With the rapid evolution of the Industrial Internet of Things (IIoT), industrial networks are required to support massive industrial devices with bounded low-latency transmission. To address these requirements, integrating the fifth-generation (5G) with time-sensitive networking (TSN) has been proposed. However, existing joint resource allocation methods struggle to achieve seamless low-latency deterministic scheduling between the 5G system (5GS) and TSN networks. Focusing on large-scale uplink transmission scenarios, a base station (gNB)-driven joint time-frequency resource allocation architecture is proposed. Within this architecture, the 5GS is modeled as a TSN bridge seamlessly integrated with the TSN cyclic queuing and forwarding (CQF) mechanism. Existing joint resource allocation algorithms suffer from local optima, low computational efficiency, and inability to capture global time-triggered (TT) flow interactions for globally optimal solutions. Accordingly, a gNB-driven multi-agent proximal policy optimization (MAPPO)-based joint time-frequency resource allocation algorithm, termed gNB-DMJRA, is further proposed. This algorithm adopts the centralized training and distributed execution (CTDE) framework, leveraging global information to better coordinate the scheduling of multiple TT flows and avoid convergence to local optimal solutions. In addition, the periodicity of TT flows is exploited to reduce computational complexity and the action space, thereby further improving learning efficiency and convergence. Simulation results demonstrate that under 1000 TT flows, the proposed algorithm reduces maximum latency by up to 74.27%, improves the scheduling success rate by 82.15%-331.75%, and achieves faster convergence than benchmarks, confirming its effectiveness and efficiency for large-scale TT flow scheduling.

To address the dual demands of high-efficiency communication for multiple devices and high-precision positioning for multiple passive radio frequency identification (RFID) tags in the Internet of Things (IoT), this paper investigates an integrated sensing and backscatter communication (ISABC) system based on a uniform planar array (UPA). Focusing on resource-constrained IoT gateways, we aim to minimize the Cramér-Rao lower bound (CRLB) for high-precision two-dimensional (2D) direction-of-arrival (DoA) estimation of passive tags via precoding design, subject to the gateway power budget and communication quality-of-service (QoS) constraints. To balance high-precision positioning with real-time processing in dynamic IoT environments, we propose a configurable framework comprising three schemes: 1) joint sensing and communication precoding (JSCP) as a performance benchmark; 2) layered-precoding communication-optimized precoding (LP-COP) to effectively balance sensing accuracy and algorithmic latency; and 3) a low-complexity fixed-beam with dynamic power allocation precoding (FB-PDPA). Simulation results demonstrate that the proposed LP-COP scheme achieves near-optimal sensing accuracy comparable to the JSCP benchmark while reducing computation time by approximately 96% and maintaining strong robustness under strict signal-to-interference-plus-noise ratio (SINR) constraints. The proposed framework provides a flexibly configurable technical path for IoT ISABC deployments.

The computing power network (CPN) serves as a key infrastructure for future networks, facilitating the connection of ubiquitous computing resources distributed across various locations. The continuous emergence of computation-intensive and delay-sensitive applications highlights the crucial need to fully utilize limited computing resources and the importance of building a resilient communication network. Primary-backup (PB) based transmission is a commonly used technique to enhance network reliability. However, implementing this approach in CPN with a consideration of load balancing introduces significant complexity and has received limited research attention. In this paper, we designed a deterministic computing power network (Det-CPN) architecture based on segment routing over IPv6 (SRv6). On top of the above architecture, we proposed a best computing node selection method based on a comprehensive index calculation and ranking (CICR) algorithm to determine the optimal computing node for task transmission. Subsequently, we developed a bandwidth sharing-based multi-path transmission (BSMT) algorithm to realize the maximization of the system efficiency. Simulation results demonstrate that in adverse network conditions (overloaded with a failure rate of 0.02), compared to the traditional dual-path redundant forwarding mechanism, the proposed solution achieves an average reduction of 22.3% in transmission latency, an average improvement of 39.94% in task success rate, a decrease of 19.4% in bandwidth occupation rate, and an increase of 31.05% in computing resource utilization rate.

With the widespread utilization of intelligent devices, massive mobile users' needs for rich multimedia services bring serious challenges in the aspects of network traffic, energy consumption and carbon emission. How to realize green content distribution by optimizing resource allocation is an urgent problem to solve in complex and dynamic networks. In this paper, we design a cross-layer cooperative scheme to promote energy efficiency in non-orthogonal multiple access (NOMA)-assisted cloud-edge-side environments. To be specific, we formulate the joint optimization issue of computation, caching and communication resources as an energy minimization model while considering request aggregation. Next, we propose a new deep reinforcement learning (DRL)-based task offloading strategy to minimize energy consumption by making optimal resource allocation decisions according to content request history and resource availability. Simulation results show that the proposed solution has better performance than current typical strategies in cloud-edge-end collaboration environments.

