Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments

Future Generation Computer Systems (

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Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs

Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments Rashmika Nawaratne a , Damminda Alahakoon a , Daswin De Silva a , Prem Chhetri b , Naveen Chilamkurti c, * a b c

Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia School of Business IT and Logistics, RMIT University, Victoria, Australia Computer Science and Computer Engineering, La Trobe University, Victoria, Australia

highlights • • • •

Justification of IoT and data interoperability for Metro Fire Brigade, Australia. Unsupervised learning enables data interoperability in IoT environments. A Growing Self Organizing Map based algorithm is proposed to address requirements. Proposed algorithm is evaluated on video surveillance data from IoT environments.

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Article history: Received 31 October 2017 Received in revised form 7 February 2018 Accepted 25 February 2018 Available online xxxx

a b s t r a c t Internet of Things (IoT) is predicted to connect 20.4 billion devices in 2020 and surge to 75 billion by 2025. Such a connected world where machines will communicate with other machines opens up huge opportunities and a very different way of life, with smart homes, self-driving vehicles and wearable devices. It is expected that such interconnectedness will enable the capture of events as data in real time and provide actionable insights to people and organizations to maximize efficiencies, be pro-active and more effective. Interconnected devices will require interoperability, and the seamless, secure and controlled exchange of data between devices and applications has been called data interoperability. Such a dynamic and volatile environment with a wide diversity of data will require a new breed of intelligent algorithms with the ability to adapt and self-learn as well as envisage and analyse events at multiple levels of abstraction to gauge association and interrelationships. This research proposes three algorithmic requirements for intelligent algorithms in such IoT environments: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. The paper first presents empirical results with real data from a fire department in Australia to highlight the need and value of IoT and data interoperability. Dynamic Self Organizing Map based unsupervised algorithms which satisfy the requirements are described and further empirical results are presented to validate the required functionality of these algorithms. © 2018 Elsevier B.V. All rights reserved.

1. Introduction The new era of Internet of Things (IoT) is a diverse space which encompasses a large variety of hardware and software ecosystems creating a paradigm that machines and devices interact with each other in a global network, thus expanding the future of technology

* Corresponding author.

E-mail addresses: [email protected] (R. Nawaratne), [email protected] (D. Alahakoon), [email protected] (D. De Silva), [email protected] (P. Chhetri), [email protected] (N. Chilamkurti).

to a wide range of application domains. Gartner Inc. forecasts that by 2020, the devices connected in IoT will grow up to 20.4 billion units from 8.4 billion which is in use worldwide by 2017 [1], which will expand the connectivity from large-scale industries to smart-homes, connecting smart appliances (washers, dryers and refrigerators), smart home safety and security systems (sensors, monitors, cameras, and alarm systems), and smart home energy equipment, like smart thermostats and smart lighting [2]. Due to the rich characteristics of such environments, being limited to one mode of data is not adequate thereby hardly provides a complete knowledge of the phenomenon of interest. With the large volume of data generated through such connected devices,

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it is essential that they work together to enhance and enrich the data to achieve common objectives [3]. It is expected that such interconnectedness will enable capturing events as data in real time and provide actionable insights to people and organizations to maximize efficiencies, be pro-active and more effective. This opens up the need for interoperability of connected devices and the seamless, secure and controlled exchange of data between connected IoT devices and applications, which has been called data interoperability. The data interoperability is incorporated with advanced analytics, employing intelligent algorithms based on Artificial Intelligence (AI), to create novel capabilities to integrate, manage and analyse the massive volumes of data and generate insights relevant to specific application domains. Such incorporation of intelligent algorithms in analysing IoT connected data is relatively novel, and mostly utilize standard supervised machine learning pipelines, that requires to be pre-trained with large amounts of labelled sample data [4]. In a dynamic and volatile environment with a wide diversity of data, will require a new breed of intelligent algorithms with the ability to adapt and self-learn as well as envisage and analyse events at multiple levels of abstraction to gauge association and interrelationships. In order to learning from continuous data in IoT environments, the intelligent algorithms, rather require to access previous training data, preserve previously acquired knowledge and accommodate new knowledge that may be introduced with streams of IoT data [5]. This research proposes three algorithmic requirements for intelligent algorithms in such IoT environments: unsupervised selflearning capability, ability to self-generate to the environment and incrementally learn with temporal changes. The paper first presents empirical results with real data from the Metropolitan Fire Brigade (MFB) in Victoria, Australia to highlight the need and value of IoT and data interoperability. Dynamic Self Organizing Map based unsupervised algorithms which satisfy the requirements are described and further empirical results are presented to validate the required functionality of these algorithms. As with the recent advancement of video analytics and the role of video data IoT environments, the empirical evaluation is conducted employing video surveillance data proving the capability of the proposed intelligent algorithms, adapting to multimodal data alongside the data interoperability. With this in mind, major contributions of this paper include the following:

• We evaluate empirical results with real data from the Metropolitan Fire Brigade (MFB) in Victoria, Australia to highlight the need and value of IoTs and data interoperability. • We investigate the need for intelligent algorithms to support data interoperability in IoT environment and identify three algorithmic requirements: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. • We develop Dynamic Self Organizing Map based unsupervised algorithms which satisfy the requirements. • We validate the proposed intelligent algorithmic solutions employing video data from IoT environments, confirming the capacity and capabilities. The rest of the paper is organized as follows; Section 2 presents an empirical evaluation with real data from a fire department in Australia to highlight the need and value of IoTs, followed by Section 3 delineating methodology and research design of the intelligent algorithms to nurture data interoperability, and workings of the proposed intelligent algorithms. Section 4 presents an empirical evaluation to validate the functionality of these algorithms and finally, the paper conclude in Section 5.

