Energy-Efficient Deployment of Mobile Sensor Networks by PSO

Wu, Xiaoling; Shu, Lei; Wang, Jin; Cho, Jinsung; Lee, Sungyoung

doi:10.1007/11610496_50

Cited by 28 publications

(10 citation statements)

References 13 publications

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“…Particle swarm optimization (PSO) is a global search technique which considers the natural behavior of bird swarming [20]. It has been used effectively to solve: the problem of resource allocation and maximization of the system throughput while keeping the minimum user rate requirement [21], sensor deployment in designing sensor networks [22], near optimal deployment of sensors with Voronoi diagram evaluating the cost function [23], and optimal tuning of fuzzy feedback filter [24]. PSO algorithm introduced in [20,25] is applicable for single objective maximization problems.…”

Section: Related Workmentioning

confidence: 99%

Location management in LTE networks using multi-objective particle swarm optimization

Hashim

Abido

2019

Computer Networks

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Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Hence, two cost functions should be minimized where the first function is the total signaling cost of TAU and paging and the second cost function is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution is presented. The proposed algorithm results in a feasible compromise solution between the two objectives, minimizing the signaling overhead, and in turn, the consumption of the power battery of the user. The efficacy and the robustness of the proposed algorithm have been proven through a large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to the results obtained by a mixed integer non-linear programming (MINLP) algorithm.

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Section: Related Workmentioning

confidence: 99%

Location management in LTE networks using multi-objective particle swarm optimization

Hashim

Abido

2019

Computer Networks

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“…Bat-inspired algorithm (BA) [9] is a novel metaheuristic algorithm, which is also suitable for optimization for a variety of applications in wireless networks, like PSO for sensor network deployment [7]. It simulates the behavior of a group of bats (agents) hunting for their food (solution) by their echolocation.…”

Section: B Bat-inspired Algorithmmentioning

confidence: 99%

A bat-inspired algorithm for router node placement with weighted clients in wireless mesh networks

Lin

Deng

2014

9th International Conference on Communications and Networking in China

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This paper considers the problem of the dynamic router node placement (dynRNP) with weighted clients in wireless mesh networks (WMNs), in which both mesh clients and mesh router have mobility, and mesh clients can switch on or off their network access at different times. The main objective of the conventional dynRNP problem is to maximize both network connectivity and client coverage, i.e., the size of the greatest subgraph component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. Furthermore, this paper considers a more complicated dynRNP problem in WMNs, in which each client is associated with a weighted value (i.e., the mesh clients with higher weights values are served with larger priority), so that the two objective measures are redefined with consideration of clients' weights. We resolve the weighted problem by a bat-inspired algorithm (BA) with dynamic local search selection scheme, which simulates the echolocation of bats to find the optimal solution. Finally, the performance of the proposed BA is compared with the original BA and PSO, and the effects of weights are analyzed.

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“…According to our previous work in [15], we assume the power consumption is proportional to the square of distance and the data length is the same. Fig.7 shows the power consumption for different approaches, from which we can see that our approach is always better than the broadcasting mechanism.…”

Section: Study Of Power Consumptionmentioning

confidence: 99%

A Swarm Intelligence inspired Autonomic Routing Scenario in Ubiquitous Sensor Networks

Wang

d’Auriol

Lee

et al. 2007

2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07)

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Autonomic computing has attracted large amount of attention as a novel computing paradigm in the past few years. In this paper, we explore the inherent accordance between autonomic computing and swarm intelligence. Then, we propose a swarm intelligence inspired autonomic routing scenario with a targeting application area in ubiquitous sensor network. This scenario covers most of the characteristics of autonomic computing. The working flow and steps of our SI inspired autonomic routing scenario are explained in detail together with some preliminary simulation results, such as the power consumption, delivery ratio etc.

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Energy-Efficient Deployment of Mobile Sensor Networks by PSO

Cited by 28 publications

References 13 publications

Location management in LTE networks using multi-objective particle swarm optimization

Location management in LTE networks using multi-objective particle swarm optimization

A bat-inspired algorithm for router node placement with weighted clients in wireless mesh networks

A Swarm Intelligence inspired Autonomic Routing Scenario in Ubiquitous Sensor Networks

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