A dynamic multiobjective hybrid approach for designing Wireless Sensor Networks

Martins, Flávio Vinícius Cruzeiro; Carrano, Eduardo G.; Wanner, Elizabeth F.; Takahashi, Ricardo H. C.; Mateus, Geraldo Robson

doi:10.1109/cec.2009.4983075

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…Some important parameters shown as follows are used in the simulation experiments: (1) for all ∈ do (2) Evaluate the node's importance ; (3) Decide the size of neighborhood ; (4) Create its candidate-node set ( ); (5) end (6) if the network is broken by a failed node ∈ (7) if nodes in ( ) can replace in terms of (15) (8) Select an optimal node from ( ); (9) else if local MCT of satisfies the network's performance (10) Decide the radius of the regional recovered region; (11) Find the recovery region; (12) else (13) Restart global optimization / * Algorithm 1 * /; (14) end if (15) …”

Section: Simulation Resultsmentioning

confidence: 99%

“…To solve the above problem, Martins et al [15] presented a dynamic hybrid approach combining the global optimization method with a local online algorithm so as to correct the failures caused by the out-of-energy nodes. The global optimization method was employed for solving the coverage problem by genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, it only considered the coverage rate of the network as a relevant criterion. The papers [14][15][16] presented hybrid algorithms based on the density control. They took the relationship among coverage, connectivity, and energy consumption of WSNs into account and focused on finding a sensor node set, which not only met the coverage requirements, but also maximized the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

Liu

Guo

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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One of the most important challenges in designing wireless sensor networks is how to recover a broken network within a very short time as the active nodes are failed or out-of-energy. Focusing on this problem, a hierarchical scheduling scheme based on node-failure pretreatment is proposed, in which the global optimization method is used to find the minimum connected tree and the local multilayer recovery algorithm is used to find a candidate sensor node instead of the failed one. Three highlights of this scheme are as follows: (1) The importance of sensor nodes is defined in terms of their locations in minimum connected tree and coverage acreage. (2) The neighborhood radius of failed sensor node varies with its importance, and then its candidate-node set is dynamically constructed. (3) A novel multilayer recovery strategy including node recovery and regional recovery is presented. Simulation results show that hierarchical scheduling scheme finds the optimal candidate sensor node in less time to make the repaired network with lowest energy consumption. Though the less sensor nodes are activated, the network lifetime is slightly shorter. Moreover, this scheme can be applied in the problem that the communication radius of sensor node is less than two times of its sensing radius.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

Liu

Guo

Chen

et al. 2015

International Journal of Distributed Sensor Networks

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“…Each of those points d i is referred to as a demand point, which can be covered by an active sensor s j if the distance between d i and s j is lower than the sensing radius of the sensor. Under this setting, the optimal design of WSNs, considering the network lifetime maximization as the objective, can be defined as (Martins et al, 2009(Martins et al, , 2011:…”

Section: Wsn-dccp Statementmentioning

confidence: 99%

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Martins

Carrano

Wanner

et al. 2014

Evolutionary Computation

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Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

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“…Different scheduling rules determine when sensors change to be active or sleep. In localized and distributed realizations, sensors periodically investigate their neighborhood and decide whether to change their operation modes [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multi-layer Genetic Algorithm for Maximum Disjoint Reliable Set Covers Problem in Wireless Sensor Networks

AbdulHalim

Attea

2014

Wireless Pers Commun

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Densely deployment of sensors is generally employed in wireless sensor networks to ensure complete target coverage for a long period of time. Many sensors scheduling techniques have been recently proposed for prolonging the network lifetime. Scheduling sensors into a maximum number of disjoint sets has been modeled, in the literature, as disjoint set covers (DSC) problem which is a well-known NP-hard optimization problem. Unlike other attempts which considers only a simple disk sensing model, this paper addresses the problem of finding the maximum number of set covers while considering a more realistic sensing model to handle uncertainty into the sensors' target-coverage reliability. The paper investigates the development of a simple multi-layer genetic algorithm (GA) whose main ingredient is to support selection of a minimum number of sensors to be assigned to maximum number of set covers. With the aid of the remaining unassigned sensors, the reliability of DSCs provided by the GA, can further be enhanced by a post-heuristic step. By identifying the upper bound of number of disjoint set covers, the GA can successively construct covers, each of which is of minimal sensor cost. Performance evaluations on solution quality in terms of both number of set covers and coverage reliability are measured and compared through extensive simulations, showing the effectiveness of the proposed GA.

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A dynamic multiobjective hybrid approach for designing Wireless Sensor Networks

Cited by 21 publications

References 13 publications

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

A Hierarchical Scheduling Scheme in WSNs Based on Node-Failure Pretreatment

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Multi-layer Genetic Algorithm for Maximum Disjoint Reliable Set Covers Problem in Wireless Sensor Networks

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