Abstract
In wireless underwater sensor networks (WUSNs), network protocols for information routing are usually designed when a significant number of nodes are present in the network. Therefore, for sparse conditions, when a noticeable reduction in the number of nodes occurs, the performance of such protocols exhibits a degraded behavior pattern. To cope with routing issues when sparse conditions prevail, two routing algorithms for WUSNs are proposed in this paper. They are energy-aware scalable reliable and void-hole mitigation routing (ESRVR) and cooperative energy-aware scalable reliable and void-hole mitigation routing (Co-ESRVR). The ESRVR uses a number of strategies. Firstly, it uses two hop neighbors' information to develop routing trajectories for information advancement, as one hop information cannot avoid a void-hole, a condition when a node is not able to find neighbors towards the sea surface, and more than two hop information is difficult to obtain when sparse conditions prevail. Secondly, when a void-hole still exists, the protocol uses a backward transmission mechanism to find other routing paths to deliver packets to the end target. Thirdly, the time by which a packet is held by a node prior to transmission is short for the nodes with low energy, depth and high count of neighbors. This reduces packets loss and avoids congestion of the channel. It also helps the nodes with no or few neighbors to hold the packet for a significant chunk of time until they find suitable neighbors, due to sea tides and currents, for further packets' advancement. The Co-ESRVR further adds reliability to information advancement by adding routing in a cooperative fashion to ESRVR, which involves packets advancement to destination along two paths: directly from source and via a relayed path. This provides multiple paths for data advancement to the sea surface, so that if one path is badly affected by the harsh sea characteristics, others may not be. Unlike the counterpart protocol, the proposed algorithms are not dependent on a node's geographical location or the distance from the sea surface, which increases their scalability and reduces the computational complexity. Performance analysis displays superior behavior patterns of the proposed algorithms over the counterpart in terms of the compared characteristics.