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Top1. Introduction
In traditional ocean monitoring approach fails in real-time monitoring, on-line reconfiguration, finding and failure detection. These issues can be resolved by connecting underwater resources by wireless links based on acoustic communications. Nowadays, such Underwater Acoustic Wireless Sensor Networks (UAWSNs) play a vital role in exploring the marine activities and applications. UAWSN is an enabling technology for oceanographic data collection, tactical surveillance, disaster prevention, and pollution monitoring applications. UAWSN consists of acoustic sensors, Autonomous Underwater Vehicles (AUVs), surface buoys and base stations. The limited bandwidth capacity, transmission loss, multipath, larger bit-error-rate, and variable propagation delay are major factors (Luo & Chen, 2012) in acoustic communication that makes communication more complex in the shallow water. These factors make deployment of acoustic nodes in the 3D aquatic environment is a challenging task. Deployment of an acoustic node in the 3D network environment at the proper place is crucial to enhance coverage and connectivity. The deployment strategy involves network reliability, communication capacity, and energy consumption of sensor nodes as major factors. The acoustic wave propagation speed is five orders slower than the radio wave magnitude (Xiao, 2010).
In the sea environment, ambient noise is caused due to water bubbles and seismic phenomena, biological creatures, etc (Hovem, 2010) which affects acoustic communication and it is also affected near the surface by water bubbles. The effectiveness of acoustic nodes’ communication in deep water is negligible and in shallow water, it needs an attention. To overcome these issues, an efficient acoustic node deployment method is required which ensures wider coverage, continuous connectivity and helps in maintaining stable network topology to monitor targeted area (Chang & Chang, 2008). The main objectives of this work are to cover more areas, minimize the deployment cost and time. Mobile agents are used in this scheme to assist the deployment and routing process due to their customized and flexible services. In (Cayirci, Tezcan, Dogan, & Coskun, 2006) and (Caruso, Paparella, Vieira, Erol, & Gerla, 2008), nodes are randomly deployed in the 3D surface and connected to buoys with wires. Further, the initial deployment of nodes in a targeted monitoring area is done on a 3D based cube system to improve coverage. In the proposed work, initially, few sensor nodes are randomly deployed in the 3D ocean surface of the targeted area. This will not assure full coverage and connectivity of the targeted area. Due to that, some more nodes have to be deployed for full coverage and seamless connectivity.
The proposed scheme consists of software-agents, which are proactive, autonomous, and adaptive. These can communicate with other agents, which are knowledge-based, self-functioning, respond on time in the environment, and self adaptable in the environments for achieving goals (Baumann, Hohl, Radouniklis, Rothermel, & Straßer, 1997). Mobile agents can sense, (re)act proactively and autonomously in a dynamic environment to achieve a set of goals. The goals of the mobile agent paradigm are mainly asynchronous interaction and reduction of network traffic. A mobile agent has capabilities such as mobility, intelligence, autonomous, recursive, asynchronous, and collaborative (Cao, Chan, Jia, & Dillon, 2001).