Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Rate allocation in wireless sensor networks with network lifetime requirement
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Mathematics and Computers in Simulation
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Active contour model via multi-population particle swarm optimization
Expert Systems with Applications: An International Journal
Cognitive radio adaptation using particle swarm optimization
Wireless Communications & Mobile Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Forepressure transmission control for wireless video sensor networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Optimal rate allocation for energy-efficient multipath routing in wireless ad hoc networks
IEEE Transactions on Wireless Communications
Distributed algorithms for maximum lifetime routing in wireless sensor networks
IEEE Transactions on Wireless Communications
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Power-rate-distortion analysis for wireless video communication under energy constraints
IEEE Transactions on Circuits and Systems for Video Technology
Resource allocation and performance analysis of wireless video sensors
IEEE Transactions on Circuits and Systems for Video Technology
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Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.