Multi-sensor task allocation framework for supply networks security using task administration protocols. Issue 18 (17th September 2017)

• Record Type: Journal Article
• Authors: Tkach, Itshak; Edan, Yael; Nof, Shimon Y.
• Is Part Of: International journal of production research. Volume 55:Issue 18(2017)
• Page Start: 5202
• Page End: 5224
• Publication Date: 2017-09-17
• Subjects: supply network security -- task administration protocols -- sensor network -- collaborative control -- reliability; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2017.1286047
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : This research proposes a multi-sensor task allocation framework for security of supply networks aimed to maximise the number of correctly detected and reported security events (defined as tasks). The framework includes a double layer system consisting of a process layer and a monitoring layer. The process layer allocates sensors to tasks using an ant colony algorithm. The monitoring layer applies four task administration protocols (TAPs) specially developed and implemented to deal with high time-consuming tasks, conflicts in task priorities and sensor failure, defined in this research as overloading, deception and tampering of sensors, respectively. A system objective function for sensor to task allocation was developed to allow computation of the expected value of system performance given the sensor and the task parameters. Sensory limitations evaluated including reliability, distance coverage and the limited number of sensors are addressed in the decision-making process. The framework enables detection of tasks as soon as they occur in every location along the supply network, based on the sensor network distribution. The dual layer system analyses reveal that TAPs increase the systems performance in the scenarios of deception, tampering and overloading by more than 64% with respect to the number of unallocated tasks in comparison to a single layer system. Overall availability was analysed using Monte Carlo simulation and the fault tolerant system yielded significantly increased number of treated tasks (by 11%, p = 0.02).