Neighbourhood area networks (NANs) lay the foundation for robust communication in smart grids to support stable and secure end-user connectivity with substations. Firstly, the current solutions are unrealistic to meet the time-bound requirements for smart grid applications with large number of intermediate node connectivity in NANs. Secondly, the existing Low-power Wireless Personal Area Network (LoWPAN) do not scale up to thousand nodes while meeting the latency requirement of delay-critical smart grid applications.

The maximum flow problem based on contact graph in DTN is very important for routing and data planning. Common topological graphs with non-sequential changes include Dinic, ISAP and other deterministic algorithms for solving the maximum flow between two nodes. But these algorithms cannot be directly applied to topological networks with time series changes. At present, Iosifidis.G has given a solution to this problem based on the time expansion graph, but his method requires high storage space, and the increase in the number of nodes means increasing high time complexity accordingly.

Abstract—This paper presents a novel approach to optimizing resource allocation in Internet of Things (IoT) networks, focusing on enhancing energy efficiency (EE) while maintaining age of information (AoI) awareness through device-to-device (D2D) communication. Our proposed solution integrates simultaneous wireless information and power transfer (SWIPT) with energy harvesting (EH) techniques. Specifically, D2D users employ time switching (TS) to harvest energy from the environment, while IoT users utilize power splitting (PS) to obtain energy from base stations (BS).

The extended bandit learning game algorithm can search the best solution for the hybrid discrete-continuous strategy space. At each learning time, the player can quickly decide based on a finite discrete strategy pool, thereby improving the learning efficiency.