Information-Oriented Traffic Management for Energy-Efficient and Loss-Resilient IoT Systems

Internet of Things (IoT) systems are driven by the massive data generation at the sensing layer. Most of data management protocols in the sensing layer target optimizing different Quality-of-Service (QoS) metrics, such as data rate, packet loss, and delay, while taking the limitations of wireless sensing networks (WSNs) into consideration. However, it is also critical to consider improving the quality of collected data, especially when the WSNs are congested by delay-sensitive data packets, leading to high packet loss. While Value-of-Information (VoI), which represents data freshness and time relevance, is commonly used to define data value, it does not provide a notion of data recoverability and tolerance-to-loss. In this article, the main contribution is to develop data management schemes to cope with inevitable data loss by identifying the data portion with a higher value to the overlaying applications. We also develop a novel metric that is referred to as information content (IC), quantifying the amount of information in data. The IC is defined such that data holding information of low-probable events have higher IC than data holding information of high-probable events. In this context, the VoI and IC are exploited in developing information-oriented traffic forwarding and reduction schemes to ensure that all dropped packets are more accurately recoverable through a traffic recovery scheme, and therefore the running applications are not disrupted. Through extensive Monte-Carlo simulations, we show that the proposed information-oriented data management improves the performance in terms of data congestion, lifetime, packet loss, delay, and data recovery accuracy.