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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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Deploying Fog Computing in Industrial Internet of Things and Industry 4.0 Mohammad Aazam, Senior Member, IEEE, Sherali Zeadally, Khaled A. Harras, Senior Member, IEEE
Abstract—Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0 will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements, and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web-services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data is often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT. Index Terms—cloud of things (CoT), fog computing; Industrial Internet of Things (IIoT); Industry 4.0; middleware.
I. I NTRODUCTION
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IRELESS sensor networking technologies have evolved significantly, and industrial sector is one of the beneficiaries. Communication technologies such as Zigbee, Bluetooth low energy (BLE), Internet Protocol version 6 (IPv6) over low-power wireless personal area network (6LoWPAN) have also helped wireless sensor networking technologies to be adopted in the industrial environment [1]. The deployment and use of wireless sensor networks (WSNs) and wireless sensor actuator/actor networks (WSANs) in industries make it possible to optimize the production line with better quality management, energy efficiency, fault prediction, product planning, and resource prediction. When data is collected from different sensors, actuators, and machines within an industrial environment and M. Aazam and K. A. Harras are with Department of Computer Science, Carnegie Mellon University, Doha, 24866, Qatar. e-mails: [email protected], [email protected]. S. Zeadally is with University of Kentucky, Lexington, KY, 40506-0224, USA. email: [email protected]
Fig. 1. IoT, IIoT, and Industry 4.0.
the access and control of the data and the devices generating it is enabled through the Internet, then such a scenario is called Industrial Internet of Things (IIoT). While the Internet of Things (IoT) is providing Internet access to any ’thing’, the IIoT restricts the ’things’ to the scenario of industry. Similar to the concept of IIoT, Industry 4.0 refers to the current fourth generation of industry focusing on the manufacturing industry scenario only which is a subset of IIoT. Figure 1 shows the concept of IIoT and Industry 4.0 within IoT. IIoT and Industry 4.0 are two terms that are often used interchangeably, however, there is a slight difference between them. Industry 4.0 is the term coined in 2011 and is an initiative of the German government. It refers to the fourth generation of industry - the current one. Industry 4.0 mainly focuses on the manufacturing industry. In other words, Industry 4.0 is the computerization of manufacturing [2]. IIoT was first introduced in 2012 by GE as an industrial Internet which entails the adoption of the IoT in the perspective of industry in general (both manufacturing and non-manufacturing). This definition is backed by the Industrial Internet Consortium (IIC), which was formed in 2014 with the support of Cisco, IBM, GE, Intel, and AT&T. The primary actors in Industry 4.0 are academic institutions. In contrast, IIoT is more businessoriented with mostly private companies and some academic institutions - hence, broader in applications [3]. IIoT mainly focuses on the transfer and control of mission critical information and responses, and relies heavily on machine-to-machine (M2M) communications [4]. IIoT encapsulates cyber physical systems (CPS), extreme automation, smart factory, industrial robots and actuators, so on. Both IIoT and Industry 4.0 are working towards making system robust, faster, and more importantly, secure. The full potential of IIoT is yet to be realized because several research challenges are still being addressed by the
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
research community. These challenges include standardization, interoperability, scalability, usability, privacy, and security. With tele-robotics and semi-autonomous machines that can be controlled remotely through virtual interfaces, precision and timely responses are required. A small error or delay beyond the acceptable limit might result in some catastrophe for various applications such as high tension power-line maintenance, inspecting underwater pipelines, manufacturing and monitoring jet aircrafts, mining, giant crane operation, so on. A middleware capable of processing the local tasks quickly according to the context would be mandatory in IIoT and Industry 4.0. In this context, fog computing can provide the required support as a middleware technology that is capable of processing urgent and complex tasks locally in a timely way. In addition to traditional standard tasks such as the delivery of fast response and computation offloading, fog will be responsible for information transparency, decentralized decision-making, technical assistance between humans and machines, interoperability, information security, and data analytics. Key advantages that fog can bring include: minimizing human error, low risk to human health, improved operational efficiency, reduced cost, better productivity, and higher quality maintenance and customer satisfaction. In an IIoT environment, the data obtained from the machines and sensors is analyzed to generate valuable information for factory operations as well as control of the devices. Extensive analysis of the industrial big data is typically done at the cloud. However, these sensors and machines generate different types of data ”continuously”. Such data may contain sensitive information and may be time-sensitive as well. Thus, the information needs to be processed locally for immediate tasks and operations. Machines require quick response and at times, undesired delays may result even in a catastrophic situation. Hence, the placement of an intermediary node that is able to perform tasks efficiently and more intelligently (which is not possible with standalone sensors and machines) is an inevitable requirement of IIoT. Fog can be such an intermediary node because of its location and its ability to perform specific tasks at the premise of an industry or smart factory in a timely manner. Several IIoT projects have been deployed in various industries such as food processing and agriculture [4]. Five main sectors of the industry including healthcare, education, transportation, manufacturing, and retail are already generating 76% of the total RFID market demand [5]. The strategic deployment of fog ensures rapid feedback based on the incoming data. In general, fog will be responsible for the following tasks: • • •
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Real-time industrial big data mining for high performance. Concurrent data collection from multiple types of sensors, robots, and machines. Fast processing of the sensed data to generate instructions for the actuators and robots within some acceptable latency. Interfacing incompatible sensors and machines through necessary protocol translation and mapping. Managing system power management.
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Data structuring and filtering to avoid sending unnecessary data to the core and the cloud. We summarize the main contributions of this work as follows: • We present the fundamental concepts of IIoT and Industry 4.0 and their relationship scope. • We describe the core components of IIoT. • We describe the role and architecture of fog as a middleware for IIoT, along with several use-case scenarios that can leverage fog in IIoT. • We discuss some of the emerging challenges related to IIoT. •
II. C OMPONENTS OF I NDUSTRIAL I NTERNET OF T HINGS The realization of the IIoT depends on incorporating some important building blocks. This section provides a list of key elements that are required by an IIoT. A. Localization of WSNs and WSANs An IIoT environment would be incomplete without WSNs and WSANs, and a middleware that can control them. Several heterogeneous sensors would be deployed in an industrial environment. For example, sensors for monitoring environment, reading temperature, gauging pressure, proximity, location, smoke, humidity, chemical reaction, gas, and so on. These sensors are networked in order to create a connected and controllable environment. To be able to interface with heterogeneous sensors and receive unstructured data, a sophisticated gateway is required. This gateway can be part of the fog middleware architecture. Since the data would be unstructured and its frequency has to be controlled, the fog’s presence would be very important. Fog can filter the captured data and send the structured data over the Internet to the service depending on those sensors. Similarly, energy consumption would be a great concern in a smart industrial environment. Efficient energy consumption according to the requirements along with adapting to alternate power sources, such as solar and thermal, would require more intelligence and real-time responses and actions. Hence, an enriched fog would be required in an IIoT. Similarly, in the case of WSANs, on many occasions, various actions are performed based on the sensed data that require high quality and reliable data. An actuator converts electrical signal into some physical action. An actor acts on the environment through one or more actuators. Besides, an actor also works as a network entity that performs networking tasks such as receive, process, transmit, and relay the data. In an IIoT, WSANs are managed over the Internet through a remote application, requiring quick response and security measures. Compared to sensors, actors generally have more resources available to them, with a higher data transmission power and longer battery life. However, by the time an action is initiated, the sensed data on the basis of which the actions is being taken must still be valid. The actors can even be unmanned aerial vehicles (UAVs) or commonly called drones. In other words, WSANs may be highly delay-sensitive at times. Additionally, the sensor and actor environment may even be
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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additional business models can be created through a CPS. Moreover, a CPS can help manage weather changes and the requirements of products that are being manufactured in the factory. A CPS has a control unit that is responsible for controlling machines, devices, sensors, and actuators. All these entities within a CPS interact with the real world, collect data, and process it to contribute to the industrial process. The CPS nodes/entities require a communication interface in order to be able to exchange data with other embedded systems, over-theInternet applications, and the cloud. The data exchange is the most vital attribute of a CPS because the acquired data may be processed centrally. As CPS interacts with machines, devices, and actuators, and is highly relevant in the manufacturing processes and therefore within the scope of the concept of Industry 4.0. Smart manufacturing, smart grid, robotic surgery, automobile manufacturing especially autonomous cars, are all good examples of a CPS-enabled industrial environment. With the recent developments in artificial intelligence and machine learning, manufacturing can be made more intelligent, dynamic, flexible, and scalable, by creating a networked and remotely accessible system of the industrial machinery [7]. Computer vision can be applied to robots and visual sensor networks to recognize the environment and control the target objects.
