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
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
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
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
Accelerated sensory data collection by greedy or aggregate mobility-based topology ranks
Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Accelerated collection of sensor data by mobility-enabled topology ranks
Journal of Systems and Software
Aggregated mobility-based topology inference for fast sensor data collection
Computer Communications
Journal of Network and Computer Applications
Hi-index | 0.00 |
We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually "learns" the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.