Simulative study of target tracking accuracy based on time synchronization error in wireless sensor networks

2008 ◽  
Author(s):  
Saeid Pashazadeh ◽  
Mohsen Sharifi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Time Synchronization ◽  
Synchronization Error ◽  
Wireless Sensor ◽  
Tracking Accuracy

Clock Drift Management Using Nature Inspired Algorithms

2012 ◽  
Vol 5 (4) ◽  
pp. 48-62
Author(s):  
Prakash Tekchandani ◽  
Aditya Trivedi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Synchronization Error ◽  
Wireless Sensor ◽  
Clock Skew ◽  
Clock Drift ◽  
Distributed Sensor ◽  
Network Applications ◽  
Nature Inspired Algorithms

Time Synchronization is common requirement for most network applications. It is particularly essential in a Wireless Sensor Networks (WSNs) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, we optimized FTSP for clock drift management using Particle Swarm Optimization (PSO), Variant of PSO and Differential Evolution (DE). The paper estimates the clock offset, clock skew, generates linear line and optimizes the value of average time synchronization error using PSO, Variant of PSO and DE. In this paper we present implementation and experimental results that produces reduced average time synchronization error using PSO, Variant of PSO and DE, compared to that of linear regression used in FTSP.

Wireless Sensor Networks to Predict the Range Covered by the Priority Scheduling Algorithm for Multi-Target Tracking

2014 ◽  
Vol 687-691 ◽  
pp. 1071-1075
Author(s):  
Yong Long Zhuang ◽  
Xiao Lan Weng ◽  
Xiang He Wei
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Scheduling Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Detection Range ◽  
Multi Target Tracking ◽  
The Relationship

Research on multi-target tracking wireless sensor networks, the main problem is how to improve tracking accuracy and reduce energy consumption. Proposed use of forecasting methods to predict the target state, the selection of target detection range forecast based on the relationship between states and between sensor nodes deployed. And in accordance with the selected detection range, to wake up and form a cluster to track the target. In multi-target tracking will use to adjust the detection range, time to time to separate the conflict node of conflict, in order to achieve a successful track multiple targets. Simulation results show that the proposed method can indeed improve the chances of success of the track.

scholarly journals SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3832
Author(s):  
Xing Wang ◽  
Xuejun Liu ◽  
Ziran Wang ◽  
Ruichao Li ◽  
Yiguang Wu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Signal Strength ◽  
Threshold Value ◽  
Wireless Sensor ◽  
Support Vector ◽  
Tracking Accuracy ◽  
Multiple Paths ◽  
The Stability

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

Simulation Study on Time Synchronization Based on RBS in Wireless Sensor Networks

2013 ◽  
Vol 321-324 ◽  
pp. 630-634
Author(s):  
Wan Xu ◽  
Guang You Yang ◽  
Jing Jing Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Simulation Study ◽  
Routing Protocol ◽  
Time Synchronization ◽  
Synchronization Error ◽  
Wireless Sensor ◽  
Synchronization Algorithm

Time synchronization is important for wireless sensor networks. In this paper RBS time synchronization algorithm principle and EECH routing protocol are introduced, and the simulation of RBS on the basis of EECH routing protocol is realized using the Synsinc software. The average synchronization error of the simulation is 25.86 us.This value is basically same with other types of simulation, which confirmed the simulation is credible.

scholarly journals Energy-Efficient Node Scheduling Method for Cooperative Target Tracking in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Weirong Liu ◽  
Yun He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Kai Gao ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Target Tracking ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Node Scheduling ◽  
Scheduling Method ◽  
Sample Interval

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.

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