Fig. 2. CPS architecture.
complex and hierarchical with a master and slave nodes’ layout [6]. A rich middleware, such as a fog micro-datacenter, can be a viable anchor node. Fog can take local actions more quickly, and help to achieve a seamless interface of heterogeneous actors and sensors with the remote application. Moreover, since WSANs open up privacy and security issues, appropriate solutions are needed for a fog-like middleware in a robust IIoT environment. B. Controlling and managing CPS A cyber-physical system (CPS) enables the networking of traditional embedded systems and devices with the cyberworld. A CPS is a subset of IoT in which machines and devices are interfaced directly or with an application that has Internet access. A CPS enables remote access and control of the embedded system and devices and hence, supports several flexible services (such as turning on the cooling/heating system remotely [2]) that can be vital to an industrial environment. Figure 2 shows the overall layered architecture of CPS. A CPS enables communications between machines, products, and humans alike. In an industrial process, a CPS can play its role in automation system, control, diagnostics, maintenance, assistance, in a cost-effective way. Likewise,
C. Industrial big data analytics When an industrial environment is automated and to a certain extent, made autonomous, multitude of data will be involved. With the deployment of WSNs, WSANs, virtual sensor networks (VSNs), robots, connected machines and appliances, a lot of data would be continuously generated. The data provide a means to create tailored services if the required data analytics are applied [8]. An IIoT would be incomplete without extensive data analytics on the sensed data. Data analytics help in: predictive maintenance, diagnostics, better fault tolerance, failure avoidance, cost-efficiency, and so on. D. Virtual sensing and virtual sensor networking A smart factory will require many sensors in order to sense the environment and products according to the changing requirements of the factory. Physical sensor deployment would be too costly in this case. Thus, virtual sensors (VSes) would be a viable solution that a nearby fog can easily provide. Sensing devices and customizing virtual sensing devices according to the requirements help to create VSNs. For example, VSes for different products, industrial environments, and in different scenarios such as during manufacturing, post-manufacturing, and so on, can be configured and machine learning techniques can be applied in the cloud. Later, feedback can be provided to the application running on the user’s mobile device. In an agricultural environment, different sensors may be required to sense the growth of different plants in different crops in different climates or even different times of the day. Virtual sensing capabilities through fog can satisfy the requirements of customizable and flexible sensor networking. In this way, cost savings can be made, and more tailored services can be provisioned.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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E. Web of things for industry When sensors and actuators are meshed up with the services and data available on the web, it is known as web of things (WoT) - a refinement of IoT. In the IIoT scenario, several services would be provisioned over the web. Moreover, integration of sensors, robots, and devices would require integration with third party web-services. For example, a recycling company’s data is integrated with a waste management company’s sensors and devices to create flexible and enhanced services. Hence, WoT would be a key element in IIoT. III. F OG ENABLED I NDUSTRIAL I NTERNET OF T HINGS In this section, we describe potential opportunities with the current IIoT and how IIoT can be enhanced to reach its true potential with the support of fog computing. The full potential of IIoT can be achieved with a middleware, such as fog, that can handle the resources and communication of underlying nodes, and provide the required local processing. An IIoT solution may have different varieties of ’things’, such as sensors, actuators, and devices, workers, so on. Many of the devices and sensors would be small in size and constrained in resources. For example, a pressure sensor on a product packing conveyer belt or a door sensor mounted on the door. Due to their small size, the devices cannot be enhanced to perform significant computational tasks such as data analytics and context-awareness. Similarly, due to their small size, sensors would be battery powered, making energy conservation another issue. To perform significant data processing and management tasks, a middleware entity such as a fog would be beneficial. Figure 3 depicts an overall fogbased architecture for IIoT. From a broader perspective, depending on the requirements within an industry, fog can be a cloudlet, an edge device, a micro-datacenter, or even a nano-datacenter (NaDa/nDC). Fog will be able to offload the sensors and perform complex tasks on their behalf. In the same way, fog can monitor the energy consumption of each sensor and adapt the frequency of data generation accordingly. Furthermore, fog can explore and manage other energy sources, such as solar, thermal, and so on. Different IoT service providers have their own proprietary systems, requiring better interoperability measures. Fog, with necessary interoperability functions, multi-protocol translation, and application programming interfaces (APIs) can address interoperability issues. Bridging short range protocols will be another vital task a fog middleware can perform in the IIoT environment. For example, a sensor operating on Bluetooth or Zigbee may require a relay to communicate to a long distance IIoT node. The publish/subscribe approach can be very useful in IIoT environment. In the publish/subscribe paradigm, information producers are known as publishers and information receivers are called subscribers. Publishers/subscribers (pub/sub), is an efficient way of anonymously disseminating information among producers and consumers [9]. In an industrial environment, the middleware can play the vital role of brokerage and pub/sub service provisioning thereby creating ways for enhancing business processes.
Fig. 3. Fog computing in Industrial IoT.
With IIoT, robots will have a significant impact on the automation of complex processes and actions. Consequently, a robot middleware system has to be flexible and powerful enough to manage robots, particularly in a multi-robotic collaborative environment such as IIoT. The Robot Operating System (ROS) is an example of one such middleware [10]. Fog computing can play an important role in almost all the sectors of the industry. The industry is broadly categorized in three sectors: primary - related to extraction, such as mining; secondary - related to manufacturing, such as automobiles; and tertiary - related to services, such as transportation. Different industries have different potential in becoming part of the IoT or the IIoT vision. We present some of the interesting areas of industry where IoT can be applied, with the help of fog, to achieve the goals of future smart industries. A. Mining Mining is one of the most popular primary industries, requiring data analytics. With a growing population, the magnitude of mining increases as well. Mining involves a lot risks and it is also expensive. According to IBM1 , currently, 1 https://www.ibm.com/blogs/internet-of-things/mining-industry-benefits/
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
each person requires around 3.11 million pounds of fuel, metals, and minerals in his/her lifetime. The use of sensors and sensor-related technologies will help improve the productivity, avoiding unnecessary costs and wastes. Machine failures and operational costs can be predicted in a better way as well. Data collected before the actual digging process starts saves cost and time. Similarly, autonomous digging or drilling system, driverless vehicles, can be a few examples of modernizing mining industry by using the standards of IIoT. The mining industry is also one of the most adventurous types of industry involving several risks. For instance, in the case of coal and mineral mining, rock sliding, suffocation, and other similar types of risks are a commonplace. Some mining processes involve hazardous gas emission and chemical reactions. Hence, the use of sensor networks to pick up the data and inform the personnel beforehand can be very useful. Additionally, accuracy can also be improved with sensor networking and in particular through fog computing, since extensive processing can be applied on the data through co-located fog. Maintenance and energy efficiency are also important concerns for the mining industry because it involves huge machinery and a lot of time is required to perform the whole mining and collection process. Better management of machinery and energy-efficiency can be achieved in mining with IIoT. B. Smart grid and power industry The smart grid is a new electric grid that has gained importance over the last decade. Smart grid involves renewable energy resources, energy efficient resources, smart meters [11], and smart appliances. In the traditional electric grid system, consumers are provided with electricity resource and billed once a month [12]. However, with the growth in automation and autonomous lifestyle, the availability of numerous electric appliances and machines, the demands are very dynamic. Hence, we need two-way communication between a consumer and electric supplier which is actually the fundamental concept behind the smart grid. In a smart grid, the power resource is distributed to local distribution companies (LDCs) that act as a micro-grid and provide electricity to the end-users [13]. As the whole concept of smart grid is not restricted to electric suppliers only, telecom operators would also be involved to realize the smart grid. Telecom operators are signing agreements with the local electric utilities to provide two-way communications through the advanced metering infrastructure (AMI) between the service providers and smart meters [14]. Through AMIs, the electricity consumption is updated at the service provider in real-time. In return, the service provider provides feedback and suggestions on the electricity consumption according to the requirements of the users as well as home appliances and the electricity cost at that time of the day to the home area network (HAN), industrial area network (IAN), building area network (BAN), smart building, or smart factory. AMI is composed of advanced sensors, smart meters, communication infrastructure (which may be through a third party), computational infrastructure, and applications & data management system.
