scholarly journals Event boundary detection using autonomous agents in a sensor network

2021 ◽  
Author(s):  
Adil Jaffer
Keyword(s):  
Sensor Network ◽  
Autonomous Agents ◽  
Boundary Detection ◽  
Boundary Node ◽  
Sensory Data ◽  
Transmission Distance ◽  
N Efficiency ◽  
Novel Approach ◽  
Event Boundary ◽  
Distance To The Boundary

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

scholarly journals Event boundary detection using autonomous agents in a sensor network

2021 ◽  
Author(s):  
Adil Jaffer
Keyword(s):  
Sensor Network ◽  
Autonomous Agents ◽  
Boundary Detection ◽  
Boundary Node ◽  
Sensory Data ◽  
Transmission Distance ◽  
N Efficiency ◽  
Novel Approach ◽  
Event Boundary ◽  
Distance To The Boundary

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

scholarly journals A REVIEW ON A NOVEL APPROACH FOR DATA COLLECTION IN WSN

2017 ◽  
Vol 16 (3) ◽  
pp. 6213-6218
Author(s):  
Ramandeep Kaur ◽  
Dinesh Kumar
Keyword(s):  
Sensor Network ◽  
Data Aggregation ◽  
Energy Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Values ◽  
Novel Approach ◽  
Wide Range ◽  
Network Application ◽  
Soft Threshold

Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. In this paper, we have proposed a modified alogirthm of leach where hard and soft threshold values will be applied for improving the overall throughput and network lifetime.

System Design and Data Fusion in Body Sensor Networks

2012 ◽  
pp. 1-25 ◽  
Author(s):  
Kai Lin ◽  
Min Chen ◽  
Joel J. P. C. Rodrigues ◽  
Hongwei Ge
Keyword(s):  
Sensor Networks ◽  
Data Fusion ◽  
System Design ◽  
Sensor Network ◽  
Evaluation Method ◽  
Data Evaluation ◽  
Body Sensor Networks ◽  
Sensory Data ◽  
Redundant Data ◽  
Healthcare Monitoring

Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.

scholarly journals Boundary Detection Method for Large-Scale Coverage Holes in Wireless Sensor Network Based on Minimum Critical Threshold Constraint

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jing ◽  
Lingfu Kong ◽  
Liang Kong
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Large Scale ◽  
Optimization Problem ◽  
Boundary Detection ◽  
Wireless Sensor ◽  
Critical Threshold ◽  
Coverage Hole ◽  
Hole Boundary ◽  
Coverage Holes

The existing coverage hole boundary detection methods cannot detect large-scale coverage hole boundary in wireless sensor network quickly and efficiently. Aiming at this problem, a boundary detection method for large-scale coverage holes in wireless sensor network based on minimum critical threshold constraint is proposed. Firstly, the optimization problem of minimum critical threshold is highlighted, and its formulaic description is constructed according to probabilistic sensing model. On the basis of this, the distributed gradient information is used to approximately solve the optimization problem. After that, local-scale rough boundary detection algorithm incorporating the minimum critical threshold and its iterative thinning algorithm are proposed according to blocking flow theory. The experimental results show that the proposed method has low computational complexity and network overhead when detecting large-scale coverage hole boundary in wireless sensor network.

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