Low communication cost (LCC) scheme for localizing mobile wireless sensor networks

znaid, Ammar M. A. Abu; Idris, Mohd Yamani Idna; Wahab, Ainuddin Wahid Abdul; Qabajeh, Liana K.; Mahdi, Omar Adil

doi:10.1007/s11276-015-1187-6

Cited by 31 publications

(15 citation statements)

References 37 publications

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“…In our previous LCC scheme (low communication cost) [72], the communication cost of MSL * is reduced by selecting the closed normal nodes in the neighborhood instead of selecting all normal nodes as in MSL * . LCC reduces the communication cost of MSL * by 18% and maintains the same localization accuracy of MSL * .…”

Section: State-of-the-art Smc Localization Schemesmentioning

confidence: 99%

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

et al. 2017

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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

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Section: State-of-the-art Smc Localization Schemesmentioning

confidence: 99%

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

et al. 2017

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“…And a large number of wireless sensor nodes are one of the key elements of wireless sensor networks [1,2]; therefore, research on location technology in wireless sensor networks is one of the key issues in the research of wireless sensor networks, which is of great significance. e DV-Hop localization algorithm [3] was first proposed by Dragos Niculescu et al of Rutgers, USA, which is a distributed localization algorithm based on distance vector routing protocol without ranging [4]. Many scholars and scientific research institutions have carried out in-depth research on the DV-Hop Localization algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid

Yin¹

2019

Mobile Information Systems

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Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.

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“…Computation and communication costs are important factors in determining the node lifetime, but limited studies have highlighted the respective issues. Based on our review on recent Monte Carlo-based localization, only Low Communication Cost (LCC) scheme [ 4 ] comprehensively addresses the issues. The LCC scheme utilizes adjacency matrix, set theory and intersection set to remove unnecessary redundant data.…”

Section: Introductionmentioning

confidence: 99%

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

Harith

Noor

Idris

et al. 2018

Sensors

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The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC.

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Low communication cost (LCC) scheme for localizing mobile wireless sensor networks

Cited by 31 publications

References 37 publications

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

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