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Impact of Edge Computing Paradigm on Energy Consumption in IoT
Proceedings, IFAC and Conference on Systems Programmable15th Devices Embedded Proceedings, 15th IFAC and Conference on Programmable Devices Embedded Systems Ostrava, Czech Republic, May 23-25, 2018 Proceedings, 15th IFAC and Conference on Available Programmable Devices Embedded Systemsonline at www.sciencedirect.com Ostrava, Czech Republic, May 23-25, 2018 Proceedings, 15th IFAC Conference on Programmable Devices and Embedded Systems Ostrava, Czech Republic, May 23-25, 2018 Programmable and Embedded Systems Ostrava, Czech Devices Republic, May 23-25, 2018 Ostrava, Czech Republic, May 23-25, 2018
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IFAC PapersOnLine 51-6 (2018) 162–167 Impact of Edge Computing Paradigm on Energy Consumption in IoT Impact of Edge Computing Paradigm Impact of Edge Computing Paradigm on on Energy Energy Consumption Consumption in in IoT IoT Impact of Edge Computing Paradigm on Energy Consumption Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** in IoT Impact of Edge Computing Paradigm on Energy Consumption in IoT Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová ****
Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** Jozef Mocnej* MartinofMiškuf** Iveta Zolotová **** *Technical University Kosice, Slovakia (e-mail: [email protected]) Peter Papcun*** University of Kosice, Slovakia (e-mail: [email protected]) ***Technical Technical University of Kosice, Slovakia (e-mail: [email protected]) *Technical University of Kosice, Slovakia (e-mail: [email protected]) ***Technical Technical University ofofKosice, Kosice, Slovakia (e-mail: [email protected]) *** TechnicalUniversity Universityof Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) ***Technical Technical University of Kosice, Kosice, Slovakia (e-mail: [email protected]) *** TechnicalUniversity Universityof ofKosice, Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) **** [email protected]) ** Technical University of Slovakia (e-mail: [email protected]) *** TechnicalUniversity UniversityofofKosice, Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) ** Technical **** Technical University University of of Kosice, Kosice, Slovakia Slovakia (e-mail: (e-mail: [email protected]) [email protected]) *** Technical **** Technical University University of of Kosice, Kosice, Slovakia Slovakia (e-mail: (e-mail: [email protected]) [email protected]) *** Technical **** Technical University of Kosice, Slovakia (e-mail: [email protected]) **** Technicalgrowth University of Kosice, Slovakiato(e-mail: [email protected]) Abstract: The exponential of devices connected the Internet, the diversity of the Internet of Abstract: Theparadigm, exponential growth of devices connected to theare Internet, the diversity of the Internet of Things (IoT) and the variety of IoT protocol stacks all factors arising concerns about IoT Abstract: The exponential growth of devices connected to the Internet, the diversity of the Internet of Things (IoT) paradigm, and the variety of IoT protocol stacks are all factors arising concerns about IoT sustainability. Aexponential promising seems to beconnected IoT integration platforms, which provide Abstract: Theparadigm, growth of devices to theare Internet, the diversity of the foundation Internet of Things (IoT) andsolution the variety of IoT protocol stacks all factors arising concerns about IoT Abstract: Theparadigm, growth of devices to the Internet, the diversity ofhas Internet of sustainability. Aexponential promising solution seems to beconnected IoT integration platforms, which provide thebeen foundation for managing connected devices in a standardized way. The first wave of IoT platforms driven Things (IoT) and the variety of IoT protocol stacks are all factors arising concerns about IoT sustainability. A promising solution seems to beprotocol IoT integration platforms, which provide the foundation Things (IoT) paradigm, and the variety ofthe IoT stacks are allof factors arising concerns about IoTa for managing connected devices in aseems standardized way. The first wave oftheIoT platforms has been driven by cloud computing with all the logic in cloud, yet taking a part logic to the edge might be sustainability. A promising solution to be IoT integration platforms, which provide the foundation for managing connected devices in aseems standardized way. The firstplatforms, wave of IoT platforms has driven sustainability. A promising to cloud, be edge IoT integration which provide thebeen foundation by cloud computing with allsolution the logic in the yet taking a part of theIoT logic to the edge might be a more suitable approach for usea cases. computing paradigm islogic going the next step for managing connected devices standardized way. The first wave platforms has been driven by cloud computing with allmany the in logic in theThe cloud, yet taking a part ofof theIoT to tothebeedge might be ina for managing connected devices in a standardized way. The first wave of platforms has been driven more suitable approach forallmany use cases. The edge computing paradigm islogic going tothebeedge the next step in the evolution of IoT platforms, but its initial complexity requires further research work to fully by cloud computing with the logic in the cloud, yet taking a part of the to might be more suitable approach forallmany use cases. The edgeyet computing paradigm islogic going the next step inaa by cloud computing with the logic inits theinitial cloud, taking arequires part ofofthe to to thebe edge might be the evolution ofpotential IoT platforms, butcases. complexity further research work to fully comprehend all benefits. This paper describes the utilization edge computing in the IoT and more suitable approach for many use The edge computing paradigm is going to be the next step in the evolution of IoT platforms, butcases. its initial complexity requires further research work fully more suitable approach for manyconsumption use The edge computing paradigm iscase going to beprovided the nextto step in comprehend all potential benefits. This paper describes the utilization offurther edge computing in the IoT and analyzes its impact on platforms, energy of IoT devices. The practical study in this the evolution ofpotential IoT but its initial complexity requires research work to fully comprehend all benefits. This paper describes the utilization of edge computing in the IoT and the evolution of IoT platforms, but its initial complexity requires further research work to fully analyzes its impact on energy consumption of IoT devices. The practical case study provided in this paper evaluates overhead caused by paper computation at the edge presents acomputing possible implementation comprehend all the potential benefits. This describes utilization of edge in the IoT and analyzes its impact on energy consumption of IoT devices. Theand practical case study provided in this comprehend all the potential benefits. This describes utilization ofconstrained edge in powered the IoT and paper evaluates overhead caused by paper computation at the edge and presents acomputing possible implementation of the edge computing paradigm to positively influence the lifespan of devices by analyzes its impact on energy consumption of IoT devices. The practical case study provided in this paper evaluates the overhead caused by computation at the edge and presentscase a possible implementation analyzes its impact on energy consumption of IoT devices. The practical study provided in this of the edge computing paradigm to positively influence the lifespan of constrained devices powered by paper evaluates the overhead caused by computation at the edge and presents a possible implementation batteries. of the evaluates edge computing paradigm to positively influence theedge lifespan constrained devices powered by paper the overhead caused by computation at the and of presents a possible implementation batteries. of the edge computing paradigm to positively influence the lifespan of constrained devices powered by batteries. © 2018, IFAC (International Federation of Automatic Control) byofValue Elsevier Ltd. All rights powered reserved. by of the edge computing paradigm toEnergy positively influence the Hosting lifespan constrained devices Keywords: IoT, Edge Computing, Consumption, ESP Module, Prediction batteries. Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction batteries. Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction analysis of how Value the edge computing paradigm can influence Keywords: Edge Computing, Energy Consumption, ESP Module, Prediction 1. IoT, INTRODUCTION analysis of life howofthe edge computing can influence 1. INTRODUCTION the battery constrained devices.paradigm analysis of how the edge computing paradigm can influence 1. INTRODUCTION the battery life of constrained devices. The Internet of Things (IoT) is a highly discussed paradigm analysis of life howofthe edge computing can influence 1. INTRODUCTION the battery constrained devices.paradigm analysis of life howof the edge computing can influence The Internet of Things (IoT)objects is a highly the main contribution is twofold: (1) The 1. INTRODUCTION aimed to connect everyday to thediscussed Internet.paradigm The IoT Consequently, the battery constrained devices.paradigm The Internet of Things (IoT) is a highly discussed paradigm Consequently, theconstrained mainand contribution is twofold: (1) The the battery life of devices. aimed to connect everyday objects thediscussed Internet. The IoT comparison of the cloud edge computing paradigms The Internet of Things (IoT) is a highly paradigm empowers systems with the ability to sense and control the the main contribution is twofold: (1) used The aimed to connect everyday objects to thediscussed Internet.paradigm The IoT Consequently, comparison of the cloud and edge computing paradigms used The Internet of Things (IoT) is a highly empowers systems with the ability to sense and control the in the IoT, and (2) the evaluation of the practical case Consequently, the main contribution is twofold: (1)study The aimed to connect everyday objects the The IoT environment around us, the which creates newInternet. possibilities for comparison of the cloud and edge computing paradigms used empowers systems with ability to sense and control the Consequently, the main contribution is twofold: (1) The in the IoT, and (2) the evaluation of the practical case study aimed to connect everyday objects to the Internet. The IoT comparison of the cloud and edge computing paradigms used environment around us, which creates new possibilities for explaining the impact of edge computing on energy the interaction between physical virtual worlds. empowers systems with the ability to and sense and control the in the IoT, and (2)cloud the evaluation of the practical case study environment around us, which creates new possibilities for comparison of the and edge computing paradigms used the impact of edge empowers systems with the ability to and sense and need control the interaction physical virtual worlds. in the IoT, and the evaluation the case study consumption in (2) the IoT. The aimofiscomputing to practical illustrateon howenergy even However, we stillbetween face challenges to the be explaining environment around us, several which creates new that possibilities for explaining the impact of edge computing on energy the interaction between physical and virtual worlds. in the IoT, and (2) the evaluation of the practical case study consumption in the IoT. The aim is to illustrate how even environment around us, which creates new possibilities for However, we still face several challenges that need to be simple decision-making process at the edge can significantly explaining the impact of edge computing on energy the interaction physical and that virtual overcome. consumption in the IoT. The aim iscomputing to illustrateonhowenergy even However, we stillbetween face several challenges needworlds. to be simple explaining the impact of devices. edge decision-making process at the edge can significantly the interaction between