MIF: Optimizing Information Freshness in Intermittently Connected Sensor Networks

We study how to maximize information freshness in intermittently connected sensor networks (ICSNs). ICSNs are emerging sensing applications and systems that are deployed in challenging environments (e.g., underwater exploration). Due to the inaccessibility of the environments, the newly generated data in ICSNs must be stored inside the network before uploading opportunities (e.g., autonomous underwater vehicles (AUVs)) become available. How to accurately quantify and effectively achieve the freshness of information stored in ICSNs pose a new challenge. We propose an algorithmic framework, referred to as MIF: maximization of information freshness, to maximize the freshness of data packets stored in ICSNs while incurring a minimum amount of energy cost in this process. We first formulate an integer linear programming (ILP) problem to solve MIF optimally. We then propose a more time-efficient greedy algorithm. Finally, simulation results show that our algorithms achieve information freshness for ICSNs under different network parameters while incurring minimum energy consumptions.