Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is open (i.e borderless) and the physical phenomena to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomena of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamic sensing environment while minimizing energy consumption. Another major challenge is to maintain network connectivity for each MSN node during relocations. To address these challenges, we propose a new distributed algorithm, Causataxis 1 , that enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. Causataxis achieves scalable control of the MSN via a backbone-tree infrastructure maintained over clusterhead nodes, and achieves agility via localized cluster formation and dissolution. Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its bio-system inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a custom tuned swarm algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis can outperform the swarm based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.
['Seokhoon Yoon', 'Onur Soysal', 'Murat Demirbas', 'Chunming Qiao']
Coordinated Locomotion of Mobile Sensor Networks