physical and that virtual worlds. overcome. extend the battery life of IoT consumption in the IoT. The aim is to illustrate how even However, we still face several challenges need to be simple decision-making process at the edge can significantly overcome. consumption in the Thedevices. aim is to illustrate how even the battery lifeIoT. of process IoT However, we still growing face several challenges thatconnected need to be The exponentially number of devices to extend simple decision-making at the edge can significantly overcome. the life ofis IoT devices. simple decision-making at theasedge can significantly The exponentially growing of number connected to extend The rest ofbattery the paper structured follows: Section II overcome. the the diversity IoT, of anddevices the variety of IoT the battery life of process IoT devices. The Internet, exponentially growing number of devices connected to extend The rest of the paper is structured as follows: Section II extend the battery life of IoT devices. the Internet, theare diversity of IoT, anddevices the variety of IoT outlines the background and related work. Section protocol stacks all factors arising concerns about The exponentially growing number of connected to The rest of the paper is structured as follows: SectionIII. II the Internet, the diversity of IoT, of anddevices the variety of IoT outlines the background and related work. Section III. The exponentially growing number connected to protocol stacks are all factors arising concerns about IoT The explains innovation in IoTas and compares cloud rest the of the paper iswaves structured follows: Section II the Internet, the diversity of IoT, andsolving the variety oftasks sustainability. Proprietary applications specific outlines background and related work. Section III. protocol stacks are all factors arising concerns about IoT The rest the oftothe paper iswaves structured follows: Section II explains innovation in IoTas IV and compares the Internet, the diversity of IoT, andsolving the variety oftasks IoT outlines sustainability. Proprietary applications specific computing edge computing. presents thecloud case the background and Section related work. Section III. protocol stacks are all factors arising concerns about IoT are easy to implement, but if we want to make explains the innovation waves in IoT and compares cloud sustainability. Proprietary applications solving specific tasks outlines theto innovation background and Section related work. Section III. computing edge computing. IV presents the case protocol stacks are all factors arising concerns about IoT explains the waves in IoT and compares cloud are easy to implement, but if we want to make study, which evaluates the overhead caused by edge sustainability. Proprietary applications solving tasks sustainable, like scalability, and mutual to innovation edge computing. Section presents thecloud case are easy toproblems implement, but if weadaptability want tospecific make IoT computing explains theon waves in of IoTanIV and compares study, which evaluates the Section overhead caused edge sustainability. Proprietary applications solving specific tasks computing to edge computing. IV presents the case sustainable, problems like scalability, adaptability and mutual energy consumption IoT device.by Finally, interoperability need to be addressed. are easy to implement, but if we want to make IoT study, which evaluates the overhead caused by edge sustainable, problems like scalability, adaptability and mutual computing to edge computing. Section IV presents the case on energy consumption of an IoT device. Finally, are easy to implement, but if we want to make IoT interoperability need to be addressed. study, which evaluates the overhead caused by edge the conclusion and future work is summarized in Section V. sustainable, problems like scalability, adaptability and mutual computing on energy consumption of an IoT device. Finally, interoperability need to be addressed. study, which evaluates the overhead caused by edge the conclusion and future work is summarized in Section V. sustainable, like and mutual A promisingproblems solution thescalability, problems adaptability described above seems computing on energy consumption of an IoT device. Finally, interoperability need toto be addressed. the conclusion and future work is summarized in Section V. on energy of an IoT device. Finally, A promising solution tobe the problems seems 2. consumption RELATED WORK interoperability need to to be IoT integration platforms. An IoTdescribed platform above provides the computing the conclusion and future work is summarized in Section V. A promising solution to theaddressed. problems described above seems 2. RELATED WORK work is summarized in Section V. to be IoT integration platforms. An IoT platform provides the the conclusion and future A promising solution to thedevices problems above seems foundation for connecting to described the Internet, acquiring 2. RELATED WORK to be IoT integration platforms. An IoT platform provides the Energy consumption has alwaysWORK been in the interest of A promising solution toprocessing thedevices problems above seems 2. RELATED foundation for connecting to described the acquiring the and inInternet, a meaningful way to begenerated IoT integration platforms. An them IoT platform provides the Energy consumption foundation fordata, connecting devices to the Internet, acquiring has always been in many the interest of 2. RELATED WORK researchers. Its optimization is critical solutions, to be IoT integration platforms. An IoT platform provides the the generated and processing them inInternet, a meaningful way Energy consumption has always been for in the interest of to get the desired output. Thus, an IoT platform offers unified foundation fordata, connecting devices to the acquiring the generated data, and processing them in a meaningful way researchers. Its optimization is critical for many solutions, especially when deployed constrained environments. Since Energy consumption has inalways been for in many the interest of foundation fordata, connecting devices to leaving the Internet, acquiring to get the desired output. Thus, an IoT platform offers unified researchers. Its optimization is critical solutions, the generated and processing them in a meaningful way management of connected devices, developers to Energy consumption in many the of to get the desired output. Thus, an IoT platform offers unified especially when deployed inalways constrained environments. Since researchers. Its is critical for solutions, IoT devices are optimization often has powered by been batteries and interest are hardly the generated data, and processing them in a meaningful way management of connected devices, leaving developers to especially when deployed in constrained environments. Since to get the desired output. IoT leaving platform offers unified focus on the added valueThus, of devices, a an particular solution. However, researchers. Its optimization is critical for many solutions, management of connected developers to IoT devices are often powered by batteries and are hardly especially when deployed in constrained environments. Since accessible, extending their lifespan to an acceptable level to get the desired output. an IoT platform offers unified focus on the added valueThus, of a can particular solution. However, devices are deployed often powered by batteries and are hardly management of developers to IoT even though IoTconnected platforms beleaving described with one especially when in constrained Since focus on the added value of devices, a particular solution. However, accessible, extending to environments. an acceptable level IoT devices are factor often their powered by batteries and are hardly may be a crucial for thelifespan implementation. management of connected devices, leaving developers to even though IoT platforms can be described with one accessible, extending their lifespan to an acceptable level focus on the added value of a particular solution. However, common definition, their actual implementation and structure IoT devices are often powered by batteries and are hardly even though IoT platforms can be described with one may be a crucial factor for the implementation. accessible, extending their lifespan to an acceptable level focus on thevary. added valueactual of a can particular solution. common definition, their implementation andHowever, structure may be a crucial factor for the implementation. can greatly even though IoT platforms be described with one accessible, extending their lifespan to ancan acceptable level Wireless Sensor Networks (WSNs), which be considered common definition, their actualcan implementation andwith structure may be a crucial factor for the implementation. even though IoT platforms be described one can greatly vary. common definition, their actual implementation and structure as Wireless Sensor Networks (WSNs), which can bethe considered may be a crucial factor for the implementation. the backbone of IoT, have discussed energy can greatly vary. Wireless Sensor Networks (WSNs), which can be considered common definition, their actual implementation structure as This paper provides an overview of two differentand approaches can greatly vary. the backbone IoT, have discussed energy conservation topic inof detail forwhich the past Rault, Wireless Sensor Networks (WSNs), can decade. bethe considered Thisgreatly paper provides an overview of– two different approaches as the backbone ofgreat IoT, have discussed the energy can vary. used in IoT platform architectures namely cloud computing Wireless Sensor Networks (WSNs), which can be considered This paper provides an overview of two different approaches conservation topic in great detail for the past decade. Rault, as the backbone of IoT, have discussed the energy Bouabdallah, and Challal (2014), as well as Khan et al. used edge in IoTcomputing platforman architectures namely cloud computing topic inofgreat detail for discussed the past decade. Rault, This paper provides of–– two different approaches and – overview and summarizes their suitability for conservation as the backbone IoT, have the energy used in IoT platform architectures namely cloud computing Bouabdallah, and Challal (2014), as well as Khan et al. conservation topic in great detail for the past decade. Rault, (2015) presented comprehensive surveys about the energy This paper provides an overview of two different approaches and edge computing – and summarizes their suitability for Bouabdallah, and in Challal asthewell asdecade. Khan Rault, et al. specific environments. The summarizes main– namely part their is cloud devoted to the used in IoT platform architectures computing conservation topic great (2014), detail for past and edge computing – and suitability for (2015) presented comprehensive surveys about the energy Bouabdallah, and Challal (2014), as well as Khan et al. used in IoT platform architectures computing specific environments. The summarizes main– namely part their is cloud devoted to the (2015) presented comprehensive surveys about the energy and edge computing – and suitability for Bouabdallah, and Challal (2014), as well as Khan et al. specific environments. The main part is devoted to the (2015) presented comprehensive surveys about the energy and edgeenvironments. computing – and suitability for specific The summarizes main part their is devoted to the Copyright © 2018 IFAC 162 (2015) presented comprehensive surveys about the energy specific © environments. The mainFederation part is ofdevoted toControl) the 162Hosting by Elsevier Ltd. All rights reserved. 2405-8963 2018, IFAC (International Automatic Copyright © 2018 IFAC Peer review©under of International Federation of Automatic Copyright 2018 responsibility IFAC 162Control. Copyright © 2018 IFAC 162 10.1016/j.ifacol.2018.07.147 Copyright © 2018 IFAC 162
2018 IFAC PDES Ostrava, Czech Republic, May 23-25, 2018 Jozef Mocnej et al. / IFAC PapersOnLine 51-6 (2018) 162–167
management in WSNs. Their taxonomies provide a summary of the recent findings in techniques, methods, and algorithms that can be used to achieve better energy efficiency.
