Wireless Sensor Networks consist of a large number of small, autonomous devices, that are able to interact with their inveronment by sensing and collaborate to fulfill their tasks, as, usually, a single node is incapable of doing so; and they use wireless communication to enable this collaboration. Each device has limited computational and energy resources, thus a basic issue in the applicastions of wireless sensor networks is the low energy consumption and hence, the maximization of the network lifetime.
The collected data is disseminated to a static control point – data sink in the network, using node to node - multi-hop data propagation. However, sensor devices consume significant amounts of energy in addition to increased implementation complexity, since a routing protocol is executed. Also, a point of failure emerges in the area near the control center where nodes relay the data from nodes that are farther away. Recently, a new approach has been developed that shifts the burden from the sensor nodes to the sink. The main idea is that the sink has significant and easily replenishable energy reserves and can move inside the area the sensor network is deployed, in order to acquire the data collected by the sensor nodes at very low energy cost. However, the need to visit all the regions of the network may result in large delivery delays.
In this work we have developed protocols that control the movement of the sink in wireless sensor networks with non-uniform deployment of the sensor nodes, in order to succeed an efficient (with respect to both energy and latency) data collection. More specifically, a graph formation phase is executed by the sink during the initialization: the network area is partitioned in equal square regions, where the sink, pauses for a certain amount of time, during the network traversal, in order to collect data.
We propose two network traversal methods, a deterministic and a random one. When the sink moves in a random manner, the selection of the next area to visit is done in a biased random manner depending on the frequency of visits of its neighbor areas. Thus, less frequently visited areas are favored. Moreover, our method locally determines the stop time needed to serve each region with respect to some global network resources, such as the initial energy reserves of the nodes and the density of the region, stopping for a greater time interval at regions with higher density, and hence more traffic load. In this way, we achieve accelerated coverage of the network as well as fairness in the service time of each region.Besides randomized mobility, we also propose an optimized deterministic trajectory without visit overlaps, including direct (one-hop) sensor-to-sink data transmissions only.
We evaluate our methods via simulation, in diverse network settings and comparatively to related state of the art solutions. Our findings demonstrate significant latency and energy consumption improvements, compared to previous research.