Interest, energy and physical-aware coalition formation and resource allocation in smart IoT applications

In this paper, the problem of coalition formation among Machine-to-Machine (M2M) communication type devices and the resource management problem is addressed. Each M2M device is characterized by its energy availability, as well as by differentiated interests to communicate with other devices based on the Internet of Things (IoT) application that they jointly serve. Physical ties among devices also exist based on their physical distance proximity and communication channel quality. Those three factors: energy availability, interest and physical ties, are considered into the coalition formation process and the coalition-head selection. Each M2M device is associated with a holistic utility function, which appropriately represents its degree of satisfaction with respect to Quality of Service (QoS) prerequisites fulfillment. Given the created coalitions among the M2M devices, a distributed power control framework is proposed towards determining each M2M device's optimal transmission power in order to fulfill its QoS prerequisites. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated.

[1]  Teng Joon Lim,et al.  Clustering and radio resource partitioning for machine-type communications in cellular networks , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[2]  Sudip Misra,et al.  Two-level mapping to mitigate congestion in machine to machine (M2M) cloud , 2015, 2015 Applications and Innovations in Mobile Computing (AIMoC).

[3]  Haige Xiang,et al.  Distributed network topology formation and resource allocation for clustered machine-to-machine communication networks , 2015 .

[4]  Sudip Misra,et al.  Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization , 2014, Inf. Sci..

[5]  Hung-Yun Hsieh,et al.  Joint Optimization of Clustering and Scheduling for Machine-to-Machine Communications in Cellular Wireless Networks , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[6]  Hung-Yun Hsieh,et al.  Minimizing Radio Resource Usage for Machine-to-Machine Communications through Data-Centric Clustering , 2016, IEEE Transactions on Mobile Computing.

[7]  Xiaoli Chu,et al.  Energy-Efficient Uplink Resource Allocation in LTE Networks With M2M/H2H Co-Existence Under Statistical QoS Guarantees , 2014, IEEE Transactions on Communications.

[8]  Cem U. Saraydar,et al.  Efficient power control via pricing in wireless data networks , 2002, IEEE Trans. Commun..

[9]  Lyman Chapin,et al.  THE INTERNET OF THINGS : AN OVERVIEW Understanding the Issues and Challenges of a More Connected World , 2015 .