Mobile Ad Hoc Networks (MANETs) are comprised of highly mobile nodes that communicate with each other without relying on a pre-existing network infrastructure. Therefore they are ideally suited for use in rescue and emergency operations. These applications have increased security requirements with low acceptable delays, in order to provide high quality service. However, MANETs were not designed with security protection in mind and they are prone to several attacks. Communication in mobile ad hoc networks comprises of route discovery and data transmission phases.In an adverse environment, both these phases are vulnerable to a variety of attacks. A misbehaving node can abide well in the route discovery phase and hence be placed on utilized routes. Later, it could tamper with the in-transit data in an arbitrary manner and degrade the network performance. This behavior can be nullified by securing the data transmitted. In this paper, we propose a novel method to enhance security in both phases. We present the design of a routing protocol based on trust, which ensures secure and undisrupted delivery of transmitted data. An end to end encryption technique is used to self encrypt the data without the necessity of a cryptographic key. Results show that our method is much more secure than other existing trust based multipath routing protocols.

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In wireless sensor network, sensor readings generated by nearby nodes are redundant and highly correlated, both in space and time domains. Since transmitting redundant and highly correlated data incurs a huge waste of energy and bandwidth, spatial and temporal correlation should be exploited in order to reduce redundant data transmission. In this paper, we propose an energy efficient data gathering protocol that uses a prediction-based filtering (EEDGPF) mechanism to solve the problem of redundant data transmissions. Our data gathering protocol organises a WSN into clusters, using data similarity that exists in readings of sensor nodes and cluster heads and uses a GARCH (1, 1) model-based non-linear predictor to exploit the temporal correlation of sensor readings. Experimental results over real dataset show that our protocol significantly outperforms linear predictor (AR(3))-based protocol proposed in Jiang et al. (2011), in terms of number of data packets delivered, number of successful predictions and average energy consumption.

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