Analysis of processing architectures for wireless sensor networks

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.