Energy-Efficient and Fault-Tolerant Positioning of Multiple Base Stations

Kim, Soo Jeong; Ko, JeongGil; Yoon, Jongwon; Lee, Heejo

doi:10.1007/978-3-540-89524-4_58

Cited by 2 publications

(3 citation statements)

References 6 publications

(10 reference statements)

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“…The following procedure is used to find the active link in the improved Q-MST algorithm (refer algorithm 2). (1) In all of the above-said models, the path loss is proportional to distance with some exponent, and exponent depends on the path model. Further, in all of the path loss models, the distance from receiver and transmitter is the key factor.…”

Section: B Active Link In Wsn Using Available Bandwidthmentioning

confidence: 99%

“…In the considered WSN, mainly four kinds of links exist namely sensor to sensor, sensor to CH, CH to CH, and CH to BS Based on the environment, sensor networks can be classified into various categories such as terrestrial WSN, underwater WSN, underground WSN, and mobile WSN. In some critical applications, such as critical-medical care [1], the sensor networks need to provide interminable services. Hence, enabling endless services in critical systems is only possible by fault tolerance.…”

Section: Introductionmentioning

confidence: 99%

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NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

et al. 2020

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A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This paper presents a novel FT approach named nodelink failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST uses the artificial bee colony (ABC) algorithm to find an alternate edge in place of the broken or failed edge in the spanning tree, in order to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.

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Section: B Active Link In Wsn Using Available Bandwidthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

et al. 2020

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“…David Jea, ArunSomasundaraet.al describes a mobile element to collect and carry data mechanically from a sensor network has many advantages over static multihop routing the network scalability and traffic may make a single mobile element insufficient [8]. The nodes near the base station relay the data from nodes that are farther away.…”

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confidence: 99%

Data Collection using Rendezvous Points Tracking for Mobility enabled Wireless Sensor Networks

Devi¹,

Anoop²,

Liba³

2013

IJCA

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Energy saving can be achieved in mobility enabled wireless sensor networks that visit sensor nodes and collect data from them through short range communication. The problem that has been faced in WSNs is the increased latency in data collection due to the speed at which the data have been collected. So in order to collect the data efficiently a rendezvous point (RP) is used. Here data are collected by the base station while visiting the rendezvous points. The rendezvous points collect the data which are being buffered from various source nodes are being aggregated at a particular point known as RP. This work proposes an efficient rendezvous design algorithm with provable performance bounds with mobility and fixed tracking.

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Energy-Efficient and Fault-Tolerant Positioning of Multiple Base Stations

Cited by 2 publications

References 6 publications

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

Data Collection using Rendezvous Points Tracking for Mobility enabled Wireless Sensor Networks

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