RSS-based localization in wireless sensor networks using SOCP relaxation

Tomic, Slaviša; Beko, Marko; Dinis, Rui; Lipovac, Vlatko

doi:10.1109/spawc.2013.6612150

Cited by 21 publications

(22 citation statements)

References 11 publications

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“…The least squares (LS) problem defined in (2) is non-linear and non-convex, hence, finding the globally optimal solution is difficult, since there may exist multiple local optima. Following [5], we show that the RSS measurement model in (1) can be approximated into a convex optimization problem, which can be solved by interior point algorithm [12], and obtain the global solution.…”

Section: Problem Formulationmentioning

confidence: 99%

“…-. According to (4), the following LS estimation problem can be formulated 1 Clearly, the corresponding LS estimator is given by (5).…”

Section: Socp Relaxationmentioning

confidence: 99%

“…For economic or other practical reasons, only a small fraction of nodes are set to be anchor nodes, hence, an efficient algorithm for node localization is necessary for WSNs. Instead of the use of GPS system, which is very expensive and limited to outdoor environments, algorithms that rely on distance measurements between neighboring nodes are emerging recently [2], [3], [4], [5], [6]. Depending on the available hardware, current distance-based algorithms extract the distance information from time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA) or received signal strength (RSS) measurements [7].…”

Section: Introductionmentioning

confidence: 99%

“…Recent RSS-based localization algorithms use a centralized concept to solve the sensor nodes localization problem [2], [3], [4], [5], [6]. A distributed approach for solving the RSS localization problem with synchronized node communication was introduced in [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed RSS-Based Localization in Wireless Sensor Networks with Asynchronous Node Communication

Tomic

Beko

Dinis

et al. 2014

Technological Innovation for Collective Awareness Systems

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Part 18: TelecommunicationsInternational audienceIn this paper we address the node localization problem in large-scale wireless sensor networks (WSNs) by using the received signal strength (RSS) measurements. According to the conventional path loss model, we first pose the maximum likelihood (ML) problem. The ML-based solutions are of particular importance due to their asymptotically optimal performance (for large enough data records). However, the ML problem is highly non-linear and non-convex, which makes the search for the globally optimal solution difficult. To overcome the non-linearity and the non-convexity of the objective function, we propose an efficient second-order cone programming (SOCP) relaxation, which solves the node localization problem in a completely distributed manner. We investigate both synchronous and asynchronous node communication cases. Computer simulations show that the proposed approach works well in various scenarios, and efficiently solves the localization problem. Moreover, simulation results show that the performance of the proposed approach does not deteriorate when synchronous node communication is not feasible

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Section: Problem Formulationmentioning

confidence: 99%

“…-. According to (4), the following LS estimation problem can be formulated 1 Clearly, the corresponding LS estimator is given by (5).…”

Section: Socp Relaxationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed RSS-Based Localization in Wireless Sensor Networks with Asynchronous Node Communication

Tomic

Beko

Dinis

et al. 2014

Technological Innovation for Collective Awareness Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…[21][22][23] Recently, RSSbased localization has received tremendous attention due to its low-cost, low-complexity, and easyimplementation solution. 24 Maximum likelihood (ML) 25,26 and least squares (LS) 17,18 are two typical estimators based on RSS measurements. Although the ML estimator is very important due to its asymptotic normality performance, the ML estimator is nonconvex and has multiple local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Received signal strength–based target localization under spatially correlated shadowing via convex optimization relaxation

Chang

Wang

et al. 2018

International Journal of Distributed Sensor Networks

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Received signal strength-based target localization methods normally employ radio propagation path loss model, in which the log-normal shadowing noise is generally assumed to follow a zero-mean Gaussian distribution and is uncorrelated. In this article, however, we represent the simplified additive noise by the spatially correlated log-normal shadowing noise. We propose a new convex localization estimator in wireless sensor networks by using received signal strength measurements under spatially correlated shadowing environment. First, we derive a new non-convex estimator based on weighted least squares criterion. Second, by using the equivalence of norm, the derived estimator can be reformulated as its equivalent form which has no logarithm in the objective function. Then, the new estimator is relaxed by applying efficient convex relaxation that is based on second-order cone programming and semi-definite programming technique. Finally, the convex optimization problem can be efficiently solved by a standard interior-point method, thus to obtain the globally optimal solution. Simulation results show that the proposed estimator solves the localization problem efficiently and is close to Cramer-Rao lower bound compared with the state-of-the-art approach under correlated shadowing environment.

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A recursive weighted least squares optimization algorithm based on RSS in wireless sensor networks

Zhai

Zhang

2021

Internet Technology Letters

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The localization of a sensor in wireless sensor network has now gained considerable attention. To study node localization based on received signal strength (RSS), a least squares optimization algorithm was proposed. Firstly, a linear model was established to calculate path loss. Then a recursive weighted least squares optimization method was presented for sensor node localization.Finally, we compared the algorithm proposed in this paper with the two other algorithms based on RSS. The simulation results showed that our proposed method has a better performance.

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RSS-based localization in wireless sensor networks using SOCP relaxation

Cited by 21 publications

References 11 publications

Distributed RSS-Based Localization in Wireless Sensor Networks with Asynchronous Node Communication

Distributed RSS-Based Localization in Wireless Sensor Networks with Asynchronous Node Communication

Received signal strength–based target localization under spatially correlated shadowing via convex optimization relaxation

A recursive weighted least squares optimization algorithm based on RSS in wireless sensor networks

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