Detection of Outdated Sensors in Wireless Network via a New Protocol

A novel method is proposed using the nonlinear mapping with kernel functions to correctly locate the outdated sensors in a wireless sensor network (WSN). Such detection system used Cornell regression and solved via the vector support regression (VSR) plus multi-dimensional backup vector regression (MBVSR). The developed method was simplistic and effective without the need of any additional hardware for any measurement. It required only the vicinity and information of location from the anchor nodes to detect the outdated sensors. It was achieved in three stages including the measurements, kernel regression, and stepping stage. First step measured the proximity information from a given grid. The relationships between the proximity and geographic distance among the sensors’ nodes were generated in the kernel regression stage. For the stepping phase, every sensor node found its location in the distributed way via the kernel regression. Simulation results showed the robustness and high efficiency of the proposed scheme.

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An Immune-Based Node Localization Algorithm for WSN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.1207 ◽

2012 ◽

Vol 490-495 ◽

pp. 1207-1211

Author(s):

Nan Zhang ◽

Jian Hua Zhang ◽

Jian Ying Chen ◽

Xiao Mei Qu

Keyword(s):

Wireless Sensor ◽

Localization Algorithm ◽

Node Localization ◽

Power Requirement ◽

The Third ◽

Third Stage ◽

Additional Amount ◽

Node Location ◽

Anchor Nodes ◽

Additional Hardware

Node localization technology is the premise and foundation of all applications in wireless sensor network. An improved DV-Hop algorithm was proposed aimed at the low-power requirement of wireless sensor networks. The distances between nodes and anchor nodes were used to calculate the node location in DV-Hop algorithm, and the immune algorithm was used to optimize the estimated location in the third stage of DV-Hop algorithm. The improved algorithm does not require additional hardware devices, and has smaller additional amount of communication and computation.

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Intrusion Detection System (IDS) with trusted nodes for improving security in Wireless Sensor Network

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i10.428435 ◽

2018 ◽

Vol 6 (10) ◽

pp. 428-435

Author(s):

S. Jawahar ◽

J. Adamkani

Keyword(s):

Wireless Sensor Network ◽

Intrusion Detection ◽

Sensor Network ◽

Intrusion Detection System ◽

Detection System ◽

Wireless Sensor

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An Efficient Anchor Nodes Distribution For Accurate Lo-calization (EDAL) In Mobile Wireless Sensor Networks

10.33977/2106-000-003-002 ◽

2020 ◽

pp. 9

Author(s):

عمار محمد أبو زنيد ◽

عين الدين واحد عبدالوهاب ◽

محمد إدريس اليمني ◽

عمر عادل مهدي ◽

ليانا خميس قباجة

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Wireless Sensor ◽

Mobile Wireless ◽

Mobile Wireless Sensor Networks ◽

Anchor Nodes ◽

Mobile Wireless Sensor

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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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Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽

10.3390/s21072538 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2538

Author(s):

Shuang Zhang ◽

Feng Liu ◽

Yuang Huang ◽

Xuedong Meng

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spread Spectrum ◽

High Efficiency ◽

Detection System ◽

Direct Sequence Spread Spectrum ◽

Detection Performance ◽

Detection Methods ◽

Direct Sequence ◽

Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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An Optimal Decision Method for Intrusion Detection System in Wireless Sensor Networks with Enhanced Cooperation Mechanism

IEEE Access ◽

10.1109/access.2021.3065571 ◽

2021 ◽

pp. 1-1

Author(s):

Yongwen Du ◽

Jinzong Xia ◽

Ji Ma ◽

Wenxian Zhang

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Wireless Sensor ◽

Optimal Decision ◽

Decision Method ◽

Cooperation Mechanism

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Nonlinear absorption and scattering of a single plasmonic nanostructure characterized by x-scan technique

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.10.211 ◽

2019 ◽

Vol 10 ◽

pp. 2182-2191 ◽

Cited By ~ 1

Author(s):

Tushar C Jagadale ◽

Dhanya S Murali ◽

Shi-Wei Chu

Keyword(s):

Laser Scanning ◽

Detection System ◽

Nonlinear Behavior ◽

Optical Nonlinearity ◽

Research Area ◽

Confocal Laser Scanning Microscope ◽

Confocal Laser ◽

Thin Sample ◽

Channel Detection ◽

Novel Method

Nonlinear nanoplasmonics is a largely unexplored research area that paves the way for many exciting applications, such as nanolasers, nanoantennas, and nanomodulators. In the field of nonlinear nanoplasmonics, it is highly desirable to characterize the nonlinearity of the optical absorption and scattering of single nanostructures. Currently, the common method to quantify optical nonlinearity is the z-scan technique, which yields real and imaginary parts of the permittivity by moving a thin sample with a laser beam. However, z-scan typically works with thin films, and thus acquires nonlinear responses from ensembles of nanostructures, not from single ones. In this work, we present an x-scan technique that is based on a confocal laser scanning microscope equipped with forward and backward detectors. The two-channel detection offers the simultaneous quantification for the nonlinear behavior of scattering, absorption and total attenuation by a single nanostructure. At low excitation intensities, both scattering and absorption responses are linear, thus confirming the linearity of the detection system. At high excitation intensities, we found that the nonlinear response can be derived directly from the point spread function of the x-scan images. Exceptionally large nonlinearities of both scattering and absorption are unraveled simultaneously for the first time. The present study not only provides a novel method for characterizing nonlinearity of a single nanostructure, but also reports surprisingly large plasmonic nonlinearities.

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