A fuzzy scheduler for MAS applied to object tracking. (March 2023)

• Record Type: Journal Article
• Authors: Barbosa, Gibson; Dantas, Marrone; Tiago de Oliveira Filho, Assis; Rodrigues, Iago Richard; Bezerra, Daniel; Sadok, Djamel; Kelner, Judith; Souza, Ricardo
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Fuzzy logic -- Object tracking -- Agents selection -- Camera selection -- Intelligent environment; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105796
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Intelligent industry and Internet of Things (IoT) rely on sensors to monitor the surroundings and allow information exchange. More sensors deployed in an environment offer additional benefits, such as increased availability and more accurate observations, as they generate information from different perspectives. Nonetheless, dealing with large amounts of data and multiple input sources to deduct unified information often requires considerable processing, communication, and energy resources. To address such a problem, this work examines intelligent agents that cooperate to generate a unified output in a Multi-Agent System (MAS) environment. The proposed approach presents a fuzzy scheduler that evaluates the processing and accuracy level to determine the relevance of each agent by comparing its information with the unified output. The proposal orchestrates when an agent can have its processing suspended. Our evaluations demonstrate that this proposal improves service level metrics, such as the rate of video frames processed per second for an increasing number of agents. We consider three experimental setups using tracking agents. First, we evaluate the threshold and how long an agent should remain idle when its observations are irrelevant. The second experiment considers an increasing number of agents. Agent scheduling achieves a frame processing rate 12.5 times faster while suffering a 6% accuracy loss for 20 agents. The third experiment focuses on using different tracking algorithms simultaneously. In this case, compared to when no agent scheduling is applied, there is a 23.9 fold increase in the frame processing rate while losing some accuracy, limited to around 6%.