One of the key requirements for future 5th Generation (5G) Industrial Internet of Things (IIoT) networks will be to deliver low latency to support different production processes. To this end, 5G New Radio (NR) provides Configured Grant (CG) scheduling for periodic traffic, additionally to conventional Grant-Based Scheduling (GBS). However, in view of the complexities introduced by spatio-temporal traffic correlations in IIoT, a fixed scheduler configuration may be suboptimal: GBS introduces excessive signaling overhead, while CG leads to inefficient resource utilization and latency degradation when traffic is not perfectly periodic. To solve these critical issues, we propose Hybrid Centralized Uplink Scheduler (HCUS), a new scheduling framework that dynamically learns the type of traffic generated by User Equipments (UEs), and adapts resource allocation accordingly. HCUS operates per-UE, and dynamically switches between GBS and Configured Grant (CG), optimizing resource allocation while preserving low End-to-End (E2E) latency. We consider both mixed periodic and aperiodic uplink traffic to model different network load conditions and IIoT applications. Extensive simulations show that HCUS achieves up to four times lower latency than GBS and CG while maintaining high reliability, even considering traffic correlations or periodicity changes, making it a robust and scalable solution for next-generation IIoT scenarios.

One of the most prominent requirements of future Industrial Internet of Things (IIoT) networks will be to provide Ultra-Reliable Low-Latency Communication (URLLC) in support of critical physical processes underlying the production chains. However, standard protocols for allocating wireless resources may not be able to optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized (e.g., grant-based) scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and then granted by the Next Generation Node B (gNB). On the other hand, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the physical resources to transmit uplink data, may lead to potentially many collisions especially when the density of UEs (and so the traffic) increases. Along these lines, in this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal sent from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available physical resources so as to minimize the number of collisions, disaggregated from the network and without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple actions in parallel. We apply DISNETS to the context of IIoT, and demonstrate by extensive simulations the superior performance of the proposed approach in addressing URLLC compared to other baselines.

The emergence of Terahertz (THz) frequency wireless networks holds great potential for enabling various high-demand services, including Industrial Internet of Things (IIoT) applications. These applications benefit significantly from the ultra-high data rates, low latency, and high spatial resolution offered by THz frequencies. However, a primary well-known challenge of THz networks is their limited coverage range due to high path loss and vulnerability to obstructions. This paper addresses this limitation by proposing two novel multi-hop protocols, Table-Less (TL) and Table-Based (TB), respectively, both avoiding centralized control and/or control plane transmissions. Indeed, both solutions are distributed, simple, and rapidly adaptable to network changes. Simulation results demonstrate the effectiveness of our approaches, as well as revealing interesting trade-offs between TL and TB routing protocols, both in a real IIoT THz network and under static and dynamic conditions.

This paper considers energy-efficient connectivity for Internet of Things (IoT) devices in a coexistence scenario between two distinctive communication models: pull- and push-based communication models. In pull-based communication, the base station (BS) decides when to retrieve a specific type of data from the IoT devices equipped with wake-up receivers, while in push-based communication, the IoT device decides when and which data to transmit. To efficiently manage both types of traffic, this paper applies content-based wake-up (CoWu) and designs a medium access control (MAC) frame. This enables the BS to activate a subset of pull-based nodes and collect the relevant data to fulfill its tasks, while receiving data from the push-based communication nodes. This paper analyzes the basic trade-off through the MAC layer operations: allocating longer duration for collecting data from pull-based nodes can lead to high retrieval accuracy while decreasing the probability of data transmission success for push-based nodes, and vice versa. Numerical results show that CoWu can manage communication requirements for both pull-based and push-based nodes while realizing high energy efficiency (up to 38%) of IoT devices, compared to the baseline.

This paper considers an Industrial Internet of Things (IIoT) scenario, where wireless devices embedded with sensors are deployed over an industrial machine, and transmit the measured data to a final Gateway (GW) using Terahertz (THz) frequencies. To mitigate the path loss of such high frequencies, the GW is equipped with multiple radiating elements, thereby generating highly directive beams, while sensors have just one single radiating element for miniaturization purposes. In this scenario, we study the applicability of a slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) from a mathematical perspective. The analytical model is validated via comparison with simulations, and the impact of different simplifying assumptions in shown. We also demonstrate the effectiveness of the CSMA/CA when compared to ALOHA, and we prove that propagation delays cannot be neglected at THz frequencies.

This paper explores the issue of enabling Ultra-Reliable Low-Latency Communications (URLLC) in view of the spatio-temporal correlations that characterize real 5th generation (5G) Industrial Internet of Things (IIoT) networks. In this context, we consider a common Standalone Non-Public Network (SNPN) architecture as promoted by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and propose a new variant of the 5G NR semi-persistent scheduler (SPS) to deal with uplink traffic correlations. A benchmark solution with a “smart” scheduler (SSPS) is compared with a more realistic adaptive approach (ASPS) that requires the scheduler to estimate some unknown network parameters. We demonstrate via simulations that the 1-ms latency requirement for URLLC is fulfilled in both solutions, at the expense of some complexity introduced in the management of the traffic. Finally, we provide numerical guidelines to dimension IIoT networks as a function of the use case, the number of machines in the factory, and considering both periodic and aperiodic traffic.

This paper addresses the problem of enabling intra-machine communication in an Industrial Internet of Things (IIoT) scenario using Terahertz (THz) frequencies. To mitigate the path loss of such high frequencies, a tree-based topology is built: each automation machine of the factory is controlled by a Router that collects data measured by wireless sensor devices located on the machine and forwards them to a final Gateway (GW) for elaboration purposes. Routers are equipped with multiple antenna elements, while sensor devices have only a single antenna element. In this scenario, we study the applicability of a contention-based access protocol, based on Carrier Sense Multiple Access, and we demonstrate its effectiveness when compared to Aloha. The performance of an uplink communication is evaluated in terms of Packet Success Probability and Network Throughput. Results demonstrate the impact of different parameters, like the sensing range of devices, the number of radiating elements at the Routers, the size and number of machines, and demonstrate that propagation delays cannot be neglected at THz frequencies.