Consolidation of a WSN and Minimax Method to Rapidly Neutralise Intruders in Strategic Installations

Abstract

Due to the sensitive international situation caused by still-recent terrorist attacks, there is a common need to protect the safety of large spaces such as government buildings, airports and power stations. To address this problem, developments in several research fields, such as video and cognitive audio, decision support systems, human interface, computer architecture, communications networks and communications security, should be integrated with the goal of achieving advanced security systems capable of checking all of the specified requirements and spanning the gap that presently exists in the current market. This paper describes the implementation of a decision system for crisis management in infrastructural building security. Specifically, it describes the implementation of a decision system in the management of building intrusions. The positions of the unidentified persons are reported with the help of a Wireless Sensor Network (WSN). The goal is to achieve an intelligent system capable of making the best decision in real time in order to quickly neutralise one or more intruders who threaten strategic installations. It is assumed that the intruders’ behaviour is inferred through sequences of sensors’ activations and their fusion. This article presents a general approach to selecting the optimum operation from the available neutralisation strategies based on a Minimax algorithm. The distances among different scenario elements will be used to measure the risk of the scene, so a path planning technique will be integrated in order to attain a good performance. Different actions to be executed over the elements of the scene such as moving a guard, blocking a door or turning on an alarm will be used to neutralise the crisis. This set of actions executed to stop the crisis is known as the neutralisation strategy. Finally, the system has been tested in simulations of real situations, and the results have been evaluated according to the final state of the intruders. In 86.5% of the cases, the system achieved the capture of the intruders, and in 59.25% of the cases, they were intercepted before they reached their objective.

Sensors in Collaboration Increase Individual Potentialities

“…(1) coverage precedence routing algorithm, ensuring full functionality, for quality of service in WSN, [ 1 ]; (2) Diffusion-based Expectation-Maximization algorithm for energy-efficient solution in WSN [ 2 ]; (3) trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm for multiple event source localization using binary information from the sensor nodes in WSN [ 3 ]; (4) collaborative localization algorithms for nodes in WSN without GPS [ 4 ]; (5) prediction (data not sent to the sink node) accuracy for data reduction in WSN [ 5 ]; (6) grid-based distributed event detection scheme for WSN [ 6 ]; (7) WSNs for intelligent transportation systems [ 7 ]; remote testbed with WSN and mobile robots equipped with a set of low-cost off-the-shelf sensors for cooperative perception [ 8 ]; (8) wireless body area networks for monitoring health parameters are useful for transmitting data externally [ 9 ]; (9) distributed and formula-based bilateration algorithm used to provide initial set of locations in WSN [ 10 ]; (10) Artificial neural network to estimate the location of a mobile station in wireless communication systems [ 11 ]; (11) WSN and minimax method in early detection to neutralize intruders in strategic installations [ 12 ].…”

On the Optimization of Wireless Multimedia Sensor Networks: A Goal Programming Approach

“…For this reason, most of the works reviewed simplify the problem formulation, just optimizing a single metric (e.g., [ 18 , 19 ]) or conducting a process where the selected metrics are optimized sequentially (not simultaneously). In order to do so, linear/non-linear programming techniques are used [ 2 , 3 , 6 , 20 ]. As a consequence, the solutions provided are not appropriate because the fully optimization of a metric does not imply optimal results for the other performance figures.…”

“…Therefore, the best results are reached for the energy consumption but the maximum data gathered at the sink is not attained. Recently, Conesa-Muñoz et al [ 20 ] solve a min-max problem in which firstly they maximize the sensing coverage, in order to later minimize the power consumption. The solution offered by this work shows a fixed scheme for data transmission which is not suitable for WMSN deployments.…”