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Sensors 2016, 16(12), 2046; doi:10.3390/s16122046

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

1
Department of Electrical Engineering, Bahria University, E-8 Naval Complex, Islamabad 44000, Pakistan
2
Department of Electrical and Computer Engineering, Dhofar University, Salalah 211, Oman
3
Department of Electronics Engineering, Sogang University, Seoul 04107, Korea
4
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Andrea Zanella and Toktam Mahmoodi
Received: 18 August 2016 / Revised: 24 November 2016 / Accepted: 28 November 2016 / Published: 2 December 2016
(This article belongs to the Special Issue Smart City: Vision and Reality)
View Full-Text   |   Download PDF [1250 KB, uploaded 7 December 2016]   |  

Abstract

This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones. View Full-Text
Keywords: RF energy harvesing; internet of things; cognitive radio; channel scheduling; quality of service; clustering; heterogenity RF energy harvesing; internet of things; cognitive radio; channel scheduling; quality of service; clustering; heterogenity
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MDPI and ACS Style

Aslam, S.; Hasan, N.U.; Jang, J.W.; Lee, K.-G. Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities. Sensors 2016, 16, 2046.

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