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In Opportunistic Networks (OppNets), the existence of an end-to-end connected path between the sender and the receiver is not possible. Thus routing in this type of networks is different from the traditional Mobile Ad hoc Networks (MANETs). MANETs assume the existence of a fixed route between the sender and the receiver before the start of the communication and till its completion. Routes are constructed dynamically as the source node or an intermediate node can choose any node as next hop from a group of neighbors assuming that it will take the message closer to the destination node or deliver to the destination itself. In this paper, we proposed a novel History Based Prediction Routing (HBPR) protocol for infrastructure-less OppNets which utilizes the behavioral information of the nodes to find the best next node for routing. The proposed protocol was compared with the Epidemic routing protocol. Through simulations it was found that the HBPR performs better in terms of number of messages delivered and the overhead ratio than the Epidemic protocol.

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SUMMARYMobile ad hoc networks (MANETs) are characterized by random, multi-hop topologies that do not have a centralized coordinating entity or a fixed infrastructure that may change rapidly over time. In addition, mobile nodes operate with portable and finite power sources. In this work, we propose an energy-efficient routing protocol for MANETs to minimize energy consumption and increase the network's consistency. Traditional works mainly focused on the shortest path-based schemes to minimize energy, which might result into network failure because some nodes might exhaust fast as they are used repetitively, while some other nodes might not be used at all. This can lead to energy imbalance and to network life reduction. We propose an energy-efficient ad hoc on-demand routing protocol that balances energy load among nodes so that a minimum energy level is maintained among nodes and the network life increases. We focused on increasing the network longevity by distributing energy consumption in the network. We also compared the simulation results with a popular existing on-demand routing protocol in this area, AODV, to establish the superiority of our approach.

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