Premium
Efficient scheduling of video camera sensor networks for IoT systems in smart cities
Author(s) -
Naeem Muhammad,
Ejaz Waleed,
Iqbal Muhammad,
Iqbal Farkhund,
Anpalagan Alagan,
Rodrigues Joel J. P. C.
Publication year - 2020
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3798
Subject(s) - computer science , probabilistic logic , computational complexity theory , scheduling (production processes) , kullback–leibler divergence , mathematical optimization , optimization problem , real time computing , algorithm , artificial intelligence , mathematics
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.