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Not only the data management becomes an issue, but with the convergence of smart grid applications and advanced technologies, a huge amount of data will be generated. This data needs to be processed for control and smart pricing. Hence, it is very critical for the service providers to have well-defined communication requirements and infrastructure. Through efficient communications between the power utilities and customers, power outages can be minimized and made predictable as well, if not completely avoided. This is vital in the case of industries because many of them face heavy losses due to unscheduled power outages. Different types of consumer premises have different data communication requirements. A smart home would be less communicative than a smart building, while a smart factory would require more frequent data communication exchanges. In this way, sophisticated data communication and data flow management technologies have to be developed and adopted. Different topological settings would require different flows especially in the case of the industrial environment. For example, as illustrated in Figure 4, one flow would be between the sensors, appliances, and machines to the smart meters. The other flow would be between the smart meters and the utility datacenter. Furthermore, with each kind of smart meter (such as gas, electric, water), the number of flows will increase accordingly. One such methodology of flow management is softwaredefined-network (SDN) and network function virtualization (NFV) [13]. With the power system becoming more and more decentralized today, the role of computation at decentralized locations would be enhanced and middleware technologies, such as the fog micro-datacenter - equipped with SDN and NFV functionalities, will be playing a key role in the management of the data. In this way, a centralized datacenter will be able to manage decentralized micro-grids. C. Transportation Transportation is an important industry and constitutes the backbone of any country. Commercial buses, metro trains and subways, cargos, public transport, and private vehicles are all parts of transportation industry. Intelligent transportation systems (ITS) is the subset of IoT that deals with transportation. A road side unit (RSU) can be equipped with a fog to enable ITS. For example, fog can enable Internet of Vehicles (IoV), support in-vehicle entertainment, provide context-aware and location-aware services, smart parking, and smart traffic lights where signals which are controlled according to the traffic load and emergency situations. Providing necessary updates to the commuters and drivers regarding road conditions, traffic load, detours, are all examples of fog computing based IoT-enabled ITS. D. Waste management industry Automating the waste management process is one of the goals of the waste management industry [15]. The timely management of waste prevents the development of different diseases. It also helps in speeding up the recycling of the waste. Waste collection and disposal can be linked to the recycling industry, so that proper resources are allocated well
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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central location. In the same way, at shopping malls and hypermarts, advertising can be made more customized for every customer. For example, a customer who always does his/her grocery from a certain hypermart (say Walmart) has a registered account with them to receive promotions and updates. When the customer enters into the store, a fog node located within the mall/hypermart communicates with the store’s Wifi through a previously registered account of the customer. Based on that information, the fog tracks the interests of the customer by assessing the kind of grocery items the customer purchases. Similarly, through location-tracking, the fog-based smart advertisement system analyzes how much time a certain customer spends in a particular aisle. There can be digital screens placed strategically at relevant aisles and the customer can see promotions and suggestions according to their past purchase history and current traces. H. Third party delivery
Fig. 4. Fog enabled smart grid.
in time according to the type and quantity of a certain kind of waste (such as glass, paper, steel, organic) [16]. Similarly, waste-to-energy (WtE) and other similar industries will be connected to the waste management industry and better planning can be achieved. More details on this topic are provided in [17]. E. Food industry Food packaging, frozen food, food analysis from organic waste are all part of the food industry. IoT-enabled food industry coupled with data analytics gives birth to the urbane food industry where food quality can be controlled and wastage can be minimized. F. Agriculture Understandably, sensor networks will be playing a key role in agriculture and more importantly, VSes would be even more important in certain scenarios where multiple sensors are required for varying conditions and states. Fog-assisted drones can be used for seeding, crop monitoring, and spraying. G. Advertisement industry Different countries have different trends and laws regarding advertising, accordingly, advertisement can be made smart. At some places, large billboards are allowed. These billboards can be a giant digital screen that can be updated from a
Other than the regular smartphone application-based taxi services, such as Uber and Careem, various delivery services are also becoming increasingly popular. A few examples of food delivery services in different countries are: UberEATS, Talabat, FoodPanda, Couch Potato. Similarly, in Pakistan, TCS Hazir and TCS HazirSubKuch are popular services to outsource shopping, delivery of goods, and mail pickup. Similarly, to deliver something urgent to a friend or a family member, to do grocery, or to get some medicines from a pharmacy, TCS HazirSubKuch is very common in Pakistan for example. Various services may require more detailed data and context-awareness information. For example, an alternate of a requested medicine that is not available, or allergy information while purchasing a medicine, or location-awareness when the delivery destination is mobile (such as a person on the move in a car). A tailored fog for such instances can be very useful in performing tasks locally. In addition to the above-mentioned applications, IIoT can help in receiving the packages as well when the recipient is not at the destined location. For example, automatically opening the door with the cell phone when the delivery person arrives at the door in customers absence. The delivery person rings the bell that gives an alert on the users phone. With a press of a button on the phone, the door opens and the delivery person can put the package inside of the door. A camera positioned to capture the door shows the event in real-time on the users phone. The door once closed, locks automatically and the lock gives a confirmation message to the users phone. Such kind of a service will be very useful where family members work and have problems in receiving their packages and posts since delivery is also made during work-hours. I. Smart parking In some countries, parking is outsourced by the city government to third parties which operate as an industry. Other than smart parking proposed by [18], in which vehicles get to know about vacant parking slots in real-time, parking can be enhanced in many ways. Suggestions can be made according to the parking rush trends and it can be more context-aware
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
according to season, weather, local events, such as sale at a certain store, and so on. IV. R ESEARCH C HALLENGES To realize the true potential of IIoT, we need to address several challenges. We discuss some of these challenges below.
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measurable - so that the acquired service can be compared with the agreed upon service; controllable - such that the factors that determine service satisfaction can be modified to achieve the required service; affordable - the SLA must be cost-effective for the involved services; and acceptable that is the involved parties need to mutually agree on the SLA, rather than one dictating it to the other [20].
A. Energy consumption and management Industries are the largest consumers of power in any country, thus, require dynamic power management. Depending on the type of the industry, energy consumption varies, which may even be different according to seasons particularly in the case of the food and textile industries. Energy consumption affects the network lifetime and is therefore an important factor in IIoT. In IIoT, not only sensors but actuators and robotic devices are also involved. Therefore, many data packets are continuously being exchanged resulting in a higher energy consumption. Since energy is a valuable resource, it affects time synchronization as well. Algorithms that are able to deal with time synchronization and energy consumption tradeoff, are more suitable for an effective and scalable IIoT environment. Dynamically managing power is an essential element of IIoT. Systematic mechanisms are required to adapt to the changing demand of an industry, during different times of the day, according to different prices and grid load. Some industries may even be run at night to cope with the power load.
D. Security and privacy of data and workers IIoT would be vulnerable to attacks that can affect the availability, confidentiality, and integrity of transmitted or stored data. With increased connectivity, more data is generated which may be susceptible to theft and misuse because several industrial deployments would be outdoor, such as construction and mining. When multiple nodes and systems communicate with each other, data communication and storage are more prone to intrusion and theft. The data can be misused and may even result in manufacturing malfunctions, which can have drastic effects on production, factory premises, and personnel working there. Moreover, interoperability features might increase security and privacy vulnerabilities in the IIoT environment, resulting in not only attacks but information misuse as well. Since different systems will be combining their resources in an interoperable IIoT scenario, there is increased likelihood that the data, information, and commands could be tampered with. E. Context and semantics-aware service provisioning
B. Interoperability of devices In IIoT, multiple subsystems and external systems would work together, causing interoperability issues [19]. For example, a smart industry is connected to an external smart grid, a production plant is connected to WoT service, and a production system of a factory is connected to storage system of the same factory, and so on. Several sensors and systems would be heterogeneous. Therefore, system and sensor integration as well as interoperability mechanisms become more of a challenge. Since many of the tasks (such as in a manufacturing environment where actuators are tasked to take actions) would be delay-sensitive, therefore, the integration and interoperability has to be not only seamless, but deliver high performance. C. Service level agreement and interoperability of services Along the same line of interoperability of nodes, services’ interoperability will be a challenge as well, especially in the case of a service level agreement (SLA). Resource federation of different IIoTs, SLA matching, SLA monitoring and violation, are important factors that need to be considered for scalable IIoT and for enabling inter-IIoTs communication. When relying on third party cloud-IoT services, the key concern is the performance delivered to the customer. Many of the IIoT applications (such as autonomous vehicles, vehicleto-vehicle communications, robotic communications in manufacturing and so on) would be sensitive to security and delay requirements. SLAs must have some of the following essential attributes: meaningful - that is relevant to the involved parties;