With the increasing demand for resource scheduling efficiency in Industrial Internet of Things (IIoT), parallel reinforcement learning (PRL) based distributed edge-cloud collaborative resource scheduling scheme has attracted enormous attention. However, the computing and communication capacities, the security degree of massive distributed edge computing servers are different. It is difficult to make a large number of edge servers carry out security and efficiency PRL based edge-cloud collaboration resource scheduling scheme. Thus, in this paper, a large-scale distributed edge-cloud collaborative resource scheduling method based on picture delegated proof of state and suspicious practical byzantine fault tolerance (pDPoSt+sPBFT) consensus algorithm is proposed. To be specific, we first propose a collaborative edge-cloud industrial network architecture to support massive industrial intelligence tasks, then a distributed PRL based resource allocation scheme is utilized. Secondly, in order to improve the efficiency and security of distributed PRL training, we propose a server filtering strategy based on pDPoSt algorithm. Finally, a sPBFT algorithm is proposed to further realize security parameter aggregation of distributed PRL. Experimental results show that the proposed method has good efficiency and security performance compared with the traditional distributed edge-cloud collaborative resource scheduling algorithm. The proposed approach has great potential in complex IIoT scenarios.

Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.

Abstract This paper focuses on the fairness‐aware nonlinear joint transceiver design for the energy‐harvesting (EH)‐powered coordinated multi‐point (CoMP) systems. In the EH‐powered CoMP systems, each node harvests the energy independently first. Then, these nodes collaborate for joint signal transmission. Since there is no conventional grid to connect the nodes, the energy among nodes cannot be coordinated. Therefore, per‐node power constraints should be satisfied, which however has not been considered in existing schemes. In this paper, a fairness‐aware nonlinear transceiver scheme is developed under the per‐node power constraints. The nonlinear transceiver design is formulated as an optimization problem to maximize the minimum signal‐to‐interference noise ratio of data streams. A two‐step optimization algorithm is proposed to obtain the closed‐form solutions in the cases with perfect channel state information (CSI) and imperfect CSI, respectively. Simulation results verify the fairness of the proposed algorithm, and demonstrate its performance enhancement in sum‐rate and bit ratio error (BER). For sum‐rate, the proposed algorithm can achieve 12% gain over WS‐MSE and 40% gain over BD at SNR = 20 dB. For BER, it can achieve an approximately 4 dB gain over WS‐MSE and 8 dB gain over BD at BER = 10 −3 .

Along with the development of 5G technology in mobile sensing and wireless communication, the internet of vehicles (IoV) has drawn much attention from the research community. The traditional centralised cloud‐based IoV network has become a bottleneck in providing computation‐intensive, high‐mobility and low‐latency services. As a promising computing paradigm, mobile edge computing (MEC) addresses such challenges. In this study, the authors propose a hierarchical IoV system, combined with MEC. They then focus on the problem of quality of service (QoS)‐enabled resource allocation for computing tasks in the system. However, the existing studies often fail to take into account different delay tolerances between different task types. In order to optimise the completion delay, they design an approach to classify tasks into different priorities according to their delay tolerances and then reorder tasks. After reordering, they use a reinforcement learning algorithm to allocate resources automatically and intelligently. Simulation results confirm that the proposed scheme is feasible and effective in the aspects of time efficiency and outage probability.

To alleviate pilot contamination for multi‐cell large‐scale multiple‐input multiple‐output (MIMO) systems, here the authors propose a constellation coordination scheme according to a constellation coordination constraint (CC constraint) on the large‐scale fading factors. In fact, a detailed analysis of the uplink process introduces the CC constraint, which reveals that if the CC constraint cannot be satisfied, the error probability will be larger than a threshold. Otherwise, the error probability goes to zero if both the number of antennas at the base station and signal‐to‐noise ratio go to infinity. Furthermore, by modelling the location of users as a Poisson point process, the authors derive a safe area threshold according to the CC constraint, through which an adaptive pilot reuse is proposed. In this scheme, users outside the dynamic safe area threshold are allowed to reuse the pilot, while other users are not allowed. Simulation results show that the CC scheme alleviates the pilot contamination effectively and the proposed pilot reuse scheme based on the safe area threshold improves the uplink achievable rate of the system significantly.

With the developments of communication technologies and smart manufacturing, Industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined IIoT (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain (BC)-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned BC. In order to improve the throughput of this BC-based SDIIoT, we jointly consider the trust features of BC nodes and controllers, as well as the computational capability of the BC system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep Q-learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11- based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.

Frequency reuse (FR) has been widely deployed to achieve increased throughput and interference mitigation. Different FR schemes can prove to be efficient under different conditions and parameters. This paper formulates a generic mathematical model which can be used to obtain an FR scheme that can be tailored according to our requirements. The paper proposes a fuzzy set theory based generic mathematical model for deriving various Soft Fractional FR (SFFR) schemes. The derived schemes are evaluated using various parameters such as average throughput, spectral and power efficiency. To the best of our knowledge, the use of fuzzy set theory for deriving different SFFR schemes is the first effort that can be found in the literature. The analysis provided in the paper discovers an optimal SFFR scheme which provides higher spectral efficiency and throughput. Our proposed scheme, at FR =1.35, improves the power efficiency (8.68 Watts/(bps/Hz)) by staggering 21% and 57% as compared to FR=1 (11 Watts/(bps/Hz)) and FR=3 (20 Watts/(bps/Hz)), respectively.

A novel framework is proposed to introduce the game theory into power control in UWB network. Firstly, the power control problem is modeled as a cooperation potential game. Secondly, a novel utility function and potential function are introduced for UWB network. Finally, the process of power control is expressed as the process of maximum potential function for each active link. For the application of the framework, a new algorithm is also proposed. The algorithm converges to an exact Nash equilibrium by the theoretical proving. Through the simulation which compares the performance of our algorithm and the traditional scheme, the result shows that our algorithm performed better in both convergence and fairness, and also saved power consumed.