PDF The Study of Coverage and Connectivity in an Underwater Sensor Network

This thesis entitled “The Study of Coverage and Connectivity in an Underwater Sensor Network”, submitted by the group mentioned below, has been accepted as satisfactory and partially meets the requirements for the degree of B.Sc. It is hereby declared that the work presented in this thesis is the result of the research and research conducted by the following students under the supervision of Dr. It is also declared that neither this thesis nor any part thereof has been submitted anywhere for the award of any degree, diploma or other qualification.

We are grateful to the Almighty for his blessings for the successful completion of our dissertation. Mahmuda Naznin, Associate Professor, BUET, Dhaka, Bangladesh, for her constant supervision, loving guidance and great encouragement and motivation. We are especially grateful to the Department of Computer Science and Engineering (CSE) of the Military Institute of Science and Technology (MIST) for providing their full support during the thesis work.

Finally, we would like to thank our families and coursemates for their valuable help, patience and suggestions during the course of our thesis. In this thesis we have made a detailed overview of various research works in the field of underwater sensor networks.

LIST OF ABBREVIATION

INTRODUCTION

• Underwater Sensor Network
• Two-dimensional Underwater Sensor Networks
• Three-dimensional Underwater Sensor Networks
• Commonly Used Terminology
• Thesis Organization

To achieve this goal, sensors and vehicles self-organize into an autonomous network that can adapt to the characteristics of the ocean environment [1]. Limited storage capacity: The amount of data that each sensor can record during a monitoring mission is limited by the capacity of the onboard storage devices (memory sticks, hard drives). Due to the extreme characteristics of the underwater channel, high bit error rates and temporary losses of connectivity (shadow areas) can occur.

For example, it is critical to avoid designing the network topology with discrete points of failure that could compromise the overall operation of the network. Network capacity is also affected by network topology. The communication architectures introduced as a basis for discussing the challenges associated with underwater acoustic sensor networks. Static two-dimensional UW-ASNs for ocean floor monitoring are formed by sensor nodes anchored to the ocean floor.

A group of sensor nodes are anchored to the bottom of the ocean with deep sea anchors. However, in UW-ASNs, the power needed to transmit may decrease with powers greater than two of the distance [7], and the uw-sink may be far from the sensor node. Three-dimensional underwater networks are used to detect and observe phenomena that cannot be adequately observed using seabed sensor nodes, i.e. to perform collaborative testing of the 3D marine environment.

For these reasons, anchoring sensor equipment at the bottom of the ocean may be another approach. The depth of the sensor can then be controlled by adjusting the length of the wire connecting the sensor to the armature, using an electronically controlled motor mounted on the sensor. A challenge that needs to be addressed in such an architecture is the effect of ocean currents on the described mechanism to regulate the depth of the sensors.

Sensor coverage: Sensors must cooperatively adjust their depth in order to achieve 3D coverage of the ocean column, according to their sensing range. Therefore, it should be possible to sample the desired phenomenon at all depths. Additionally, AUVs can be used either for the installation and maintenance of sensor network infrastructure or to deploy new sensors.

Therefore, solar-powered AUVs can acquire continuous information for time periods on the order of months [14]. Volume coefficient: This is the ratio of the volume of a polyhedron to the volume of its circular sphere.

Figure 1.1: Architecture for 2D underwater sensor networks [6]

PRELIMINARIES

• Some Research Work on UWSN
• Hierarchical and Nonhierarchical UWSN Architecture
• Coverage Preserving Deployment in UWSN
• Coverage and Connectivity in 3D UWSN

The following table shows the power consumption ratio of each of the four models compared to the TO model. This maximum distance is essentially the diameter of the cell and is equal to twice the corresponding radius. With this configuration, full coverage can be achieved by adjusting the distance between the sensors (i.e. the sides of an equilateral triangle) and this is d=√.

In [21], the authors work on the coverage and connectivity problems of 3D networks, where the goal is to find a node placement strategy with 100% sensing coverage of a 3D space, while reducing the number of nodes needed is for surveillance is minimized and also to Find out the minimum ratio between the transmission range and the detection range with such a placement strategy. The detection range is much smaller than the length, width or height of the 3D space to be covered, so the boundary effect is negligible and can therefore be ignored. The minimum transmission range required to maintain connectivity between adjacent nodes depends on the choice of polyhedron.

Their research results indicate that using the Voronoi tessellation of 3D space to create truncated octahedral cells produces the best strategy. According to them, almost all coverage strategies are based on the premise in which sensors are deployed. The detection model is therefore a sphere and all sensors adopt the beam of the detection sphere model.

A coverage optimization algorithm is performed to change the radio and detection circle locations in the sample plans. The circle center is the projection of the sensing sphere onto the sample plane, and the radius of the circle is . Then the sensor nodes corresponding to the sensing circles are repositioned according to the new radios and locations of the sensing circles.

However, they find a feasible solution after limiting the limitations of the optimization algorithm in vertical planes. First, a plane parallel to the - plane is sampled in interval (0,R/2). Then the positions and radii of the detection circles in the sampling plane are calculated when the detection spheres intersect on the sampling plane. The detection radius of the underwater sensor node is 20 meters, and the communication nodes deployed on the water surface can communicate with each other.

The server implements the COS algorithm [22] and transmits the new position of the underwater sensor nodes to the coordinator. The sampling plane coverage rate of the COS algorithm outperforms the random approach by about 10% (Figures 2.23 and 2.24), which is not a significant improvement.

Figure 2.1: Comparison of different placement models for hierarchical network [19]

COMPARATIVE STUDY

Comparative Study Table

Summary of the Table

CONCLUSION

Fratantoni, "Multi-AUV control and adaptive sampling in Monterey Bay", In Proceedings of IEEE Autonomous Underwater Vehicles 2004: Workshop on Multiple AUV Operations (AUV04), June 2004. 20] Pompili, D, Melodia, T and Akyildiz, I.F , "Three-Dimensional and Two-Dimensional Deployment Analysis for Underwater Acoustic Sensor Networks", Ad Hoc Networks (Elsevier) Journal, vol Haas, "Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks", Wireless Communication and Mobile Computing, Volume 8, Pages 995-1009 , October 2008.

22] Du Xiaoyu, Sun Lijuan and Liu Linfeng, “Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks”, International Journal of Distributed.