The Internet of Things (IoT) is a network of interconnected objects, in which every object in the world seeks to communicate and exchange information actively. This exponential growth of interconnected objects increases the demand for wireless spectrum. However, providing wireless channel access to every communicating object while ensuring its guaranteed quality of service (QoS) requirements is challenging and has not yet been explored, especially for IoT-enabled mission-critical applications and services. Meanwhile, Cognitive Radio-enabled Internet of Things (CR-IoT) is an emerging field that is considered the future of IoT. The combination of CR technology and IoT can better handle the increasing demands of various applications such as manufacturing, logistics, retail, environment, public safety, healthcare, food, and drugs. However, due to the limited and dynamic resource availability, CR-IoT cannot accommodate all types of users. In this paper, we first examine the availability of a licensed channel on the basis of its primary users' activities (e.g., traffic patterns). Second, we propose a priority-based secondary user (SU) call admission and channel allocation scheme, which is further based on a priority-based dynamic channel reservation scheme. The objective of our study is to reduce the blocking probability of higher-priority SU calls while maintaining a sufficient level of channel utilization. The arrival rates of SU calls of all priority classes are estimated using a Markov chain model, and further channels for each priority class are reserved based on this analysis. We compare the performance of the proposed scheme with the greedy non-priority and fair proportion schemes in terms of the SU call-blocking probability, SU call-dropping probability, channel utilization, and throughput. Numerical results show that the proposed priority scheme outperforms the greedy non-priority and fair proportion schemes.

Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. Haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nano-devices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nano-devices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated non-convex stochastic problem, an improved time-varying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

Edge-enabled Industrial Internet of Things (E-IIoT) has gained massive attention as a new type of IIoT for hosting emerging low-latency applications. However, due to device variations and complex communication environments, the edge servers and channel information are usually uncertain which poses significant challenges to the computational capacities in practical E-IIoT networks. To address these challenges, in this paper, we introduce a risk-sensitive task offloading scheme for the practical E-IIoT networks. Firstly, a distributed robust offloading optimization problem is formulated by jointly considering the high latency risks caused by network uncertainty and the average latency of the system. Then, by characterizing the latency risk as the conditional value, the formulated problem is transformed into a distributed robust mean conditional value-at-risk (CVaR) optimization problem. Further, we use a fuzzy set to capture the uncertainty of the first and second-order statistical information of the system to overcome the computational difficulties of the problem, and a distributionally robust optimization (DRO) method is employed to transform the mean-CVaR optimization problem into a computable semi-definite programming (SDP) problem. Finally, an improved DRO-based task offloading algorithm is proposed to obtain the optimal decision under uncertain E-IIoT networks. The simulation results show that the proposed offloading scheme not only improves the reliability in computation but also reduces the high latency risks. Therefore, our proposed model is more suitable for practical E-IIoT networks.

In Vehicle-to-Everything (V2X) communication, the high mobility of vehicles generates the Doppler shift which leads to channel uncertainties. Moreover, the reasons for channel uncertainties also include the finite channel feedback, channel state information (CSI) loss and latency. With this concern, we formulate a joint spectrum and power allocation problem for V2X communication with imperfect CSI. Specifically, the sum capacity of cellular user equipments (CUEs) is maximized subject to the minimum Signal-to-Interference-and-Noise Ratio (SINR) requirements of CUEs and the outage probability constraints of vehicular user equipments (VUEs). Then, two different robust resource allocation approaches are designed to solve the problem. One is Bernstein Approximation-based Robust Resource Allocation approach. More specifically, Bernstein approximations are employed to convert the chance constraint into a calculable constraint, and Bisection search method is proposed to obtain the optimal allocation solution with low complexity. Then, for further reducing the computational complexity, Self-learning Robust Resource Allocation approach, which includes a learning method and an analytical mapping method, is proposed as the second approach. The learning method is devised to learn the uncertainty set which transforms the chance constraint into calculable constraints, and the analytical mapping method is proposed to obtain closed-form solutions of the resource allocation problem. Finally, the simulation results prove that the proposed approaches can improve the capacity of all CUEs effectively whilst ensuring the reliability of the channel.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.