Power and Buffer Overflow Optimization in Wireless Sensor Nodes

Prolonging the life span of the network is the prime focus in highly energy constrained wireless sensor networks. Sufficient number of active nodes can only ensure proper coverage of the sensing field and connectivity of the network. If most of the nodes get their batteries depleted then it is not possible to maintain the network. In order to have long lived network it is mandatory to have long lived sensor nodes and hence power optimization at node level becomes equally important as power optimization at network level. In this paper need for a dynamically adaptive sensor node is signified in order to optimize power at individual nodes.

We have analyzed a wireless sensor node using queuing theory. A sensor node is looked upon as a tandem queue in which first server is the processor or micro controller and the second server in series is the transmitter. Both the servers have finite and very small buffers associated with them as the sensor nodes are tiny devices and have very limited hardware.

In this paper we have analyzed and simulated sensor node models. First we have considered a sensor node working with fixed service rate (processing rate and transmission rate). Secondly we have considered an adaptive sensor node which is capable of varying its service rates as per the requirement and ensure the quality of service. We have simulated both the models using MATLAB and compared their performances like life time, power consumption, buffer overflow probability and idle time etc.

We have compared the performances of both the models under normal work loads as well as when the catastrophe (heavy wok load) occurs. In both the situations an adaptive service model out performs the fixed service model as it saves the power during normal period and increases the lifetime and during catastrophe period it consumes more power but ensures the QoS (Quality of Service) by reducing the overflow probability.