Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity

Sengupta, Shamik; Das, Swagatam; Nasir, Md.; Panigrahi, B. K.

doi:10.1016/j.engappai.2012.05.018

Cited by 137 publications

(79 citation statements)

References 23 publications

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“…The aim of the evolutionarybased approaches reported in [19][20][21][22][23][24][25] is to dynamically cluster the sensor nodes in the setup phase in such a way that some criteria (e.g., energy consumption, clustering distances, etc.,) to be optimized. For nodes, there are totally 2 − 1 different solutions, where in the every solution, each node can be selected as a cluster head or not.…”

Section: -14 Aslpr (Application-specific Low Power Routing)mentioning

confidence: 99%

“…Recently, evolutionary algorithms have attracted attentions from researchers to develop clustering protocols in wireless sensor networks [19][20][21][22][23][24][25][26][27]. The aim of the evolutionarybased approaches reported in [19][20][21][22][23][24][25] is to dynamically cluster the sensor nodes in the setup phase in such a way that some criteria (e.g., energy consumption, clustering distances, etc.,) to be optimized.…”

Section: -14 Aslpr (Application-specific Low Power Routing)mentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Advanced LEACH-based Protocols

Fanian¹,

Rafsanjani

Bardsiri

2016

IJEIC

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Section: -14 Aslpr (Application-specific Low Power Routing)mentioning

confidence: 99%

Section: -14 Aslpr (Application-specific Low Power Routing)mentioning

confidence: 99%

A Survey of Advanced LEACH-based Protocols

Fanian¹,

Rafsanjani

Bardsiri

2016

IJEIC

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“…Cardei et al model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach [8]. In [22] the sensor node deployment task has been formulated as a constrained multiobjective optimization problem where the aim is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes while maintaining connectivity between each sensor node and the sink node for proper data transmission.…”

Section: Related Workmentioning

confidence: 99%

“…The application of linear programming in coverage problem focuses on point (or target) coverage, where the objective is to cover a set of points (targets) [6,8,[20][21][22]. Zhao and Gurusamy consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity.…”

Section: Related Workmentioning

confidence: 99%

Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks

Sun

Liu

2013

International Journal of Distributed Sensor Networks

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We discuss the problem of maximizing the sensor field coverage for a specific number of sensors while minimizing the distance traveled by the sensor nodes. Thus, we define the movement task as an optimization problem that involves the adjustment of sensor node positions in a coverage optimization mission. We propose a coverage optimization algorithm based on sampling to enhance the coverage of 3D underwater sensor networks. The proposed coverage optimization algorithm is inspired by the simple random sampling in probability theory. The main objective of this study is to lessen computation complexity by dimension reduction, which is composed of two detailed steps. First, the coverage problem in 3D space is converted into a 2D plane for heterogeneous networks via sampling plane in the target 3D space. Second, the optimization in the 2D plane is converted into an optimization in a line segment by using the line sampling method in the sample plane. We establish a quadratic programming mathematical model to optimize the line segment coverage according to the intersection between sensing circles and line segments while minimizing the moving distance of the nodes. The intersection among sensors is decreased to increase the coverage rate, while the effective sensor positions are identified. Simulation results show the effectiveness of the proposed approach.

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“…Network reliability is based on the node deployment [11,12], localization [13], communication protocol [14], and energy-aware routing protocols. In particular, transporting the required information in time is a challenging task in the wireless sensor network because of time constraints.…”

Section: Introductionmentioning

confidence: 99%

PriTLP: A Priority-based Transport Layer Protocol for Low Rate Wireless Sensor Networks

Sarode¹,

Bakal²

2017

AJST

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This paper presents a solution for priority-based data transmission in heterogeneous wireless sensor networks. Considering the requirements of each event separately while delivering a data simultaneously is a challenging task in the sensor networks. Therefore, we present the PriTLP protocol for prioritizing the information at various data processing units in the multi-hop topology network. It brings a distributed approach for taking decisions on behalf of the sink node which fastens the data processing, reduces the communication delays, and prolongs the network life. It performs two operations, namely data prioritization and scheduling; and identifying the congestion degree of the network. The reported work is implemented and validated over the RF TestBed by managing the buffer level. It shows high packet delivery ratio, greater throughput, and fewer communication delays of high priority-based traffic flow over regular flows.

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Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity

Cited by 137 publications

References 23 publications

A Survey of Advanced LEACH-based Protocols

A Survey of Advanced LEACH-based Protocols

Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks

PriTLP: A Priority-based Transport Layer Protocol for Low Rate Wireless Sensor Networks

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