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popular cloud platforms that have found their place in production as well as science (Silva et al. (2016)). While the centralized architecture (Fig. 1(a)) of these platforms is simple and straightforward, it also means the necessity of pushing all data up to the cloud itself before making any decision process, eventually leading to overhead.
Besides WSNs, researchers have also focused on how to improve the energy conservation in the IoT as well. Kaur and Sood (2017) proposed an energy efficient architecture for IoT that dynamically switches IoT devices to the sleep mode based on the parameters such as available battery power, quality of extracted information, conflict factor, and coefficient of variation. A slightly different approach was taken by Ventura et al. (2014), who utilized benefits of machine learning to teach devices their usage pattern so that they could adapt to the environment and optimize their consumption. The whole learning process was executed on the centralized server, though.
A better approach may be to move a part of the decisionmaking capabilities from the cloud to the edge of the network, e.g. to a gateway or even to end nodes themselves. The distribution of logic can greatly reduce the volume of transferred data, improving the network traffic and lowering the requirements for cloud services (Lojka et al. (2015)). The next wave of IoT platforms will be driven by the edge computing paradigm, enabling real-time analytics and decision making. However, the decentralized architecture (Fig. 1(b)) also increases the complexity of management process, creating a high demand for the efficient architecture design.
More recently, IoT applications revealed the potential advantages in shifting a part of the logic from the cloud to the edge. Shi et al. (2016) wrote a survey paper about the vision and possible gains of distributing logic across the network, but also mentioned the challenges that need to be overcome. It is necessary to point out that the edge computing brings additional computation to the edge, which can create some overhead. However, as Harrison et al. (2016) outlined, the biggest energy consumer in a device is transceiver, not CPU. Therefore, we can assume the edge computing can still be energy efficient, but we need to know the size of the overhead at first.
(a) Centralized
(b) Decentralized
Fig. 1 Types of IoT architectures. Consequently, there is a trade-off between the initial complexity and the gained benefits. While a centralized architecture has an advantage in easy set-up and management, a decentralized architecture can offer more efficient communication. Table 1 provides characteristic features to ease the selection process of the right architecture for an IoT platform in a particular environment. When we want to connect a lower number of devices with less frequent sending rates, the network connectivity is reliable, cloud resources are not restricted, and the response rate can be in hundreds of ms, a simple and straightforward centralized architecture may be a suitable choice. On the contrary, in situations where we need to efficiently utilize the available network and cloud resources, and the response is required in tens of ms, a decentralized architecture with computation at the edge will be preferable.
All of the aforementioned papers deal with the energy conservation in constrained environments. However, to the best of our knowledge, there is no a practical case study measuring the exact overhead caused by the edge computing paradigm and how it influences the battery life of IoT devices. Consequently, we have decided to write this paper in order to analyze the impact of the edge computing on energy consumption of IoT devices. 3. EDGE COMPUTING PARADIGM 3.1 Innovation Waves and Comparison to Cloud Computing The early stage of IoT solutions was based on proprietary applications solving specific tasks they were designed for. However, these solutions were unable to interact with each other as the expectations of a system evolved over time, which eventually lead to the requirement of more dynamic approaches. Consequently, the trend has shifted from specifically-oriented tight-coupled applications to more open and loosely-coupled IoT integration platforms (Gula, and Žaková (2017)).
Table 1. Decision characteristics for selecting a suitable IoT architecture
Number of devices
The first wave of IoT platforms has been driven by the cloud computing paradigm with the characteristic feature of placing the whole logic in the cloud. Amazon Web Services 1, Microsoft Azure2, and Bluemix3 are just a few examples of
Sending rates Connectivity Availability of resources Response rate
1
https://aws.amazon.com/ https://portal.azure.com/ 3 https://www.ibm.com/cloud/ 2
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Centralized architecture
Decentralized architecture
Low
High
Less frequent, tens of s interval Must be reliable, high network throughput Abundance of resources Hundreds of ms
More frequent, s or ms interval Can be unreliable, constrained networks Resources need to be used efficiently Tens of ms
2018 IFAC PDES 164 Ostrava, Czech Republic, May 23-25, 2018 Jozef Mocnej et al. / IFAC PapersOnLine 51-6 (2018) 162–167
Nevertheless, these two approaches do not exclude each other. In fact, a whole solution can be the combination of both of them. As the complexity of IoT solutions rises, we can expect different services using different logic distribution in order to achieve the desired goal. Actual implementation will be a use case dependent and IoT platforms should be dynamic enough to adapt to these changing requirements accordingly.
module that integrates general-purpose input/output (GPIO), pulse-width modulation (PWM), inter-integrated circuit (IIC), 1-Wire, and analog-to-digital converter (ADC). Since this device offers several pins and has built-in Wi-Fi chip, it can be easily used for the typical IoT scenario – plug-in sensors, acquire sensed data to the cloud in a regular interval and be in sleep mode for the rest of the time. However, instead of merely sending all data to the cloud, we have implemented a value prediction model based on a linear regression that represents the decision-making process at the edge. The model is present in both the end device and the gateway that collects data from the network. Since we used simple linear regression, the prediction process is defined by the formula:
3.2 Impact on IoT Devices To sum up the comparison, the edge computing paradigm can bring several advantages, such as network traffic reduction, faster response rate, lower requirements of network connection reliability, more efficient utilization of cloud services, etc. The initial complexity of solutions based on the edge computing is higher, but the gained benefits will eventually pay off the invested effort.
Y *X ,
( 1)
where X is the independent (explanatory) variable, Y is the dependent variable, α is the Y-Intercept of the regression line, and β is the slope of the regression line.
So far, we described the edge computing only from a whole system point of view. However, it is important to look at one more aspect – how the edge computing influences IoT devices themselves.
The main purpose is to send messages only when necessary. If a predicted value falls within the acceptable error bound
[ , ], , nothing will be sent. The gateway will simply use the predicted value. On the contrary, the incorrectly predicted value will cause the end device to recreate the prediction model and send the new parameters α and β together with the correct value. This way, two states can happen:
End devices in IoT networks are constrained many times. The computing capabilities are low, and the energy resources can be limited. Particularly application areas with the difficult accessibility to IoT devices are very demanding on the lifespan of devices, since replacing them may not be an easy task. Smart environment (Ramesh (2014); Virmani and Jaim (2016)), smart agriculture (Elliot et al. (2014); Pimentel and Peshin (2014)), smart city (Djahel et al. (2015); Catania and Ventura (2014)), smart manufacturing and retail (Wang et al. (2016); Xu et al. (2016)) are just a few examples of application domains requiring good longevity because of the constrained energy resources. In these situations, the only option is to utilize resources wisely. Therefore, we have decided to analyze the impact of the edge computing on energy consumption in IoT. Our aim is to prove that the edge computing is not only suitable for more powerful machines but can also spare resources even on constrained devices.
1.
The model predicts a value correctly – the end device will put itself to sleep mode without sending any value.
2.
The model predicts a value incorrectly – the end device will recreate the prediction model and send new parameters together with the correct value to the gateway. Then goes to sleep mode.
Obviously, the model spares energy consumption with the first option, whereas the latter creates the undesirable overhead. Now, the question is how big the overhead is and under what circumstances we should consider implementing the edge computing even on the most constrained devices.
According to Harrison et al. (2016), small devices used in wireless sensor networks (WSNs) draws the most energy with the transceiver turned on, while computing operations are much less energy demanding. Consequently, the assumption is that the edge computing should have a positive effect on the energy conservation if utilized properly.
4.2 Implementation To measure the amount of overhead and answer the aforementioned question, we need to examine the behavior of a NodeMCU ESP-12E device. In particular, we are interested in observing power consumption and time spent by executing different activities.
