Abstract-Energy optimized Path Unaware Layered RoutingProtocol (E-PULRP) for dense 3D Underwater Sensor Network (UWSN) is proposed and analysed in this paper. In the proposed E-PULRP, sensor nodes report events to a stationary sink node using ℎ routing. E-PULRP consists of a layering phase and communication phase. In the layering phase, a layering structure is presented wherein nodes occupy different layers in the form of concentric shells, around a sink node. The layer widths and transmission energy of nodes in each layer are chosen taking into consideration the probability of successful packet transmission and minimization of overall energy expenditure in packet transmission. During the communication phase, we propose a method to select intermediate relay nodes ℎ , for delivering packets from the source node to sink node. We develop a mathematical framework to analyse the energy optimization achieved by E-PULRP. We further obtain expressions for throughput, delay and derive performance bounds for node densities and packet forwarding probabilities, for given traffic conditions. A comparison is made between the results obtained based on simulations and analytical expressions. The energy efficiency is also demonstrated in comparison with existing routing protocol for underwater sensor networks.Index Terms-Under sensor networks, energy aware routing, end-to-end throughput.

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In the need to conserve communication energy we consider one bit aggregation in wireless sensor network (WSN) in the context of event detection. In this paper we propose a novel Adaptive Weighted Aggregation Scheme (AdWAS) for star as well as tree topology. We compare the performance of the proposed adaptive weighted aggregation scheme with existing one bit non-adaptive aggregation schemes. In non-adaptive schemes, a slight variation in the topology or performance indices necessitate recalculation of the initial setup. However, in the proposed adaptive scheme we just need to fine tune the weights starting from the previously adapted weights to compensate for any small variation in the topology. Moreover, there is hardly any performance degradation when using AdWAS. This clearly makes the adaptive setup more appealing.

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