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Usage of mobile sink(s) for data gathering in wireless sensor networks(WSNs) improves the performance of WSNs in many respects such as power consumption, lifetime, etc. In some applications, the mobile sink $MS$ travels along a predefined path to collect data from the nearby sensors, which are referred as sub-sinks. Due to the slow speed of the $MS$, the data delivery latency is high. However, optimizing the {\em data gathering schedule}, i.e. optimizing the transmission schedule of the sub-sinks to the $MS$ and the movement speed of the $MS$ can reduce data gathering latency. We formulate two novel optimization problems for data gathering in minimum time. The first problem determines an optimal data gathering schedule of the $MS$ by controlling the data transmission schedule and the speed of the $MS$, where the data availabilities of the sub-sinks are given. The second problem generalizes the first, where the data availabilities of the sub-sinks are unknown. Plane sweep algorithms are proposed for finding optimal data gathering schedule and data availabilities of the sub-sinks. The performances of the proposed algorithms are evaluated through simulations. The simulation results reveal that the optimal distribution of data among the sub-sinks together with optimal data gathering schedule improves the data gathering time., Comment: 13 pages, 13 figures

In energy-constrained wireless sensor networks (WSNs), maximizing the data collection using mobile sink(s) with minimum energy consumption is one of the practical challenging issues. In this article, we consider the problem of efficient data collection along with a pre-specified path using a mobile sink with constant speed. We refer the problem as a Maximizing data gathering with minimum energy consumption (MDGMEC) problem. So far, existing works have heuristic algorithms for MDGMEC problem. Therefore, based on network flow optimization approach, we propose a deterministic algorithm called data gathering using mobile sink for path constraint environment (DGAMSPCE) to handle the MDGMEC problem. The proposed DGAMSPCE scheme runs in polynomial time and is easily scalable for the networks with a large number of nodes. Based on the data receiving models used by the mobile sink, another algorithm called single access based data gathering using mobile sink for path constraint environment (SADGAMSPCE) is also proposed. We evaluate the proposed schemes and compare these with the existing schemes. The simulation experiments in MATLAB show that our proposed schemes outperform other existing schemes in terms of collecting the amount of data and the total energy consumption of the network, significantly., Comment: Not published

Coverage problem in wireless sensor networks measures how well a region or parts of it is sensed by the deployed sensors. Definition of coverage metric depends on its applications for which sensors are deployed. In this paper, we introduce a new quality control metric/measure called road coverage. It will be used for measuring efficiency of a sensor network, which is deployed for tracking moving/mobile objects in a road network. A road segment is a sub-part of a road network. A road segment is said to be road covered if an object travels through the entire road segment then it must be detected somewhere on the road segment by a sensor. First, we propose different definitions of road coverage metrics. Thereafter, algorithms are proposed to measure those proposed road coverage metrics. It is shown that the problem of deploying minimum number of sensors to road cover a set of road segments is NP-hard. Constant factor approximation algorithms are proposed for road covering axis-parallel road segments. Experimental performance analysis of our algorithms are evaluated through simulations., Comment: 11 pages, 12 figures

In Wireless Sensor Network (WSN) sensor nodes are deployed to sense useful data from environment. Sensors are energy-constrained devices. To prolong the sensor network lifetime, now a days mobile robots (sometimes refer as data sink, data mules, or data collectors) are used for collecting the sensed data from the sensors. In this environment sensor nodes directly transfer their sensed data to the data mules. Sensed data are sometime time sensitive; therefore, the data should be collected within a predefined period. Hence, depending on the speed of the data mules the trajectory lengths of the data mules have upper limits. In this paper an approximation algorithm is proposed for collecting data from the mobile sensors using mobile data collectors, Comment: 7 pages, 3 figures

In Wireless Sensor Networks, sensors are used for tracking objects, monitoring health and observing a region/territory for different environmental parameters. Coverage problem in sensor network ensures quality of monitoring a given region. Depending on applications different measures of coverage are there. Barrier coverage is a type of coverage, which ensures all paths that cross the boundary of a region intersect at least one sensor's sensing region. The goal of the sensors is to detect intruders as they cross the boundary or as they penetrate a protected area. The sensors are dependent on their battery life. Restoring barrier coverage on sensor failure using mobile sensors with minimum total displacement is the primary objective of this paper. A centralized barrier coverage restoring scheme is proposed to increase the robustness of the network. We formulate restoring barrier coverage as bipartite matching problem. A distributed restoring of barrier coverage algorithm is also proposed, which restores it by first finding existing alternate barrier. If alternate barrier is not found, an alternate barrier is reconstructed by shifting existing sensors in a cascaded manner. Detailed simulation results are shown to evaluate the performance of our algorithms., Comment: 20 pages, 8 figures

The coverage problem in wireless sensor networks deals with the problem of covering a region or parts of it with sensors. In this paper, we address the problem of covering a set of line segments in sensor networks. A line segment ` is said to be covered if it intersects the sensing regions of at least one sensor distributed in that region. We show that the problem of finding the minimum number of sensors needed to cover each member in a given set of line segments in a rectangular area is NP-hard. Next, we propose a constant factor approximation algorithm for the problem of covering a set of axis-parallel line segments. We also show that a PTAS exists for this problem., Comment: 16 pages, 5 figures