Given the dynamic environment in industry, the ability to discover web-services on the go to create an extended and flexible business process is much needed. An example of a context-aware service in a smart factory environment can be different temperature settings according to different products in a factory. In the case of IIoT, context-awareness can be primary [5], such as gathering context without any existing contextual information; or secondary, such as gathering context from an existing contextual information. Both types have different complexities and outcomes, and hence require more intelligence and efficiency. The same data can be used to derive different insights for different scenarios or even domains commonly known as sensing as a service. For example, data on different temperature settings for different products can be used in the transportation or cargo industry when designing the cargo compartment and cooling equipment. In the case of the medical domain, food stabilizers can be developed according to the related contextual information such as products ingredients, age, life, and required temperature settings. Moreover, developing semantic web-based services can be very important in the industrial scenario. Services are annotated on the basis of shared ontologies, which help in the discovery of web services, according to the semantics [21]. F. Fault detection and reconfiguration As the IIoT system becomes increasingly automated and heterogeneous entities are involved, the chances of failure increase. Device malfunction, delayed communication, and
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
connectivity failures are some common examples. A complete IIoT system must be robust and be capable of not only detecting and withstanding common faults but also be capable of detecting faults in time. Advanced fault detection algorithms will have to be applied at the hub, gateway, or middleware that is responsible for coordinating different machines and devices. Accuracy and timeliness are also important in detecting faults because one faulty object may hinder the whole manufacturing or industrial process, resulting in financial loss, energy loss, and loss of other related resources. A faulty network of sensors or machines should be able to reconfigure itself with no human intervention. If a sensor is not working due to some malfunctioning, it can be put to sleep until replaced and the sensor network settings can be reconfigured. In this way, not only the robustness is ensured, but energy is also saved. G. User-friendliness in the product deployment and usage Industrial workers may not be fluent with the most recent technologies, which makes it even more challenging to design devices and user-interfaces for an IIoT system. Unlike the typical traditional IT system and common IoT systems where either user interaction is low, or the user is well-aware of the technology he/she is using, in the industrial domain, factory workers with different backgrounds and experience are distributed over a large area. For example, in a factory or agricultural field, people in these areas will be deploying the devices and sensors with a low knowledge of electronics and communication. Creating seamless adaptation of the IIoT technology and user-friendly interfaces can help acceptance of IIoT. V. C ONCLUSION Recent advances in virtual sensor networking, robotics, and communication technologies have paved the way for autonomous industrial environment. Theoretically, there is a huge potential of IIoT and Industry 4.0. However, it brings a lot of challenges as well, which can be converted to opportunities if better planning and standardization are done. In this paper, we present an architectural introduction of IIoT and Industry 4.0 and discuss that how a middleware, such as a fog, can help realize different use cases in emerging industrial paradigms. We discuss some interesting use case scenarios related to different industries, such as transportation, smart grid, food & restaurants, advertisement, and tourism. We describe how IoT and fog can help such industries in becoming more efficient in the future. We also discuss some noteworthy challenges and how they can be converted into opportunities. Some of the challenges include context-aware and semantics-aware service provisioning in IIoT, interoperability of different devices and services, security & privacy, and energy consumption. This work can be very useful in terms of understanding the potential of IIoT and Industry 4.0. It will also motivate future research directions in IIoT for a wide range of application domains. ACKNOWLEDGMENTS
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of Qatar Foundation. The statements made herein are solely the responsibility of the authors. We thank the anonymous reviewers for their valuable comments which helped us to improve the content and presentation of this paper. R EFERENCES [1] L. Shu, M. Mukherjee, M. Pecht, N. Crespi, and S. N. Han, “Challenges and research issues of data management in iot for large-scale petrochemical plants,” IEEE Systems Journal, 2017. [2] N. Jazdi, “Cyber physical systems in the context of industry 4.0,” in Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on. IEEE, 2014, pp. 1–4. [3] R. Geissbauer, J. Vedso, and S. Schrauf, “Industry 4.0: Building the digital enterprise, pwc (2016),” 2017. [4] T. Qiu, Y. Zhang, D. Qiao, X. Zhang, M. L. Wymore, and A. K. Sangaiah, “A robust time synchronization scheme for industrial internet of things,” IEEE Transactions on Industrial Informatics, 2017. [5] C. Perera, C. H. Liu, S. Jayawardena, and M. Chen, “A survey on internet of things from industrial market perspective,” IEEE Access, vol. 2, pp. 1660–1679, 2014. [6] W. Lee, K. Nam, H.-G. Roh, and S.-H. Kim, “A gateway based fog computing architecture for wireless sensors and actuator networks,” in Advanced Communication Technology (ICACT), 2016 18th International Conference on. IEEE, 2016, pp. 210–213. [7] L. Monostori, “Cyber-physical production systems: roots, expectations and r&d challenges,” Procedia Cirp, vol. 17, pp. 9–13, 2014. [8] B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, and Q. Yang, “Incorporating intelligence in fog computing for big data analysis in smart cities,” IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2140–2150, 2017. [9] C. Ca˜nas, E. Pacheco, B. Kemme, J. Kienzle, and H.-A. Jacobsen, “Graps: A graph publish/subscribe middleware,” in Proceedings of the 16th Annual Middleware Conference. ACM, 2015, pp. 1–12. [10] V. Monajjemi, J. Wawerla, and R. Vaughan, “Drums: A middlewareaware distributed robot monitoring system,” in Computer and Robot Vision (CRV), 2014 Canadian Conference on. IEEE, 2014, pp. 211– 218. [11] S. S. S. R. Depuru, L. Wang, and V. Devabhaktuni, “Smart meters for power grid: Challenges, issues, advantages and status,” Renewable and sustainable energy reviews, vol. 15, no. 6, pp. 2736–2742, 2011. [12] H. Farhangi, “The path of the smart grid,” IEEE power and energy magazine, vol. 8, no. 1, 2010. [13] D. A. Chekired, L. Khoukhi, and H. T. Mouftah, “Decentralized cloudsdn architecture in smart grid: A dynamic pricing model,” IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1220–1231, 2018. [14] V. C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, and G. P. Hancke, “Smart grid technologies: Communication technologies and standards,” IEEE transactions on Industrial informatics, vol. 7, no. 4, pp. 529–539, 2011. [15] R. SHUKLA, “Smart waste management.” [16] Y. Glouche and P. Couderc, “A smart waste management with selfdescribing objects,” in The Second International Conference on Smart Systems, Devices and Technologies (SMART’13), 2013. [17] M. Aazam, M. St-Hilaire, C.-H. Lung, and I. Lambadaris, “Cloud-based smart waste management for smart cities,” in Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2016 IEEE 21st International Workshop on. IEEE, 2016, pp. 188–193. [18] S. Andreev, O. Galinina, A. Pyattaev, M. Gerasimenko, T. Tirronen, J. Torsner, J. Sachs, M. Dohler, and Y. Koucheryavy, “Understanding the iot connectivity landscape: a contemporary m2m radio technology roadmap,” IEEE Communications Magazine, vol. 53, no. 9, pp. 32–40, 2015. [19] O. Givehchi, K. Landsdorf, P. Simoens, and A. W. Colombo, “Interoperability for industrial cyber-physical systems: An approach for legacy systems,” IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 3370–3378, 2017. [20] A. V. Papadopoulos, S. A. Asadollah, M. Ashjaei, S. Mubeen, H. PeiBreivold, and M. Behnam, “Slas for industrial iot: Mind the gap,” in Future Internet of Things and Cloud Workshops (FiCloudW), 2017 5th International Conference on. IEEE, 2017, pp. 75–78. [21] K. Sivashanmugam, K. Verma, A. P. Sheth, and J. Miller, “Adding semantics to web services standards,” 2003.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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Mohammad Aazam (S’11, M’15, SM’16) is currently working as a postdoc research associate at Carnegie Mellon University in Qatar since July 2017 and a part-time postdoctoral fellow at Ryerson University, Canada since March 2017. Previously, he has been a sessional faculty and postdoc fellow with Carleton University, Canada from July 20152017. He is also working as a postdoc collaborator with University of Kentucky, USA and University of Ontario Institute of Technology (UOIT), Canada. He completed his Ph.D. Computer Engineering from Kyung Hee University, Korea, in 2015. In addition to that, he has completed a course on Data Science with R from Harvard University, USA in 2017, in which he topped the course securing 100% score. He also completed a course on Internet of Things (IoT) from King’s College London, UK, in 2016. He has around 100 publications, including 3 patents. He is also a recipient of IEEE AIYEHUM 2016 award. For additional details: www.aazamcs.com.
Sherali Zeadally is an associate professor in the College of Communication and Information at the University of Kentucky. He received his bachelor and doctorate degrees in computer science from the University of Cambridge, England, and the University of Buckingham, England, respectively. He is a Fellow of the British Computer Society and the Institution of Engineering Technology, England.
Khaled A. Harras (S’05, M’09, SM’16) ([email protected]) is an associate professor at Carnegie Melon University Qatar (CMUQ), and Director of the Computer Science program there. Dr. Harras is the founder and director of the Networking Systems Lab (NSL) at CMUQ. He has more than 100 refereed publications and 4 US patents. Along with his research group in the past few years, he has won the best national computing research award twice, received two best paper awards, and his work has been featured online various venues like MIT Tech Review and Tech the Future. To date, he has been involved in or managing research grants that amount to more than 3 Million USD, and has supervised over 30 different personnel including undergraduate and graduate students, postdoctoral researchers, and research engineers.
