Abstract: Network lifetime plays an integral role in setting up an efficient wireless sensor network. The objective is in twofold. The first one is to deploy sensor nodes at optimal locations such that the theoretically computed network lifetime is maximum. The second is to schedule these sensor nodes such that the network attains the maximum lifetime. Thus, the overall objective is to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network lifetime is maximum with the required coverage level. Since the upper bound of the network lifetime for a given network can be computed mathematically, use this knowledge to compute locations of deployment such that the network lifetime is maximum. Further, the nodes are scheduled to achieve this upper bound. In this proposed system uses Sleep-wake scheduling is an effective mechanism to prolong the lifetime of energy-constrained wireless sensor networks. However, sleep–wake scheduling could result in substantial delays because a transmitting node needs to wait for its next-hop relay node to wake up. An interesting line of work attempts to reduce these delays by developing “anycast”-based packet forwarding schemes, where each node opportunistically forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. It develops an anycast packet-forwarding scheme to reduce the event-reporting delay and to prolong the lifetime of wireless sensor networks employing asynchronous sleep–wake scheduling. Specifically, studies two optimization problems. First, when the wake-up rates of the sensor nodes are given, develop an efficient and distributed algorithm to minimize the expected event-reporting delay from all sensor nodes to the sink. Second, using a specific definition of the network lifetime, studies the lifetime-maximization problem to optimally control the sleep–wake scheduling policy and the any cast policy in order to maximize the network lifetime subject to an upper limit on the expected end-to-end delay.
Keywords: Sleep/Wake scheduling, Energy Consumption, Improve the network life time, Minimizing Delay, Wireless sensor Network