Distributed TDMA Scheduling in Tree based Wireless Sensor Networks with Multiple Data Attributes and Multiple Sinks

Abstract

Data collection is an important application of wireless sensor networks. Sensors are newlinedeployed in given region of interest. They sense physical quantity like temperature, newlinepressure, solar radiation, speed and many others. One or more sinks are also deployed in the network along with sensor nodes. The sensor nodes send sensed data to the sink(s). This operation is known as converge cast operation. Once nodes are deployed, logical tree is formed. Every node identi_es its parent node to transmit data towards sink. As TDMA (Time Division Multiple Access) completely prevents collisions, it is preferred over CSMA (Carrier Sense Multiple Access). The next step after tree formation is to assign time slot to every node of the tree. A node transmits only during the assigned slot. Once tree formation and scheduling is done, data transfer from sensors to sink takes place. We have proposed an algorithm known as SLBMHT (Schedule Length Balancing for Multi-sink HeTerogeneous Networks). It is an extension of SLBMHM. The proposed algorithm is capable of balancing schedule lengths no matter whether imbalance is caused due to di_erence in density or di_erence in heterogeneity. It is also evaluated through simulations. It is found that the SLBMHT algorithm results in maximum upto 56% reduction in schedule length di_erence, maximum upto 20% reduction in overall schedule length and 2% to 17% reduction in energy consumption per TDMA frame during data transfer phase. It results in maximum 7% more energy consumption during control phase. As control phase does not take place very frequently, increase in energy consumption during control phase can be balanced by reduction in energy consumption during data x phase. As a result, network lifetime is going to increase. newline newline