Authors:
Ijeoma Okeke
;
Alastair Allen
;
David Hendry
and
Fabio Verdicchio
Affiliation:
University of Aberdeen, United Kingdom
Keyword(s):
Energy Analysis, Distributed Processing, Architecture, Fairness, Network Lifetime, Wireless Sensor Networks.
Related
Ontology
Subjects/Areas/Topics:
Data Manipulation
;
Multi-Sensor Data Processing
;
Network Performance
;
Obstacles
;
Power Management
;
Sensor Networks
;
Wireless Information Networks
Abstract:
Wireless Sensor Networks (WSN) are networks of low-cost communication devices with sensing and computational
capabilities enabling remote, real-time measurement, monitoring and control of divers physical and
environmental parameters. As WSNs are typically battery powered, energy-aware techniques are critical for
extending its lifetime. Aside from energy-efficient communication protocols, distributed processing strategies
are being explored whereby,computational capabilities of sensor nodes are utilised to locally process sensed
data in order to reduce communication cost. However, as local processing increases, the impact of processing
energy cost becomes significant creating a need to analyse WSNs under this emergent scenario as previous
work have focused mostly on communication cost. We analysed the energy cost for WSN under different
processing architectures. We used a fairness metric to quantify the fairness of energy cost distribution in the
network. Our results showed a positive
correlation between fairness and network lifetime. Hence, we argue
that local processing can be exploited to reduce transmission and improve system performance without
adversely reducing network lifetime. We conclude that although local processing marginally increases node
energy consumption, it improves overall network life time as energy cost is evenly distributed in the network.
Moreover, it enhances network maintenance as nodes have similar lifetimes.
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