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2. Background 2.1. Fire prevention, Internet of Things (IoT) and interconnected environments Smart homes filled with connected devices in a IoT environment, loaded with possibilities to make household more convenient, and more comfortable. The connected devices include but not limited to smart appliances (washers, dryers and refrigerators), smart home safety and security systems (sensors, monitors, cameras, and alarm systems), and smart home energy equipment, like smart thermostats and smart lighting. Fire detection and prevention employing IoT devices is a prime concern in smart home safety systems, however, it is still evolving in baby steps. [6] suggests an IoT based intelligent fire emergency response system that can control directional guidance intelligently according to the time and location of a disaster. The emergency response system is designed by incorporating integrated control system using wireless sensor networks, and guide lights and smart phone application to direct evacuees to identify exit points preventing directional confusion of the emergency lights and inappropriate evacuation guidance, as well firefighters to locate the fire origins. [7] proposes an implementation for IoT in a smart home environment to monitor and control the home appliances via World Wide Web. The solution controls home appliances via smartphone using Wi-Fi as communication protocol and raspberry pi as server system, employing low power communication protocols like ZigBee and, Wi-Fi. This system encompasses protection from fire accidents, by detecting fire using fire sensors and immediately an alert message along with the image and video taken in camera is sent to mobile phone and an automatic phone call is made to nearby fire station. Similar approach is proposed in [8] using Android based user interface for control of home appliances and alert system to trigger in fire incidents. The multiple approaches prevailing IoT in smart home environment to overcome fire incidents use a detection approach with extensive use of fire detection sensors. 2.2. Data interoperability It is imperative that the connected devices can exchange data among themselves as well as with third-party applications and services to realize truly connected smart home environment. Based on [9], interoperability challenges results in a barriers for the smart home market growth. For a sustainable growth of smart home sector, accomplishing interoperability at all three levels: device-to-device connectivity, device to-platform and app-to-app is essential. However, the cost associated with a generic smart home solution to achieve interoperability is a main issue [10]. [11] proposes a web based system architecture based on the REST architectural model exploiting the HTTP protocol in achieving interoperability of smart home devices. [12] exploits use of service robots in improving interoperability in of heterogeneous devices in a smart home network. Similarly, a handful of protocols have been proposed in multiple researchers in achieving interoperability in a smart home networks, however, a lot of research and work still needs to be done. As concludes in [13], currently the extensive amount of devices in smart networks employ different protocols that cannot communicate with each other, and even the products with the same protocols are not necessarily interoperable across different profiles. Which opens up the necessity for an advanced big data solutions and intelligent algorithms are needed to deal with the vast amount of data generated within the IoT.

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2.3. Justification for fire prevention IoT devices in smart homes In this section we use data collected by the Metropolitan Fire Brigade (MFB), in the Victorian state of Australia to demonstrate the benefits of fire prevention IoT devices in smart homes. To our knowledge there has been no such detailed study of real fire incidents conducted focussing on justifying the adoption of new technologies such as IoT and to identify the algorithmic requirements for such adoption. The rationale for using the MFB data are as follows:

• Provides the opportunity to understand the current home fire incidents from multiple dimensions such as locations, cause, times, seasons, household information etc., supported by a large number of real incidents over the period of 10 years. • Contains reports of the incidents providing a rich source of textual data which can be analysed to identify details which could be lost in other sources such as numeric and categorical data. • Contains valuable information which to identify limitations in current fire detection techniques and enable planning for better and more pro-active approach to use of new technology and design for fire prevention. State of Victoria in Australia has a population of approximately five million people. Similar to many other regions, home fires are a major concern in Victoria and the Metropolitan Fire Brigade is the authority that attends to all such incidents. The MFB Fire Incident dataset consists detailed records of a total of 17849 fire incidents which occurred during the 10-year period between June 2005 to May 2015. The dataset includes fire incidents involving property, for example, dwellings, public buildings and business buildings, described by information such as date and time of the incident, whether the incident was suspicious, whether the incident was contaminated, the area of fire origin and a text description of the incident and the actions taken to resolve the situation. Over 97% of incidents relate to residential property of which 61% with single families, 18% where 2–20 families reside and 21% with over 20 families. 2.3.1. Analysis of causes, origin and times of fire The MFB fire incident dataset contains a number of numeric and categorical variables such as the place of origin as well as textual description of the cause of the fire. Primarily, the source of ignition was identified based on the text description. Nearly 19% of fire incidents (3365 incidents) were identified as ignited from the stove. Followed by gas, fan, pot, oven respectively 11%, 9%, 8% and 7% of the total incidents. Inconsiderate use of cigarettes is responsible for 3% (637) of the total incidents. From Fig. 1(a), it is evident that a significant number of fire incidents caused by the misuse of cooking appliance, typically leaving the appliance unattended while some incidents were due to placing articles too close to a heat source, mostly close to cooking appliances. Whilst, the highest amount of fire incidents ignited from the kitchen or cooking area, cigarettes and cigarette butts are also responsible for a considerable number of incidents. Analysis if the source of origin shown in Fig. 1(b) indicate that nearly a half of the cases originate from the kitchen which corresponds to the causes identified. It is also seen that a considerable number of fire incidents originate from the bedrooms, lounge area, roof, exterior walls and the garage. The analysis tells us that home fires can originate from various places in a house and are caused due to a range of reasons. Fig. 2 illustrates the distribution of fire incidents over the duration of the day. The highest number of incidents are reported in the evening, specifically from 5 pm to 9 pm, where the peak falls 6 pm