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Deploying Fog Computing in Industrial Internet of Things and Industry 4.0 Mohammad Aazam, Senior Member, IEEE, Sherali Zeadally, Khaled A. Harras, Senior Member, IEEE
Abstract—Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0 will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements, and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web-services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data is often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT. Index Terms—cloud of things (CoT), fog computing; Industrial Internet of Things (IIoT); Industry 4.0; middleware.
I. I NTRODUCTION
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IRELESS sensor networking technologies have evolved significantly, and industrial sector is one of the beneficiaries. Communication technologies such as Zigbee, Bluetooth low energy (BLE), Internet Protocol version 6 (IPv6) over low-power wireless personal area network (6LoWPAN) have also helped wireless sensor networking technologies to be adopted in the industrial environment [1]. The deployment and use of wireless sensor networks (WSNs) and wireless sensor actuator/actor networks (WSANs) in industries make it possible to optimize the production line with better quality management, energy efficiency, fault prediction, product planning, and resource prediction. When data is collected from different sensors, actuators, and machines within an industrial environment and M. Aazam and K. A. Harras are with Department of Computer Science, Carnegie Mellon University, Doha, 24866, Qatar. e-mails: [email protected], [email protected]. S. Zeadally is with University of Kentucky, Lexington, KY, 40506-0224, USA. email: [email protected]
Fig. 1. IoT, IIoT, and Industry 4.0.
the access and control of the data and the devices generating it is enabled through the Internet, then such a scenario is called Industrial Internet of Things (IIoT). While the Internet of Things (IoT) is providing Internet access to any ’thing’, the IIoT restricts the ’things’ to the scenario of industry. Similar to the concept of IIoT, Industry 4.0 refers to the current fourth generation of industry focusing on the manufacturing industry scenario only which is a subset of IIoT. Figure 1 shows the concept of IIoT and Industry 4.0 within IoT. IIoT and Industry 4.0 are two terms that are often used interchangeably, however, there is a slight difference between them. Industry 4.0 is the term coined in 2011 and is an initiative of the German government. It refers to the fourth generation of industry - the current one. Industry 4.0 mainly focuses on the manufacturing industry. In other words, Industry 4.0 is the computerization of manufacturing [2]. IIoT was first introduced in 2012 by GE as an industrial Internet which entails the adoption of the IoT in the perspective of industry in general (both manufacturing and non-manufacturing). This definition is backed by the Industrial Internet Consortium (IIC), which was formed in 2014 with the support of Cisco, IBM, GE, Intel, and AT&T. The primary actors in Industry 4.0 are academic institutions. In contrast, IIoT is more businessoriented with mostly private companies and some academic institutions - hence, broader in applications [3]. IIoT mainly focuses on the transfer and control of mission critical information and responses, and relies heavily on machine-to-machine (M2M) communications [4]. IIoT encapsulates cyber physical systems (CPS), extreme automation, smart factory, industrial robots and actuators, so on. Both IIoT and Industry 4.0 are working towards making system robust, faster, and more importantly, secure. The full potential of IIoT is yet to be realized because several research challenges are still being addressed by the
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
research community. These challenges include standardization, interoperability, scalability, usability, privacy, and security. With tele-robotics and semi-autonomous machines that can be controlled remotely through virtual interfaces, precision and timely responses are required. A small error or delay beyond the acceptable limit might result in some catastrophe for various applications such as high tension power-line maintenance, inspecting underwater pipelines, manufacturing and monitoring jet aircrafts, mining, giant crane operation, so on. A middleware capable of processing the local tasks quickly according to the context would be mandatory in IIoT and Industry 4.0. In this context, fog computing can provide the required support as a middleware technology that is capable of processing urgent and complex tasks locally in a timely way. In addition to traditional standard tasks such as the delivery of fast response and computation offloading, fog will be responsible for information transparency, decentralized decision-making, technical assistance between humans and machines, interoperability, information security, and data analytics. Key advantages that fog can bring include: minimizing human error, low risk to human health, improved operational efficiency, reduced cost, better productivity, and higher quality maintenance and customer satisfaction. In an IIoT environment, the data obtained from the machines and sensors is analyzed to generate valuable information for factory operations as well as control of the devices. Extensive analysis of the industrial big data is typically done at the cloud. However, these sensors and machines generate different types of data ”continuously”. Such data may contain sensitive information and may be time-sensitive as well. Thus, the information needs to be processed locally for immediate tasks and operations. Machines require quick response and at times, undesired delays may result even in a catastrophic situation. Hence, the placement of an intermediary node that is able to perform tasks efficiently and more intelligently (which is not possible with standalone sensors and machines) is an inevitable requirement of IIoT. Fog can be such an intermediary node because of its location and its ability to perform specific tasks at the premise of an industry or smart factory in a timely manner. Several IIoT projects have been deployed in various industries such as food processing and agriculture [4]. Five main sectors of the industry including healthcare, education, transportation, manufacturing, and retail are already generating 76% of the total RFID market demand [5]. The strategic deployment of fog ensures rapid feedback based on the incoming data. In general, fog will be responsible for the following tasks: • • •
• •
Real-time industrial big data mining for high performance. Concurrent data collection from multiple types of sensors, robots, and machines. Fast processing of the sensed data to generate instructions for the actuators and robots within some acceptable latency. Interfacing incompatible sensors and machines through necessary protocol translation and mapping. Managing system power management.
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Data structuring and filtering to avoid sending unnecessary data to the core and the cloud. We summarize the main contributions of this work as follows: • We present the fundamental concepts of IIoT and Industry 4.0 and their relationship scope. • We describe the core components of IIoT. • We describe the role and architecture of fog as a middleware for IIoT, along with several use-case scenarios that can leverage fog in IIoT. • We discuss some of the emerging challenges related to IIoT. •
II. C OMPONENTS OF I NDUSTRIAL I NTERNET OF T HINGS The realization of the IIoT depends on incorporating some important building blocks. This section provides a list of key elements that are required by an IIoT. A. Localization of WSNs and WSANs An IIoT environment would be incomplete without WSNs and WSANs, and a middleware that can control them. Several heterogeneous sensors would be deployed in an industrial environment. For example, sensors for monitoring environment, reading temperature, gauging pressure, proximity, location, smoke, humidity, chemical reaction, gas, and so on. These sensors are networked in order to create a connected and controllable environment. To be able to interface with heterogeneous sensors and receive unstructured data, a sophisticated gateway is required. This gateway can be part of the fog middleware architecture. Since the data would be unstructured and its frequency has to be controlled, the fog’s presence would be very important. Fog can filter the captured data and send the structured data over the Internet to the service depending on those sensors. Similarly, energy consumption would be a great concern in a smart industrial environment. Efficient energy consumption according to the requirements along with adapting to alternate power sources, such as solar and thermal, would require more intelligence and real-time responses and actions. Hence, an enriched fog would be required in an IIoT. Similarly, in the case of WSANs, on many occasions, various actions are performed based on the sensed data that require high quality and reliable data. An actuator converts electrical signal into some physical action. An actor acts on the environment through one or more actuators. Besides, an actor also works as a network entity that performs networking tasks such as receive, process, transmit, and relay the data. In an IIoT, WSANs are managed over the Internet through a remote application, requiring quick response and security measures. Compared to sensors, actors generally have more resources available to them, with a higher data transmission power and longer battery life. However, by the time an action is initiated, the sensed data on the basis of which the actions is being taken must still be valid. The actors can even be unmanned aerial vehicles (UAVs) or commonly called drones. In other words, WSANs may be highly delay-sensitive at times. Additionally, the sensor and actor environment may even be
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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additional business models can be created through a CPS. Moreover, a CPS can help manage weather changes and the requirements of products that are being manufactured in the factory. A CPS has a control unit that is responsible for controlling machines, devices, sensors, and actuators. All these entities within a CPS interact with the real world, collect data, and process it to contribute to the industrial process. The CPS nodes/entities require a communication interface in order to be able to exchange data with other embedded systems, over-theInternet applications, and the cloud. The data exchange is the most vital attribute of a CPS because the acquired data may be processed centrally. As CPS interacts with machines, devices, and actuators, and is highly relevant in the manufacturing processes and therefore within the scope of the concept of Industry 4.0. Smart manufacturing, smart grid, robotic surgery, automobile manufacturing especially autonomous cars, are all good examples of a CPS-enabled industrial environment. With the recent developments in artificial intelligence and machine learning, manufacturing can be made more intelligent, dynamic, flexible, and scalable, by creating a networked and remotely accessible system of the industrial machinery [7]. Computer vision can be applied to robots and visual sensor networks to recognize the environment and control the target objects.
