A Fuzzy Ranking Collaborative Multi-Tasks Data Collection Scheme in Ubiquitous Environments

Abstract

In Ubiquitous Computing (UC) and the Internet of Things (IoTs), numerous devices collect and transmit data for sensing and control. However, connecting these devices effectively to foster collaboration is crucial for optimizing system performance. With the increasing number of connected sensing devices in IoT, efficient task completion through collaboration becomes imperative. Therefore, selecting and assigning sensing tasks to maximize system benefits is a significant challenge requiring resolution. To address this challenge, this paper proposes a Data Collector Selection Method for Collaborative Multi-Tasks. This method considers task preferences and uncertainty in data collectors’ contributions. It integrates three key components: 1) Utilizing Fuzzy Analytical Hierarchy Process (FAHP) to determine optimal weights for task preferences; 2) Ranking data collectors using Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) based on preferences and determined weights; and 3) Introducing Contribution Density with Ranking as a metric to assess individual data collectors’ contributions to specific tasks. Extensive experiments validate the proposed strategy’s efficacy, demonstrating superior performance in terms of Task Completion Rate, Total Profit, Total Reward, and Total Selected data collectors compared to existing approaches. Overall satisfaction scores improved significantly, surpassing existing models by 31% to 61%.

Keywords

• Ubiquitous computing (UC)
• mobile crowdsourcing
• data collector selection
• task unit bid
• contribution density
• FAHP