A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks

Kulkarni, Vaishali R; Desai, Veena; Kulkarni, Raghavendra V.

doi:10.1007/s11276-019-01994-9

Cited by 35 publications

(9 citation statements)

References 36 publications

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“…In addition, the consideration of the NLOS paths in the positioning signal transmission makes the problem to be discontinuous. This favors the employment of heuristic methods to address the NLP in the literature [ 59 ].…”

Section: Node Location Problem In Wireless Sensor Networkmentioning

confidence: 99%

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Verde

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.

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Section: Node Location Problem In Wireless Sensor Networkmentioning

confidence: 99%

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Verde

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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“…The assessment results show that HPSOVNS performed better than PSO algorithms, and advanced positioning algorithms such as GEPM, NLLE and RSSI-LSSVR. An improved version of the Modified Shuffled Frog Leaping Algorithm (MSFLA) is proposed for accurate positioning of sensors [36]. MSFLA results are compared with Trilateration, ABC and PSO algorithms.…”

Section: Previous Workmentioning

confidence: 99%

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Kamfar¹,

Zandhessami²,

Alborzi³

2021

Preprint

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Nowadays, Wireless Sensor Networks (WSNs) are significantly applied in engineering and scientific research. WSNs consist of a group of distributed space sensors that track the environment's physical conditions and control the collected data at one central location. Examples of these sensors' applications are smart cities, transport, volcano surveillance and environmental activity, earthquake monitoring, medicine, post-disaster response, and military control. Wireless sensor networks have a lot of research issues like access to the media, implementation, time synchronization, network security and localization of the nodes. One of the most critical problems in this network research is the optimum position of the sensors to have access to maximum coverage and increase network life span to decrease maintenance costs, develop and manage the network. One of the main causes of the failure in these networks is running out of sensor battery and replacing them which impose high costs to maintenance and managing of the network. In order to solve the issues related to optimization and localization, researchers have focused on the algorithms like Swarm Intelligence (SI), because they enable us to solve complicated issues of optimization and NP-Hard issues to solve optimization. However, most of these algorithms are specialized for a purpose or a special program, and the majority of the solutions are not compatible with most of the wireless network sensors. The DV-Hop is one of the most popular node algorithms. But the main problem of the DV-Hop is the possibility of error in calculating the assessed distance between the unknown node and the nodes of anchor. Therefore, minimizing this error is the key to improve this algorithm. To reduce the problem of high localization error, two meta-heuristic algorithms have been proposed based on a combination. In this paper, a new optimization method based on a combination of Krill Herd Algorithm (KHA) and Particle Swarm Optimization (PSO) called KHAPSO is suggested to improve DV-Hop. Simulation results in MATLAB 2016 show that the KHAPSO model has a lower mean error compared to the DV-Hop, DV-Hop-KHA and DV-Hop-PSO models. Also, energy consumption in the KHAPSO model is less in comparison to the other models. The KHAPSO model with 400 unknown nodes and 30 anchor nodes was able to reduce energy consumption by about 35% and at the same time 27% reduction in Average Localization Error (ALE) compared to DV-Hop.

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“…A number of different EAs like DE, PSO, ABC, and CS have been widely applied in order to solve the localization problem, in terms of determining the unknown position of the target [36]. In this regard, the PSO algorithm and its improved variants have been applied to accurately estimate 3 Journal of Sensors the position of the passive target using TDOA measurements [37].…”

Section: Background and Related Workmentioning

confidence: 99%

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks

Cited by 35 publications

References 36 publications

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

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