Solving Priority-Based Target Coverage Problem in Directional Sensor Networks with Adjustable Sensing Ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have recently attracted a great deal of attention. DSNs primarily operate based on simultaneously observing a group of events (targets) occurring in a set area and maximizing network lifetime, as there are...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Wireless personal communications Jg. 95; H. 2; S. 847 - 872
Hauptverfasser: Razali, Mohd Norsyarizad, Salleh, Shaharuddin, Mohamadi, Hosein
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2017
Springer Nature B.V
Schlagworte:
Algorithms
Batteries
Communications Engineering
Computer Communication Networks
Detection
Engineering
Greedy algorithms
Machine learning
Maximization
Networks
Scheduling
Scheduling algorithms
Sensors
Signal,Image and Speech Processing
Directional sensor networks
Cover set formation
Greedy-based algorithm
Scheduling algorithms
Learning automata
ISSN:0929-6212, 1572-834X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The extensive applications of directional sensor networks (DSNs) in a wide range of situations have recently attracted a great deal of attention. DSNs primarily operate based on simultaneously observing a group of events (targets) occurring in a set area and maximizing network lifetime, as there are limitations to the directional sensors’ sensing angle and battery power. The higher the number of sensing ranges of the sensors and the more different the coverage requirements for the targets, the more complex this issue will be. Also known as priority-based target coverage with adjustable sensing ranges (PTCASR), this issue, which has not yet been investigated in the field of study, is the highlight of this research. A potential solution to this problem, based on the fact that sensors are frequently densely deployed, would be to organize the sensors into a few cover sets. After that the cover sets needs to be successively activated—this process is referred to as the scheduling technique. This paper aims to resolve the issue of PTCASR with the proposal of two scheduling algorithms i.e. greedy-based and learning automata-based algorithms. These proposed algorithms were assessed for their performance via a number of experiments. Additionally, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. Both algorithms were successful in solving the problem; however, the learning automata-based scheduling algorithm proved relatively superior to the greedy-based algorithm when it came to extending network lifetime.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-016-3801-z