In an energy-constrained underwater system environment it is very important to find ways to improve the life expectancy of the sensors. Compared to the sensors of a terrestrial Ad Hoc Wireless Sensor Network (WSN), underwater sensors cannot use solar energy to recharge the batteries, and it is difficult to replace the batteries in the sensors. This paper reviews the research progress made to date in the area of energy consumption in underwater sensor networks (UWSN) and suggests further research that needs to be carried out in order to increase the energy efficiency of the UWSN system. An underwater network is typically made up of many autonomous and individual sensor nodes that perform data collection operations as well as store and forwarding operations to route the data that has been collected to a central node. The main challenges of deploying such a network are the cost, the computational power, the memory, the communication range and most of all the limited battery resources of each individual sensor node. As the life time of any individual sensor in the UWSN is limited, the number of sensor nodes that stop working due to the power loss increases with a lengthened deployment time, therefore the coverage area of WSN will shrink. It is obvious that the issue of limited battery resources is particularly important and it is a challenge for researchers to obtain long operating time without sacrificing system performance. Therefore new, energy efficient protocols must be developed for all of the UWSN nodes' functions. Battery TechnologyTo increase network lifetime, energy must be saved in every hardware and software solution composing the network architecture. One way to resolve the battery problem is for the UWSN sensor to generate energy by itself. This can be achieved by using chemistry or mechanical methods such as current movement. On the other hand if the type of battery is chosen, the Li-ion systems are the most promising technology for the underwater sensors mainly due to their higher energy and power densities compared to other technologies such as Nickel Cadmium and Nickel Metal Hydride. The main features of lithium ion technology are the low life cycle cost (long cycle life, no memory effect, no maintenance), the low discretion rate (no thermal or magnetic signature), the design flexibility (battery systems are independent, secured and communicant) and the readable battery status (with electronics and easy state of charge evaluation) [4]. Aside from analyzing the physical design characteristics of Li-ion cells and batteries, Yardney Technical Products, Inc.[5] have performed extensive research and testing on various chemistries suitable for underwater environment. Figure 1 compares Li-ion technology to other battery technologies. The "HP" bubble relates to the highest power systems, the "Typical Military" bubble corresponds to the general performance window of fielded military systems and the "HE" bubble corresponds to system where discharge rates are low and the temperature is no colder than ...

ABSTRACT

The flooding method, which is used by many mobile ad-hoc routing protocols, is a process in which a route request packet (RREQ) is broadcasted from a source node to other nodes in the network. This often results in unnecessary re-transmissions, causing packet collisions and congestion in the network, a phenomenon called broadcast storm. This article presents firstly the impact of a different message forwarding probability on the RREQ and secondly a RREQ message forwarding scheme which is implemented on Ad-hoc On-Demand Distance Vector Routing (AODV) routing protocol, a Bayesian probability based the AODV extended version based on a modified version of Bayesian probability (AODV_EXT_BP) that reduces routing overheads, by calculating the probability with respect to the neighbour density as well as the posterior probability. The performance of the AODV_EXT_BP is compared to that of extended version of AODV (AODV_EXT), AODV, Destination Sequenced Distance Vector, dynamic source routing and Optimized Link State Routing protocols and the simulation results show that the AODV_EXT_BP protocol achieves better results in all sectors.

In our paper we discuss how elements of algebraic hyperstructure theory can be used in the context of underwater wireless sensor networks (UWSN). We present a mathematical model which makes use of the fact that when deploying nodes or operating the network we, from the mathematical point of view, regard an operation (or a hyperoperation) and a binary relation. In this part of the paper we relate our context to already existing topics of the algebraic hyperstructure theory such as quasi-order hypergroups, E L -hyperstructures, or ordered hyperstructures. Furthermore, we make use of the theory of quasi-automata (or rather, semiautomata) to relate the process of UWSN data aggregation to the existing algebraic theory of quasi-automata and their hyperstructure generalization. We show that the process of data aggregation can be seen as an automaton, or rather its hyperstructure generalization, with states representing stages of the data aggregation process of cluster protocols and describing available/used memory capacity of the network.

Energy efficiency is an important issue during the design and the overall performance evaluation of an UWSN system. Clustering sensor nodes have proven to be an effective method to improve the load balancing and scalability of the network while minimizing the system's overall energy consumption. In this paper, a new clustering algorithm is proposed to provide an improved cluster system against clusterhead failures. This study suggests that system CH failures could be further minimized when simultaneously a CH (primary CH) and a vice/backup CH are selected. Thus, when a primary CH fails due to an irreparable fault, a backup CH will take its place and it will operates as a head node. This study proposes two major procedures in order this to be accomplished, the detection failure and the recovery procedures. The first one initially detects any failures that occurred in the network and then reports this information to the relevant nodes to initiate recovery. The recovery procedure actually decides who and when will trigger the recovery function according to the origin of the CH node failure which can be either the energy depletion of the CH's battery or a software/hardware malfunction. The simulation results clearly indicate that there is an improvement in terms of network lifetime expectancy and energy consumption.