Beacon Node Placement for Minimal Localization Error

OBJECTIVEThe aim of this study was to compare the accuracy of optical frameless neuronavigation (ON) and robot-assisted (RA) stereoelectroencephalography (SEEG) electrode placement in children, and to identify factors that might increase the risk of misplacement.METHODSThe authors undertook a retrospective review of all children who underwent SEEG at their institution. Twenty children were identified who underwent stereotactic placement of a total of 218 electrodes. Six procedures were performed using ON and 14 were placed using a robotic assistant. Placement error was calculated at cortical entry and at the target by calculating the Euclidean distance between the electrode and the planned cortical entry and target points. The Mann-Whitney U-test was used to compare the results for ON and RA placement accuracy. For each electrode placed using robotic assistance, extracranial soft-tissue thickness, bone thickness, and intracranial length were measured. Entry angle of electrode to bone was calculated using stereotactic coordinates. A stepwise linear regression model was used to test for variables that significantly influenced placement error.RESULTSBetween 8 and 17 electrodes (median 10 electrodes) were placed per patient. Median target point localization error was 4.5 mm (interquartile range [IQR] 2.8–6.1 mm) for ON and 1.07 mm (IQR 0.71–1.59) for RA placement. Median entry point localization error was 5.5 mm (IQR 4.0–6.4) for ON and 0.71 mm (IQR 0.47–1.03) for RA placement. The difference in accuracy between Stealth-guided (ON) and RA placement was highly significant for both cortical entry point and target (p < 0.0001 for both). Increased soft-tissue thickness and intracranial length reduced accuracy at the target. Increased soft-tissue thickness, bone thickness, and younger age reduced accuracy at entry. There were no complications.CONCLUSIONSRA stereotactic electrode placement is highly accurate and is significantly more accurate than ON. Larger safety margins away from vascular structures should be used when placing deep electrodes in young children and for trajectories that pass through thicker soft tissues such as the temporal region.

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An Obstacle-resistant Relay Node Placement in Constrained Environment

10.36227/techrxiv.11454246 ◽

2019 ◽

Author(s):

Abhishek Verma ◽

Virender Ranga

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Mobile Robots ◽

Relay Node ◽

Wireless Sensor ◽

Relay Node Placement ◽

Node Placement ◽

Simulation Results ◽

Additional Hardware ◽

Shape And Size

Relay node placement in wireless sensor networks for constrained environment is a critical task due to various unavoidable constraints. One of the most important constraints is unpredictable obstacles. Handling obstacles during relay node placement is complicated because of complexity involved to estimate the shape and size of obstacles. This paper presents an Obstacle-resistant relay node placement strategy (ORRNP). The proposed solution not only handles the obstacles but also estimates best locations for relay node placement in the network. It also does not involve any additional hardware (mobile robots) to estimate node locations thus can significantly reduce the deployment costs. Simulation results show the effectiveness of our proposed approach.

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Improved DV-Hop Localization Scheme for Randomly Deployed WSNs

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666190208161350 ◽

2020 ◽

Vol 10 (1) ◽

pp. 94-109 ◽

Cited By ~ 1

Author(s):

Rekha Goyat ◽

Mritunjay Kumar Rai ◽

Gulshan Kumar ◽

Hye-Jin Kim ◽

Se-Jung Lim

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Research Area ◽

Sensor Nodes ◽

Wireless Sensor ◽

Localization Error ◽

Anchor Nodes ◽

Least Energy ◽

Range Free

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

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A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666181206103304 ◽

2019 ◽

Vol 9 (2) ◽

pp. 224-236

Author(s):

Vaishali R. Kulkarni ◽

Veena Desai ◽

Raghavendra Kulkarni

Keyword(s):

Firefly Algorithm ◽

Heuristic Algorithms ◽

Computing Time ◽

Sensor Nodes ◽

Convergence Time ◽

Localization Error ◽

Location Awareness ◽

Cultural Algorithm ◽

Localization Accuracy ◽

Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

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