Edge Computing, IoT and Social Computing in Smart Energy Scenarios

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

[1]  Antonio F. Gómez-Skarmeta,et al.  Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings , 2017, Sensors.

[2]  Amjad Anvari-Moghaddam,et al.  Demand Side Management Using the Internet of Energy Based on Fog and Cloud Computing , 2017, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[3]  Edoardo Patti,et al.  A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids , 2019, IEEE Internet of Things Journal.

[4]  Juan M. Corchado,et al.  Electrical power consumption monitoring in hotels using the n-Core Platform , 2016, 2016 Clemson University Power Systems Conference (PSC).

[5]  Inés Sittón-Candanedo,et al.  Machine Learning Predictive Model for Industry 4.0 , 2018, KMO.

[6]  Juan M. Corchado,et al.  CAFCLA: A Framework to Design, Develop, and Deploy AmI-Based Collaborative Learning Applications , 2015 .

[7]  Khaled S. Balkhair,et al.  Sustainable and economical small-scale and low-head hydropower generation: A promising alternative potential solution for energy generation at local and regional scale , 2017 .

[8]  Rajkumar Buyya,et al.  Hedonic Pricing of Cloud Computing Services , 2018, IEEE Transactions on Cloud Computing.

[9]  Ned Djilali,et al.  Renewable resources portfolio optimization in the presence of demand response , 2016 .

[10]  Fei Zeng,et al.  Coordinated Control System of Multi-Level Belt Conveyors for Promotion the Energy Efficiency Based on IoT-Technology , 2018, 2018 5th International Conference on Information Science and Control Engineering (ICISCE).

[11]  Chih-Heng Ke,et al.  Efficiency Network Construction of Advanced Metering Infrastructure Using Zigbee , 2019, IEEE Transactions on Mobile Computing.

[12]  Deepak Kumar Sharma,et al.  IoT Architecture for Preventive Energy Conservation of Smart Buildings , 2019, Energy Conservation for IoT Devices.

[13]  Juan M. Corchado,et al.  Social computing in currency exchange , 2019, Knowledge and Information Systems.

[14]  Athanasios V. Vasilakos,et al.  IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges , 2017, IEEE Internet of Things Journal.

[15]  Zita Vale,et al.  A stochastic model for energy resources management considering demand response in smart grids , 2017 .

[16]  Juan M. Corchado,et al.  Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review , 2018, Energies.

[17]  J. M. Corchado,et al.  CAFCLA, a framework to ease design, development and deployment AmI-based collaborative learning applications , 2012, 7th Iberian Conference on Information Systems and Technologies (CISTI 2012).

[18]  Jianhua Li,et al.  Fog Computing-Enabled Secure Demand Response for Internet of Energy Against Collusion Attacks Using Consensus and ACE , 2018, IEEE Access.

[19]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

[20]  V. Kethareswaran,et al.  An Indian Perspective on the adverse impact of Internet of Things (IoT) , 2017, DCAI 2017.

[21]  Alberto Leon-Garcia,et al.  On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid , 2018, IEEE Transactions on Smart Grid.

[22]  Juan M. Corchado,et al.  A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building , 2018, Sensors.

[23]  P. G. V. Naranjo,et al.  Big Data Over SmartGrid-A Fog Computing Perspective , 2016 .

[24]  Juan M. Corchado,et al.  Evaluating the n-Core Polaris Real-Time Locating System in an Indoor Environment , 2012, PAAMS.

[25]  Albert Y. Zomaya,et al.  Edge-based Energy Management for Smart Homes , 2018, 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[26]  Hongnian Yu,et al.  Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond , 2017, IEEE Access.

[27]  Soumya Kanti Datta,et al.  Home automation using edge computing and Internet of Things , 2017, 2017 IEEE International Symposium on Consumer Electronics (ISCE).

[28]  Weisong Shi,et al.  The Promise of Edge Computing , 2016, Computer.

[29]  Sandeep K. Sood,et al.  An Energy-Efficient Architecture for the Internet of Things (IoT) , 2017, IEEE Systems Journal.

[30]  T. Csoknyai,et al.  Analysis of energy consumption profiles in residential buildings and impact assessment of a serious game on occupants’ behavior , 2019, Energy and Buildings.