Fig. 2. CPS architecture.
complex and hierarchical with a master and slave nodes’ layout [6]. A rich middleware, such as a fog micro-datacenter, can be a viable anchor node. Fog can take local actions more quickly, and help to achieve a seamless interface of heterogeneous actors and sensors with the remote application. Moreover, since WSANs open up privacy and security issues, appropriate solutions are needed for a fog-like middleware in a robust IIoT environment. B. Controlling and managing CPS A cyber-physical system (CPS) enables the networking of traditional embedded systems and devices with the cyberworld. A CPS is a subset of IoT in which machines and devices are interfaced directly or with an application that has Internet access. A CPS enables remote access and control of the embedded system and devices and hence, supports several flexible services (such as turning on the cooling/heating system remotely [2]) that can be vital to an industrial environment. Figure 2 shows the overall layered architecture of CPS. A CPS enables communications between machines, products, and humans alike. In an industrial process, a CPS can play its role in automation system, control, diagnostics, maintenance, assistance, in a cost-effective way. Likewise,
C. Industrial big data analytics When an industrial environment is automated and to a certain extent, made autonomous, multitude of data will be involved. With the deployment of WSNs, WSANs, virtual sensor networks (VSNs), robots, connected machines and appliances, a lot of data would be continuously generated. The data provide a means to create tailored services if the required data analytics are applied [8]. An IIoT would be incomplete without extensive data analytics on the sensed data. Data analytics help in: predictive maintenance, diagnostics, better fault tolerance, failure avoidance, cost-efficiency, and so on. D. Virtual sensing and virtual sensor networking A smart factory will require many sensors in order to sense the environment and products according to the changing requirements of the factory. Physical sensor deployment would be too costly in this case. Thus, virtual sensors (VSes) would be a viable solution that a nearby fog can easily provide. Sensing devices and customizing virtual sensing devices according to the requirements help to create VSNs. For example, VSes for different products, industrial environments, and in different scenarios such as during manufacturing, post-manufacturing, and so on, can be configured and machine learning techniques can be applied in the cloud. Later, feedback can be provided to the application running on the user’s mobile device. In an agricultural environment, different sensors may be required to sense the growth of different plants in different crops in different climates or even different times of the day. Virtual sensing capabilities through fog can satisfy the requirements of customizable and flexible sensor networking. In this way, cost savings can be made, and more tailored services can be provisioned.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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E. Web of things for industry When sensors and actuators are meshed up with the services and data available on the web, it is known as web of things (WoT) - a refinement of IoT. In the IIoT scenario, several services would be provisioned over the web. Moreover, integration of sensors, robots, and devices would require integration with third party web-services. For example, a recycling company’s data is integrated with a waste management company’s sensors and devices to create flexible and enhanced services. Hence, WoT would be a key element in IIoT. III. F OG ENABLED I NDUSTRIAL I NTERNET OF T HINGS In this section, we describe potential opportunities with the current IIoT and how IIoT can be enhanced to reach its true potential with the support of fog computing. The full potential of IIoT can be achieved with a middleware, such as fog, that can handle the resources and communication of underlying nodes, and provide the required local processing. An IIoT solution may have different varieties of ’things’, such as sensors, actuators, and devices, workers, so on. Many of the devices and sensors would be small in size and constrained in resources. For example, a pressure sensor on a product packing conveyer belt or a door sensor mounted on the door. Due to their small size, the devices cannot be enhanced to perform significant computational tasks such as data analytics and context-awareness. Similarly, due to their small size, sensors would be battery powered, making energy conservation another issue. To perform significant data processing and management tasks, a middleware entity such as a fog would be beneficial. Figure 3 depicts an overall fogbased architecture for IIoT. From a broader perspective, depending on the requirements within an industry, fog can be a cloudlet, an edge device, a micro-datacenter, or even a nano-datacenter (NaDa/nDC). Fog will be able to offload the sensors and perform complex tasks on their behalf. In the same way, fog can monitor the energy consumption of each sensor and adapt the frequency of data generation accordingly. Furthermore, fog can explore and manage other energy sources, such as solar, thermal, and so on. Different IoT service providers have their own proprietary systems, requiring better interoperability measures. Fog, with necessary interoperability functions, multi-protocol translation, and application programming interfaces (APIs) can address interoperability issues. Bridging short range protocols will be another vital task a fog middleware can perform in the IIoT environment. For example, a sensor operating on Bluetooth or Zigbee may require a relay to communicate to a long distance IIoT node. The publish/subscribe approach can be very useful in IIoT environment. In the publish/subscribe paradigm, information producers are known as publishers and information receivers are called subscribers. Publishers/subscribers (pub/sub), is an efficient way of anonymously disseminating information among producers and consumers [9]. In an industrial environment, the middleware can play the vital role of brokerage and pub/sub service provisioning thereby creating ways for enhancing business processes.
Fig. 3. Fog computing in Industrial IoT.
With IIoT, robots will have a significant impact on the automation of complex processes and actions. Consequently, a robot middleware system has to be flexible and powerful enough to manage robots, particularly in a multi-robotic collaborative environment such as IIoT. The Robot Operating System (ROS) is an example of one such middleware [10]. Fog computing can play an important role in almost all the sectors of the industry. The industry is broadly categorized in three sectors: primary - related to extraction, such as mining; secondary - related to manufacturing, such as automobiles; and tertiary - related to services, such as transportation. Different industries have different potential in becoming part of the IoT or the IIoT vision. We present some of the interesting areas of industry where IoT can be applied, with the help of fog, to achieve the goals of future smart industries. A. Mining Mining is one of the most popular primary industries, requiring data analytics. With a growing population, the magnitude of mining increases as well. Mining involves a lot risks and it is also expensive. According to IBM1 , currently, 1 https://www.ibm.com/blogs/internet-of-things/mining-industry-benefits/
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
each person requires around 3.11 million pounds of fuel, metals, and minerals in his/her lifetime. The use of sensors and sensor-related technologies will help improve the productivity, avoiding unnecessary costs and wastes. Machine failures and operational costs can be predicted in a better way as well. Data collected before the actual digging process starts saves cost and time. Similarly, autonomous digging or drilling system, driverless vehicles, can be a few examples of modernizing mining industry by using the standards of IIoT. The mining industry is also one of the most adventurous types of industry involving several risks. For instance, in the case of coal and mineral mining, rock sliding, suffocation, and other similar types of risks are a commonplace. Some mining processes involve hazardous gas emission and chemical reactions. Hence, the use of sensor networks to pick up the data and inform the personnel beforehand can be very useful. Additionally, accuracy can also be improved with sensor networking and in particular through fog computing, since extensive processing can be applied on the data through co-located fog. Maintenance and energy efficiency are also important concerns for the mining industry because it involves huge machinery and a lot of time is required to perform the whole mining and collection process. Better management of machinery and energy-efficiency can be achieved in mining with IIoT. B. Smart grid and power industry The smart grid is a new electric grid that has gained importance over the last decade. Smart grid involves renewable energy resources, energy efficient resources, smart meters [11], and smart appliances. In the traditional electric grid system, consumers are provided with electricity resource and billed once a month [12]. However, with the growth in automation and autonomous lifestyle, the availability of numerous electric appliances and machines, the demands are very dynamic. Hence, we need two-way communication between a consumer and electric supplier which is actually the fundamental concept behind the smart grid. In a smart grid, the power resource is distributed to local distribution companies (LDCs) that act as a micro-grid and provide electricity to the end-users [13]. As the whole concept of smart grid is not restricted to electric suppliers only, telecom operators would also be involved to realize the smart grid. Telecom operators are signing agreements with the local electric utilities to provide two-way communications through the advanced metering infrastructure (AMI) between the service providers and smart meters [14]. Through AMIs, the electricity consumption is updated at the service provider in real-time. In return, the service provider provides feedback and suggestions on the electricity consumption according to the requirements of the users as well as home appliances and the electricity cost at that time of the day to the home area network (HAN), industrial area network (IAN), building area network (BAN), smart building, or smart factory. AMI is composed of advanced sensors, smart meters, communication infrastructure (which may be through a third party), computational infrastructure, and applications & data management system.
