Motivated by emerging applications, we consider sensor networks where the sensors themselves (not just the sinks) are mobile. We focus on mobility scenarios characterized by heterogeneous, highly changing mobility roles in the network. To capture these high dynamics we propose a novel network parameter, the mobility level, which, although simple and local, quite accurately takes into account both the spatial and speed characteristics of motion. We then propose adaptive data dissemination protocols that use the mobility level estimation to improve performance. By basically exploiting high mobility (redundant message ferrying) as a cost-effective replacement of flooding, e.g., the sensors tend to dynamically propagate less data in the presence of high mobility, while nodes of high mobility are favored for moving data around. These dissemination schemes are enhanced by a distance-sensitive probabilistic message flooding inhibition mechanism that further reduces communication cost, especially for fast nodes of high mobility level, and as distance to data destination decreases. Our simulation findings demonstrate significant performance gains of our protocols compared to non-adaptive protocols, i.e., adaptation increases the success rate and reduces latency (even by 15%) while at the same time significantly reducing energy dissipation (in most cases by even 40%). Also, our adaptive schemes achieve significantly higher message delivery ratio and satisfactory energy-latency trade-offs when compared to flooding when sensor nodes have limited message queues.
For networks of mobile sensors, REF suggests exploiting the sensor motion to adaptively disseminate data, e.g. propagate redundant data when mobility is low while propagating less data in the presence of high mobility; in contrast to our approach the sink is assumed to be static, and data travels many hops towards the sink.
Adaptive redundancy for data propagation exploiting dynamic sensory mobility
MSWiM '08
Computer Science