RSS-based sensor localization with unknown transmit power

Vaghefi, Reza Monir; Gholami, Mohammad; Ström, Erik G.

doi:10.1109/icassp.2011.5946987

Cited by 72 publications

(48 citation statements)

References 11 publications

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“…2) P T is not known: The worst case complexity of "SOCP3" approach is O(N 3.5 ), while the complexity of [4] and [5] is O(N 3.5 ) and O(N ), and we call them here "SDP" and "WANG", respectively.…”

Section: Complexity Analysis 1) P T Is Knownmentioning

confidence: 99%

“…3 depicts the RMSEs versus different N . The red solid line with "⊲" denotes the performance of "SOCP3", while the black solid line and the blue dashed line with "⊲" represent the performances of [5] and [4], called here "WANG" and "SDP", respectively. As before, the black dotted line plots the unbiased CRB.…”

Section: A P T Knownmentioning

confidence: 99%

“…Wang and Yang showed that for cooperative scenarios the WLS formulation can be relaxed as a mixed semidefinite and second-order cone programming (SDP/SOCP), whereas for noncooperative scenarios, the WLS formulation can be solved by the bisection method. In [4], Vaghefi et al addressed the RSS localization problem for the case when the sensor nodes are not aware of the source transmit power, P T . In order to tackle this problem, an SDP approach by approximating the ML problem to a convex optimization problem was introduced.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RSS-based localization in wireless sensor networks using SOCP relaxation

Tomic¹,

Beko

Dinis

et al. 2013

2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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This paper addresses the problem of locating a single source from noisy received signal-strength (RSS) measurements in wireless sensor networks (WSNs). To overcome the non-convexity of the maximum likelihood (ML) optimization problem, we provide an efficient convex relaxation that is based on the second order cone programming (SOCP), for both cases of known and unknown source transmit power, and we use a simple iterative procedure to solve the problem when the transmit power and the path loss exponent (PLE) are simultaneously unknown. Simulation results demonstrate that the new approach outperforms the existing ones in terms of the estimation accuracy, while in terms of the complexity, it represents a good balance when compared to the existing approaches 1 .

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Section: Complexity Analysis 1) P T Is Knownmentioning

confidence: 99%

Section: A P T Knownmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RSS-based localization in wireless sensor networks using SOCP relaxation

Tomic¹,

Beko

Dinis

et al. 2013

2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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show abstract

“…Note that replacing the ℓ2 norm with the ℓ1 norm for solving optimization problems can also be found in other work, e.g., in [24]. By introducing di = ∥x − si∥ 2 > 0 and the slack variable ti ≥ 0, we can convert the optimization problem (7) into…”

Section: Rss-based Approachmentioning

confidence: 99%

RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios.

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“…The ML estimator of RSS localization was derived in [3]. It can be shown that the cost function of the ML estimator is highly nonlinear and nonconvex [8,9]. The ML estimator does not have a closed-form solution, but it can be approximately solved by iterative algorithms [10,11].…”

Section: Introductionmentioning

confidence: 99%

Received signal strength-based sensor localization in spatially correlated shadowing

Vaghefi

Buehrer

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Wireless sensor localization using received signal strength (RSS) measurements is investigated in this paper. Most studies for RSS localization assume that the shadowing components are uncorrelated. However in this paper, we assume that the shadowing is spatially correlated. Under this condition, it can be shown that the localization accuracy can be improved if the correlation among links is taken into consideration. Avoiding the maximum likelihood (ML) convergence problem, we derive a novel semidefinite programming (SDP) approach by converting the corresponding noncovex ML estimator into a convex one. The performance of the proposed SDP estimator is compared with the ML estimator and previously considered estimators. Computer simulations show that the proposed SDP estimator outperforms the previously considered estimators in both uncorrelated and correlated shadowing environments.

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RSS-based sensor localization with unknown transmit power

Cited by 72 publications

References 11 publications

RSS-based localization in wireless sensor networks using SOCP relaxation

RSS-based localization in wireless sensor networks using SOCP relaxation

RSS-based sensor localization in underwater acoustic sensor networks

Received signal strength-based sensor localization in spatially correlated shadowing

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