Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Alia, Osama Moh’d; Al-Ajouri, Alaa

doi:10.1109/jsen.2016.2633409

Cited by 101 publications

(36 citation statements)

References 35 publications

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“…Although, they did not prove that the proposed parsing crossover strategy is better than the original genetic approach. Authors in [12] propose an algorithm which is based on a harmony search to optimize the number of deployed sensors and the coverage. The disadvantage of this work is that the proposed network model is simplistic.…”

Section: Related Work On the 2d-3d Deployment Problem Using Optimizamentioning

confidence: 99%

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A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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h i g h l i g h t s• Proving the efficiency of optimization algorithms in solving real-world problems.• A new concept of accent birds introduced to the particle swarm optimization. • A new hybrid scheme that integrates PSO and MAS.• The hybridization improves the performance of the original tested algorithms. • A comparison between simulation and experimental validation is given. a r t i c l e i n f o Keywords: Accent based PSO Multi-agent Many-objective optimization Experimental validation 3D indoor deployment DL-IoT collection networks a b s t r a c tThe 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird's accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird's accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) ''die'' and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.✩ This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. * Correspondence to: bureau C107, Batiment C, 1 Place Georges positions and the topology of the network. In this study, we are interested in the deployment of nodes in a three-dimensional space that reflects the real topology of the RoI (region of interest) better than the two-dimensional case. We are particularly interested in improving the initial 3D indoor deployment by adding new nodes while optimizing a set of objectives such as coverage, connectivity, localization, quality of links and network utilization. These objectives will be detailed in the modeling section. To resolve the deployment problem in WSN, the topology of the network can be modeled as an identification problem in a graph as addressed in [1] and [2]. In this static case, the deployment algorithm is run off-...

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Section: Related Work On the 2d-3d Deployment Problem Using Optimizamentioning

confidence: 99%

“…PB is represented using the Eqs. (2), (6), (8), (11) and (13) subject to the constraints (3), (4), (5), (7), (9), (12) and (14). The used parameters are listed in Table 1.…”

Section: System Modeling and Notationmentioning

confidence: 99%

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

et al. 2019

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“…In [26], Yang et al proposed an optimal coordination strategy for sensor motion to maximize the tracking accuracy and the search space was reduced to improve efficiency. A harmony search based deployment algorithm was proposed by Mohd Alia et al that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost in [27]. In this paper, we utilized the harmony search algorithm to solve the optimization problem of node depth.…”

Section: Related Workmentioning

confidence: 99%

Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search

Liu

Zhang

et al. 2017

Sensors

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Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.

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“…Other papers related to similar problems are present in [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 95%

A Statistical Method for Area Coverage Estimation and Loss Probability Analysis on Mobile Sensor Networks

Eiras

Zucchi

2019

The 6th International Electronic Conference on Sensors and Applications

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Sensor networks are formed by fixed or mobile sensor nodes and their functions are to capture the events that occur within a certain area and then relay to a central node. Normally sensor nodes are not able to transmit or receive information over long distances due to the need to use less energy and thus extend their useful life. Therefore, the number of sensor nodes in a given area directly influences the coverage of this area and the ability of information to be relayed by several sensors to the central node. If there are many missed messages the application will have its performance compromised. In this paper we use a statistical method-based on Monte Carlo approach to estimate the probability of message loss and area coverage. The position and proper motion of the sensors are randomly chosen and from that we estimate how many nodes can communicate with the central node directly or through another sensor working as relay. The free variables in our analysis are node density, node displacement velocity and sensor quantity. The results obtained are compared analytically with simple cases in order to validate the results obtained by the simulations performed.

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Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Cited by 101 publications

References 35 publications

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search

A Statistical Method for Area Coverage Estimation and Loss Probability Analysis on Mobile Sensor Networks

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