TCWTP: Time-Constrained Weighted Targets Patrolling Mechanism in Wireless Mobile Sensor Networks

Abstract

Target coverage problems have received much attention in recent years. In a large monitoring environment where targets are distributed over an entire monitored region, deploying static sensors leads to high hardware costs because a high number of sensors may be required to achieve network connectivity. This paper considers the target-patrol issue where a set of mobile data mules (DMs) are dispatched to efficiently patrol the given targets under a predefined time constraint. The targets are assigned weights indicating their importance, where more important targets should be visited more frequently by the DMs. Accordingly, this paper proposes a time-constrained weighted targets patrolling (TCWTP) algorithm for locally constructing efficient patrol paths, thereby ensuring globally stable intervals between visits to all target points. A performance analysis revealed that the proposed TCWTP mechanism outperforms existing works in terms of the average interval between visits, quality of monitoring satisfaction ratio, and monitoring fairness ratio.

Multi-Robot Patrolling with Sensing Idleness and Data Delay Objectives

“…In [26] the long term goal of minimizing the idleness is converted into a short horizon control law that selects the next sensing location that should be visited by a robot. In [5] tour planning, dispatching robots on tours and controlling the speed to meet the revisit constraints of points of interest in a wireless sensor network setting with data mules is considered.…”

“…Assume the longest path from any tour in V starts at vertex 27 (Figure 9a). This path determines a path (2,7,3,6,5) in G (which is added to the tree T ) and the directions d 7 = d 6 = cw, d 3 = d 5 = ccw (Figure 9c). In the next step the path starting at 14 is considered (Figure 9b).…”

“…If the robots need to be deployed in the environment the schedule determined by Algorithm 1 (d) Figure 9: Two steps of the tree generation of Algorithm 3. First, the path in G starting from 27 is considered (bold edges in Figure 9a), which results in the path (2,7,3,6,5) in G and the directions d 7 = d 6 = cw, d 3 = d 5 = ccw (bold lines in Figure 9c). Next, the path starting at 14 is considered (Figure 9b), which results in the path (1,4,3,5) in G. Since 3 is already in the tree, only the branch (1, 4, 3) with d 4 = ccw is added (Figure 9c, the dashed line indicates the discarded part of the path).…”

“…Scheduling robots on given tours considering some idleness criterion is a recurring problem in literature, e.g. [28], [27], [36], [5], and [16].…”

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

“…Mobile data gathering has also attracted much attention in the past few years, where one or more mobile collectors are designated to take responsibility for collecting data from sensor nodes [ 33 , 34 , 35 , 36 , 37 , 38 ]. The reason to use mobile collector(s) for data gathering is to reduce energy expenditure at each node.…”