4. EDGE COMPUTING CASE STUDY 4.1 Description
ESP-12E module supports three sleep modes by default – modem sleep, light sleep and deep sleep. The differences between them are summarized in Table 2. Differences between ESP-12E modes. We tested out all modes and measured their power consumption. The input voltage was set to 5V. A regular ON mode had 70 mA current draw, whereas merely turning the transceiver off reduced current to 17.2
This paper provides a practical case study of the edge computing paradigm implemented in an IoT device with the intention to evaluate its power consumption. The device is represented by NodeMCU ESP8266 ESP-12E due to its versatile capabilities, large availability and low price. NodeMCU is a development board based on the ESP8266 164
2018 IFAC PDES Ostrava, Czech Republic, May 23-25, 2018 Jozef Mocnej et al. / IFAC PapersOnLine 51-6 (2018) 162–167
mA. The lowest current draw, 2.3 mA, was achieved during deep sleep when everything except the real-time clock is off.
value took 3572 ms, which is slightly more than regular duty cycling. This time difference represents the overhead of our model when the prediction model is not accurate.
To eliminate the potential confusion, be aware that our measured values are slightly higher than current mentioned in datasheets for the ESP-12E module. The difference is caused by the NodeMCU development board, which itself consumes some energy.
Table 3 provides numbers used for the evaluation in the next section. The duty cycling had 70 mA current draw in ON mode for the total period of 3352 ms. A value prediction model drew merely 17.2 mA for 18 ms when a value was predicted correctly, whereas the incorrectly predicted value consisted of 17.2 mA current draw during the model recreation phase and 70 mA during the sending phase.
Table 2. Differences between ESP-12E modes
Wi-Fi CPU System clock Real-time clock Consumption
ON mode ON ON ON
Modem sleep OFF ON ON
Light sleep OFF Pending OFF
Deep sleep OFF OFF OFF
ON
ON
ON
ON
70 mA
17.2 mA
3.2 mA
2.3 mA
165
Table 3. Summary of the measured execution times and energy consumption Duty cycling Correctly predicted value Incorrectly predicted value
The difference in power consumption between modes is significant. Nevertheless, it is necessary to include one more factor – time. To estimate the lifespan of a device, we need to know how much time is spent on executing a certain task.
Execution time 3352 ms
Consumption 70 mA
18 ms
17.2 mA
3572 ms
17.2 & 70 mA
4.3 Evaluation Once we knew the energy consumption and execution time of all expected device’s states, we could evaluate the results using different configuration settings. Therefore, we compared a regular duty cycling approach with the value prediction model. In the latter case, we assumed three scenarios based on the prediction success rate: the unrealistic 100% and 0% success rates to illustrate the best and the worst possible use cases, and the more probable 60% success rate, which is easy to achieve, and we can expect the prediction model would not go below in most environments. Moreover, we did the calculations with several sending rates to see how a sending interval influences the energy consumption of devices. The intervals differed from 1 message per hour up to 1 message every 10 seconds.
Therefore, we have implemented the described model into NodeMCU ESP-12E and measured the execution time. Our findings are depicted in Fig. 2. Predicting the next value took 18 ms on average. When the predicted value was out of the
acceptable error bound [ , ], , recreating a new linear regression model based on the last five values took 20 ms. The transition from modem sleep to ON mode lasted 3 ms. The biggest time-consuming activity was connecting to the Wi-Fi network, which took approximately 3.5 sec with the static IP. When testing with DHCP, assigning a new IP extended the time by another 500 ms. We tried different routers with the similar or even worse results. This discovery set the lower boundary of the duty cycling frequency for us. The minimum sending rate should be no less than 4 sec, otherwise it is not worth to turn the transceiver OFF. Lastly, we used MQTT protocol for transferring messages from the device to broker, and this activity took 53 ms.
Fig. 3 summarizes the differences in energy consumption of a NodeMCU device per hour depending on the used configuration. The numbers were calculated using the formula:
J / hr Aa * t a * MH As * ( 3600 ( t a * MH )) , ( 2) where J/hr is joules per hour, Aa is current of a device in active mode, ta is the execution time spent in active mode, MH is measurements per hour, and As is current of a device in sleep mode. The chart offers two interesting facts. The first one is the correlation between the duty cycling and value prediction model. It is worth to notice that even the theoretical worst possible scenario with the 0% prediction success rate consumes just negligibly more energy than the regular duty cycling without any computations. Nevertheless, the increasing prediction success rate can significantly reduce the consumed energy. Therefore, we can confirm that the computation tasks on the edge do not necessarily have to increase energy consumption and the appropriate logic can actually be more efficient.
Fig. 2 Execution time of different activities. In summary, a regular duty cycling, which consisted of measuring a new value and sending it to the broker, took 3352 ms on average. When the model was implemented at the edge, the correctly predicted value required 18 ms of the execution time. On the contrary, the incorrectly predicted 165
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The second fact worth to notice is the impact of the sending rate on the overall energy consumption of a device. While the 1-hour sending interval resulted in the minimum differences between each variant, the gap is starting to be considerable from the 5-min interval and higher. This trend is caused by the total time amount of a device spent in sending mode. The more messages a device is required to send, the bigger energy conservation can be achieved using the value prediction model. However, as we mentioned in Implementation section, the upper bound of a sending interval is limited by the logic execution time, including the time spent on connecting to Wi-Fi.
contrary, even the worst possible use case, where the prediction model has 0% success rate, does not add too big overhead. Actually, the lifespan of a device would not be shortened by more than three days. And it is very unlikely to happen. Consequently, we can conclude that the additional overhead caused by computations at the edge is negligible and can be easily overridden even with the low prediction success rate. Our evaluation proved that the edge computing paradigm can be suitable not only for powerful machines but also for the constrained devices with the limited energy resources.
Fig. 3 Energy consumption of NodeMCU ESP-12E device per hour. Fig. 4 Estimated lifespan of NodeMCU ESP-12E device when powered by 10Ah battery.
Once we knew the energy consumed per hour, we could estimate the lifespan of a NodeMCU ESP-12E device. The calculations were realized assuming 5V input voltage, 10 Ah battery and using the formula: DL
V * Ah * 3600
,
5. CONCLUSION
( 3)
J / hr * 24
where DL is expected device lifespan, V is the input voltage of a device, Ah is a battery capacity in ampere-hours, and J/hr is the calculated device consumption per hour in joules. The results are depicted in Fig. 4. The configurations remained the same; we compared the regular duty-cycling with the value prediction model considering three prediction success rates – two unrealistic to show the upper and lower bounds, and one that can be easily achieved in most scenarios. The sending interval differed from 1 hour to 10 sec.
IoT platforms have already shown some of their potential in addressing the arising sustainability issues caused by the exponential growth of devices connected to the Internet. Although the first wave of IoT platforms was strictly cloudcentric, the requirements have created the demand for decentralized approaches as well. The edge computing is a paradigm that takes a part of logic from the cloud and moves it closer to the network and devices for more efficient communication. This paper provided the comparison between the cloud and edge computing to highlight their network characteristics and mutual differences. The main part was devoted to the practical case study showing the impact of the edge computing overhead on the overall energy consumption of IoT devices. We put in contrast a regular duty cycling model with value prediction based on linear regression and implemented both alternatives into NodeMCU ESP12-E module to evaluate their energy consumptions. As this case study revealed, the additional computation of the value prediction model caused the minimal overhead, which was
As it can be seen, the gap between the duty cycling and value prediction model with achievable 60% success rate has enlarged even more. While the difference between them is merely three days at 1-hour sending interval, it can grow up to ninety-seven days with 10-sec sending rate. On the 166
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easily overridden even with the low prediction success rate. Consequently, we could conclude the edge computing paradigm has proved to be suitable for the constrained devices with the limited battery life if utilized properly.
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In future work, we would like to analyze changes in the gained information value caused by the computation at the edge. In particular, our aim is to propose a representational model for monitoring an IoT system at runtime so that it would be possible to determine how data modifications influence the output quality of a system. Ultimately, we would like to control available resources of a system based on the desired output quality to achieve better energy efficiency. ACKNOWLEDGMENT This publication is the result of the following projects implementation: FEI Grant - FEI-2017-46, 2017-2018 (40%); University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology – II. phase, ITMS: 313011D232 (30%); Grant KEGA - 033TUKE-4/2018 AICybS (30%). REFERENCES Catania, Vincenzo, and Daniela Ventura. (2014). An approach for monitoring and smart planning of urban solid waste management using smart-M3 platform. In: Proceedings of 15th Conference of Open Innovations Association FRUCT, IEEE. Djahel, Soufiene, et al. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. In: IEEE Communications Surveys & Tutorials, 17.1, 125-151. Elliott, Joshua, et al. (2014). Constraints and potentials of future irrigation water availability on agricultural production under climate change. In: Proceedings of the National Academy of Sciences, 111.9, 3239-3244. Gula, Miroslav, and Katarína Žaková (2017). Proposal of Component Based Architecture for Internet of Things: online laboratory case study. In: IFAC-PapersOnLine, 50.1, 337-342. Harrison, D. C., et al. (2016). Busting myths of energy models for wireless sensor networks. In: Electronics Letters, 52.16, 1412-1414. Kaur, Navroop, and Sandeep K. Sood (2017). An energyefficient architecture for the Internet of Things (IoT). In: IEEE Systems Journal, 11.2, 796-805. Khan, Junaid Ahmed, Hassaan Khaliq Qureshi, and Adnan Iqbal (2015). Energy management in wireless sensor networks: A survey. In: Computers & Electrical Engineering, 41, 159-176. Lojka, Tomáš, Marek Bundzel, and Iveta Zolotová (2015). Industrial gateway for data acquisition and remote control. In: Acta Electrotechnica et Informatica, 15.2, 43-48. Pimentel, David, and Rajinder Peshin (2014). Integrated pest management: pesticide problems. In: Springer Science & Business Media, 3.
