Optimization Problems in Wireless Sensor Networks

Gogu, Ada; Nace, Dritan; Dilo, Arta; Mertnia, Nirvana

doi:10.1109/cisis.2011.50

Cited by 29 publications

(14 citation statements)

References 49 publications

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“…Ants have been successfully applying their routing scheme in finding the shortest path from their nest to the food source for many years [16]. Ants follow the pheromone trails laid by other ants and this process is known as stigmergy.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…The limited energy, processing capabilities and memory, requires efficient resource management. Energy efficiency is considered to be the major performance criterion and much of the energy is wasted in routing of data from the sensor nodes to the base station which is considered to be a combinatorial problem, and ACO could be an alternative for solving this problem [16].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

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Fuzzy Based Ant Colony Optimization Approach for Wireless Sensor Network

Tomar¹,

Sharma

Kumar

2015

Wireless Pers Commun

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Wireless sensor networks have spread their presence to every other domain we could think of with the technological advancements in the Information Technology. The core component of the WSN are the sensor nodes, which gather the environmental information of the area in which they are deployed and forwards it to the base station for further processing. WSNs are associated with the low network lifetime problem, which restricts in achieving maximum performance. To increase the lifetime, fuzzy system has gained popularity among the systems which are associated with redundant and non-exact information and is being widely used in the optimization problems. In this paper a cluster based hierarchy approach similar to LEACH algorithm has been proposed with fuzzy inference system for the cluster head election along with the ant colony optimization, which is a swarm intelligence based technique used for the routing of data between the sensor nodes and the base station. The proposed approach has been proved to be better as compared to the LEACH algorithm and can be observed from the simulation results where the proposed approach outperforms in terms of residual energy of the system, the number of packets transmitted to the base station and the stability period of the system.

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Fuzzy Based Ant Colony Optimization Approach for Wireless Sensor Network

Tomar¹,

Sharma

Kumar

2015

Wireless Pers Commun

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“…Applications Technique [7] Interference-free localization Maximum flow [10], [32] Nodes deployment Quadratic Assignment [23] Network lifetime Set Cover Problem and variant [30] [43] Routing Graph Coloring Problem [33] Routing Voronoi based path searching [44], [47] Energy Saving Multipath [28] Network lifetime Linear programming [12] QoS Multipath [24] Minimizing Energy Binary Integer Linear Programming [27] Energy saving Mixed Integer Linear Programming [48] Routing and sink location Integer programming [29] Energy Minimum Dominant set [34] Routing Dominant Set [31] Node localization Integer linear programming [38] Node localization Mathematical programing [39] Node localization, network lifetime Linear Programming [41] Interference [7], [10], [27], [29], [32] [34], [41], [43], [45], [48], [49], [52], [58], [59], [62], [65] √ √ [12], [23], [24], [28], [33], [38], [39], [44], [46], …”

Section: Referencesmentioning

confidence: 99%

“…Their algorithm finds the connected minimal weighted dominated set, and used it as a support over sensors to route the data, and then idle the remaining nodes. In [30], authors presented a survey on optimization techniques to address several problems in WSNs, such as coverage, topology control, mobility, scheduling and routing. In particular, authors noted that in several investigations, the time-slot allocation problem (scheduling) is formulated as a graph-coloring problem, to model the fact that two edges adjacent to the same sensor cannot use the same time slot.…”

Section: Operations Research Techniques In Energy Savingmentioning

confidence: 99%

Operation Research Based Techniques in Wireless Sensors Networks

Mahlous¹,

Tounsi²

2017

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In recent years, we have seen an increasing interest in developing and designing Wireless Sensor Networks (WSNs). WSNs consist of large number of nodes, with wireless communications and computation abilities that can be used in variety of domains. It has been used in areas that have direct contact with monitoring and gathering data, to name few, health monitoring, military surveillance, geological monitoring (Earthquakes, Volcanoes, Tsunami), agriculture control and many more. However, the design and implementation of WSNs face many challenges, due to the power limitation of sensor nodes, deployment and localization, data routing and data aggregation, data security, limited bandwidth, storage capacity and network management. It is known that Operation Research (OR) has been widely used in different areas to solve optimization problems; such as improving network performance and maximizing lifetime of system. In this survey, we present the most recent OR based techniques applied to solve different WSNs problems: the node scheduling problem, energy management problems, nodes allocating issues and other WSNs related complex problems. Different Operational Research techniques are presented and discussed in details here, including graph theory based techniques, linear programing and mixed integer programming related approaches.

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“…Successful network design and deployment include understanding and modelling several problems related to these factors, which determine the accessible extent and data rate of a WSN, as well as cost and battery lifetime [2]. Therefore, studies, intended to researchers and graduate fields related to operations research, applied mathematics and computer science, give some highlights on a number of representative network problems in WSN and focus on their respective optimization problems [3]. NP-hard optimization problems induce the necessity of optimization methods like metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

TLBO-Based Routing Approach for Wireless Sensor Networks

Ghazi¹,

Ahiod²,

Abbad³

2018

IJIES

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Routing in Wireless Sensor Networks (WSN) differs from routing in classical networks, as in WSN links are unreliable. Indeed, there is no infrastructure and the sensors have limited energy resources and low processing capacity. Routing problems in many cases are considered NP-hard problems. In these cases, metaheuristics can be the best solution for this kind of problems. Thus, many metaheuristics are used for routing in WSNs. Ant colony optimization (ACO) is among the most used metaheuristics, as it showed an excellent performance for routing. Due to the adaptation difficulties related to the ACO parameters, in this paper we propose a new optimization approach for routing problem using the Teaching Learning Based Optimization (TLBO). TLBO is a recent nature inspired metaheuristic based on the influence of the teacher in the class and learners interactions. In this work, TLBO algorithm is used for routing as it was proposed for continuous optimization problems, despite the fact that the routing in WSNs is a discrete optimization problem. Our proposed approach is shown to be competitive with ACObased approaches in terms of energy consumption and WSN lifetime. The experiments performed in varied scenarios and the results found, illustrate the efficiency of the TLBO approach against the well-known ACO approaches: Energy Efficient Ant Based Routing (EEABR) Algorithm and the Improved Ant Colony Optimization Routing Protocol (IACO).

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Optimization Problems in Wireless Sensor Networks

Cited by 29 publications

References 49 publications

Fuzzy Based Ant Colony Optimization Approach for Wireless Sensor Network

Fuzzy Based Ant Colony Optimization Approach for Wireless Sensor Network

Operation Research Based Techniques in Wireless Sensors Networks

TLBO-Based Routing Approach for Wireless Sensor Networks

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