Industrial Internet of Things (IIoT) communication serves as the core for connecting industrial devices, systems, and platforms. In addition to real-time performance, reliability, and security, fairness in device access and data transmission has become increasingly important. This paper integrates Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) technology in 6G communications, establishing a synergistic integration to jointly elevate spectral efficiency and fairness. Deploying Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS), which provides$\mathbf{3 6 0}$-degree coverage, in factory environments helps improve signal coverage and reduce bit error rates. To tackle challenges such as beamforming coupling, dynamic decoding order, reflection path optimization, and phase adjustment -with particular attention to fairness for low-rate devices - a fairness-oriented optimization model is proposed. This model focuses on reflective channel beamforming, quality of service (QoS), and STAR-RIS phase shift matrices optimization. Pursuing the maximization of the minimum achievable rate under QoS constraints for this intricate non-convex problem, a two-layer iterative method is employed. The outer layer handles adaptive SIC decoding order updates, nesting an inner layer that tackles the coupled optimization of the base station beamforming and STAR-RIS coefficients. Simulation verification reveals: 1) The proposed algorithm significantly improves system fairness. 2) The integration of STAR-RIS and NOMA provides higher gain in device communication. 3) Optimizing RIS phase shifts and beamforming effectively enhances communication rates. 4) The proposed approach exhibits superior performance relative to other schemes.

The diversity of devices, systems, and applications imposes stringent requirements on the Industrial Internet of Things (IIoT) regarding agility, reliability, and delay sensitivity. Network function virtualization (NFV) can provide on-demand service and flexible resource management for the IIoT. Of course, a highefficiency and time-saving NFV placement solution is critical for IIoT. Information processing requests in an actual industrial scenario is generally continuous and dynamic. In addition, industrial information systems pay more attention to the real-time nature of the information. Considering the above challenges and the fact that most previous optimization-based solutions cannot cope with the characteristics of dynamic requests, we propose a deep reinforcement learning-based method to solve the virtual network function (VNF) placement problem, called DeepVNP, which automatically places the VNF according to the current physical network state, aiming to reduce the placement cost and improve the profit of the NFV system. In addition, the Information Age (AoI) is introduced to measure the real-time freshness of information, and it is naturally integrated with delay constraints. Through simulations, we evaluate the convergence and performance of DeepVNP. Numerical results show that, compared with several existing solutions, DeepVNP performs well in terms of request acceptance rate, resource utilization, total system cost, and average AoI.

Network Function Virtualization (NFV) supported Mobile Edge Cloud (MEC) is considered an ideal platform for low-latency Internet of Things (IoT) applications, where IoT application requests are represented as Service Function Chains (SFCs) which consists of a sequence of ordered Virtual Network Functions (VNFs). However, MEC’s limited resources can only support a limited number of IoT applications. In this scenario, how to effectively place SFCs to improve resource utilization and service quality under latency, resource constraints while considering the dynamic changes of network is a critical concern for infrastructure providers. In this paper, we study the SFC placement problem in NFV-enabled MEC and propose a Proximal Policy Optimization (PPO) based online SFC placement algorithm called SFCP-PPO. SFCP-PPO achieves the goal of maximizing long-term average revenue through the integration of two critical components: the Multi-Head Attention Mechanism (MHA), capable of extracting information from diverse network representation spaces, and the Recurrent Neural Network (RNN) that addresses scalability challenges posed by varying sizes of SFCs and reduces the frequency of acquiring physical network states during the SFC placement process. We demonstrate the effectiveness of SFCP-PPO through extensive experiments. Compared to existing benchmark algorithms, SFCP-PPO achieves an improvement of 8% in acceptance ratio and 6.5% in long-term average revenue with low running time.

The emerging software defined networking enables the separation of control plane and data plane and saves the resource consumption of the network. Breakthrough in this area has opened up a new dimension to the design of software defined method in wireless sensor networks (WSNs). However, the limited routing strategy in software defined WSNs (SDWSNs) imposes a great challenge in achieving the minimum traffic load. In this paper, we propose a flow splitting optimization (FSO) algorithm for solving the problem of traffic load minimization (TLM) in SDWSNs by considering the selection of optimal relay sensor node and the transmission of optimal splitting flow. To this end, we first establish the model of different packet types and describe the TLM problem. We then formulate the TLM problem into an optimization problem which is constrained by the load of sensor nodes and the packet similarity between different sensor nodes. Afterwards, we present a Levenberg-Marquardt algorithm for solving the optimization problem of traffic load. We also provide the convergence analysis of the Levenberg-Marquardt algorithm. Finally, we implement the FSO algorithm in the NS-2 simulator and give extensive simulation results to verify the efficiency of FSO algorithm in SDWSNs.

In wireless sensor networks (WSNs), there usually exist spatial overlap and data correlation among sensors. Reducing data redundancy is crucial for prolonging network lifetime inWSNs. Source coding is an effective method for data aggregation to reduce data redundancy. However, source coding was regarded as an independent problem in previous work. Little work pays attention to optimal coding rate and associates it with underlying protocols. In this paper, we adopt Slepian-Wolf theorem to achieve the boundary of coding rate, and propose a cross-layer optimization framework to give the optimal source coding rate and flow allocation. We seek to establish a structure-free, multipath transmission model. To the best of our knowledge, this is the first work to solve the optimal source coding rate allocation problem in WSNs. Our extensive simulation results demonstrate that the proposed framework can reduce network traffic and extend network lifetime significantly.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

This paper considers applying simultaneously wireless information and power transfer (SWIPT) technique to cooperative clustered wireless sensor networks, aiming at prolonging the lifetime of relay nodes and maximizing the energy efficiency of data transmission. To this end, we first formulate the energy-efficient cooperative transmission (eCotrans) problem for SWIPT as a non-convex optimization problem. By exploiting fractional programming and dual decomposition, we design a distributed iteration algorithm for power allocation, power splitting and relay selection to solve the non-convex optimization problem. Our simulation results illustrate that the proposed algorithm can converge within a few iterations and provide practical insights into the effect of the number of relay nodes and the maximum transmission power allowance on energy efficiency.