This paper investigates the age of information (AoI) and energy efficiency of wireless powered industrial Internet of Everything (IIoE) network, where multiple low-power IIoE devices (IIoEDs) are wirelessly charged by a hybrid access point (HAP) to transmit their sensing information to the control nodes. To enhance the system’s information timeliness with high energy efficiency, we define a novel performance metric, i.e., age-energy efficiency (AEE), which depicts the achievable AoI gain per unit energy consumption. Then, an optimization problem is formulated to maximize the system long-term AEE by jointly optimizing the IIoEDs scheduling and the HAP’s transmit power. Due to the non-convexity of the formulated problem and the intractable challenges with discrete binary variables, we first model the problem as a two-stage discrete-time Markov decision process (MDP) with carefully designed state spaces, action spaces, and reward functions. We then propose a deep reinforcement learning (DRL)-based approach to find the effective scheduling strategy and transmit power. To improve the accuracy of the learned policy, we design a dual-layer deep Q-network (DLDQN) algorithm with fast convergence. Simulation results show that our proposed DLDQN algorithm can improve the AEE by at least 25% when the number of IIoEDs exceeds 50 compared with benchmarks. Moreover, with the proposed DLDQN algorithm, the system long-term AEE can be improved with the increase of the number of IIoEDs.

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.

This paper investigates secure computation offloading in device-collaborative mobile edge computing (MEC) networks, where mobile users (MUs) can process a part of their tasks locally and offload the other part to the MEC server or other idle paired MUs. All the transmission links are exposed to a potential eavesdropper. To prevent information leakage, artificial noise (AN) is employed and the secrecy capacity is considered. For such a system, we formulate a weighted latency-energy-aware cost minimization problem by jointly optimizing the subchannel allocation, the offloading proportions, the MUs' transmit powers, the MUs' and the MEC server's CPU frequencies, and the AN power while guaranteeing the secrecy capacities of all transmission links. Since the variables are multi-dimensional and coupled, the formulated problem is challenging to solve with the traditional optimization method. We first propose a swap-matching-based algorithm to achieve the optimal subchannel allocation. We then propose a two-layer asynchronous advantage actor-critic (A3C) algorithm to optimize the rest of the resource variables. Specifically, in the inner layer, by utilizing the Karush-Kuhn-Tucker conditions, we derive the optimal CPU frequency with semi-closed-form expressions. In the outer layer, we propose an A3C-based algorithm to achieve near-optimal solutions with fast convergence speed based on the obtained optimal CPU frequency. Simulation results demonstrate that our proposed scheme can decrease the latency-energy-aware cost compared with other learning-based algorithms.

Recently, air quality issues have attracted much more attention. This paper aims to find an effective way to analyse the buildings’ effects on the air quality variation between indoor and outdoor. To do so, we treat the building as a dynamic filter system by regarding the outdoor PM $_{2.5}$ , the indoor PM $_{2.5}$ , and the building as the input, the output, and a response system, respectively. To analyze the filtering effect produced by buildings, the statistical distribution of the indoor PM $_{2.5}$ per hour is studied, and the interrelationship between the indoor and the outdoor PM $_{2.5}$ is explored in time domain. Some interesting physical laws are discovered by using the collected data. First, the indoor PM $_{2.5}$ per hour follows Gaussian distribution in most cases. Second, the indoor PM $_{2.5}$ has a positive correlation with the corresponding outdoor one. Third, a linear regression model with high accuracy on analyzing the indoor PM $_{2.5}$ is presented, which indicates that the indoor PM $_{2.5}$ consists of two parts—one comes from the outdoor PM $_{2.5}$ penetrating into the building and the other comes from the indoor environment. Fourth, by applying different system identification methods, it is found that the B–J model is the best one in characterizing the memory effects of the building for both long time and short time scales. Particularly, for the long time memory effect, the indoor PM $_{2.5}$ average memory duration (AMD) is about 2 h, and the indoor PM $_{2.5}$ AMD to the outdoor PM $_{2.5}$ is about 7 h, while for the short time memory effect, the indoor PM $_{2.5}$ AMD is also about 2 h but that to the outdoor PM $_{2.5}$ is about 5 h. Additionally, the continuance of outdoor PM $_{2.5}$ has much greater effect on the indoor one than its concentration.

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

This paper investigates a multi-fog server (FS) assisted non-orthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD’s performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation and the computation frequency under multiple system constraints. Since the problem is non-convex with no known solution, an efficient solution approach is designed to achieve the.-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD’s computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with non-linear EH model adopted, the more the helping FSs are deployed, the more the performance loss caused by traditional linear EH model can be reduced.