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IFAC PapersOnLine 51-6 (2018) 162–167 Impact of Edge Computing Paradigm on Energy Consumption in IoT Impact of Edge Computing Paradigm Impact of Edge Computing Paradigm on on Energy Energy Consumption Consumption in in IoT IoT Impact of Edge Computing Paradigm on Energy Consumption Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** in IoT Impact of Edge Computing Paradigm on Energy Consumption in IoT Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová ****
Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** Jozef Mocnej* Martin Miškuf** Peter Papcun*** Iveta Zolotová **** Jozef Mocnej* MartinofMiškuf** Iveta Zolotová **** *Technical University Kosice, Slovakia (e-mail: [email protected]) Peter Papcun*** University of Kosice, Slovakia (e-mail: [email protected]) ***Technical Technical University of Kosice, Slovakia (e-mail: [email protected]) *Technical University of Kosice, Slovakia (e-mail: [email protected]) ***Technical Technical University ofofKosice, Kosice, Slovakia (e-mail: [email protected]) *** TechnicalUniversity Universityof Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) ***Technical Technical University of Kosice, Kosice, Slovakia (e-mail: [email protected]) *** TechnicalUniversity Universityof ofKosice, Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) **** [email protected]) ** Technical University of Slovakia (e-mail: [email protected]) *** TechnicalUniversity UniversityofofKosice, Kosice,Slovakia Slovakia(e-mail: (e-mail:[email protected]) [email protected]) ** Technical **** Technical University University of of Kosice, Kosice, Slovakia Slovakia (e-mail: (e-mail: [email protected]) [email protected]) *** Technical **** Technical University University of of Kosice, Kosice, Slovakia Slovakia (e-mail: (e-mail: [email protected]) [email protected]) *** Technical **** Technical University of Kosice, Slovakia (e-mail: [email protected]) **** Technicalgrowth University of Kosice, Slovakiato(e-mail: [email protected]) Abstract: The exponential of devices connected the Internet, the diversity of the Internet of Abstract: Theparadigm, exponential growth of devices connected to theare Internet, the diversity of the Internet of Things (IoT) and the variety of IoT protocol stacks all factors arising concerns about IoT Abstract: The exponential growth of devices connected to the Internet, the diversity of the Internet of Things (IoT) paradigm, and the variety of IoT protocol stacks are all factors arising concerns about IoT sustainability. Aexponential promising seems to beconnected IoT integration platforms, which provide Abstract: Theparadigm, growth of devices to theare Internet, the diversity of the foundation Internet of Things (IoT) andsolution the variety of IoT protocol stacks all factors arising concerns about IoT Abstract: Theparadigm, growth of devices to the Internet, the diversity ofhas Internet of sustainability. Aexponential promising solution seems to beconnected IoT integration platforms, which provide thebeen foundation for managing connected devices in a standardized way. The first wave of IoT platforms driven Things (IoT) and the variety of IoT protocol stacks are all factors arising concerns about IoT sustainability. A promising solution seems to beprotocol IoT integration platforms, which provide the foundation Things (IoT) paradigm, and the variety ofthe IoT stacks are allof factors arising concerns about IoTa for managing connected devices in aseems standardized way. 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The edge computing paradigm islogic going tothebeedge the next step in the evolution of IoT platforms, but its initial complexity requires further research work to fully by cloud computing with the logic in the cloud, yet taking a part of the to might be more suitable approach forallmany use cases. The edgeyet computing paradigm islogic going the next step inaa by cloud computing with the logic inits theinitial cloud, taking arequires part ofofthe to to thebe edge might be the evolution ofpotential IoT platforms, butcases. complexity further research work to fully comprehend all benefits. This paper describes the utilization edge computing in the IoT and more suitable approach for many use The edge computing paradigm is going to be the next step in the evolution of IoT platforms, butcases. its initial complexity requires further research work fully more suitable approach for manyconsumption use The edge computing paradigm iscase going to beprovided the nextto step in comprehend all potential benefits. This paper describes the utilization offurther edge computing in the IoT and analyzes its impact on platforms, energy of IoT devices. The practical study in this the evolution ofpotential IoT but its initial complexity requires research work to fully comprehend all benefits. This paper describes the utilization of edge computing in the IoT and the evolution of IoT platforms, but its initial complexity requires further research work to fully analyzes its impact on energy consumption of IoT devices. The practical case study provided in this paper evaluates overhead caused by paper computation at the edge presents acomputing possible implementation comprehend all the potential benefits. This describes utilization of edge in the IoT and analyzes its impact on energy consumption of IoT devices. Theand practical case study provided in this comprehend all the potential benefits. This describes utilization ofconstrained edge in powered the IoT and paper evaluates overhead caused by paper computation at the edge and presents acomputing possible implementation of the edge computing paradigm to positively influence the lifespan of devices by analyzes its impact on energy consumption of IoT devices. The practical case study provided in this paper evaluates the overhead caused by computation at the edge and presentscase a possible implementation analyzes its impact on energy consumption of IoT devices. The practical study provided in this of the edge computing paradigm to positively influence the lifespan of constrained devices powered by paper evaluates the overhead caused by computation at the edge and presents a possible implementation batteries. of the evaluates edge computing paradigm to positively influence theedge lifespan constrained devices powered by paper the overhead caused by computation at the and of presents a possible implementation batteries. of the edge computing paradigm to positively influence the lifespan of constrained devices powered by batteries. © 2018, IFAC (International Federation of Automatic Control) byofValue Elsevier Ltd. All rights powered reserved. by of the edge computing paradigm toEnergy positively influence the Hosting lifespan constrained devices Keywords: IoT, Edge Computing, Consumption, ESP Module, Prediction batteries. Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction batteries. Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction Keywords: IoT, Edge Computing, Energy Consumption, ESP Module, Value Prediction analysis of how Value the edge computing paradigm can influence Keywords: Edge Computing, Energy Consumption, ESP Module, Prediction 1. IoT, INTRODUCTION analysis of life howofthe edge computing can influence 1. INTRODUCTION the battery constrained devices.paradigm analysis of how the edge computing paradigm can influence 1. INTRODUCTION the battery life of constrained devices. The Internet of Things (IoT) is a highly discussed paradigm analysis of life howofthe edge computing can influence 1. INTRODUCTION the battery constrained devices.paradigm analysis of life howof the edge computing can influence The Internet of Things (IoT)objects is a highly the main contribution is twofold: (1) The 1. INTRODUCTION aimed to connect everyday to thediscussed Internet.paradigm The IoT Consequently, the battery constrained devices.paradigm The Internet of Things (IoT) is a highly discussed paradigm Consequently, theconstrained mainand contribution is twofold: (1) The the battery life of devices. aimed to connect everyday objects thediscussed Internet. The IoT comparison of the cloud edge computing paradigms The Internet of Things (IoT) is a highly paradigm empowers systems with the ability to sense and control the the main contribution is twofold: (1) used The aimed to connect everyday objects to thediscussed Internet.paradigm The IoT Consequently, comparison of the cloud and edge computing paradigms used The Internet of Things (IoT) is a highly empowers systems with the ability to sense and control the in the IoT, and (2) the evaluation of the practical case Consequently, the main contribution is twofold: (1)study The aimed to connect everyday objects the The IoT environment around us, the which creates newInternet. possibilities for comparison of the cloud and edge computing paradigms used empowers systems with ability to sense and control the Consequently, the main contribution is twofold: (1) The in the IoT, and (2) the evaluation of the practical case study aimed to connect everyday objects to the Internet. The IoT comparison of the cloud and edge computing paradigms used environment around us, which creates new possibilities for explaining the impact of edge computing on energy the interaction between physical virtual worlds. empowers systems with the ability to and sense and control the in the IoT, and (2)cloud the evaluation of the practical case study environment around us, which creates new possibilities for comparison of the and edge computing paradigms used the impact of edge empowers systems with the ability to and sense and need control the interaction physical virtual worlds. in the IoT, and the evaluation the case study consumption in (2) the IoT. The aimofiscomputing to practical illustrateon howenergy even However, we stillbetween face challenges to the be explaining environment around us, several which creates new that possibilities for explaining the impact of edge computing on energy the interaction between physical and virtual worlds. in the IoT, and (2) the evaluation of the practical case study consumption in the IoT. The aim is to illustrate how even environment around us, which creates new possibilities for However, we still face several challenges that need to be simple decision-making process at the edge can significantly explaining the impact of edge computing on energy the interaction physical and that virtual overcome. consumption in the IoT. The aim iscomputing to illustrateonhowenergy even However, we stillbetween face several challenges needworlds. to be simple explaining the impact of devices. edge decision-making process at the edge can significantly the interaction between physical and that virtual worlds. overcome. extend the battery life of IoT consumption in the IoT. The aim is to illustrate how even However, we still face several challenges need to be simple decision-making process at the edge can significantly overcome. consumption in the Thedevices. aim is to illustrate how even the battery lifeIoT. of process IoT However, we still growing face several challenges thatconnected need to be The exponentially number of devices to extend simple decision-making at the edge can significantly overcome. the life ofis IoT devices. simple decision-making at theasedge can significantly The exponentially growing of number connected to extend The rest ofbattery the paper structured follows: Section II overcome. the