Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility

Keskin, Muhammed Emre; Altınel, İ. Kuban; Aras, Necatı; Ersoy, Cem

doi:10.1016/j.adhoc.2014.01.003

Cited by 98 publications

(69 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the well-known commercial MILP-solver Cplex (URL: http://www-01.ibm.com/software/commerce/optimization/ cplex-optimizer) performed poorly in instances of a formulation provided in [25], which is similar to CASD, while Gurobi produced decent networks. Hence, Gurobi was used in order to produce better networks.…”

Section: Performance Of Psshmentioning

confidence: 99%

See 1 more Smart Citation

Lifetime maximization of wireless sensor networks with sink costs

Keskin¹

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Balanced distribution of the energy load of the sensors is important for the elongation of wireless sensor network (WSN) lifetimes. There are four main WSN design issues affecting the energy distribution among the sensors: sensor locations, sensor activity schedules, mobile sink routes, and data flow routes. Many studies try to make energy usage more efficient through optimal determination of these design issues. However, only very few studies handle these four design issues in a combined manner. Additionally, the cost of the sinks is neglected in all studies. In this study, a mixed integer linear program is first proposed, in which the cost of the sinks is taken into consideration in terms of lifetime hours and the four design issues are integrated. Next, a heuristic solution procedure for the solution of large network instances is offered and the efficiency of the heuristic is proven by comparing its performance with that of commercial solvers in extensive numerical instances.

show abstract

Section: Performance Of Psshmentioning

confidence: 99%

“…The solution method of [24] depends on column generation. Alternatively, the models in [25] are improved in [22][23][24] by making the sinks mobile. In [26], design issue integration's effect on network lifetime is analyzed via an empirical approach.…”

Section: Introductionmentioning

confidence: 99%

Lifetime maximization of wireless sensor networks with sink costs

Keskin¹

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…The research shows that the wireless sensor node energy consumption mainly lies in data sending and receiving [13]. For easy analysis, this paper ignores the other energy consumption.…”

Section: System Modelmentioning

confidence: 99%

Optimization Deployment Strategy of Mine Roadway Wireless Sensor Network

Wu¹,

Luo²,

Tang³

2015

TOAUTOCJ

View full text Add to dashboard Cite

Abstract:Further research has been done in order to reduce the energy consumption of the mine roadway wireless sensor nodes and prolong the lifetime of the network. On the basis of analysis and study on the existing linear wireless sensor network nodes deployment, the node distance optimization deployment scheme with sinks in the center is proposed. The calculation method for optimized distance between nodes is provided. The experimental simulation prove that, compared to the traditional deployment scheme, this scheme can effectively solve the problem of "energy hole", greatly reduce the energy consumption of single sensor node and whole system, and prolong the network life cycle. For mine roadway with long distance, the optimization deployment scheme with multisinks shall be used. According to number of optimized sinks, the monitoring area in the mine roadway is divided, the total energy consumption of system is minimized, the energy consumption of sensor nodes is balanced, and the network life cycle is significantly lengthened.

show abstract

“…Different research groups have discussed this issue [16,17], from the point of view of network power management software and protocol but not in terms of extending battery lifetime.…”

Section: Linear Networkmentioning

confidence: 99%