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to 7 pm. After 9 pm, the frequency of incidents gradually declines until 5 am and starts to increase again. From 6 am, the incident count gradually increases and by 12 noon there is a slight local maximum. Considering the season of the year, the analysis illustrates that maximum number of fire incidents are reported in the winter period, 27% of all incidents — 4808 in total, which falls from June to August annually. Autumn and Spring have almost equal distribution making up 25% of the total. Summer accounts for lowest being 24% of incidents. This analysis tells us that most home fires occur when residents are at home and awake — during evening ‘family’ times as well as in winter where people tend to be home rather than summer. Since the focus of the analysis is to better understand the origin, causes and times of fire to determine whether technology such as IoTs can be utilized to improve fire detection, further analysis of the text descriptions were conducted. 2.3.2. Analysis of textual descriptions of fires This analysis was conducted using text clustering and Natural Language Processing (NLP) separately on the main sources of origin of home fires identified from previous analysis. (See Table 1.) It is important to note that highest percentage of fire incidents were reported within the duration of 5 pm to 9 pm. Over 80% incidents were due to not attending to cooking and leaving the food on the kitchenware. 855 of the total incidents were originated from the lounge area and resulted in 3 clusters as shown in Table 2. 445 incidents originated from the garage/vehicle port. Clustering resulted in 5 clusters as described in Table 3. The second highest location of fire origin was the bedroom, containing 1529 incidents. The clustering resulted in 5 clusters as described in Table 4. The unsupervised cluster analysis of the MFB fire data provides indicators on the possible causes respective to different locations and other situational conditions in a home environment. The analysis demonstrate that fires are caused by a combination of factors and in various locations in a house at differing times and also impacted by seasonal weather patterns as well as human behaviours. At present the smoke detector is the main device which is utilized to alert residents of potential fires, which is mainly used to warn residents once a fire has actually started for the purpose of evacuation, contact fire authorities or if minor incident, put out by themselves. The analysis also reveals that the post fire reports by human observers contain valuable details which if known prior could be utilized to prevent or reduce the damage by such fires. IoT and smart technology could provide us the ability to automatically generate such knowledge prior to the incident. 2.4. IoT and smart technology for home fire prevention The future smart home will be equipped with a variety of IoT devices and surveillance cameras which has the technological capability for transforming the current reactive ‘fire detection’ into a proactive fire prevention situation, which not only can save lives but help to minimize damage to property and reduce need for costly intervention by fire authorities. Table 5 summarizes the origin and causes identified previously and proposes preventive action and potential technology in a smart home environment that could be involved in each situation. Based on the analysis we propose our first hypothesis: Data captured by IoTs and smart home technologies can be used to automatically capture potential causes of an event such as a home fire to generate a possible fire scenario prior to the actual incident. As represented in Table 5, multiple IoT devices need to operate simultaneously connected to each other in achieving the proposed

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Fig. 1. Causes (a) and origin (b) of home fires.

Fig. 2. Fire incident spread over time of the day — accumulated hourly (a) and over the seasons of the year (b). Table 1 Analysis of fire incidents originated from the kitchen. Cluster

Key events identified using text analytics and NLP

Most frequently used words

Possible situation and cause

1

Related to burnt food in kitchenware such as hotplate, toaster, microwave, stove

burnt, foodstuffs, toast, alarm, toaster, activated, room, damage, unit, staff

Residents attending to other activities at home while food is being prepared

2

The fire incidents took place in the kitchen from electrical items

Advised, contact, electrician, isolated, power, motor, owner, burning, checked, area

Malfunctioning or over use of electric items

3

The fire incidents that were reported to the MFB by neighbours or a third. The MFB had to force entry to the property in most of the cases

Alerted, neighbours, entry, alarm, operating, forced, detector, returned, front, gain

Incidents while residents not at home

Table 2 Analysis of fire incidents originated from the lounge area. Cluster

Key events identified using text analytics and NLP

Most frequently used words

Possible situation and cause

1

Incidents that occur during the midnight time. 50% of the incidents deemed suspicious.

Deemed, suspicious, search, police, handed, vacant, alight, well-conducted, arrival

Occurred around mid-night, cause unknown but considered suspicious (police involved)

2

Incidents mostly occurred during 5 pm to 9 pm. The prime cause for the fire was a short circuit of electric equipment in the living area.

