scholarly journals Construction and Optimization of Biconnected and Wide-Coverage Topology Based on Node Mobility

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 791
Author(s):  
Peng Zhao ◽  
Jianzhong Wang ◽  
Lingren Kong
Keyword(s):  
Distributed Algorithm ◽  
Nearest Neighbor ◽  
Wireless Sensor ◽  
Node Mobility ◽  
Effective Coverage ◽  
Limit Value ◽  
Nearest Neighbor Rule ◽  
Movement Distance ◽  
Simulation Results ◽  
Motion Constraint

Constructing a communications topology with fault tolerance and effective coverage plays an important role in wireless sensor networks. This paper is aimed at constructing and maintaining a biconnected topology, while minimizing the movement distance of the nodes and maximizing the coverage of the field of interest. First, it presents a new model with the motion constraint. If the nodes move at distance within the limit value calculated by the model, the topology is always connected, whether the neighbors of nodes are dynamic or static. Secondly, it improves the coverage strategy based on the nearest neighbor rule (NNR) and finds a rule of nodes’ spreading so that the nodes are distributed evenly and the spacing of the adjacent nodes is controllable. In addition, the nodes move only when necessary according to the added judgment conditions. Consequently, the movement distance is reduced. The simulation results prove the feasibility and effectiveness of the Localized Topology Optimized Method (LTOM) proposed by this paper. The connected indicators of the system’s topology during implementing LTOM are consistent, and the transformation of topology by LTOM is symmetric. Compared with the other distributed algorithm, NNR, LTOM reduces the movement distance of nodes, improves the connected probability, and maximizes the coverage of the topological structures under the biconnected conditions.

Sybil Node Discovery Based on Group Location Information

2014 ◽  
Vol 1061-1062 ◽  
pp. 1088-1095
Author(s):  
Peng Lu ◽  
Guang Wei Zhang ◽  
Xiao Bin Xu ◽  
Fang Chun Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Clustering Analysis ◽  
Distributed Algorithm ◽  
Computational Cost ◽  
Wireless Sensor ◽  
Location Information ◽  
Relative Location ◽  
Simulation Results ◽  
Discovery Method ◽  
Node Group

The paper proposed a Wireless Sensor Network (WSN)’s Sybil node discovery method based on groups relative location information. By setting the distributed algorithm of node group, the relative locations of each node with its surrounding and interactive nodes are analyzed location, interactions of surrounding location information are done by a certain mechanism. According to group location information and data filed, the clustering analysis of location information is carried out. This is used to discover the nodes which are obviously closer than others location. Therefore recognition and tolerance of Sybil nodes are implemented. The simulation results indicate that the proposed method has much lower computational cost and implements load balancing among different nodes,. In addition, the proposed method is able to discover Sybil nodes in wireless sensor networks timely as well.

An Obstacle-resistant Relay Node Placement in Constrained Environment

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.

Deterministic Deployment for Wireless Image Sensor Nodes

2014 ◽  
Vol 8 (1) ◽  
pp. 668-674
Author(s):  
Junguo Zhang ◽  
Yutong Lei ◽  
Fantao Lin ◽  
Chen Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Visual Information ◽  
Image Sensor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Effective Coverage ◽  
Area Of Interest ◽  
Key Issues

Wireless sensor networks composed of camera enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. The node deployment is one of the key issues in the application of wireless sensor networks. In this paper, we take the effective coverage and connectivity as the evaluation indices to analyze the effect of the perceivable angle and the ratio of communication radius and sensing radius for the deterministic circular deployment. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes. When the nodes are deployed in the same monitoring area in the premise of ensuring connectivity, rhombus deployment is optimal when √2 < rc / rs < √3 . The research results of this paper provide an important reference for the deployment of the image sensor networks with the given parameters.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

Research and Improvement about LEACH Routing Protocol for Wireless Sensor Networks

2012 ◽  
Vol 241-244 ◽  
pp. 1028-1032
Author(s):  
Li Wang ◽  
Qi Lin Zhu
Keyword(s):  
Routing Protocol ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Selection Algorithm ◽  
Energy Factor ◽  
Random Cluster ◽  
Simulation Results ◽  
Node Energy

In recent years, as the development of wireless sensor network, people do some deep researches on cluster-based protocol, most around the prolongation of the lifetime of WSN and decline of energy consumed by the sensors. This paper analyses of classical clustering routing protocol based on LEACH, aiming at the node energy foot presents energy improved clustering routing algorithm, the random cluster head selection algorithm of threshold to be changed, lowering the threshold, in the original threshold increases the node's remaining energy factor, reduces the communication load of cluster nodes, and simulation. The simulation results show that the LEACH-E improved algorithm, energy saving, reducing balance node energy consumption, effectively prolongs the network lifetime.

A Small World Routing Protocol in Wireless Sensor Networks

2011 ◽  
Vol 474-476 ◽  
pp. 828-833
Author(s):  
Wen Jun Xu ◽  
Li Juan Sun ◽  
Jian Guo ◽  
Ru Chuan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Small World ◽  
Wireless Sensor ◽  
Small World Network ◽  
Average Delay ◽  
Network Diameter ◽  
Simulation Results

In order to reduce the average path length of the wireless sensor networks (WSNs) and save the energy, in this paper, the concept of the small world is introduced into the routing designs of WSNs. So a new small world routing protocol (SWRP) is proposed. By adding a few short cut links, which are confined to a fraction of the network diameter, we construct a small world network. Then the protocol finds paths through recurrent propagations of weak and strong links. The simulation results indicate that SWRP reduces the energy consumption effectively and the average delay of the data transmission, which leads to prolong the lifetime of both the nodes and the network.

Performance Analysis of OQPSK Signals with Bi-Kappa Noise for Wireless Sensor Networks

2013 ◽  
Vol 330 ◽  
pp. 957-960
Author(s):  
Qiao Ling Du ◽  
Zhi Rui Wang ◽  
Yu Pei ◽  
Yi Ding Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Error Rate ◽  
Fading Channel ◽  
Rayleigh Fading ◽  
Symbol Error Rate ◽  
Wireless Sensor ◽  
Channel Simulation ◽  
Simulation Results

This paper investigates the performance analysis of OQPSK in HF band for wireless sensor networks. An analytical model for getting symbol error rate (SER) is given in presence of Bi-Kappa noise in HF band. And the SER of OQPSK is given in AWGN and Rayleigh fading channel. Simulation results HF noise as Bi-Kappa noise should be investigated in HF band for WSN.

Based on RSSI Ranging Technology for DV-Hop Localization Algorithm of Wireless Sensor Network

2012 ◽  
Vol 442 ◽  
pp. 360-365 ◽  
Author(s):  
Yang Jun Zhong
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Approximate Calculation ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Original Algorithm ◽  
Approximate Distance ◽  
Anchor Nodes ◽  
Simulation Results

For the DV-Hop algorithm of wireless sensor networks,there is an error arising problem that anchor nodes and location node hop distance is only an approximate calculation. A method based on the original Algorithm introducing RSSI ranging technique is proposed.Using RSSI ranging technology,we accord that if the anchor nodes is only a hop away from the location node,then decide whether using the DV-Hop algorithm to approach to the approximate distance between them. Simulation results show that the algorithm can effectively improve the error problems of calculating the hop distance between the anchor nodes and the location nodes, meanwhile improve the positioning accuracy of the node.

scholarly journals Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1782
Author(s):  
Yulong Deng ◽  
Chong Han ◽  
Jian Guo ◽  
Lijuan Sun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Missing Data ◽  
Nearest Neighbor ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Temporal And Spatial ◽  
Two Parameters

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

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