The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization; routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this paper, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems’ self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are realtime based; they require fast computation, real-time optimal solutions, an
This paper studies a novel wireless powered Internet of Things (IoT) network that consists of (a) a Hybrid Access Point (HAP) that charges devices and also helps facilitate backscattering transmissions, (b) devices that use active Radio Frequency (RF) and backscattering transmissions, and (c) a mobile data collector. Our aim is to maximize the amount of data received by the HAP and data collector. The main problem is to determine the charging duration of the HAP and link activation schedule of devices. We formulate a novel Mixed Integer Linear Program (MILP) and also propose a heuristic algorithm named Reduced-Set Linear Program Approximation (RS-LPA). The results show that (i) throughput increases with the number of backscatter transmission sets, (ii) smaller amount of data is uploaded to the mobile collector when sampling cost is low, and (iii) the throughput of RS-LPA is on average 10.55% lower than MILP.