Electric, short, circuit, wall, caused, shut, heater, ventilated, notified, lounge

Over use of electrical items (possibly, TV, games, music etc.) during evening with most residents are home

3

The incidents related to electric equipment and which were caused by children.

Light, switch, wall, started, power, inside, children, investigation, isolated, board

Unsupervised children and electrical points without child safety measures

solutions for preventive actions. At present, data from such devices are fragmented and locked in silos due to differences in frequencies, granularities, type of data and incompatibility of formats in proprietary platforms and in numerous evolving standards that focus on limited domains. For instance, video surveillance cameras provide continues streams of real-time video footages, heat

sensors and weather sensors provide respective data in as a data stream, and smoke detector would provide information when it detects a smoke. In practise, a diverse range of vendors and vendorspecific middleware can be found on different IoT devices, that have resulted in multiple standards with incompatible data models and definitions for IoT devices [15]. We propose the development

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Table 3 Analysis of fire incidents originated from the garage. Cluster

Key events identified using text analytics and NLP

Most frequently used words

Possible situation and cause

1

Fire in garage which spread to other adjacent areas

Lit, contained, detached, investigated, cigarette, adjacent, open, alarm, battery

Leaving cigarette butts in the garage or lighting a fire on wooden/metal items

2

Fire originated in garage. Most of the incidents occur in the daytime in spring, summer and autumn.

Destroyed, approx., contents, dwelling, heat, single, power, spreading, well, roof

Due to heating of garage roof in hot weather conditions and flammable contents in garage

3

Incidents caused by electric items, such as wire boards, light bulbs.

Board, carpark, wiring, electrical, contact, causing, property, burning, started, damaged

Faulty or over use of electricity points or electrical items in garage

4

Includes the incidents, where the cause of the fire is unknown causing a suspicion.

Unknown, persons, details, set, const, constable, damaging, alight, mfb, structure

Cause unknown considered suspicious, police involved

5

Incidents originated from the rubbish bin. Most cases were cigarette butts in the rubbish bin.

Confined, bin, front, occupant, rubbish, damage, window, discarded, cigarette, owner

Rubbish bin being confined to garage and placing cigarette butts in such bins

Table 4 Analysis of fire incidents originated from the bedroom. Cluster

Key events identified using text analytics and NLP

Most frequently used words

Possible situation and cause

1

This contains the issues which deemed suspicious and handed over to the police. The bedroom, which is the origin of fire was locked out and the firefighters had to force entry. The incidents caused physical harm to residents and taken away to hospitals.

Forced, entry, flat, hospital, window, door, handed, damaged, issuing, side

Possible suicide attempts

2

The incident caused physical damages to residents as well as the smoke exposure caused issues in breathing.

Hospital, transported, male, safe, caused, av, contained, mas, minor, year

Bedroom fires while residents were sleeping while the doors and windows closed

3

Most of the incidents caused by malfunctioning/extreme use of electrical equipment.

Forced, fitting, entry, light, persons, wiring, attendance, wall, ventilated, investigated

Electrical equipment malfunction or overuse

4

Incidents directly caused by smoking of cigarette. The residents have carelessly dumped cigarette butts near bedroom prior to sleep, which has caused a fire.

Smoking, cigarette, lamp, ignited, beddings, mouldering, small, occupant, extinguished, water

Smoking in bedroom and falling asleep

5

Short circuit of electric equipment.

Electric, turned, blanket, appeared, left, circuit, short, fan, result, hot

Minor fires due to electrical equipment malfunction or overuse

of a fire incident profile (or signature) using the variety of data to enable the more comprehensive representation of a home fire incident using variables — time, weather, location (room, position), fire causing factors (heat, smoke, condition of electrical items, relevant input from surveillance cameras, etc.). A major problem to be resolved in such a situation is the need for interoperability between such a variety of devices. Interoperability is often claimed by all connected IoT devices, such as home appliances, surveillance cameras, from different vendors if all of them implement in a standard specification and employ a common middleware. In such a landscape, we propose to address the connectedness and interoperability at the level of aggregation level in means of data interoperability. Regardless of differences in frequencies, granularities and different formats of data as originally encoded by the device, the aggregation level provides a common form that allows to interoperate the data in multiple IoT devices. Consider the scenario based on the previous analysis, of threat of fire incident originated at lounge area of a house, caused by over use of electrical items (possibly, TV, games, music etc.) during evening with most residents are home. One approach to detect such fire incident in advance is by installing heat sensors on identified electrical items that use regularly and use of smart power outlets. Further, improve the possibility of identifying the threat of fire by detecting sparks igniting from electrical equipment through employing surveillance camera. In the proposed technology architecture in Table 5, we employ three devices: surveillance camera, heat sensors and smart power

outlet. The data collected through the three devices are from different in frequencies, granularities, and mainly different formats. Table 6 contains characteristics of sample devices. In order to identify potential fire threats, data from all three devices needs to be analysed together, creating a need for data interoperability. However, the diverse characteristics of data such as frequencies, granularities and formats, formulates a challenge in data interoperability, which can be overcome by transforming the diverse data into a common representation at the aggregation level. Generation of actionable insights, such as fire prediction, can then be utilized through employing intelligent algorithms, by facilitating data interoperability. We propose our second hypothesis focussing on the intelligent algorithmic requirements which can facilitate the requirements identified above in achieving the first hypothesis: Hypothesis two: Unsupervised self-evolving intelligent algorithms, which can self-generate to suit the data and environment with the ability to work with data on multiple granularities can facilitate data interoperability in and environment with smart devices. 3. Unsupervised intelligent algorithms for interoperability in IoT environments This section proposes several intelligent algorithmic features which can facilitate interoperability as discussed in Section 2.4. To highlight the broader view and the context in which the algorithmic features are proposed we present a three-tier architecture,