the diversity IoT, of anddevices the variety of IoT the battery life of process IoT devices. The Internet, exponentially growing number of devices connected to extend The rest of the paper is structured as follows: Section II extend the battery life of IoT devices. the Internet, theare diversity of IoT, anddevices the variety of IoT outlines the background and related work. Section protocol stacks all factors arising concerns about The exponentially growing number of connected to The rest of the paper is structured as follows: SectionIII. II the Internet, the diversity of IoT, of anddevices the variety of IoT outlines the background and related work. Section III. The exponentially growing number connected to protocol stacks are all factors arising concerns about IoT The explains innovation in IoTas and compares cloud rest the of the paper iswaves structured follows: Section II the Internet, the diversity of IoT, andsolving the variety oftasks sustainability. Proprietary applications specific outlines background and related work. Section III. protocol stacks are all factors arising concerns about IoT The rest the oftothe paper iswaves structured follows: Section II explains innovation in IoTas IV and compares the Internet, the diversity of IoT, andsolving the variety oftasks IoT outlines sustainability. Proprietary applications specific computing edge computing. presents thecloud case the background and Section related work. Section III. protocol stacks are all factors arising concerns about IoT are easy to implement, but if we want to make explains the innovation waves in IoT and compares cloud sustainability. Proprietary applications solving specific tasks outlines theto innovation background and Section related work. Section III. computing edge computing. IV presents the case protocol stacks are all factors arising concerns about IoT explains the waves in IoT and compares cloud are easy to implement, but if we want to make study, which evaluates the overhead caused by edge sustainability. Proprietary applications solving tasks sustainable, like scalability, and mutual to innovation edge computing. Section presents thecloud case are easy toproblems implement, but if weadaptability want tospecific make IoT computing explains theon waves in of IoTanIV and compares study, which evaluates the Section overhead caused edge sustainability. Proprietary applications solving specific tasks computing to edge computing. IV presents the case sustainable, problems like scalability, adaptability and mutual energy consumption IoT device.by Finally, interoperability need to be addressed. are easy to implement, but if we want to make IoT study, which evaluates the overhead caused by edge sustainable, problems like scalability, adaptability and mutual computing to edge computing. Section IV presents the case on energy consumption of an IoT device. Finally, are easy to implement, but if we want to make IoT interoperability need to be addressed. study, which evaluates the overhead caused by edge the conclusion and future work is summarized in Section V. sustainable, problems like scalability, adaptability and mutual computing on energy consumption of an IoT device. Finally, interoperability need to be addressed. study, which evaluates the overhead caused by edge the conclusion and future work is summarized in Section V. sustainable, like and mutual A promisingproblems solution thescalability, problems adaptability described above seems computing on energy consumption of an IoT device. Finally, interoperability need toto be addressed. the conclusion and future work is summarized in Section V. on energy of an IoT device. Finally, A promising solution tobe the problems seems 2. consumption RELATED WORK interoperability need to to be IoT integration platforms. An IoTdescribed platform above provides the computing the conclusion and future work is summarized in Section V. A promising solution to theaddressed. problems described above seems 2. RELATED WORK work is summarized in Section V. to be IoT integration platforms. An IoT platform provides the the conclusion and future A promising solution to thedevices problems above seems foundation for connecting to described the Internet, acquiring 2. RELATED WORK to be IoT integration platforms. An IoT platform provides the Energy consumption has alwaysWORK been in the interest of A promising solution toprocessing thedevices problems above seems 2. RELATED foundation for connecting to described the acquiring the and inInternet, a meaningful way to begenerated IoT integration platforms. An them IoT platform provides the Energy consumption foundation fordata, connecting devices to the Internet, acquiring has always been in many the interest of 2. RELATED WORK researchers. Its optimization is critical solutions, to be IoT integration platforms. An IoT platform provides the the generated and processing them inInternet, a meaningful way Energy consumption has always been for in the interest of to get the desired output. Thus, an IoT platform offers unified foundation fordata, connecting devices to the acquiring the generated data, and processing them in a meaningful way researchers. Its optimization is critical for many solutions, especially when deployed constrained environments. Since Energy consumption has inalways been for in many the interest of foundation fordata, connecting devices to leaving the Internet, acquiring to get the desired output. Thus, an IoT platform offers unified researchers. Its optimization is critical solutions, the generated and processing them in a meaningful way management of connected devices, developers to Energy consumption in many the of to get the desired output. Thus, an IoT platform offers unified especially when deployed inalways constrained environments. Since researchers. Its is critical for solutions, IoT devices are optimization often has powered by been batteries and interest are hardly the generated data, and processing them in a meaningful way management of connected devices, leaving developers to especially when deployed in constrained environments. Since to get the desired output. IoT leaving platform offers unified focus on the added valueThus, of devices, a an particular solution. However, researchers. Its optimization is critical for many solutions, management of connected developers to IoT devices are often powered by batteries and are hardly especially when deployed in constrained environments. Since accessible, extending their lifespan to an acceptable level to get the desired output. an IoT platform offers unified focus on the added valueThus, of a can particular solution. However, devices are deployed often powered by batteries and are hardly management of developers to IoT even though IoTconnected platforms beleaving described with one especially when in constrained Since focus on the added value of devices, a particular solution. However, accessible, extending to environments. an acceptable level IoT devices are factor often their powered by batteries and are hardly may be a crucial for thelifespan implementation. management of connected devices, leaving developers to even though IoT platforms can be described with one accessible, extending their lifespan to an acceptable level focus on the added value of a particular solution. However, common definition, their actual implementation and structure IoT devices are often powered by batteries and are hardly even though IoT platforms can be described with one may be a crucial factor for the implementation. accessible, extending their lifespan to an acceptable level focus on thevary. added valueactual of a can particular solution. common definition, their implementation andHowever, structure may be a crucial factor for the implementation. can greatly even though IoT platforms be described with one accessible, extending their lifespan to ancan acceptable level Wireless Sensor Networks (WSNs), which be considered common definition, their actualcan implementation andwith structure may be a crucial factor for the implementation. even though IoT platforms be described one can greatly vary. common definition, their actual implementation and structure as Wireless Sensor Networks (WSNs), which can bethe considered may be a crucial factor for the implementation. the backbone of IoT, have discussed energy can greatly vary. Wireless Sensor Networks (WSNs), which can be considered common definition, their actual implementation structure as This paper provides an overview of two differentand approaches can greatly vary. the backbone IoT, have discussed energy conservation topic inof detail forwhich the past Rault, Wireless Sensor Networks (WSNs), can decade. bethe considered Thisgreatly paper provides an overview of– two different approaches as the backbone ofgreat IoT, have discussed the energy can vary. used in IoT platform architectures namely cloud computing Wireless Sensor Networks (WSNs), which can be considered This paper provides an overview of two different approaches conservation topic in great detail for the past decade. Rault, as the backbone of IoT, have discussed the energy Bouabdallah, and Challal (2014), as well as Khan et al. used edge in IoTcomputing platforman architectures namely cloud computing topic inofgreat detail for discussed the past decade. Rault, This paper provides of–– two different approaches and – overview and summarizes their suitability for conservation as the backbone IoT, have the energy used in IoT platform architectures namely cloud computing Bouabdallah, and Challal (2014), as well as Khan et al. conservation topic in great detail for the past decade. Rault, (2015) presented comprehensive surveys about the energy This paper provides an overview of two different approaches and edge computing – and summarizes their suitability for Bouabdallah, and in Challal asthewell asdecade. Khan Rault, et al. specific environments. The summarizes main– namely part their is cloud devoted to the used in IoT platform architectures computing conservation topic great (2014), detail for past and edge computing – and suitability for (2015) presented comprehensive surveys about the energy Bouabdallah, and Challal (2014), as well as Khan et al. used in IoT platform architectures computing specific environments. The summarizes main– namely part their is cloud devoted to the (2015) presented comprehensive surveys about the energy and edge computing – and suitability for Bouabdallah, and Challal (2014), as well as Khan et al. specific environments. The main part is devoted to the (2015) presented comprehensive surveys about the energy and edgeenvironments. computing – and suitability for specific The summarizes main part their is devoted to the Copyright © 2018 IFAC 162 (2015) presented comprehensive surveys about the energy specific © environments. The mainFederation part is ofdevoted toControl) the 162Hosting by Elsevier Ltd. All rights reserved. 2405-8963 2018, IFAC (International Automatic Copyright © 2018 IFAC Peer review©under of International Federation of Automatic Copyright 2018 responsibility IFAC 162Control. Copyright © 2018 IFAC 162 10.1016/j.ifacol.2018.07.147 Copyright © 2018 IFAC 162
2018 IFAC PDES Ostrava, Czech Republic, May 23-25, 2018 Jozef Mocnej et al. / IFAC PapersOnLine 51-6 (2018) 162–167
management in WSNs. Their taxonomies provide a summary of the recent findings in techniques, methods, and algorithms that can be used to achieve better energy efficiency.
