An intelligent, uncertainty driven management scheme for software updates in pervasive IoT applications

Abstract

The era of the Internet of Things (IoT) involves a huge number of autonomous devices (nodes) capable of monitoring and interacting with their environment. The autonomous devices are also able of being interconnected, thus, they can exchange data. Pervasive computing applications can be built on top of this infrastructure offering efficient solutions for multiple domains. Nodes can execute intelligent, light-weight processing of the collected data being capable of responding in case of events. Apart from the software necessary to perform the discussed processing tasks, nodes are coming with pre-installed software necessary to perform basic functionalities e.g., communication. When nodes act in dynamic environments, it is necessary to update the software necessary for their functionalities. Updates involve software extensions and patches important to secure a high level performance of the IoT nodes. In this paper, we propose a distributed updates management scheme enhancing the autonomous nature of nodes. Legacy models deal with centralized approaches (i.e., a central server) where complex algorithms are adopted to derive the protocols for the distribution of the updates. In our approach, each node is responsible to, independently, initiate and conclude the update process. The central server is responsible only for indicating when the updates are available to the nodes. Every node monitors a set of performance metrics (either for the node itself or the network) and based on an intelligent scheme decides the appropriate time to conclude the update process. We adopt an ensemble forecasting model on top of a pool of estimators and an optimization model to derive the right time for initiating the update process. We are based on the solution of the known Santa Fe bar problem to perform load balancing in the retrieval of the updates. The aim is to have the nodes deciding the conclusion of the update process in different time intervals, thus, to keep the load of the network at low levels. We provide specific formulations and the analysis of our problem while extensive simulations and a comparison assessment reveal the advantages of the proposed solution.