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Table 5 Fire incident preventive techniques using IoT and smart technology. Origin of fire

Cause

Preventive actions

IoT and smart technology

Kitchen

Residents attending to other activities at home while food is being prepared

Employ smart cooking appliances for preparing food and use smart power outlets in case of a power shortage

Smart cooking appliances (such as electric smoker, kitchen thermometer, connected coffee makers, intelligent ovens), smart power outlets and heat sensors

Malfunctioning or over use of electric items

Use of smart power outlets to control power supply and incorporate surveillance cameras to detect sparks caused by electricity using intelligent algorithms

Smart power outlets, surveillance cameras and heat sensors

Incidents while residents not at home

Employ smart cooking appliances for preparing food, and detect unattended cooking areas for longer durations using smart cameras incorporated with intelligent algorithms

Smart cooking appliances, smart power outlets, Surveillance camera and heat sensors

Over use of electrical items (possibly, TV, games, music etc.) during evening with most residents are home

Install heat sensors on identified electrical items that use regularly and use of smart power outlets. Identification of causality for fire ignition can accelerate through detecting spark employing surveillance camera.

Smart power outlets, heat sensors and surveillance camera

Unsupervised children and electrical points without child safety measures

Install smart power outlets in areas where children mostly spend time. Further, install surveillance cameras integrated with intelligent algorithms to detect dangers

Surveillance camera and smart power outlets

Bedroom fires while residents were sleeping while the doors and windows closed

Install both heat sensors near electric items in bedroom and smoke detectors to alert prior to ignition

Heat sensors, smoke detectors

Smoking in bedroom, dumping cigarette butt bedside and falling asleep

Use smart ashtrays that prevent fire arousing from cigarette butt

Smart ashtrays

Rubbish bin being confined to garage and placing cigarette butts in such bins

Detect and alert when the rubbish bin is filled and ready to clean as well it is on fire

Smart rubbish bins [14]

Faulty or over use of electricity points or electrical items in garage

Use of smart power outlets to control power supply and incorporate surveillance cameras to detect sparks caused by electricity using intelligent algorithms

Surveillance camera, smart power outlets

Due to heating of garage roof in hot weather conditions and flammable contents in garage

Detect heat and weather conditions and identify potential overheating and fire ignition by employing intelligent algorithms

Heat sensors, weather sensors

Lounge

Bedroom

Garage

Table 6 Characteristics of IoT devices. Device

Characteristics

Surveillance camera

Surveillance cameras come in diverse types and number of feature options including, filed of view, sound, resolution (480 pixels to 1080 pixels), network capability (wired, Wi-Fi, Bluetooth, etc.), night vision, motion detection, video compression (Motion JPEG, MPEG-4, H.264) and frequency (15 to 30 frames per second) [16]

Heat detector

Heat detectors are designed to respond when the thermal energy of a fire increases in the heat sensitive element. Network capabilities, reading frequencies, trigger thresholds and operation type (rate-of-rise or fixed temperature) are few characteristics that differs in heat detectors.

Smart power outlet

Smart power outlets enable to turn devices on and off remotely, and monitor energy usage through networks. The characteristics include, network connectivity type (Wi-Fi, Bluetooth, ZigBee, LTE, etc.), frequency and further allows to set automation rules for intelligent behaviour.

illustrated in Fig. 3, primarily for capturing data from the IoT devices such as smart kitchen appliances, surveillance cameras, and other smart home sensors. This preliminary data is fed to a cloud gateway at an aggregation level in order to provide a bridge from the IoT data to the data interoperable analytics platform. The actionable insights generated from the analytics and intelligence layers of the architecture will be employed to inform the residents,

authorities such as fire department and the police, and the government for statistical purposes to enhance the capabilities further with feedback. Our contribution in this paper mainly focuses in achieving data interoperability through intelligent algorithms, aligned with AI and machine learning, as illustrated in the Analytics tier of the smart home architecture in Fig. 3. We propose three algorithmic

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Fig. 3. Smart home architecture [17].

