Scheduling Resource-Bounded Monitoring Devices for Event Detection and Isolation in Networks

Abstract

In networked systems, monitoring devices such as sensors are typically deployed to monitor various target locations. Targets are the points in the physical space at which events of some interest, such as random faults or attacks, can occur. Most often, these devices have limited energy supplies, and they can operate for a limited duration. As a result, energyefficient monitoring of various target locations through a set of monitoring devices with limited energy supplies is a crucial problem in networked systems. In this paper, we study optimal scheduling of monitoring devices to maximize network coverage for detecting and isolating events on targets for a given network lifetime. The monitoring devices considered could remain active only for a fraction of the overall network lifetime. We formulate the problem of scheduling of monitoring devices as a graph labeling problem, which unlike other existing solutions, allows us to directly utilize the underlying network structure to explore the trade-off between coverage and network lifetime. In this direction, first we propose a greedy heuristic to solve the graph labeling problem, and then provide a game-theoretic solution to achieve optimal graph labeling. Moreover, the proposed setup can be used to simultaneously solve the scheduling and placement of monitoring devices, which yields improved performance as compared to separately solving the placement and scheduling problems. Finally, we illustrate our results on various networks, including real-world water distribution networks.

Graph-Theoretic Approach for Increasing Participation in Social Sensing

“…us, the removal of a node from a network may cause a cascading e ect, or unraveling, due to which nodes that initially satisfy the condition (2) may also get removed from the network. In the end, we are le with a subnetwork of nodes that all satisfy the engagement rule (2). We call the remaining subnetwork the (r, s)-core of the network.…”

“…is network has ten nodes, each of which has s = 2 labels from the set R = {1, 2, 3, 4, 5}. Initially, there are two nodes x and that do not satisfy the condition (2). As a result, they leave the network, which leads to further node removals.…”

“…To obtain the (r, s)-core with anchors, we iteratively remove those non-anchor nodes from G (V , E) that do not satisfy condition (2). We repeat this until we are le with a subgraphG A (Ṽ A ,Ẽ A ), which is the (r , s)-core with anchors.…”

“…We note that this heuristic belongs to a class of learning algorithms, known as log-linear learning [5,14], which are known to converge to an optimal solution (asymptotically) if the problem can be formulated as a potential game. In [2], we showed that the problem of assigning labels to nodes such that the number of labels missing from the neighborhood of nodes is minimized can be formulated as a potential game. In Algorithm 4, we formally describe our heuristic for relabeling nodes to increase the size of the (r, s)-core.…”