Self-Organized Ad Hoc Mobile (SOAM) Underwater Sensor Networks.

Política de acceso abierto tomada de: https://beta.sherpa.ac.uk/id/publication/3570The need of underwater wireless sensor networks (UWSNs) having mobile sensor nodes has been there for a long time in form of underwater warfare or explorations by autonomous underwater vehicles (AUVs) or remote unmanned vehicles (ROVs). There are very few protocols for ad hoc mobile UWSNs (AMUWSNs). Designing a protocol for AMUWSN is quite challenging because of continuous random movement of the sensor nodes. In addition to random movement, the challenges to design a routing protocol for AMUWSN are more demanding than terrestrial ad hoc networks due to acoustic communications, which has large propagation delay in water. In this article, we present a self-organized ad hoc mobile (SOAM) routing protocol for AMUWSN. The sensor nodes may need to communicate with each other to the gateway (GW). The protocol, which we also refer to as SOAM, is a reactive, self-configuring, and self-organizing cluster-based routing protocol that uses received signal strength (RSS) for distance estimation. A beacon (BCN) packet will be sent by the GW, which will traverse through all the cluster heads (CHs) to form forwarding paths between the GW and the CHs. The ordinary sensor nodes (OSNs) will select the CHs every time they intend to forward a packet based on the BCN and they will receive from CHs. The formation of the forwarding path between the GW and the CHs and the selection CHs by OSN is explained in Section IV

Self-organizing Fast Routing Protocols for Underwater Acoustic Communications Networks

To address this problem, in this thesis we propose a cross-layer proactive routing initialization mechanism that does not require additional measurements and, at the same time, is energy efficient. Two routing protocols are proposed: Self-Organized Fast Routing Protocol for Radial Underwater Networks (SOFRP) for radial topology and Self-organized Proactive Routing Protocol for Non-uniformly Deployed Underwater Networks (SPRINT) for a randomly deployed network. SOFRP is based on the algorithm to recreate a radial topology with a gateway node, such that packets always use the shortest possible path from source to sink, thus minimizing consumed energy. Collisions are avoided as much as possible during the path initialization. The algorithm is suitable for 2D or 3D areas, and automatically adapts to a varying number of nodes. In SPRINT the routing path to the gateway is formed on the basis of the distance, measured by the signal strength received. The data sending node prefers to choose the neighbor node which is closest to it. It is designed to achieve high data throughput and low energy consumption of the nodes. There is a tradeoff between the throughput and the energy consumption: more distance needs more transmission energy, and more relay nodes (hops) to the destination node affects the throughput. Each hop increases the packet delay and decreases the throughput. Hence, energy consumption requires nearest nodes to be chosen as forwarding node whereas the throughput requires farthest node to be selected to minimize the number of hops. Fecha de lectura de Tesis Doctoral: 11 mayo 2020Underwater Wireless Sensor Networks (UWSNs) constitute an emerging technology for marine surveillance, natural disaster alert and environmental monitoring. Unlike terrestrial Wireless Sensor Networks (WSNs), electromagnetic waves cannot propagate more than few meters in water (high absorption rate). However, acoustic waves can travel long distances in underwater. Therefore, acoustic waves are preferred for underwater communications, but they travel very slow compare to EM waves (typical speed in water is 1500 m/s against 2x10^8 m/s for EM waves). This physical effect makes a high propagation delay and cannot be avoided, but the end-to-end packet delay it can be reduced. Routing delay is one of the major factors in end-to-end packet delay. In reactive routing protocols, when a packet arrives to a node, the node takes some time to select the node to which the data packet would be forwarded. We may reduce the routing delay for time-critical applications by using proactive routing protocols. Other two critical issues in UWSNs are determining the position of the nodes and time synchronization. Wireless sensor nodes need to determine the position of the surrounding nodes to select the next node in the path to reach the sink node. A Global Navigation Satellite System (GNSS) cannot be used because of the very short underwater range of the GNSS signal. Timestamping to estimate the distance is possible but the limited mobility of the UWSN nodes and variation in the propagation speed of the acoustic waves make the time synchronization a challenging task. For these reasons, terrestrial WSN protocols cannot be readily used for underwater acoustic networks

Self-Organized Fast Routing Protocol for Radial Underwater Networks

An underwater wireless sensor networks (UWSNs) is an emerging technology for environmental monitoring and surveillance. One of the side effects of the low propagation speed of acoustic waves is that routing protocols of terrestrial wireless networks are not applicable. To address this problem, routing strategies focused on different aspects have been proposed: location free, location based, opportunistic, cluster based, energy efficient, etc. These mechanisms usually require measuring additional parameters, such as the angle of arrival of the signal or the depth of the node, which makes them less efficient in terms of energy conservation. In this paper, we propose a cross-layer proactive routing initialization mechanism that does not require additional measurements and, at the same time, is energy efficient. The algorithm is designed to recreate a radial topology with a gateway node, such that packets always use the shortest possible path from source to sink, thus minimizing consumed energy. Collisions are avoided as much as possible during the path initialization. The algorithm is suitable for 2D or 3D areas, and automatically adapts to a varying number of nodes, allowing one to expand or decrease the networked volume easily