requirements for intelligent algorithms in such IoT environments: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. 3.1. Unsupervised self-evolving AI Application of machine learning is mainly based on learning from examples in a training dataset i.e. the learning algorithm is trained using a labelled dataset where each record is assigned a label of a known behaviour. With the extensive use of large volumes of IoT sensors and data, in most of the practical applications, finding labelled data is unrealistic and generating such labelled data is time-consuming and expensive. However, when considering human cognition, learning is an incremental process. A human will develop a basic understanding from available information at first, and incrementally refine the understanding as new information arrives. Thus, learn, un-learn and re-learn is a continuous process in human cognition. Inspired by the human cognition, we developed our methodology to exploit both predictive machine learning algorithms, as well incorporate unsupervised self-evolving AI algorithms to address three key capabilities in achieving interoperability in the IoT environment. As depicted in Fig. 3, the analytics platform consists of two prime components, (i) unsupervised self-evolving AI module (Fig. 4), and (ii) predictive analytics module. The data stream will initially self-organized based on its similarity, exploiting Growing Self-Organizing Maps (GSOM) algorithm that have the capability of hierarchical abstraction of multiple granularities of the data. Hierarchical abstraction is achieved using the spread factor, a primary parameter of the GSOM algorithm. The temporal changes will be acquired into its knowledge base by exploiting Incremental Knowledge Acquisition and Self Learning (IKASL) algorithm. The self-incremental learned knowledge will then be assessed with the predictive analytics module to generate actionable insights in the intelligence layer of the intelligent smart home architecture.

Fig. 4. Unsupervised self-evolving AI module.

3.2. Unsupervised self-learning for automatically capturing previously unknown patterns Most of the environments today are dynamic and ever-evolving, generating high-volume of correlated contextual information of varying quality and complexity. Analysis and generating actionable insights from the ever-evolving IoT data requires the contextaware applications to dynamically adapt their behaviour at run time. [18] With the high-volume and complexity, manual operation involving human operators is unrealistic and not. Such unsupervised self-learning AI methods are useful in mining largevolumes of IoT sensor data in finding unforeseen and novel patterns in the data that would support generating actionable insights. The Growing Self-Organizing Maps (GSOM) algorithm is one prominent unsupervised self-learning algorithm, comprising of competition and correlative learning, inspired by Hebbian learning rule and topography preservation in cortical maps. It is capable of unconstrained learning and node growth in the neural network and ensure the development of a natural topology of the data space. The GSOM initialize with four nodes and dynamically grows to represent the input data. During the node growth of the neural network, the weight values of the nodes are self-organized. In the growing phase of the GSOM algorithm, the closest weight vector will be identified for each input as the winner, and accumulate

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3.4. Incremental learning to adapt temporal changes in the data

Fig. 5. Self-learn by weight adaptation in GSOM.

as an error value of the winner neuron. If a neuron’s error value contributes significantly to the total error of the network, then its Voronoi region [19] is under-represented by the neuron. Consequently, a new neuron is generated as a neighbour of the neuron to achieve a better representation of the region. The node generation is determined by following two conditions; 1. If the network has adequate neurons to process the input data, the weight vectors of the neurons are adapted to represent the input vector distribution as illustrated in Fig. 5(a); 2. If the network has inadequate neurons to process the input data, the inputs which would have been spread out to neighbouring neurons will be accumulated on a new neuron as illustrated in Fig. 5(b). The closest neuron will be identified using a distance matrix, a vector-based similarity measure such as Euclidean distance or Cosine similarity. For a comprehensive reading of the GSOM algorithm refer [20]. 3.3. Self-evolving and generative structure to generate abstract representations from data in a hierarchical approach

One of the most important aspects of the IoT data is that the incremental, time series nature of the sensor data. The IoT platforms such as surveillance video feeds, smart home sensor data, weather data acts as data streams, continuously accumulating large-volumes of data. This phenomenon emerges the necessity for the analysis algorithms to have the capability to incremental learn from the data streams. The GSOM algorithms extends its capabilities to incrementally acquire knowledge from the data streams and self-learn, in the form of an advanced incremental learning algorithm — Incremental Knowledge Acquisition and Self-Learning (IKASL) [21]. The IKASL algorithm can be perceived as an n-layer structure, where ‘n’ is the number of learning sequences. Each learning sequence comprises of two sub-layers, a learning layer and a generalization layer. The learning layer will encompass the GSOM functionality organizing the inputs into clusters based on its closeness property. The generalization layer encodes a generalized representation of the immediate learning layer, which will act as the starting layer for the next learning layer. The structural formation of the IKASL model is depicted in Fig. 7. The starting layer acts as the inception for the IKASL model. The inception encompasses the same as GSOM algorithm, such that as the first sequence of the dataset is fed into the model as tuples, a winning node will be selected based on a selected distance measure. The winning node will adjust its weight based on the input tuple as well the neighbouring nodes will adjust their weights to reflect accordingly. Following one iteration of learning, all winning nodes for in that layer are identified and generalized such that the result will reflect two important outcomes of the learning process; the knowledge embodied in the weight vectors of the winning node, and knowledge embodied in the weight vectors of the neighbourhood of winning nodes. The fuzzy integral [22] is used to combine the weight values into a single weight vector at the generalization layer. This generalization layer will represent the knowledge acquired through the self-organizing process in the learning layer and form the basis for further learning for the next sequences in the dataset. The same phases; learning and generalization, will continue for all learning sequences. The complete proceedings of the IKASL algorithm is presented in the original paper [5]. 4. Empirical evaluation

GSOM algorithm has the capability to control its growth enabling to observe the most significant clusters first and then, once obtain an idea of the overall dataset, to further spread out the mapping and visualize finer clusters. This is achieved by the parameter spread factor, which is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, that can be compared and analysed with better accuracy. This self-evolving and generative structure will also facilitate IoT sensor data to be hierarchically analysed in means such that decisions be made on finer cluster regions of interest. The interest, generally, depends on the abnormality, volume and frequency of the data. The data will self-learn with a low spread factor at the beginning of the analysis and then gradually increase the spread factor for finer observations of selection regions of the data. The spread factor takes values between zero and one and is independent of the number of dimensions in the data. Fig. 6 illustrates the data being self-evolved in a generative structure with the GSOM algorithm. Fig. 6(a), (b) and (c) depicts finer analysis of IoT data analysed from GSOM algorithm respectively with a spread factor of 0.3, 0.5 and 0.83.

