Real-time adaptation of decision thresholds in sensor networks for detection of moving targets

Automatica ◽  
2011 ◽  
Vol 47 (1) ◽  
pp. 185-191 ◽  
Author(s):  
Kushal Mukherjee ◽  
Asok Ray ◽  
Thomas Wettergren ◽  
Shalabh Gupta ◽  
Shashi Phoha
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Moving Targets

An Enhanced CPA Algorithm for Real-Time Target Tracking in Wireless Sensor Networks

2009 ◽  
Vol 5 (5) ◽  
pp. 619-643 ◽  
Author(s):  
Qing Yang ◽  
Alvin Lim ◽  
Kenan Casey ◽  
Raghu-Kisore Neelisetti
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Field Experiments ◽  
Tracking Error ◽  
Wireless Sensor ◽  
Target Velocity ◽  
Moving Targets ◽  
Target Trajectory ◽  
Real Time Tracking

Real-time tracking of moving targets using wireless sensor networks has been a challenging problem because of the high velocity of the targets and the limited resources of the sensors. CPA (closest point of approach) algorithms are appropriate for tracking fast-moving targets since the tracking error is roughly inversely proportional to the square root of the target velocity. However, this approach requires a specific node configuration with reference to the target trajectory which may not always be possible in randomly deployed sensor networks. Moreover, our mathematical analysis of the original CPA algorithm shows that it suffers from huge localization errors due to inaccuracies in sensor location and measured CPA times. To address these issues, we propose an enhanced CPA (ECPA) algorithm which requires only five sensors around the target to achieve the reliability and efficiency we want for computing the bearing of the target trajectory, the relative position between the sensors and the trajectory, and the velocity of the target. To validate the ECPA algorithm, we designed and implemented this algorithm over an actual data-centric acoustic sensor network as well as simulating it in an NS-2 simulator. The results of our field experiments and simulations show that we can achieve our goals of detecting the target and predicting its location, velocity and direction of travel with reasonable accuracy.

Real-Time Routing Based on On-Demand Multi-Hop Lookahead in Wireless Sensor Networks

2011 ◽  
Vol E94-B (2) ◽  
pp. 569-572
Author(s):  
Soochang PARK ◽  
Euisin LEE ◽  
Juhyun JUNG ◽  
Sang-Ha KIM
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Wireless Sensor ◽  
On Demand

Two-Hop Neighborhood Information-Based Real-Time Routing Design for Sensor Networks

2010 ◽  
Vol 20 (7) ◽  
pp. 1931-1942 ◽  
Author(s):  
Yan-Jun LI ◽  
Zhi WANG ◽  
You-Xian SUN
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Neighborhood Information ◽  
Routing Design

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

2006 ◽  
Author(s):  
Tian He ◽  
Lin Gu ◽  
Liqian Luo ◽  
Ting Yan ◽  
John A. Stankovic ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Aggregation ◽  
Real Time Tracking

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 63-78
Author(s):  
Neetika Jain ◽  
Sangeeta Mittal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Routing Protocol ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Packet Delivery ◽  
High Data ◽  
Hard Real Time

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

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