Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure

Yarinezhad, Ramin

doi:10.1016/j.adhoc.2018.09.016

Cited by 75 publications

(35 citation statements)

References 13 publications

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“…LBCP has been solved by different methods such as genetic algorithm [10, 12], differential evolution [13], multi‐objective particle swarm optimisation (PSO) [14], greedy algorithm [15, 16], ant colony optimisation [17], cuckoo search [18], and PSO [8, 19], which are heuristic approaches. Simulation has been used to evaluate these methods.…”

Section: Previous Work On Lbcpmentioning

confidence: 99%

Exact and approximate algorithms for clustering problem in wireless sensor networks

Yarinezhad

Hashemi

2020

IET Communications

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Section: Previous Work On Lbcpmentioning

confidence: 99%

Exact and approximate algorithms for clustering problem in wireless sensor networks

Yarinezhad

Hashemi

2020

IET Communications

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“…lim ( 1) ( ) 0 t t n f n f n →+∞ + − = (17) In reality, the number of RNs is very finite in a cluster. Generally, as long as we guarantee the marginal increment between ( 1) t f n + and ( ) t f n is minimum, that is, min( ( 1) ( )) t t f n f n + − (18) the number of RNs can be illustrated arg min( ( 1) ( )) t t n f n f n + − (19)…”

Section: Definition 5 (Utility Function) a Utility Function About Enmentioning

confidence: 99%

“…However, it will take more time to collect data because ME generally moves with a slow speed and regularly stops at a prearranged location after tracing predetermined paths, which is very fatal for data collection system in terms of delivering emergency data as quickly as possible. In consequence, it is of importance to concentrate on the problem of delay problem for data collection by combination of both [19]. Additionally, the transmission model is also regarded as one of critical influence factors for the data collection in terms of the energy efficiency [20].…”

Section: Introductionmentioning

confidence: 99%

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

2020

KSII TIIS

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The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

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“…This behaviour subsequently not only leads to power consumption and energy deduction of nodes, but also has a significant impact on the network, due to frequent updates among nodes. Various techniques were proposed in [15], including a new algorithm which uses a multi-ring shaped infrastructure to announce the sink positions regularly.…”

Section: Related Workmentioning

confidence: 99%

IoT Energy Efficiency through Centrality Metrics

Alhaisoni¹

2019

AETiC

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The Internet of Things is the current and next revolution in integrating various technologies and wireless communications. It has been shown to make an important contribution in various modes of communication, in homes, offices and other buildings. However, certain research issues are still remain, such as life span of the network and a definition of the most influential nodes in communications, which affect the overall energy distribution. This paper introduces a new approach to enhance the communication over the internet of things, by combining two different domains, the computer network and network science. Various scenarios have been thoroughly implemented and tested over different network topologies. The results show clear enhancements on network centrality and overall energy distribution.

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Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure

Cited by 75 publications

References 13 publications

Exact and approximate algorithms for clustering problem in wireless sensor networks

Exact and approximate algorithms for clustering problem in wireless sensor networks

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

IoT Energy Efficiency through Centrality Metrics

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