A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks

Du, Huazheng; Xia, Na; Jiang, Jianguo; Xu, Lina; Zheng, Rong

doi:10.1007/s11390-013-1355-z

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Chen et al [9] have studied the sniffer-channel selection problem for monitoring Wireless Local Area Networks (WLANs), formulating the two optimization problems: how to minimize the maximum number of channels that a sniffer listens to; how to minimize the total number of channels that the sniffers listen to. The recent works [10], [11] have studied the sniffer-channel selection problem, with the goal to maximize the quality of monitoring. Du et al [10] presented a Monte Carlo enhanced Particle Swarm Optimization (MC-PSO) algorithm, while Xia et al [11] proposed a Multiple Quantum Immune Clone Algorithm (MQICA).…”

Section: Related Workmentioning

confidence: 99%

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Toward Optimal Distributed Monitoring of Multi-Channel Wireless Networks

Shin

Bagchi

Wang

2016

IEEE Trans. on Mobile Comput.

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This paper studies an optimal channel assignment problem for passive monitoring in multi-channel wireless networks, where a set of sniffers capture and analyze the network traffic to monitor the wireless network. The objective of this problem is to maximize the total amount of traffic captured by sniffers by judiciously assigning the radios of sniffers to a set of channels. This problem is NP-hard, with the computational complexity growing exponentially with the number of sniffers. We develop distributed online solutions for largescale and dynamic networks. The dynamism in the network may arise from mobility of the nodes being monitored. Our algorithm is guaranteed to achieve at least 1 1 e times the optimum, regardless of the network topology and the channel assignment of nodes to be monitored, while providing a distributed solution amenable to online implementation. Further, our algorithm is cost-effective, in terms of communication and computational overheads, due to the use of purely local communication and the incremental adaptation to network changes. We present two operational modes of our algorithm for two types of networks that change at different rates; one is a proactive mode for fast-varying networks, while the other is a reactive mode for slowly-varying networks. Simulation results demonstrate the effectiveness of the two modes of our algorithm and compare it to the theoretically optimal algorithm.

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Section: Related Workmentioning

confidence: 99%

“…The recent works [10], [11] have studied the sniffer-channel selection problem, with the goal to maximize the quality of monitoring. Du et al [10] presented a Monte Carlo enhanced Particle Swarm Optimization (MC-PSO) algorithm, while Xia et al [11] proposed a Multiple Quantum Immune Clone Algorithm (MQICA).…”

Section: Related Workmentioning

confidence: 99%

Toward Optimal Distributed Monitoring of Multi-Channel Wireless Networks

Shin

Bagchi

Wang

2016

IEEE Trans. on Mobile Comput.

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“…This is a global best and called “gbest”. In the past several years, PSO has been successfully applied in many research and application areas [ 30 , 31 ], and its parameters setting has been well discussed [ 32 , 33 ].…”

Section: Pssd (Particle Swarm Inspired Underwater Sensor Deployment) mentioning

confidence: 99%

Particle Swarm Inspired Underwater Sensor Self-Deployment

Xia

Zheng

2014

Sensors

Self Cite

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Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation.

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“…Many evolutionary approaches for multi-objective optimization problems are studied and a historical review of evolutionary can be seen. These involved approaches are mainly designed to solve unconstrained optimization problems [2]- [5]. In addition, researchers have also enriched these algorithms to solve multi-objective optimization (MOO) problems with constraints [6]- [7].…”

Section: Introductionmentioning

confidence: 99%

An effective hybrid search mode for multi-objective optimization with constraints

Chen

Yuan

2022

MATEC Web Conf.

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In this paper a new search strategy for multi-objective optimization (MOO) with constraints is proposed based on a hybrid search mode (HSM). The search processes for feasible solutions and optimal solutions are executed in a mixed way for the existing methods. With regard to HSM, a hybrid search mode is proposed, which consists of two processes: Feasibility search mode (FSM) and optimal search mode (OSM). The executions of these two search modes are independent relatively and also adjusted according to the population distribution. In the early stage, FSM plays the leading role for exploring the feasible space since most of the individuals are infeasible. With the increase of the feasible individuals, OSM is the primary operation for the search of optimal individuals. The proposed method is simple to implement and need few extra parameter tuning. The handing method of constraints is tested on several multi-objective optimization problems with constraints. The remarkable results demonstrate its effectiveness and good performance.

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A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks

Cited by 12 publications

References 23 publications

Toward Optimal Distributed Monitoring of Multi-Channel Wireless Networks

Toward Optimal Distributed Monitoring of Multi-Channel Wireless Networks

Particle Swarm Inspired Underwater Sensor Self-Deployment

An effective hybrid search mode for multi-objective optimization with constraints

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