Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
An efficient leader election protocol for mobile networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Sink mobility protocols for data collection in wireless sensor networks
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Cluster-based routing protocol for mobile sensor networks
QShine '06 Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks
Energy optimal data propagation in wireless sensor networks
Journal of Parallel and Distributed Computing
Fast and energy efficient sensor data collection by multiple mobile sinks
Proceedings of the 5th ACM international workshop on Mobility management and wireless access
LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
ANSS-41 '08 Proceedings of the 41st Annual Simulation Symposium (anss-41 2008)
Adaptive redundancy for data propagation exploiting dynamic sensory mobility
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
Fast sensory data collection by mobility-based topology exploration
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
MobiRoute: routing towards a mobile sink for improving lifetime in sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Aggregated mobility-based topology inference for fast sensor data collection
Computer Communications
Journal of Network and Computer Applications
Hi-index | 0.00 |
We investigate the problem of efficient data collection in wireless sensor networks where both the sensors and the sink move. We especially study the important, realistic case where the spatial distribution of sensors is non-uniform and their mobility is diverse and dynamic. The basic idea of our protocol is for the sink to benefit of the local information that sensors spread in the network as they move, in order to extract current local conditions and accordingly adjust its trajectory. Thus, sensory motion anyway present in the network serves as a low cost replacement of network information propagation. In particular, we investigate two variations of our method: a)the greedy motion of the sink towards the region of highest density each time and b)taking into account the aggregate density in wider network regions. An extensive comparative evaluation to relevant data collection methods (both randomized and optimized deterministic), demonstrates that our approach achieves significant performance gains, especially in non-uniform placements (but also in uniform ones). In fact, the greedy version of our approach is more suitable in networks where the concentration regions appear in a spatially balanced manner, while the aggregate scheme is more appropriate in networks where the concentration areas are geographically correlated.