NDLI: Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

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Staying Alive: System Design for Self-Sufficient Sensor Networks

Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

Toward Stable Network Performance in Wireless Sensor Networks: A Multilevel Perspective

GreenLocs: An Energy-Efficient Indoor Place Identification Framework

TOC: Localizing Wireless Rechargeable Sensors with Time of Charge

Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

Optimized Cooperative Dynamic Coverage in Mixed Sensor Networks

Integrated Intrusion Detection and Tolerance in Homogeneous Clustered Sensor Networks

Toward Optimal Monitoring of Flow-Based Systems Using Mobile Wireless Sensor Networks

Distributed Low-Latency Data Aggregation Scheduling in Wireless Sensor Networks

Geospatial Estimation-Based Auto Drift Correction in Wireless Sensor Networks

Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks

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ACM Transactions on Sensor Networks (TOSN) : Volume 1

Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

Content Provider	ACM Digital Library
Author	Vasilakos, Athanasios V. Khokhar, Ashfaq Ansari, Rashid Xu, Xi
Copyright Year	2015
Abstract	Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.
Starting Page	1
Ending Page	25
Page Count	25
File Format	PDF
ISSN	15504859
e-ISSN	15504867
DOI	10.1145/2700264
Volume Number	11
Issue Number	3
Journal	ACM Transactions on Sensor Networks (TOSN)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-02-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wireless sensor networks Clustering Compressive sensing Energy efficiency In-network processing and aggregation Modeling
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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