This section presents experiments designed to demonstrate the capabilities of the proposed methodology to achieve interoperability in IoT environments through self-learning and incremental learning intelligent algorithms. The empirical evaluation was conducted by employing video surveillance data. The evaluation consists of two parts, (i) demonstrating the unsupervised selflearning, and self-evolving structure of the proposed algorithms, and (ii) demonstrating the incremental learning capability of the proposed algorithms. The Action Recognition Dataset from [23] was selected for the experiment as it contains 44 short video clips acquired under unconstrained real-world conditions. The videos are 640 × 480 pixels in size and were recorded using a consumer handheld camera. The video dataset contains a number of scenarios broadly grouped under road surveillance and indoor surveillance. Road surveillance scenarios contain videos where pedestrians crossing the road, walking on the sidewalk, waiting near a bus stop, walking in groups and having conversations beside the road. In the indoor surveillance video scenarios, videos of people having conversations in shopping malls, waiting in queues for delivery at food courts and people walking in an indoor environment were contained.

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Fig. 6. Hierarchical clustering capability of GSOM.

From a heuristics-based evaluation each 5th frame adequately captures the change of movement of the video and thereby was sufficient for the experiment [24], resulting in 38 frame sequences. Subsequently, Microsoft Vision API [25] was used to compute feature vectors by identifying key objects, actions, behaviour, and background by obtaining, (i) description of the video frame (TA) — a complete English sentence and set of keywords that describe the frame with the highest confidence, which includes the confidence score, (ii) tags describing the frame (TB) — an extensive list of keywords related to the frame which contains objects, actions, behaviour and environmental factors. The features were engineered to represent the feature space employing the Vector Space Model (VSM) [26]. In this experiment, a feature vector is a dictionary that contains the complete list of vocabulary with a weight for each keyword. The weight (w) of the VSM was calculated as,

⎧ 0.0, ⎪ ⎨ 0.3, w= ⎪ ⎩0.7 × conf (t), 0.3 + 0.7 × conf (t),

t t t t

̸∈ TA ∆ t ̸∈ TB ∈ TB ∈ TA ∈ TA ∆ t ∈ TB

(1)

where ‘t’ is the keyword and conf (t) is the confidence score of term ‘t’ in TA. TA contains an ordered list of tags with its confidence score whereas TB contains an unordered list of tags that are relevant to the specific video frame. Therefore, the relevance of tags in TA is considered prominent than TB for the pathway generation with IKASL, consequently defined weight 0.7 for tags in TA and 0.3 for tags in TB in Eq. (1). The quality of clustering in action recognition dataset was used to derive the weights. 4.1. Unsupervised self-learning, and self-evolving structure of the proposed algorithms Initially the first frames of the multiple video streams were analysed employing the GSOM algorithm. The parameters of the algorithm were set as, spread factor, SF = 0.3, initial learning rate, α0 = 0.3 and initial neighbourhood radius, N0 = 4. Further, employed 100 growing iterations and 100 smoothing iterations. Subsequently, the spread factor was increased, SF = 0.83, to explore the fine granularities. Fig. 8(a) illustrates the initial clustering of the surveillance data. Contrasting with Fig. 6(b), it can be realized that the marked clusters A and B has expanded into finer details. Fig. 6(a) — cluster A contains videos of people walking on pathways both on sidewalks, between buildings and in parks. In Fig. 8(b), the same cluster has expanded and the videos were sub clustered into multiple granularities such that one sub cluster contains, only people walking on roadside sidewalks and another cluster contains people walking on sidewalks near parks. One video, which the people walk between

