Active transmission time analysis of a sensor in a mobile wireless sensor network

Abstract

This paper studies the epidemic-based information dissemination model proposed by Ko and Gautam. Considering the limitations of local information retention and the restricted energy capacity of each sensor in harsh environments, Ko and Gautam’s model was proposed for efficient information dissemination in mobile wireless sensor networks (MWSNs) and has been proven to be a flexible and scalable efficient model in previous studies. However, previous research identified a lack of measurement and evaluation at the individual sensor level within the system. Therefore, the goal of this study is to propose a new evaluation metric for applying and operating the epidemic-based information propagation model proposed by Ko and Gautam to real systems and a methodology to obtain it. As a new evaluation metric, we propose ”survival time,” which is the duration that each sensor node remains active. This study focuses on two of the three models proposed in previous research based on transmission intervals: the Synchronous time model and the Asynchronous time model with random inter-transmission time. In this study, we constructed discrete-time Markov chains (DTMC) and continuous-time Markov chains (CTMC) to describe the two target models. We proposed a methodology to probabilistically calculate the newly proposed evaluation metric, ”survival time,” by leveraging the characteristics of Markov chains. By comparing the numerical results obtained through the proposed methodology with the average results obtained from simulation experiments, we validated the proposed methodology. The proposed evaluation metric, ”survival time,” can be used to assess the energy consumption of each sensor node based on its activation time in mobile wireless sensor networks following the information dissemination model proposed by Ko and Gautam. Additionally, it is expected to serve as a foundational study for system optimization research such as energy efficiency optimization and maintenance planning.