Efficient scheduling of video camera sensor networks for IoT systems in smart cities. Issue 5 (29th November 2019)

• Record Type: Journal Article
• Authors: Naeem, Muhammad; Ejaz, Waleed; Iqbal, Muhammad; Iqbal, Farkhund; Anpalagan, Alagan; Rodrigues, Joel J. P. C.
• Other Names: Kerrache Chaker Abdelaziz guestEditor.; Amadeo Marica guestEditor.; Ahmed Syed Hassan guestEditor.; Liang Chengchao guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 31:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-29
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3798
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Video camera sensor networks (VCSN) has numerous applications in smart cities, including vehicular networks, environmental monitoring, and smart houses. Scheduling of video camera sensor networks (VCSN) can reduce the computational complexity, increase energy efficiency, and enhance throughput for the Internet of things (IoT) systems. In this paper, we apply the iterative low‐complexity probabilistic evolutionary method for scheduling video cameras to maximize throughput in VCSNs for IoT systems. Scheduling of video cameras in VCSNs to maximize throughput is a combinatorial optimization problem whose computational complexity increases exponentially with the increase in the number of video cameras. We propose an iterative probabilistic method named as cross‐entropy optimization (CEO), which is an evolutionary algorithm. The combinatorial optimization problems can be solved using the CEO which is a generalized Monte Carlo technique. The proposed method updates its selected population (video cameras) at each iteration based on the Kullback Leibler (KL) distance/divergence. The KL distance/divergence is minimized using the probability distribution obtained from the learned from the group of selected samples of better solutions found in the previous iterations. The effectiveness of the CEO is verified in terms of optimality and simplicity through simulations. In addition, the results of the CEO are better than the suboptimal algorithms (ie, best norm‐based algorithm, genetic algorithm, and capacity upper‐bound–based greedy algorithm) and maximum of 2%‐3% deviation from the exhaustive search (optimal) with less complexity. The trade‐off between CEO and optimal is the computational complexity. Abstract : ‐We mathematically model scheduling of VCSNs for IoT systems. ‐We have used a probabilistic learning method by minimizing the Kullback Leibler (KL) distance/divergence. ‐We evaluated the performance of the CEO for scheduling of VCSNs in terms of average sum capacity and computational complexity.