buildings, has moved to a single node with no similar videos as it was the only video of that kind. Similarly, Fig. 8(a) — cluster B, contains videos of people crossing the road, and vehicles moving. The same cluster has expanded in Fig. 8(b) such that the videos which the vehicles are passing by have clustered together where the videos of people crossing the road have clustered together. However, it further illustrates that the cluster C in both GSOM maps are together. The granularity which the spread was increased has not been adequate to expand the cluster, which includes videos of indoor, in a shopping environment where people formed as groups. 4.2. Incremental learning Twenty time-steps from the video footages were processed and input into the IKASL-based incremental learning algorithm. The learning process resulted in generating five pathways up to the 20th iteration. Fig. 9 illustrates the pathways generated by the algorithm and are numbered based on the generation time. At the first time-step the input video frames were categorized into three clusters and in the 2nd time-step a new cluster was generated based on the activity of the video frames. Based on the distances of the feature vectors, at the 2nd timestep, IKASL algorithm identifies the 3rd cluster contains 2 subcategories, one containing videos of group of people walking on sidewalks and standing beside street, and the other cluster containing videos of individual people walking on sidewalks. Similarly, at the 19th timestep, 2nd cluster is divided into two new subcategories, one containing individual people walking and the other containing group of people standing together. Fig. 10 illustrates the final clusters, the IKASL algorithm incrementally learned from the video footages. Cluster 1 contains videos of people in indoor environments, cluster 2 contains videos of people crossing the road and vehicles moving, cluster 5 contains videos of people walking on sidewalks, cluster 3 contains videos of people structured as a queue waiting for bus or to purchase from a shop and final cluster 4 contains videos of park environments that contains greenery and trees. Contrasting with Fig. 7, the cluster 2 and cluster 5, both contains videos of roadside environments, which have divided into sub clusters only at the 19th timestep. 5. Discussion and conclusion This research highlights the advantages of unsupervised selfevolving algorithms in facilitating interoperability in IoT and smart technology environments. Due to the fast increasing adoption of IoTs, video surveillance and other smart devices, streams of fast changing, dynamic and volatile data are being generated at a unprecedented rate. The technology platforms, intelligent algorithms

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Fig. 7. The IKASL model [5].

and are important for attaining the potential benefits from this data. But a crucial issue is the interoperability of these technologies, devices and the data generated and captured by such devices. In this research we focus on data interoperability and propose several intelligent algorithms which have the capacity to facilitate data interoperability. The key features of the algorithms are: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. The paper initially uses a dataset from the Metropolitan Fire Brigade from a state in Australia to demonstrate the value of human observer generated reports in identifying the causes of home fires. This detailed analysis is used as the basis to derive the hypothesis: Data captured by IoTs and smart home technologies can be used to automatically capture potential causes of an event

such as a home fire to generate a possible fire scenario prior to the actual incident. The need of data interoperability for achieving such outcomes is discussed and the intelligent algorithmic features are the introduced, which can facilitate such functionality. This leads to the second hypothesis: Unsupervised self-evolving intelligent algorithms, which can self-generate to suit the data and environment with the ability to work with data on multiple granularities can facilitate data interoperability in and environment with smart devices. The unsupervised algorithms are discussed in detail and empirical results with video surveillance data are presented to demonstrate each of the three features in the algorithms. Future work will focus on collecting IoT and other sensor data and trialling the algorithms integrating video data to comprehensively demonstrate the practicality of the system.

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Fig. 8. (a) GSOM with SF = 0.3, (b) GSOM with SF = 0.83.

Fig. 9. Incremental learning of video sequence data from IKASL algorithm.

Fig. 10. Final clusters resulted from the incremental learning from video streams.

Acknowledgement The authors would like to thank the Metropolitan Fire Brigade (MFB) in Victoria, Australia for providing real fire incident dataset for the research purposes. References [1] Gartner Says 8.4 Billion Connected, (2017). www.gartner.com/newsroom/id/ 3598917. (Accessed 28 October 2017).

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– Rashmika Nawaratne received the bachelor’s degree, with a first class as the top in his batch, from the Department of Computer Science and Engineering of University of Moratuwa, Sri Lanka, in 2014. He is currently pursuing his Ph.D. studies in Data Analytics and Cognition at La Trobe University, Australia. His research interests include self-learning, incremental learning, video analytics, deep learning and human cognition. Prior to commencing his Ph.D., he was employed at a Software Product Development organization in the capacity of a Technical Lead.

Damminda Alahakoon is Professor of Business Analytics at the La Trobe Business School and the Director of the Research Centre for Data Analytics and Cognition. Damminda has over 15 years’ experience as an academic in several Australian Universities as well as over 10 years’ in the IT and finance industries. His research expertise lies in the areas of Data Mining, Predictive Analytics, Text Analytics, Machine Learning and Business Intelligence and the harnessing of such theories for practical tools and innovative technology for industry.

Daswin De Silva is a senior lecturer at La Trobe University, Australia. Daswin completed his Ph.D. at Monash University on Machine Learning and Artificial Intelligence. His research interests include cognitive computing, autonomous learning algorithms, incremental knowledge acquisition, stream mining, social media and text mining.

Prem Chhetri is a Professor of geo-logistics at RMIT University in Australia. He was a Deputy Head for Industry Engagement at RMIT and the Programme Director for Open Australia Universities. Prem obtained a Ph.D. in Geospatial Science from RMIT University in 2003. His recent research focused on port logistics, climate change, urban modelling, tourism potential mapping, emergency response, skills and training, and the application of GIS and GPS in transport, infrastructure and logistics planning.

Naveen Chilamkurti obtained his Ph.D. from La Trobe University and currently is the Acting Head of Department, Computer Science and Computer Engineering, La Trobe University, Australia. He is the Inaugural Editorin-Chief for International Journal of Wireless Networks and Broadband Technologies launched in July 2011. He has published about 165 Journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia and wireless sensor networks. He currently serves on the editorial boards of several international journals including Wiley IJCS, SCN, Inderscience JETWI, and IJIPT. He is a Senior Member of IEEE.

Please cite this article in press as: R. Nawaratne, et al., Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments, Future Generation Computer Systems (2018), https://doi.org/10.1016/j.future.2018.02.049.