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Not only the data management becomes an issue, but with the convergence of smart grid applications and advanced technologies, a huge amount of data will be generated. This data needs to be processed for control and smart pricing. Hence, it is very critical for the service providers to have well-defined communication requirements and infrastructure. Through efficient communications between the power utilities and customers, power outages can be minimized and made predictable as well, if not completely avoided. This is vital in the case of industries because many of them face heavy losses due to unscheduled power outages. Different types of consumer premises have different data communication requirements. A smart home would be less communicative than a smart building, while a smart factory would require more frequent data communication exchanges. In this way, sophisticated data communication and data flow management technologies have to be developed and adopted. Different topological settings would require different flows especially in the case of the industrial environment. For example, as illustrated in Figure 4, one flow would be between the sensors, appliances, and machines to the smart meters. The other flow would be between the smart meters and the utility datacenter. Furthermore, with each kind of smart meter (such as gas, electric, water), the number of flows will increase accordingly. One such methodology of flow management is softwaredefined-network (SDN) and network function virtualization (NFV) [13]. With the power system becoming more and more decentralized today, the role of computation at decentralized locations would be enhanced and middleware technologies, such as the fog micro-datacenter - equipped with SDN and NFV functionalities, will be playing a key role in the management of the data. In this way, a centralized datacenter will be able to manage decentralized micro-grids. C. Transportation Transportation is an important industry and constitutes the backbone of any country. Commercial buses, metro trains and subways, cargos, public transport, and private vehicles are all parts of transportation industry. Intelligent transportation systems (ITS) is the subset of IoT that deals with transportation. A road side unit (RSU) can be equipped with a fog to enable ITS. For example, fog can enable Internet of Vehicles (IoV), support in-vehicle entertainment, provide context-aware and location-aware services, smart parking, and smart traffic lights where signals which are controlled according to the traffic load and emergency situations. Providing necessary updates to the commuters and drivers regarding road conditions, traffic load, detours, are all examples of fog computing based IoT-enabled ITS. D. Waste management industry Automating the waste management process is one of the goals of the waste management industry [15]. The timely management of waste prevents the development of different diseases. It also helps in speeding up the recycling of the waste. Waste collection and disposal can be linked to the recycling industry, so that proper resources are allocated well
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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central location. In the same way, at shopping malls and hypermarts, advertising can be made more customized for every customer. For example, a customer who always does his/her grocery from a certain hypermart (say Walmart) has a registered account with them to receive promotions and updates. When the customer enters into the store, a fog node located within the mall/hypermart communicates with the store’s Wifi through a previously registered account of the customer. Based on that information, the fog tracks the interests of the customer by assessing the kind of grocery items the customer purchases. Similarly, through location-tracking, the fog-based smart advertisement system analyzes how much time a certain customer spends in a particular aisle. There can be digital screens placed strategically at relevant aisles and the customer can see promotions and suggestions according to their past purchase history and current traces. H. Third party delivery
Fig. 4. Fog enabled smart grid.
in time according to the type and quantity of a certain kind of waste (such as glass, paper, steel, organic) [16]. Similarly, waste-to-energy (WtE) and other similar industries will be connected to the waste management industry and better planning can be achieved. More details on this topic are provided in [17]. E. Food industry Food packaging, frozen food, food analysis from organic waste are all part of the food industry. IoT-enabled food industry coupled with data analytics gives birth to the urbane food industry where food quality can be controlled and wastage can be minimized. F. Agriculture Understandably, sensor networks will be playing a key role in agriculture and more importantly, VSes would be even more important in certain scenarios where multiple sensors are required for varying conditions and states. Fog-assisted drones can be used for seeding, crop monitoring, and spraying. G. Advertisement industry Different countries have different trends and laws regarding advertising, accordingly, advertisement can be made smart. At some places, large billboards are allowed. These billboards can be a giant digital screen that can be updated from a
Other than the regular smartphone application-based taxi services, such as Uber and Careem, various delivery services are also becoming increasingly popular. A few examples of food delivery services in different countries are: UberEATS, Talabat, FoodPanda, Couch Potato. Similarly, in Pakistan, TCS Hazir and TCS HazirSubKuch are popular services to outsource shopping, delivery of goods, and mail pickup. Similarly, to deliver something urgent to a friend or a family member, to do grocery, or to get some medicines from a pharmacy, TCS HazirSubKuch is very common in Pakistan for example. Various services may require more detailed data and context-awareness information. For example, an alternate of a requested medicine that is not available, or allergy information while purchasing a medicine, or location-awareness when the delivery destination is mobile (such as a person on the move in a car). A tailored fog for such instances can be very useful in performing tasks locally. In addition to the above-mentioned applications, IIoT can help in receiving the packages as well when the recipient is not at the destined location. For example, automatically opening the door with the cell phone when the delivery person arrives at the door in customers absence. The delivery person rings the bell that gives an alert on the users phone. With a press of a button on the phone, the door opens and the delivery person can put the package inside of the door. A camera positioned to capture the door shows the event in real-time on the users phone. The door once closed, locks automatically and the lock gives a confirmation message to the users phone. Such kind of a service will be very useful where family members work and have problems in receiving their packages and posts since delivery is also made during work-hours. I. Smart parking In some countries, parking is outsourced by the city government to third parties which operate as an industry. Other than smart parking proposed by [18], in which vehicles get to know about vacant parking slots in real-time, parking can be enhanced in many ways. Suggestions can be made according to the parking rush trends and it can be more context-aware
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
according to season, weather, local events, such as sale at a certain store, and so on. IV. R ESEARCH C HALLENGES To realize the true potential of IIoT, we need to address several challenges. We discuss some of these challenges below.
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measurable - so that the acquired service can be compared with the agreed upon service; controllable - such that the factors that determine service satisfaction can be modified to achieve the required service; affordable - the SLA must be cost-effective for the involved services; and acceptable that is the involved parties need to mutually agree on the SLA, rather than one dictating it to the other [20].
A. Energy consumption and management Industries are the largest consumers of power in any country, thus, require dynamic power management. Depending on the type of the industry, energy consumption varies, which may even be different according to seasons particularly in the case of the food and textile industries. Energy consumption affects the network lifetime and is therefore an important factor in IIoT. In IIoT, not only sensors but actuators and robotic devices are also involved. Therefore, many data packets are continuously being exchanged resulting in a higher energy consumption. Since energy is a valuable resource, it affects time synchronization as well. Algorithms that are able to deal with time synchronization and energy consumption tradeoff, are more suitable for an effective and scalable IIoT environment. Dynamically managing power is an essential element of IIoT. Systematic mechanisms are required to adapt to the changing demand of an industry, during different times of the day, according to different prices and grid load. Some industries may even be run at night to cope with the power load.
D. Security and privacy of data and workers IIoT would be vulnerable to attacks that can affect the availability, confidentiality, and integrity of transmitted or stored data. With increased connectivity, more data is generated which may be susceptible to theft and misuse because several industrial deployments would be outdoor, such as construction and mining. When multiple nodes and systems communicate with each other, data communication and storage are more prone to intrusion and theft. The data can be misused and may even result in manufacturing malfunctions, which can have drastic effects on production, factory premises, and personnel working there. Moreover, interoperability features might increase security and privacy vulnerabilities in the IIoT environment, resulting in not only attacks but information misuse as well. Since different systems will be combining their resources in an interoperable IIoT scenario, there is increased likelihood that the data, information, and commands could be tampered with. E. Context and semantics-aware service provisioning
B. Interoperability of devices In IIoT, multiple subsystems and external systems would work together, causing interoperability issues [19]. For example, a smart industry is connected to an external smart grid, a production plant is connected to WoT service, and a production system of a factory is connected to storage system of the same factory, and so on. Several sensors and systems would be heterogeneous. Therefore, system and sensor integration as well as interoperability mechanisms become more of a challenge. Since many of the tasks (such as in a manufacturing environment where actuators are tasked to take actions) would be delay-sensitive, therefore, the integration and interoperability has to be not only seamless, but deliver high performance. C. Service level agreement and interoperability of services Along the same line of interoperability of nodes, services’ interoperability will be a challenge as well, especially in the case of a service level agreement (SLA). Resource federation of different IIoTs, SLA matching, SLA monitoring and violation, are important factors that need to be considered for scalable IIoT and for enabling inter-IIoTs communication. When relying on third party cloud-IoT services, the key concern is the performance delivered to the customer. Many of the IIoT applications (such as autonomous vehicles, vehicleto-vehicle communications, robotic communications in manufacturing and so on) would be sensitive to security and delay requirements. SLAs must have some of the following essential attributes: meaningful - that is relevant to the involved parties;
