Internet of Things for Residential Areas: Toward Personalized Energy Management Using Big Data

Intelligent management of machines, particularly in a residence area, has been of interest for many years. However, such system design has always been limited to simple control of machines from a local area or remotely from the Internet. In this report, for the first time, an intelligent system is proposed, where not only provides intelligent control ability of machines to user, but also utilizes big data and optimization techniques to provide promotional offers to the user to optimize energy consumption of machines. Since a high traffic communication is involved among the machines and the optimization-big data core of system, the communication core of the proposed system is designed based on cloud, where many challenging issues such as spectrum assignment and resource management are involved. To deal with that, the communication network in the home area network (HAN) is designed based on the cognitive radio system, where a new spectrum assignment method based on the ant colony optimization (ACO) algorithm is proposed to perform spectrum assignment to the machines in the HAN. Performance evaluation of the proposed spectrum assignment method shows its performance in fair spectrum assignment among machines.

[1]  Shahryar Rahnamayan,et al.  3D localization in large-scale Wireless Sensor Networks: A micro-differential evolution approach , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[2]  Fereydoun Farrahi Moghaddam Carbon-profit-aware job scheduling and load balancing in geographically distributed cloud for HPC and web applications , 2014 .

[3]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[4]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[5]  Ben Y. Zhao,et al.  Utilization and fairness in spectrum assignment for opportunistic spectrum access , 2006, Mob. Networks Appl..

[6]  Hojjat Salehinejad,et al.  Cognitive radio networks spectrum allocation: An ACS perspective , 2012, Sci. Iran..

[7]  Zhe Chen,et al.  Cognitive Radio for Smart Grid: Theory, Algorithms, and Security , 2011, Int. J. Digit. Multim. Broadcast..

[8]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[9]  Zhen Peng,et al.  Cognitive radio spectrum allocation using evolutionary algorithms , 2009, IEEE Transactions on Wireless Communications.

[10]  Mohsen Guizani,et al.  Cognitive radio based hierarchical communications infrastructure for smart grid , 2011, IEEE Network.

[11]  Shahryar Rahnamayan,et al.  Micro-differential evolution: Diversity enhancement and a comparative study , 2015, Appl. Soft Comput..

[12]  Ivan Stojmenovic,et al.  A hybrid channel assignment approach using an efficient evolutionary strategy in wireless mobile networks , 2005, IEEE Transactions on Vehicular Technology.

[13]  Hojjat Salehinejad,et al.  Dynamic Fuzzy Logic-Ant Colony System-Based Route Selection System , 2010, Appl. Comput. Intell. Soft Comput..

[14]  Rong Yu,et al.  Hybrid spectrum access in cognitive Neighborhood Area Networks in the smart grid , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[15]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[16]  Brian M. Sadler,et al.  Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy , 2006, ArXiv.

[17]  Hojjat Salehinejad,et al.  A metaheuristic approach to spectrum assignment for opportunistic spectrum access , 2010, 2010 17th International Conference on Telecommunications.

[18]  Jiming Chen,et al.  Sensing-delay tradeoff for communication in cognitive radio enabled smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[19]  Xin Liu,et al.  The U.S. Environmental Protection Agency , 2010 .