An integrated mobile surveillance and wireless sensor (iMouse) system and its detection delay analysis

Abstract

Wireless sensor networks (WSN) provide an inexpensive and convenient way to monitor physical environments. Integrating the context-aware capability of WSN into surveillance systems is an attractive direction. We thus propose an integrated mobile surveillance and wireless sensor (iMouse) system, which consists of a large number of inexpensive static sensors and a small number of more expensive mobile sensors. The former is to monitor the environment, while the latter can move to certain locations and takes more advanced actions. The iMouse system is a mobile, context-aware surveillance system. We demonstrate our current prototyping for home security applications. Besides, we analyze its event detection delay under an any-sensor-detection model.

A Branch and Bound Algorithm for the Critical Grid Coverage Problem in Wireless Sensor Networks

“…The separation on the node (3, 1) of level 1 (L1) will provide in level 2 (L2) the following partial solution border positions: 𝑆 = {(2, 1); (2, 2); (3, 3); (4, 1)}. We observe in level 2 (L2) a partial solution equal to (3, 1); (2,2) which is equal to the partial solution (3, 1); (2, 2) already treated. Hence, the elimination of the (2, 2) sensor position from the remaining position of the node (3, 1) in the first level of the search tree will improve the performance of the B&B algorithm in terms of fathomed nodes and computation time.…”

“…WSNs are dense but low cost networks, which are formed by wireless nodes that sense certain phenomena in the area of interest and report their observations to a base station for further analysis [1]. WSNs are used in many applications such as environmental monitoring [2], emergency navigation [3], and smart spaces and industrial diagnostics [4, 5].…”

“…Figure3, represents the partial solution border of the node with the partial solution (2, 2);(2,4);(3,1);(3,3);(5,1).The branching scheme is achieved as follows: we first define the partial solution border of the first level by determining all the (𝑖, 𝑗) positions verifying (𝑙 − 𝑖) 2 + (𝑤 − 𝑗) 2 ≤ 𝑅 2 where (𝑙, 𝑤) corresponds to the sink position. After that, the separation on the last node of the first level is achieved by adding to the node its remaining sensor positions determined by the node partial solution border 𝑆.…”

Global Sensor Deployment and Local Coverage-Aware Recovery Schemes for Smart Environments

“…A WSN is widely used for habitat and environmental surveillance, medical application (with the purpose of improving quality of health care), agricultural assistance, and as solutions to military problems [8], [17], [22], [23]. Several experimental testbeds are also implemented to investigate various aspects of WSN-related performance issues [16], [27], [30], [32]. Imagine an indoor sensing environment, as depicted in Fig.…”

Using event detection latency to evaluate the coverage of a wireless sensor network

“…Given an event appearing in a WSN, the event detection latency is to model the time that it takes for the WSN to be aware of the event. Such latency is an important metric to measure the monitoring capability of a WSN's deployment for real-time applications such as surveillance [8][9][10] or object tracking [11][12][13].…”