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popular cloud platforms that have found their place in production as well as science (Silva et al. (2016)). While the centralized architecture (Fig. 1(a)) of these platforms is simple and straightforward, it also means the necessity of pushing all data up to the cloud itself before making any decision process, eventually leading to overhead.
Besides WSNs, researchers have also focused on how to improve the energy conservation in the IoT as well. Kaur and Sood (2017) proposed an energy efficient architecture for IoT that dynamically switches IoT devices to the sleep mode based on the parameters such as available battery power, quality of extracted information, conflict factor, and coefficient of variation. A slightly different approach was taken by Ventura et al. (2014), who utilized benefits of machine learning to teach devices their usage pattern so that they could adapt to the environment and optimize their consumption. The whole learning process was executed on the centralized server, though.
A better approach may be to move a part of the decisionmaking capabilities from the cloud to the edge of the network, e.g. to a gateway or even to end nodes themselves. The distribution of logic can greatly reduce the volume of transferred data, improving the network traffic and lowering the requirements for cloud services (Lojka et al. (2015)). The next wave of IoT platforms will be driven by the edge computing paradigm, enabling real-time analytics and decision making. However, the decentralized architecture (Fig. 1(b)) also increases the complexity of management process, creating a high demand for the efficient architecture design.
More recently, IoT applications revealed the potential advantages in shifting a part of the logic from the cloud to the edge. Shi et al. (2016) wrote a survey paper about the vision and possible gains of distributing logic across the network, but also mentioned the challenges that need to be overcome. It is necessary to point out that the edge computing brings additional computation to the edge, which can create some overhead. However, as Harrison et al. (2016) outlined, the biggest energy consumer in a device is transceiver, not CPU. Therefore, we can assume the edge computing can still be energy efficient, but we need to know the size of the overhead at first.
(a) Centralized
(b) Decentralized
Fig. 1 Types of IoT architectures. Consequently, there is a trade-off between the initial complexity and the gained benefits. While a centralized architecture has an advantage in easy set-up and management, a decentralized architecture can offer more efficient communication. Table 1 provides characteristic features to ease the selection process of the right architecture for an IoT platform in a particular environment. When we want to connect a lower number of devices with less frequent sending rates, the network connectivity is reliable, cloud resources are not restricted, and the response rate can be in hundreds of ms, a simple and straightforward centralized architecture may be a suitable choice. On the contrary, in situations where we need to efficiently utilize the available network and cloud resources, and the response is required in tens of ms, a decentralized architecture with computation at the edge will be preferable.
All of the aforementioned papers deal with the energy conservation in constrained environments. However, to the best of our knowledge, there is no a practical case study measuring the exact overhead caused by the edge computing paradigm and how it influences the battery life of IoT devices. Consequently, we have decided to write this paper in order to analyze the impact of the edge computing on energy consumption of IoT devices. 3. EDGE COMPUTING PARADIGM 3.1 Innovation Waves and Comparison to Cloud Computing The early stage of IoT solutions was based on proprietary applications solving specific tasks they were designed for. However, these solutions were unable to interact with each other as the expectations of a system evolved over time, which eventually lead to the requirement of more dynamic approaches. Consequently, the trend has shifted from specifically-oriented tight-coupled applications to more open and loosely-coupled IoT integration platforms (Gula, and Žaková (2017)).
Table 1. Decision characteristics for selecting a suitable IoT architecture
Number of devices
The first wave of IoT platforms has been driven by the cloud computing paradigm with the characteristic feature of placing the whole logic in the cloud. Amazon Web Services 1, Microsoft Azure2, and Bluemix3 are just a few examples of
Sending rates Connectivity Availability of resources Response rate
1
https://aws.amazon.com/ https://portal.azure.com/ 3 https://www.ibm.com/cloud/ 2
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Centralized architecture
Decentralized architecture
Low
High
Less frequent, tens of s interval Must be reliable, high network throughput Abundance of resources Hundreds of ms
More frequent, s or ms interval Can be unreliable, constrained networks Resources need to be used efficiently Tens of ms
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Nevertheless, these two approaches do not exclude each other. In fact, a whole solution can be the combination of both of them. As the complexity of IoT solutions rises, we can expect different services using different logic distribution in order to achieve the desired goal. Actual implementation will be a use case dependent and IoT platforms should be dynamic enough to adapt to these changing requirements accordingly.
module that integrates general-purpose input/output (GPIO), pulse-width modulation (PWM), inter-integrated circuit (IIC), 1-Wire, and analog-to-digital converter (ADC). Since this device offers several pins and has built-in Wi-Fi chip, it can be easily used for the typical IoT scenario – plug-in sensors, acquire sensed data to the cloud in a regular interval and be in sleep mode for the rest of the time. However, instead of merely sending all data to the cloud, we have implemented a value prediction model based on a linear regression that represents the decision-making process at the edge. The model is present in both the end device and the gateway that collects data from the network. Since we used simple linear regression, the prediction process is defined by the formula:
3.2 Impact on IoT Devices To sum up the comparison, the edge computing paradigm can bring several advantages, such as network traffic reduction, faster response rate, lower requirements of network connection reliability, more efficient utilization of cloud services, etc. The initial complexity of solutions based on the edge computing is higher, but the gained benefits will eventually pay off the invested effort.
Y *X ,
( 1)
where X is the independent (explanatory) variable, Y is the dependent variable, α is the Y-Intercept of the regression line, and β is the slope of the regression line.
So far, we described the edge computing only from a whole system point of view. However, it is important to look at one more aspect – how the edge computing influences IoT devices themselves.
The main purpose is to send messages only when necessary. If a predicted value falls within the acceptable error bound
[ , ], , nothing will be sent. The gateway will simply use the predicted value. On the contrary, the incorrectly predicted value will cause the end device to recreate the prediction model and send the new parameters α and β together with the correct value. This way, two states can happen:
End devices in IoT networks are constrained many times. The computing capabilities are low, and the energy resources can be limited. Particularly application areas with the difficult accessibility to IoT devices are very demanding on the lifespan of devices, since replacing them may not be an easy task. Smart environment (Ramesh (2014); Virmani and Jaim (2016)), smart agriculture (Elliot et al. (2014); Pimentel and Peshin (2014)), smart city (Djahel et al. (2015); Catania and Ventura (2014)), smart manufacturing and retail (Wang et al. (2016); Xu et al. (2016)) are just a few examples of application domains requiring good longevity because of the constrained energy resources. In these situations, the only option is to utilize resources wisely. Therefore, we have decided to analyze the impact of the edge computing on energy consumption in IoT. Our aim is to prove that the edge computing is not only suitable for more powerful machines but can also spare resources even on constrained devices.
1.
The model predicts a value correctly – the end device will put itself to sleep mode without sending any value.
2.
The model predicts a value incorrectly – the end device will recreate the prediction model and send new parameters together with the correct value to the gateway. Then goes to sleep mode.
Obviously, the model spares energy consumption with the first option, whereas the latter creates the undesirable overhead. Now, the question is how big the overhead is and under what circumstances we should consider implementing the edge computing even on the most constrained devices.
According to Harrison et al. (2016), small devices used in wireless sensor networks (WSNs) draws the most energy with the transceiver turned on, while computing operations are much less energy demanding. Consequently, the assumption is that the edge computing should have a positive effect on the energy conservation if utilized properly.
4.2 Implementation To measure the amount of overhead and answer the aforementioned question, we need to examine the behavior of a NodeMCU ESP-12E device. In particular, we are interested in observing power consumption and time spent by executing different activities.
4. EDGE COMPUTING CASE STUDY 4.1 Description
ESP-12E module supports three sleep modes by default – modem sleep, light sleep and deep sleep. The differences between them are summarized in Table 2. Differences between ESP-12E modes. We tested out all modes and measured their power consumption. The input voltage was set to 5V. A regular ON mode had 70 mA current draw, whereas merely turning the transceiver off reduced current to 17.2
This paper provides a practical case study of the edge computing paradigm implemented in an IoT device with the intention to evaluate its power consumption. The device is represented by NodeMCU ESP8266 ESP-12E due to its versatile capabilities, large availability and low price. NodeMCU is a development board based on the ESP8266 164
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mA. The lowest current draw, 2.3 mA, was achieved during deep sleep when everything except the real-time clock is off.
value took 3572 ms, which is slightly more than regular duty cycling. This time difference represents the overhead of our model when the prediction model is not accurate.
