A Network Lifetime Enhancement Method for Sink Relocation and Its Analysis in Wireless Sensor Networks

Wang, Chu‐Fu; Shih, Jau-Der; Pan, Bohan; Wu, Tin-Yu

doi:10.1109/jsen.2014.2306429

Cited by 157 publications

(72 citation statements)

References 25 publications

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“…This algorithm not only balanced the load but also reduced energy consumption increasing the life time and eliminating the hotspot problem. C. Wang [4] carried out work on sink relocation. The nodes near the sink experience too much load so as to reduce burden on them sink is relocated to different places.…”

Section: Related Workmentioning

confidence: 99%

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Mobile Sink Path Optimization for Data Gathering Using Neural Networks in WSN

Kaura¹,

Narula²

2017

IJWMT

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Wireless sensor networks are being used for various applications for collection of heterogeneous data. Hotspot problem is major issue of concern that affects the connectivity of entire network along with decreasing lifetime of network. The focus in this paper is lies on optimizing the path followed by the mobile sink for collection of data. The proposed work aims at reducing the hotspot problem and increasing the lifetime. A trained neural network is used to select the best route to be followed by mobile sink. In the proposed work, the stop points are identified which allow the communication between the nodes and the movable sink. The experimental results of the work carried out show that tour length of the sink is greatly reduced and the network lifetime (number of rounds) is increased. Increased lifetime also handles the problem of hotspots.Index Terms: Wireless sensor network, trained neural network, stop points, cluster head.

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Section: Related Workmentioning

confidence: 99%

“…The sink then passes the information to the observer via internet. [4]Sensor, memory, actuator and a processor together form a sensor node. Communication of the nodes is through wireless medium that is infrared, radio frequencies.…”

Section: Introductionmentioning

confidence: 99%

Mobile Sink Path Optimization for Data Gathering Using Neural Networks in WSN

Kaura¹,

Narula²

2017

IJWMT

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“…In addition to reliable and energy-efficient data transmission it is essential to enhance lifetime of the sink node as it forms a bottleneck zone in a network [35]. By assigning cost to wireless links based on remaining energy at each sensor node [36], the life time of sensor nodes and sensor nodes near to sink can be maximized [37][38] [39]. Reliability and energy efficiency are crucial requirements for data dissemination in WSNs.…”

Section: Related Workmentioning

confidence: 99%

LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

S.R¹,

S.²,

M.³

2017

IJCA

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Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing (LR 3 ) protocol for WSNs to accomplish reliable and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost estimated to find forwarding nodes on mentor path that includes link reliability, delay, status of queue at forwarding node and packet advancement at the forwarding node. LR 3 is simulated using NS-2 and results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost [1] [2].

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“…The energy dissipation in the radio depends on the different characteristics of the radio. The energy model used in this work is adopted from [20,19,42] and summarised here. The energy dissipation for transmitting b bits to d distance is shown in Equation 7.…”

Section: Energy Model For Optimisationmentioning

confidence: 99%