Optimization of Sensor Node Locations in a Wireless Sensor Network

Low, Kay Soon; Nguyen, Hong-Quang; Guo, Hao

doi:10.1109/icnc.2008.670

Cited by 26 publications

(18 citation statements)

References 11 publications

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“…Beacon localization scheme in [24]. This scheme assumes a presence of four beacons deployed roughly on boundaries of the sensor field.…”

Section: Pso-4 Beacon: Low Et Al Have Proposed Pso-4mentioning

confidence: 99%

“…Environmental path loss exponent α plays an important role in distance estimation from the received signal strength. In the scheme proposed in [24], the target node at location O localizes by solving geometrical equations if the value of α is known. The target node uses PSO to find the best value of α and uses a Kalman filter based recursive estimation to localize itself.…”

Section: Pso-4 Beacon: Low Et Al Have Proposed Pso-4mentioning

confidence: 99%

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Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

626

244

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Abstract-Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bio-inspired techniques. Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering and data-aggregation. This paper outlines issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.

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“…Beacon localization scheme in [24]. This scheme assumes a presence of four beacons deployed roughly on boundaries of the sensor field.…”

Section: Pso-4 Beacon: Low Et Al Have Proposed Pso-4mentioning

confidence: 99%

Section: Pso-4 Beacon: Low Et Al Have Proposed Pso-4mentioning

confidence: 99%

Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

626

244

View full text Add to dashboard Cite

show abstract

“…In literature [17], the inertia weight is varied linearly from 0.9 to 0.4 according to the following equation:…”

Section: B the Selection Of The Parameters Of Psomentioning

confidence: 99%

A novel three-dimensional localization algorithm for Wireless Sensor Networks based on Particle Swarm Optimization

Dong

Yanze

Liu

2011

2011 18th International Conference on Telecommunications

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The paper presents a three-dimensional localization algorithm for Wireless Sensor Networks (WSN) based on Particle Swarm Optimization (PSO). According to a direct proportion relationship of the measured distance with the measuring errors, an improved three-dimensional localization objective function is defined with weighted the measured distance, and which is optimized by using PSO. In the process of solving the measured distance equations set which is an overdetermined system of equations, for reducing the order of the equations, the minimum distance equation in the system of equations is selected to subtract other equations instead of random selection. The simulation results show that the process can reduce localization errors. The effects of the amount and the distribution of the beacon nodes are analyzed, and the experimental results show that the localization errors under the marginal distribution of the beacon nodes are smaller than that one under the random distribution of the beacon nodes. The final simulation results indicate that the proposed threedimensional localization algorithm has a higher accuracy and lower affection of the non-line-of-sight error than the least square algorithm and the BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, but the proposed algorithm is at cost of more localization time.

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“…The second level of this figure presents several algorithms that have been proposed so far. Lateration algorithms use the distance between the known and unknown nodes to calculate the estimated position of the unknown including LANDMARC (Ni et al, 2004) algorithms use not only the distance but also the angulations between t to calculate the estimated position of the unknown node, such as Ellipse PSO (Low et al, 2008). Centralized algorithms typically use complex mathematics to calculate the estimated position of the unknown node in multidimensional, such as SDP MDS-MAP (Stojmenovic, 2005), and PSO are based on statistical inference to calculate the most possible position of the unknown node, such as Bayesian (Ito and Kawaguchi, 2005) All abovementioned localization algorithms, except for probabilistic, angulations between unknown and known nodes.…”

Section: Introductions Of Localization Hardwarementioning

confidence: 99%