Given the dynamic environment in industry, the ability to discover web-services on the go to create an extended and flexible business process is much needed. An example of a context-aware service in a smart factory environment can be different temperature settings according to different products in a factory. In the case of IIoT, context-awareness can be primary [5], such as gathering context without any existing contextual information; or secondary, such as gathering context from an existing contextual information. Both types have different complexities and outcomes, and hence require more intelligence and efficiency. The same data can be used to derive different insights for different scenarios or even domains commonly known as sensing as a service. For example, data on different temperature settings for different products can be used in the transportation or cargo industry when designing the cargo compartment and cooling equipment. In the case of the medical domain, food stabilizers can be developed according to the related contextual information such as products ingredients, age, life, and required temperature settings. Moreover, developing semantic web-based services can be very important in the industrial scenario. Services are annotated on the basis of shared ontologies, which help in the discovery of web services, according to the semantics [21]. F. Fault detection and reconfiguration As the IIoT system becomes increasingly automated and heterogeneous entities are involved, the chances of failure increase. Device malfunction, delayed communication, and
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
connectivity failures are some common examples. A complete IIoT system must be robust and be capable of not only detecting and withstanding common faults but also be capable of detecting faults in time. Advanced fault detection algorithms will have to be applied at the hub, gateway, or middleware that is responsible for coordinating different machines and devices. Accuracy and timeliness are also important in detecting faults because one faulty object may hinder the whole manufacturing or industrial process, resulting in financial loss, energy loss, and loss of other related resources. A faulty network of sensors or machines should be able to reconfigure itself with no human intervention. If a sensor is not working due to some malfunctioning, it can be put to sleep until replaced and the sensor network settings can be reconfigured. In this way, not only the robustness is ensured, but energy is also saved. G. User-friendliness in the product deployment and usage Industrial workers may not be fluent with the most recent technologies, which makes it even more challenging to design devices and user-interfaces for an IIoT system. Unlike the typical traditional IT system and common IoT systems where either user interaction is low, or the user is well-aware of the technology he/she is using, in the industrial domain, factory workers with different backgrounds and experience are distributed over a large area. For example, in a factory or agricultural field, people in these areas will be deploying the devices and sensors with a low knowledge of electronics and communication. Creating seamless adaptation of the IIoT technology and user-friendly interfaces can help acceptance of IIoT. V. C ONCLUSION Recent advances in virtual sensor networking, robotics, and communication technologies have paved the way for autonomous industrial environment. Theoretically, there is a huge potential of IIoT and Industry 4.0. However, it brings a lot of challenges as well, which can be converted to opportunities if better planning and standardization are done. In this paper, we present an architectural introduction of IIoT and Industry 4.0 and discuss that how a middleware, such as a fog, can help realize different use cases in emerging industrial paradigms. We discuss some interesting use case scenarios related to different industries, such as transportation, smart grid, food & restaurants, advertisement, and tourism. We describe how IoT and fog can help such industries in becoming more efficient in the future. We also discuss some noteworthy challenges and how they can be converted into opportunities. Some of the challenges include context-aware and semantics-aware service provisioning in IIoT, interoperability of different devices and services, security & privacy, and energy consumption. This work can be very useful in terms of understanding the potential of IIoT and Industry 4.0. It will also motivate future research directions in IIoT for a wide range of application domains. ACKNOWLEDGMENTS
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of Qatar Foundation. The statements made herein are solely the responsibility of the authors. We thank the anonymous reviewers for their valuable comments which helped us to improve the content and presentation of this paper. R EFERENCES [1] L. Shu, M. Mukherjee, M. Pecht, N. Crespi, and S. N. Han, “Challenges and research issues of data management in iot for large-scale petrochemical plants,” IEEE Systems Journal, 2017. [2] N. Jazdi, “Cyber physical systems in the context of industry 4.0,” in Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on. IEEE, 2014, pp. 1–4. [3] R. Geissbauer, J. Vedso, and S. Schrauf, “Industry 4.0: Building the digital enterprise, pwc (2016),” 2017. [4] T. Qiu, Y. Zhang, D. Qiao, X. Zhang, M. L. Wymore, and A. K. Sangaiah, “A robust time synchronization scheme for industrial internet of things,” IEEE Transactions on Industrial Informatics, 2017. [5] C. Perera, C. H. Liu, S. Jayawardena, and M. Chen, “A survey on internet of things from industrial market perspective,” IEEE Access, vol. 2, pp. 1660–1679, 2014. [6] W. Lee, K. Nam, H.-G. Roh, and S.-H. Kim, “A gateway based fog computing architecture for wireless sensors and actuator networks,” in Advanced Communication Technology (ICACT), 2016 18th International Conference on. IEEE, 2016, pp. 210–213. [7] L. Monostori, “Cyber-physical production systems: roots, expectations and r&d challenges,” Procedia Cirp, vol. 17, pp. 9–13, 2014. [8] B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, and Q. Yang, “Incorporating intelligence in fog computing for big data analysis in smart cities,” IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2140–2150, 2017. [9] C. Ca˜nas, E. Pacheco, B. Kemme, J. Kienzle, and H.-A. Jacobsen, “Graps: A graph publish/subscribe middleware,” in Proceedings of the 16th Annual Middleware Conference. ACM, 2015, pp. 1–12. [10] V. Monajjemi, J. Wawerla, and R. Vaughan, “Drums: A middlewareaware distributed robot monitoring system,” in Computer and Robot Vision (CRV), 2014 Canadian Conference on. IEEE, 2014, pp. 211– 218. [11] S. S. S. R. Depuru, L. Wang, and V. Devabhaktuni, “Smart meters for power grid: Challenges, issues, advantages and status,” Renewable and sustainable energy reviews, vol. 15, no. 6, pp. 2736–2742, 2011. [12] H. Farhangi, “The path of the smart grid,” IEEE power and energy magazine, vol. 8, no. 1, 2010. [13] D. A. Chekired, L. Khoukhi, and H. T. Mouftah, “Decentralized cloudsdn architecture in smart grid: A dynamic pricing model,” IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1220–1231, 2018. [14] V. C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, and G. P. Hancke, “Smart grid technologies: Communication technologies and standards,” IEEE transactions on Industrial informatics, vol. 7, no. 4, pp. 529–539, 2011. [15] R. SHUKLA, “Smart waste management.” [16] Y. Glouche and P. Couderc, “A smart waste management with selfdescribing objects,” in The Second International Conference on Smart Systems, Devices and Technologies (SMART’13), 2013. [17] M. Aazam, M. St-Hilaire, C.-H. Lung, and I. Lambadaris, “Cloud-based smart waste management for smart cities,” in Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2016 IEEE 21st International Workshop on. IEEE, 2016, pp. 188–193. [18] S. Andreev, O. Galinina, A. Pyattaev, M. Gerasimenko, T. Tirronen, J. Torsner, J. Sachs, M. Dohler, and Y. Koucheryavy, “Understanding the iot connectivity landscape: a contemporary m2m radio technology roadmap,” IEEE Communications Magazine, vol. 53, no. 9, pp. 32–40, 2015. [19] O. Givehchi, K. Landsdorf, P. Simoens, and A. W. Colombo, “Interoperability for industrial cyber-physical systems: An approach for legacy systems,” IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 3370–3378, 2017. [20] A. V. Papadopoulos, S. A. Asadollah, M. Ashjaei, S. Mubeen, H. PeiBreivold, and M. Behnam, “Slas for industrial iot: Mind the gap,” in Future Internet of Things and Cloud Workshops (FiCloudW), 2017 5th International Conference on. IEEE, 2017, pp. 75–78. [21] K. Sivashanmugam, K. Verma, A. P. Sheth, and J. Miller, “Adding semantics to web services standards,” 2003.
This publication was made possible by NPRP grant # 81645-1-289 from the Qatar National Research Fund, a member 1551-3203 (c) 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TII.2018.2855198, IEEE Transactions on Industrial Informatics IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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Mohammad Aazam (S’11, M’15, SM’16) is currently working as a postdoc research associate at Carnegie Mellon University in Qatar since July 2017 and a part-time postdoctoral fellow at Ryerson University, Canada since March 2017. Previously, he has been a sessional faculty and postdoc fellow with Carleton University, Canada from July 20152017. He is also working as a postdoc collaborator with University of Kentucky, USA and University of Ontario Institute of Technology (UOIT), Canada. He completed his Ph.D. Computer Engineering from Kyung Hee University, Korea, in 2015. In addition to that, he has completed a course on Data Science with R from Harvard University, USA in 2017, in which he topped the course securing 100% score. He also completed a course on Internet of Things (IoT) from King’s College London, UK, in 2016. He has around 100 publications, including 3 patents. He is also a recipient of IEEE AIYEHUM 2016 award. For additional details: www.aazamcs.com.
Sherali Zeadally is an associate professor in the College of Communication and Information at the University of Kentucky. He received his bachelor and doctorate degrees in computer science from the University of Cambridge, England, and the University of Buckingham, England, respectively. He is a Fellow of the British Computer Society and the Institution of Engineering Technology, England.
Khaled A. Harras (S’05, M’09, SM’16) ([email protected]) is an associate professor at Carnegie Melon University Qatar (CMUQ), and Director of the Computer Science program there. Dr. Harras is the founder and director of the Networking Systems Lab (NSL) at CMUQ. He has more than 100 refereed publications and 4 US patents. Along with his research group in the past few years, he has won the best national computing research award twice, received two best paper awards, and his work has been featured online various venues like MIT Tech Review and Tech the Future. To date, he has been involved in or managing research grants that amount to more than 3 Million USD, and has supervised over 30 different personnel including undergraduate and graduate students, postdoctoral researchers, and research engineers.
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