In industrial edge caching networks, to ensure long-term accurate decision making of industrial applications, it is critical to obtain fresh sensing contents with low sensor energy consumption. The acquisition of sensing contents consists of cache update and content delivery, jointly determining the Age of Information (AoI) of applications. However, cache update suffers from the large sensor energy consumption and the mismatch between content offerings and demands. Content delivery suffers from the limited fronthaul capacity. Furthermore, contents from multiple sensors typically need to be aggregated, allowing the AoI of applications to be determined by the co-AoI of all correlated sensors. It is challenging to make the tradeoff between the energy efficiency of each sensor and the co-AoI performance of all correlated sensors. In our work, the weighted sum of application AoI and sensor energy consumption is minimized by jointly optimizing cache update and content delivery, which is formulated as a long-term stochastic optimization problem. Next, two caching schemes, access point centric scheme (APCS) and request adaptive caching scheme (RACS), are presented. In APCS, we fully decouple cache update and content delivery by applying statistical probability of application requests to control update. In RACS, cached contents are updated along with content delivery according to real-time requests. Thus, we introduce the concept of decision reward to transform the stochastic problem into the per-time slot reward maximization problem and propose online algorithms to solve it. Simulation results show that proposed schemes can reduce the sensor energy consumption by 40% while guaranteeing the application AoI.

In Industrial Internet of Things (IIoT), it is essential to acquire timely device status information to ensure efficient operation. In this article, we consider a wireless monitoring system in IIoT and employ the concept of Age of Information (AoI) to characterize the timeliness of device status information in the system. Considering the impact of resource constraints on information acquisition, we apply a pull-based model to control the entire process of sampling, transmission, and processing associated with device status updates, which constitutes a system-wide AoI minimization problem. The formulated problem is a mixed-integer nonconvex problem, due to the temporal correlation of AoI and the intractability of the implicit AoI-associated objective function. We introduce the concept of average AoI earnings to equivalently substitute the optimization objective. The original problem in consecutive time slots is decomposed into the per-time slot average AoI earnings maximization problem to deal with the temporal correlation of AoI. Then, an online slot-by-slot optimization algorithm (SBSA) is proposed to control device status updates without long-term system state information. Simulation results show that SBSA can significantly improve the AoI performance of the system. However, the problem decomposition in SBSA inevitably brings approximation error. Hence, based on the actual transmission and processing in the system, we get the lower bound of the system total AoI by designing a multislot optimization algorithm (MSA) and analyze the approximate error caused by SBSA. Through simulation results, SBSA has a substantially lower computational complexity, while maintaining acceptable approximation error in comparison to MSA.

In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

Recently, ultrareliable and low-latency communication (URLLC) has attracted a significant interest for mission-critical applications in future wireless communication systems. Achieving strict requirements of latency and reliability for URLLC with a fixed infrastructure is challenging, and unmanned aerial vehicles (UAVs) have been deemed as promising enablers to handle this issue due to its salient attributes, such as high maneuverability, flexible deployment, and high probability of line-of-sight links. This article investigates a novel UAV-assisted URLLC service system, where the blocklength of channel codes is finite in Internet-of-Things (IoT) networks. Considering the limited energy of IoT devices, the average uplink transmit power of the IoT devices are minimized by jointly optimizing the device scheduling and association, power control and resource allocation, as well as UAV deployment. The formulated problem is a mixed-integer nonconvex optimization problem because of the finite blocklength regime. To tackle the problem, we derive the approximation of the achievable rate and propose an effective iteration algorithm by applying the block coordinate descent (BCD) and Lagrange dual decomposition techniques. Furthermore, the convergence of our proposed algorithm is analyzed and illustrated. The minimum average transmit power of IoT devices is calculated with a different resource allocation scheme. Simulation results demonstrate that our proposed iterative algorithm can obtain a performance gain of 15%-20% in terms of the average transmit power for URLLC. Moreover, compared with the average bandwidth allocation scheme, our proposed algorithm can get a stable minimum as the total bandwidth increases.

Ultra-reliable and low-latency communication (URLLC) is proposed as one of the three key services of 5G for Internet of Things (IoT), especially for mission-critical applications. This paper investigates the minimum power of devices in uplink in IoT networks for URLLC. Unmanned aerial vehicles (UAVs) are utilized to assistant the IoT system because they have flexible deployment and high probability to establish line-of-sight (LoS) communication links. First, we formulate a minimum average transmit power problem under the constraints of latency and reliability in modern industry. The deployment of UAVs and device association need to be jointly optimized, making the problem non-linear and non-convex. Then the block error probability which characterizes the reliability is derived under finite blocklength regime and an iteration algorithm is proposed. Additionally, the minimum average transmit power of IoT devices in URLLC is also calculated by deploying different number of UAVs. Simulation results are presented to show that the transmit power can be greatly reduced by appropriately deploying more UAVs or relaxing the tolerance of latency.

Fog computing can aggregate the computing resources to handle the unprecedented amounts of data and becomes a promising technology in the future 5G smart Internet of Things (IoT) networks. This paper considers an IoT video data analysis system where smart IoT cameras can transmit all data to the base station or analyze the data locally. After receiving smart cameras offloading data, the base station can partially redistribute the analyzing task to the smart user equipment. The smart cameras and base station task offloading scheme and the uplink-downlink bandwidth allocation are jointly optimized to minimize the system level delay. The problem is a mixed integer non-linear problem, and the objective function contains the sum of several segmented maximum, which makes it very challenging to solve. Firstly, the smart device 0-1 binary task offloading is relaxed into a continuous form, with adding an upper bound to guarantee the solution can be as close as possible to the integer. Then introduced by a change of variables in handling the segmented maximum, all non-convex constraints are transformed with slack variables and successive convex approximation. To further ensure the iteration algorithm convergence, the disciplined iteration algorithm is proposed to prevent the iteration from getting stuck. The simulation results verify that the assisted smart user equipment can reduce the system delay combining with the proposed resource allocation algorithm.