[31]  Kazem Sohraby,et al.  IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems , 2017, IEEE Internet of Things Journal.

[32]  Bruno Volckaert,et al.  Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services , 2018, Sustainability.

[33]  Alagan Anpalagan,et al.  Efficient Energy Management for the Internet of Things in Smart Cities , 2017, IEEE Communications Magazine.

[34]  Mugen Peng,et al.  Edge computing technologies for Internet of Things: a primer , 2017, Digit. Commun. Networks.

[35]  Angela Lee,et al.  The impact of occupants’ behaviours on building energy analysis: A research review , 2017 .

[36]  Zita Vale,et al.  Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs , 2018 .

[37]  Teruo Higashino,et al.  Edge-centric Computing: Vision and Challenges , 2015, CCRV.

[38]  Aurelio Tommasetti,et al.  A Review of Smart Cities Based on the Internet of Things Concept , 2017 .

[39]  Carlos Henggeler Antunes,et al.  An Energy Management System Aggregator Based on an Integrated Evolutionary and Differential Evolution Approach , 2015, EvoApplications.

[40]  Juan M. Corchado,et al.  A review of edge computing reference architectures and a new global edge proposal , 2019, Future Gener. Comput. Syst..

[41]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[42]  Mohammad Abdullah Al Faruque,et al.  Energy Management-as-a-Service Over Fog Computing Platform , 2015, IEEE Internet of Things Journal.

[43]  Juan M. Corchado,et al.  A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring , 2017, Sensors.

[44]  Vanessa De Luca,et al.  Triggering Electricity-Saving Through Smart Meters: Play, Learn And Interact Using Gamification And Social Comparison , 2016 .

[45]  Ignacio Zabalza Bribián,et al.  Information and Communications Technologies (ICTs) for energy efficiency in buildings: Review and analysis of results from EU pilot projects , 2016 .

[46]  Juan M. Corchado,et al.  Energy Efficiency in Public Buildings through Context-Aware Social Computing , 2017, Sensors.

[47]  James A. Rodger,et al.  A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings , 2014, Expert Syst. Appl..

[48]  Xinyu Yang,et al.  A Survey on the Edge Computing for the Internet of Things , 2018, IEEE Access.

[49]  Juan M. Corchado,et al.  Cloud Computing and Multiagent Systems, a Promising Relationship , 2016 .

[50]  Hyeonjoon Moon,et al.  A Survey on Internet of Things and Cloud Computing for Healthcare , 2019, Electronics.

[51]  Alfonso González Briones,et al.  Review of the Main Security Problems with Multi-Agent Systems used in E-commerce Applications , 2016 .

[52]  Juan M. Corchado,et al.  Tendencies of Technologies and Platforms in Smart Cities: A State-of-the-Art Review , 2018, Wirel. Commun. Mob. Comput..

[53]  Óscar García,et al.  A Serious Game to Reduce Consumption in Smart Buildings , 2017, PAAMS.

[54]  Omid Abrishambaf,et al.  Demand response implementation in smart households , 2017 .

[55]  James K. Scarborough,et al.  Increasing Energy Efficiency With Entertainment Media , 2015 .

[56]  Soumya Kanti Datta,et al.  IoT and Machine Learning Based Prediction of Smart Building Indoor Temperature , 2018, 2018 4th International Conference on Computer and Information Sciences (ICCOINS).

[57]  Xinyu Yang,et al.  A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications , 2017, IEEE Internet of Things Journal.

[58]  Lei Zhang,et al.  Smart Home Electricity Demand Forecasting System Based on Edge Computing , 2018, 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS).

[59]  Kire Trivodaliev,et al.  A review of Internet of Things for smart home: Challenges and solutions , 2017 .

[60]  Manuel Díaz,et al.  On blockchain and its integration with IoT. Challenges and opportunities , 2018, Future Gener. Comput. Syst..

[61]  Abdulsalam Yassine,et al.  IoT big data analytics for smart homes with fog and cloud computing , 2019, Future Gener. Comput. Syst..

[62]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[63]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[64]  Xiaoming Fu,et al.  Cloud-Assisted Data Fusion and Sensor Selection for Internet of Things , 2016, IEEE Internet of Things Journal.