Improved DV-Hop Algorithm Based on Minimum Hops Correction and Reevaluate Hop Distance

Fang, Wangsheng; Xu, Haomin; Yang, Geng

doi:10.1109/infoman.2019.8714669

Cited by 7 publications

(13 citation statements)

References 5 publications

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“…The larger the minimum hop-count value from the localization node to the beacon node, the greater the cumulative error value [ 27 ]. On account of the error is directly proportional to the hop-count value, when the hop-count value is large, the localization result will be inaccurate if the estimated distance with large error is used to calculate the node localization coordinates [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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“…Further, the investigation of the equation (26) shows that the ODR is more and more dependent upon R rather thanHop count , which establishes its efficiency to exploit the antenna capabilities of a sensor more than the DV-Hop and IDV, because in DV-Hop and IDV the prominent factor isHop count rather than R . Even the equation (26) shows that the localization error will remain quite low in contrast with DV-Hop and IDV.…”

Section: Analytical Examination Of Resultsmentioning

confidence: 98%

“…The equation (26) along with Table 5 exposes the cause behind the reduction in localization error due to the increase in the communication range. Table 5 demonstrates an interesting trend that with the rise in communication range the hop counts start falling.…”

Section: Analytical Examination Of Resultsmentioning

confidence: 99%

“…Therefore R andHop count are inversely proportional to each other. Further all the three prepositions of the equation (26) state that the effect of Hop count is many more times than the R . Hence if at any stage R is enhanced, the drop inHop count becomes strongly prominent to pull back the localization error several times.…”

Section: Analytical Examination Of Resultsmentioning

confidence: 99%

“…In the next step, distance is estimated with the help of a weight coefficient factor dependent upon hop counts. On the same line of action, W. Fang et al [26] calculated the weight coefficient factor as a ratio of the estimated and actual hop counts to calculate the updated distance values.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Distance Range Free Localization Algorithm for WSN

Kumar

Batra

Authorea

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DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hop path is not a straight line, which leads to a faulty location estimation. The proposed model Optimized Distance Range Free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally.

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MWLP‐DP: Mobile war‐fighters location prediction for dark phase in Internet of Battlefield Things

Kumar

Lobiyal

2021

Trans Emerging Tel Tech

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In a battlefield, a base station is always interested to track the real-time location of moving war-fighters and other armaments because of its strategic importance. The base station estimates their real-time location by using the collected localization-essentials information like hop-counts matrix of the nodes. This information is collected at the base station in active phase through satellite communication among the base station and the sink nodes. In the harsh environment of the battlefield, if this satellite communication breaks accidentally or purposefully then the whole network is disconnected from the base station, and the network enters into the dark phase. In this dark phase, the base station wants to localize the mobile war-fighters and other armaments also. It motivates us to propose a novel method MWLP-DP: mobile war-fighters location prediction for dark phase in Internet of Battlefield Things to localize the moving war-fighters.It works in two stages: localization of sink nodes (LSN) and localization of blind nodes (LBN). In LSN, by using geometric Brownian motion (GBM), the mobility pattern of the sink nodes is analyzed. The stochastic nature of GBM needs repeated trials to ascertain the locations of the sink nodes. Further, computational efforts are reduced by finding the optimum counts of repeated trials of GBM using the central limit theorem for localizing the sink nodes. In LBN, the localization of remaining nodes (blind nodes) is inspired by the DV-Hop using the location of sink nodes obtained in LSN stage. Further, the erroneous distance estimations between the node pairs are optimized through linear programming to reduce localization error. The simulation shows that the proposed MWLP-DP localizes the sink nodes and blind nodes in the dark phase with 89% and 64% average accuracy, respectively. INTRODUCTIONInternet of Battlefield Things (IoBT) has been highlighted by U.S. Army Research Laboratory in 2016 1 by summarizing their discussion of a strategic planning meeting that was held on November 9-10, 2015 on a defense report 2 -"The Army Operating Concept 2020-2040: Winning in a Complex World" of United States Army's Institute of Land Warfare. The meeting explains IoBT as a technical paradigm that converts the battlefield to a smart battlefield by providing soldiers additional sensory capability with their negotiable and collaborative connect with sensors, convoy, robots, weapons, and

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Improved DV-Hop Algorithm Based on Minimum Hops Correction and Reevaluate Hop Distance

Cited by 7 publications

References 5 publications

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Optimized Distance Range Free Localization Algorithm for WSN

MWLP‐DP: Mobile war‐fighters location prediction for dark phase in Internet of Battlefield Things

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