Distributed energy efficient adaptive clustering protocol with data gathering for large scale wireless sensor networks

Hierarchical routing is an efficient way to lower energy consumption within a cluster, performing data aggregation and fusion in order decrease the number of transmitted messages to the BS. In this paper, a novel hierarchical approach called distributed energy efficient adaptive clustering protocol with Data Gathering (DEACP) is proposed for Wireless sensor network. Since nodes in a sensor network have limited energy, prolonging the network lifetime and improving scalability become important. we have proposed (DEACP) approach to reach the following objectives: reduce the overall network energy consumption, balance the energy consumption among the sensors and extend the lifetime of the network, the clustering must be completely distributed, the clustering should be efficient in complexity of message and time, the cluster-heads should be well-distributed across the network, the load balancing should be done well, the clustered WSN should be fully-connected. As a result transmission power of the node is reduce which subsequently reduces the energy consumption of the node. Our proposed work is simulated through Network Simulator (NS-2). We consider the problem of conserving energy in a single node in a wireless sensor network by turning off the node's radio for periods of a fixed time length. While packets may continue to arrive at the node's buffer during the sleep periods, the node cannot transmit them until it wakes up. The objective is to design sleep control laws that minimize the expected value of a cost function representing both energy consumption costs and holding costs for backlogged packets. The network scenario is established by considering 1000 × 1000 area and displaying randomly moving nodes using TCL. The resource reservation is used to decompose the total simulation time of network into smaller time slots depending upon number of nodes in the network using TDMA technique. Simulations show that (DEACP) clusters have good performance characteristics.