1. Introduction:-

The coverage problem here is to cover maximum area by using static and mobile WSN Nodes. Recent improvements in affordable and efficient integrated electronic devices have a considerable impact on advancing the state of wireless sensor networks, which constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring.

An important problem receiving increased consideration recently is the sensor coverage problem, centered on a fundamental question: How well do the sensors observe the physical space?

2. Aim and objective:-

Sensitive applications of wireless sensor networks require high level of connectivity as well as coverage. My aim is to propose a method to schedule the sensor nodes such that only minimum number of sensor nodes will be active, satisfying connectivity and coverage requirement with performance enhancement.

3. Research plan

3.1 Literature review

An important problem addressed in literature is the sensor coverage problem. This problem is centered on a fundamental question: "How well do the sensors observe the physical space?"
Several researchers have addressed target coverage problem without considering connectivity. Simple coverage problem has been addressed in [1, 2]. K-coverage problem has been addressed. Q-coverage problem has been addressed in. None of these address the connected coverage problem.
Zhou et al. [3] present a centralized approximation algorithm and a distributed version of the algorithm to solve connected k-coverage problem. The distributed priority algorithm is more efficient in applications where the query is executed for less than a few hundred times. For longer running queries, the distributed greedy algorithm is more efficient.

Lu et al. [4] generalize the sleep/active mode by adjusting sensing range to maximize total number of rounds and present a distributed heuristic. A more generic connectivity condition that can be used even when the transmission range is less than twice the sensing range is considered. It deals with the case of scheduling sensors activity by self configuring sensing ranges, in the environment where both discrete target coverage and network connectivity are satisfied.

Gupta et al. [5] design and analyze algorithms for self-organization of a sensor network into an optimal logical topology in response to a query. A distributed version of the approximation algorithm that is run by the sensors in the network and results in a self-organization of the network into a topology involving a near-optimal number of sensors is also designed.
Zhao and Gurusamy [6] consider the Connected Target Coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink.
Kar and Banerjee [7] also address the problem of optimal node placement for ensuring connected coverage in sensor networks and propose two scenarios. The first scenario requires a complete area to be provided with connected coverage and the second scenario needs a given set of points in the region to be covered and connected

3.2 Implementation in Queuing theory:-
Data packets are transmitted and processed in collaboration by the sink nodes, transmission nodes and boundary nodes. For a large scale WSN, a Queuing network model can be used to analyze its performance. But how to configure resources to find the best value hardware using trends of changing the parameters of performance is an important reference for node design. The definition of the threshold of node buffer capacity is given below:
When the queuing network system is stable, node's hardware buffer capacity just accommodate the maximum length of the queue to be processed. Buffer size value at the moment is called the node threshold, denoted by Nt.

*Ni>= Nt, node queue length will never be processed over the buffer capacity when a system is in a steady state. Therefore, the packets that have not been timely processing data will be placed in the packet buffer. Newly arrived packets will not cause the blocking node server.
* Ni< Nt when the packet buffer of node is full, the link paths that include the nodes are blocked, and lead to the processing efficiency of the whole WSN down. On the other hand, when the link path is blocked, all the nodes are in an active state in the link path. Therefore, energy consumption of the node is larger, and individual nodes are invalidated due to energy exhaustion

3.3 Implementation in Markov chain model:-
It helps to minimizing power consumption in each sensor node locally while ensuring two global (i.e., network wide) properties :(i) communication connectivity, and (ii) sensing coverage. A sensor node saves energy by suspending its sensing and communication activities according to a Markovian stochastic process. It is shown that a power level to induce a coverage radius r is sufficient for providing effective connectivity. This Markov model and its solution is used for steady State distributions to determine the operation of a single node. Given the steady state probabilities, a non-linear optimization problem can be constructed to minimize the power consumption.

3.4 Task planning
1. Background and motivation for my work.
2. Review current state of research.
3. My contribution to achieve goal.
4. Problem findings.
5. Methodology and techniques to be used for my research.
6. Implementations and experimentations.
7. Publication and conference presentation.
8. Tools requirement to complete my research.
9. Collection of my cited research papers to read further.
10. Thesis preparations and final presentation.

3.5 Over all research Challenges:-
Since sensor nodes are dense and randomly deployed, the best way to prolong network lifetime is by scheduling the nodes such that the required level of coverage and connectivity is achieved with minimum number of sensor nodes.
• Area Coverage where the sensor nodes are deployed to cover a specific area or region
• Target Coverage Problem where the sensor nodes are deployed to cover a specific set of targets or points.
• Coverage dealing with the determination of the maximum support or breach path.

4. References:-

[1] S. Slijepcevic and M. Potkonjak, "Power efficient organization of wireless sensor networks," in Proceedings of the IEEE International Conference on Communications (ICC '01), pp. 472-476, Helsinki, Finland, June 2001.
[2] M. Cardei,M. T. Thai, Y. Li, and W.Wu, "Energy-efficient target coverage in wireless sensor networks," in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM '05), pp. 1976-1984, Miami, Fla, USA, March 2005.
[3] Z. Zhou, S. Das, and H. Gupta, "Connected k-coverage problem in sensor networks," in Proceedings of the 13th International Conference on Computer Communications and Networks (ICCCN '04), pp. 373-378, Chicago, Ill, USA, October 2007.
[4] M. Lu, J. Wu, M. Cardei, and M. Li, "Energy-efficient connected coverage of discrete targets in wireless sensor networks," in Proceedings of the 3rd International Conference on Computer Network and Mobile Computing (ICCNMC '05), pp. 43-52, Zhangjiajie, China, August 2009.
[5] H. Gupta, S. R. Das, and Q. Gu, "Connected sensor cover: self organization of sensor networks for efficient query execution," in Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '03), pp. 189-200, Annapolis, Md, USA, June 2010.
[6] Q. Zhao and M. Gurusamy, "Lifetime maximization for connected target coverage in wireless sensor networks," IEEE/ACM Transactions on Networking, vol. 16, no. 6, pp. 1378-1391, 2010.
[7] K. Kar and S. Banerjee, "Node placement for connected coverage in sensor networks," in Proceedings of the 1st International Symposium on Modeling and Optimization in Mobile, Ad-Hoc and Wireless Networks (WiOpt '03), Sophia-Antipolis, France, Dec 2011.

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