Parameterized energy–latency trade-offs for data propagation in sensor networks
Section snippets
Introduction and contribution summary
A wireless sensor network (WSN) is an ad-hoc collection of large numbers of geographically distributed autonomous nodes that communicate over a wireless link. Each node can directly communicate with other nodes lying within its transmission range. In greedy data propagation, for a packet to reach the destination (e.g. sink, or base station), a node forwards the packet to a suitably chosen neighbor, which in turn forwards data to one of its neighbors, and so on, until the data reaches the final
The model
We consider a two-dimensional (plane) sensor network, in which the sensors and the single sink node are static. We abstract the network by a graph G(V, E), where V denotes the set of nodes (sensors), while E ⊆ V2 represents the set of edges (wireless links). The deployment of the sensors is random uniform and various densities (low, medium, high) are considered. An edge between two nodes in the graph exists iff the distance between the corresponding sensors in the network is below a certain limit,
Rigorous performance analysis
We below analyze some important performance properties (energy dissipation, data propagation latency) of each of the three basic routing methods.
The simulation environment
Our simulation environment for making the experiments is the environment of Matlab 7.9.0. We deploy uniformly at random nodes in the network area. We choose as a communication model the unit disk graph. This means that each node is able to send a message to another iff the distance between them is at most a given threshold (in particular, the wireless transmission range R is taken 5). Using the unit disk graph means that the expected number of neighbors per node is close to dπ, where d is the
Conclusions
We study the problem of greedy data propagation, aiming mainly to reduce the energy dissipation of the routing algorithm. Towards parameterized energy–latency trade-offs we provide hybrid combinations of two greedy optimization criteria, as any single criterion does not simultaneously satisfy both energy efficiency and low latency.
We rigorously analyzed the direction-aware DAR protocol, the location-aware LAR protocol and the Nearest with Forward Progress (NFP) Protocol. Also, we compared
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