To eliminate the potential confusion, be aware that our measured values are slightly higher than current mentioned in datasheets for the ESP-12E module. The difference is caused by the NodeMCU development board, which itself consumes some energy.
Table 3 provides numbers used for the evaluation in the next section. The duty cycling had 70 mA current draw in ON mode for the total period of 3352 ms. A value prediction model drew merely 17.2 mA for 18 ms when a value was predicted correctly, whereas the incorrectly predicted value consisted of 17.2 mA current draw during the model recreation phase and 70 mA during the sending phase.
Table 2. Differences between ESP-12E modes
Wi-Fi CPU System clock Real-time clock Consumption
ON mode ON ON ON
Modem sleep OFF ON ON
Light sleep OFF Pending OFF
Deep sleep OFF OFF OFF
ON
ON
ON
ON
70 mA
17.2 mA
3.2 mA
2.3 mA
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Table 3. Summary of the measured execution times and energy consumption Duty cycling Correctly predicted value Incorrectly predicted value
The difference in power consumption between modes is significant. Nevertheless, it is necessary to include one more factor – time. To estimate the lifespan of a device, we need to know how much time is spent on executing a certain task.
Execution time 3352 ms
Consumption 70 mA
18 ms
17.2 mA
3572 ms
17.2 & 70 mA
4.3 Evaluation Once we knew the energy consumption and execution time of all expected device’s states, we could evaluate the results using different configuration settings. Therefore, we compared a regular duty cycling approach with the value prediction model. In the latter case, we assumed three scenarios based on the prediction success rate: the unrealistic 100% and 0% success rates to illustrate the best and the worst possible use cases, and the more probable 60% success rate, which is easy to achieve, and we can expect the prediction model would not go below in most environments. Moreover, we did the calculations with several sending rates to see how a sending interval influences the energy consumption of devices. The intervals differed from 1 message per hour up to 1 message every 10 seconds.
Therefore, we have implemented the described model into NodeMCU ESP-12E and measured the execution time. Our findings are depicted in Fig. 2. Predicting the next value took 18 ms on average. When the predicted value was out of the
acceptable error bound [ , ], , recreating a new linear regression model based on the last five values took 20 ms. The transition from modem sleep to ON mode lasted 3 ms. The biggest time-consuming activity was connecting to the Wi-Fi network, which took approximately 3.5 sec with the static IP. When testing with DHCP, assigning a new IP extended the time by another 500 ms. We tried different routers with the similar or even worse results. This discovery set the lower boundary of the duty cycling frequency for us. The minimum sending rate should be no less than 4 sec, otherwise it is not worth to turn the transceiver OFF. Lastly, we used MQTT protocol for transferring messages from the device to broker, and this activity took 53 ms.
Fig. 3 summarizes the differences in energy consumption of a NodeMCU device per hour depending on the used configuration. The numbers were calculated using the formula:
J / hr Aa * t a * MH As * ( 3600 ( t a * MH )) , ( 2) where J/hr is joules per hour, Aa is current of a device in active mode, ta is the execution time spent in active mode, MH is measurements per hour, and As is current of a device in sleep mode. The chart offers two interesting facts. The first one is the correlation between the duty cycling and value prediction model. It is worth to notice that even the theoretical worst possible scenario with the 0% prediction success rate consumes just negligibly more energy than the regular duty cycling without any computations. Nevertheless, the increasing prediction success rate can significantly reduce the consumed energy. Therefore, we can confirm that the computation tasks on the edge do not necessarily have to increase energy consumption and the appropriate logic can actually be more efficient.
Fig. 2 Execution time of different activities. In summary, a regular duty cycling, which consisted of measuring a new value and sending it to the broker, took 3352 ms on average. When the model was implemented at the edge, the correctly predicted value required 18 ms of the execution time. On the contrary, the incorrectly predicted 165
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The second fact worth to notice is the impact of the sending rate on the overall energy consumption of a device. While the 1-hour sending interval resulted in the minimum differences between each variant, the gap is starting to be considerable from the 5-min interval and higher. This trend is caused by the total time amount of a device spent in sending mode. The more messages a device is required to send, the bigger energy conservation can be achieved using the value prediction model. However, as we mentioned in Implementation section, the upper bound of a sending interval is limited by the logic execution time, including the time spent on connecting to Wi-Fi.
contrary, even the worst possible use case, where the prediction model has 0% success rate, does not add too big overhead. Actually, the lifespan of a device would not be shortened by more than three days. And it is very unlikely to happen. Consequently, we can conclude that the additional overhead caused by computations at the edge is negligible and can be easily overridden even with the low prediction success rate. Our evaluation proved that the edge computing paradigm can be suitable not only for powerful machines but also for the constrained devices with the limited energy resources.
Fig. 3 Energy consumption of NodeMCU ESP-12E device per hour. Fig. 4 Estimated lifespan of NodeMCU ESP-12E device when powered by 10Ah battery.
Once we knew the energy consumed per hour, we could estimate the lifespan of a NodeMCU ESP-12E device. The calculations were realized assuming 5V input voltage, 10 Ah battery and using the formula: DL
V * Ah * 3600
,
5. CONCLUSION
( 3)
J / hr * 24
where DL is expected device lifespan, V is the input voltage of a device, Ah is a battery capacity in ampere-hours, and J/hr is the calculated device consumption per hour in joules. The results are depicted in Fig. 4. The configurations remained the same; we compared the regular duty-cycling with the value prediction model considering three prediction success rates – two unrealistic to show the upper and lower bounds, and one that can be easily achieved in most scenarios. The sending interval differed from 1 hour to 10 sec.
IoT platforms have already shown some of their potential in addressing the arising sustainability issues caused by the exponential growth of devices connected to the Internet. Although the first wave of IoT platforms was strictly cloudcentric, the requirements have created the demand for decentralized approaches as well. The edge computing is a paradigm that takes a part of logic from the cloud and moves it closer to the network and devices for more efficient communication. This paper provided the comparison between the cloud and edge computing to highlight their network characteristics and mutual differences. The main part was devoted to the practical case study showing the impact of the edge computing overhead on the overall energy consumption of IoT devices. We put in contrast a regular duty cycling model with value prediction based on linear regression and implemented both alternatives into NodeMCU ESP12-E module to evaluate their energy consumptions. As this case study revealed, the additional computation of the value prediction model caused the minimal overhead, which was
As it can be seen, the gap between the duty cycling and value prediction model with achievable 60% success rate has enlarged even more. While the difference between them is merely three days at 1-hour sending interval, it can grow up to ninety-seven days with 10-sec sending rate. On the 166
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easily overridden even with the low prediction success rate. Consequently, we could conclude the edge computing paradigm has proved to be suitable for the constrained devices with the limited battery life if utilized properly.
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In future work, we would like to analyze changes in the gained information value caused by the computation at the edge. In particular, our aim is to propose a representational model for monitoring an IoT system at runtime so that it would be possible to determine how data modifications influence the output quality of a system. Ultimately, we would like to control available resources of a system based on the desired output quality to achieve better energy efficiency. ACKNOWLEDGMENT This publication is the result of the following projects implementation: FEI Grant - FEI-2017-46, 2017-2018 (40%); University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology – II. phase, ITMS: 313011D232 (30%); Grant KEGA - 033TUKE-4/2018 AICybS (30%). REFERENCES Catania, Vincenzo, and Daniela Ventura. (2014). An approach for monitoring and smart planning of urban solid waste management using smart-M3 platform. In: Proceedings of 15th Conference of Open Innovations Association FRUCT, IEEE. Djahel, Soufiene, et al. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. In: IEEE Communications Surveys & Tutorials, 17.1, 125-151. Elliott, Joshua, et al. (2014). Constraints and potentials of future irrigation water availability on agricultural production under climate change. In: Proceedings of the National Academy of Sciences, 111.9, 3239-3244. Gula, Miroslav, and Katarína Žaková (2017). Proposal of Component Based Architecture for Internet of Things: online laboratory case study. In: IFAC-PapersOnLine, 50.1, 337-342. Harrison, D. C., et al. (2016). Busting myths of energy models for wireless sensor networks. In: Electronics Letters, 52.16, 1412-1414. Kaur, Navroop, and Sandeep K. Sood (2017). An energyefficient architecture for the Internet of Things (IoT). In: IEEE Systems Journal, 11.2, 796-805. Khan, Junaid Ahmed, Hassaan Khaliq Qureshi, and Adnan Iqbal (2015). Energy management in wireless sensor networks: A survey. In: Computers & Electrical Engineering, 41, 159-176. Lojka, Tomáš, Marek Bundzel, and Iveta Zolotová (2015). Industrial gateway for data acquisition and remote control. In: Acta Electrotechnica et Informatica, 15.2, 43-48. Pimentel, David, and Rajinder Peshin (2014). Integrated pest management: pesticide problems. In: Springer Science & Business Media, 3.
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