Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems

The Internet of Things (IoT) brings internet connectivity to everyday devices. These devices generate a large volume of information that needs to be transmitted to the nodes running the IoT applications, where they are processed and used to make some output decisions. On the one hand, the quality of these decisions is typically affected by the freshness of the received information, thus requesting frequent updates from the IoT devices. On the other hand, the severe energy, memory, processing, and communication constraints of IoT devices and networks pose limitations in the frequency of sensing and reporting. So, it is crucial to minimize the energy consumed by the device for sensing the environment and for transmitting the update messages, while taking into account the requirements for information freshness. Edge-caching can be effective in reducing the sensing and the transmission frequency; however, it requires a proper refreshing scheme to avoid staleness of information, as IoT applications need timeliness of status updates. Recently, the Age of Information (AoI) metric has been introduced: it is the time elapsed since the generation of the last received update, hence it can describe the timeliness of the IoT application’s knowledge of the process sampled by the IoT device. In this work, we propose a model-driven and AoI-aware optimization scheme for information caching at the network edge. To configure the cache parameters, we formulate an optimization problem that minimizes the energy consumption, considering both the sampling frequency and the average frequency of the requests sent to the device for refreshing the cache, while satisfying an AoI requirement expressed by the IoT application. We apply our caching scheme in an emulated IoT network, and we show that it minimizes the energy cost while satisfying the AoI requirement. We also compare the case in which the proposed caching scheme is implemented at the network edge against the case in which there is not a cache at the network edge. We show that the optimized cache can significantly lower the energy cost of devices that have a high transmission cost because it can reduce the number of transmissions. Moreover, the cache makes the system less sensitive to higher application-request rates, as the number of messages forwarded to the devices depends on the cache parameters.