Chain Routing for Convergecast Small Scale Wireless Sensor Networks

Wireless sensor networks have many applications involving autonomous sensors transmitting their data to a sink placed in the network. A protocol by name Chain Routing for Convergecast Small Scale (CRCSS) Wireless sensor networks is proposed in this paper. The set of sensor nodes in the network send the data periodically to the sink located in the area of interest. The nodes who cannot reach sink in one hop choose one of the neighbours for forwarding the data to the sink by forming a chain of links. The selection of forwarding node and the waiting period before forwarding plays an important role in the protocol. The proposed CRCSS protocol exhibits improvement in energy spent per packet and latency per packet for a wireless sensor network as compared to ConverSS protocol for small scale wireless sensor networks. In CRCSS protocol energy spent per packet is independent of the network radius

Minimum Latency Aggregation Convergecast in Wireless Sensor Networks

In wireless sensor networks, sensor nodes are used to collect data from the environment and send it to a data collection point or a sink node using a convergecast tree. Considerable savings in energy can be obtained by aggregating data at intermediate nodes along the way to the sink. We study the problem of finding a minimum latency aggregation tree and transmission schedule in wireless sensor networks. This problem is referred to as Minimum Latency Aggregation Scheduling (MLAS) in the literature and has been proven to be NP-Complete even for unit disk graphs. We present a new simpler proof of the NP-Completeness of the MLAS Problem for arbitrary networks and unit disk graphs. We give tight bounds for the latency of aggregation convergecast for grids, tori, and trees. For regular unit interval graphs, we provide an algorithm which is guaranteed to have a latency that is within one time slot of the optimal latency. Finally, for unit interval graphs we give a 2-approximation algorithm to solve the same problem. For arbitrary graphs, we introduce a new algorithm for building an aggregation tree. Furthermore, we propose two new approaches for building a transmission schedule to perform aggregation on a given tree. We evaluate the performance of our algorithms through extensive simulations on randomly generated graphs and we compare them to the previous state of the art. Our results show that one of our algorithms has a latency that is 38% less than the latency of the previous best algorithm