A mixed-integer linear programming approach for energy-constrained mobile anchor path planning in wireless sensor networks localization

Kouroshnezhad, Sahar; Peiravi, Ali; Haghighi, Mohammad Sayad; Zhang, Qi

doi:10.1016/j.adhoc.2018.12.014

Cited by 19 publications

(10 citation statements)

References 24 publications

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“…TDL [2] has significantly minimizes the spatial and temporal measurement numbers and pose better effectiveness and robustness, but the communication cost is high. MILP [3] achieves better determination of beacon point and has improved trajectory efficiency, localization accuracy and sensor nodes lifetime. The main issues related to this methodology is, it needs solution to mobile anchor path planning optimization problem.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

Narasapuram

Subramanyam

Bindu

2022

Preprint

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Mobile Target Tracking is one of the most important applications in Wireless Sensor Networks (WSNs), particularly for surveillance purposes. The tracking accuracy is highly dependent on distance estimation or localization, and so far more works has been done in this aspect. This paper proposes a new energy-saving target tracking scheme with two phases: (i) Mobility Target Tracking and (ii) Target Movement Prediction. At first, the target tracking is attained by Extended Kalman Filter. Following this, the target movement is predicted with the aid of input factors such as Angle of Arrival (AoA) and Received Signal Strength (RSS), thereby the mobile node's optimal movement is predicted. This scenario is considered as the optimization crisis as the prediction of optimal node movement is one of most significant problems in WSN. In order to make the optimal prediction more precise, a new hybrid algorithm named Lion Mutated-Crow Search Algorithm (LM-CS) is introduced. The proposed algorithm combines the concept of Lion Algorithm (LA) and Cuckoo Search algorithm (CS), respectively. To the end, the performance of proposed work is evaluated over other models with respect to convergence analysis, error analysis and so on.

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Section: Related Workmentioning

confidence: 99%

Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

Narasapuram

Subramanyam

Bindu

2022

Preprint

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“…The symbols used in Equation ( 8) are described in paper. 37 Kouroshnezhad et al 38 have proposed the OPTEC scheme. The proposed method follows the MILP approach selecting an optimal path for the sensor nodes which are not aware of their location.…”

Section: F I G U R E 1 6 Machine Learning Techniques For Localizationmentioning

confidence: 99%

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Yadav

Sharma

2022

Int J Communication

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“…However, the length of the path traveled by the MA is long. Kouroshnezhad et al 41 and Xu et al 42 are some of the studies using the LMAT mobility model. The LMAT path planning is shown in Figure 3C.…”

Section: Literature Reviewmentioning

confidence: 99%

A path planning model based on nested regular hexagons using weighted centroid localization

Karagöl

Yıldız

2021

Int J Communication

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Summary In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non‐collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.

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A mixed-integer linear programming approach for energy-constrained mobile anchor path planning in wireless sensor networks localization

Cited by 19 publications

References 24 publications

Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

A path planning model based on nested regular hexagons using weighted centroid localization

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