The use of directional antennas in high-frequency bands (e.g., millimeter-wave) is essential to support applications requiring high throughput and low latency. However, communications using directional antennas require intricate scheduling by a central coordinator to avoid collision and deafness problems. Thus, in this study, we propose a directional medium access control (DMAC) protocol based on a deep$Q$-network (DQN) framework wireless ad hoc networks (WANETs) for Internet of Things (IoT). In our model, even though there is no central coordinating unit (e.g., edge/cloud server), each IoT device can intelligently avoid the collision and deafness through its learning agent. In addition, to maximize the throughput, we design a reinforcement learning (RL) architecture and propose a DQN-based DMAC such that each IoT device intelligently selects the time-slot and transmitting beam without any central coordinator. The proposed schemes are evaluated using carrier-sense multiple access (CSMA) and adaptive learning-based DMAC (AL-DMAC) protocols. The evaluation results reveal that the proposed double DQN scheme outperforms the existing schemes by approximately 54.1% and 57.2% in terms of the throughput.

Mobile chargers (MCs) equipped with radio-frequency (RF)-based wireless power transfer (WPT) modules have been suggested as a possible solution to battery constraints in wireless rechargeable sensor networks (WRSNs). In RF-based WPT, charging efficiency decreases significantly as the charging distance increases. Therefore, single charging consumes less energy than multicharging because it can generally charge a sensor node at a closer range. However, when the density of nodes is high, multicharging may achieve higher efficiency. We propose an energy-efficient adaptive directional charging (EEADC) algorithm that considers the density of sensor nodes to adaptively choose single charging or multicharging. The EEADC exploits directional antennas to concentrate the energy and improve energy efficiency and identifies the optimum charging points and beam directions to minimize energy consumption. In the EEADC, clustering is performed by considering the density of the sensor nodes. After clustering, the clusters are classified into single-charging/multicharging clusters according to the number of sensor nodes in each cluster. Next, the charging strategy is determined according to the type of cluster. In the case of a multicharging cluster, the problem is nonconvex. Therefore, a discretized charging strategy decision (DCSD) algorithm is proposed. The performance evaluation indicates that EEADC outperforms two existing methods in terms of power consumption and charging delay by 10% and 9%, respectively.

Technological advances in fog computing are precipitating an evolution in conventional vehicle networks to a new paradigm called fog-enabled connected vehicle networks (FCVNs). FCVNs provide communication efficiency for ensuring safe transportation through the massive Internet of vehicles. In FCVNs, massive vehicles tend to associate with roadside units and high power nodes, which act as fog nodes (FNs), when they have a good channel quality and/or popular contents. This circumstance may lead to a load imbalance among the FNs. This problem significantly decreases the resource utilization efficiency and service capability of the networks. In this article, we propose a dynamic resource orchestration (DRO) scheme to harmonize resource allocation for connected vehicles by migrating the offloaded services among FNs. A graph-theoretic approach is utilized to transform the FCVN into a directed graph model, where the maximum resource reduction obtained by service migrations is considered the weight of the link between every two FNs. Subsequently, the maximum weight matching solution is used to determine optimal pairs of FNs for migrating services to maximize network resource utilization. Our simulation results reveal that the proposed DRO scheme achieves significant improvements in terms of service capability, throughput, and resource utilization efficiency as compared with existing algorithms.

To prolong the battery lifetime of Internet-of-Things (IoT) devices, they can be charged by a mobile charger (MC) equipped with radio frequency (RF)-based wireless power transfer (WPT) capability. By concentrating power toward IoT devices, the energy efficiency of the MC increases when using a directional antenna instead of an omnidirectional counterpart. However, directional antennas have a narrow beamwidth, and thus, several IoT devices cannot be charged simultaneously. In this article, we propose a multiple-directional MC (MDMC) scheme that exploits multiple-directional beams to reduce the charging delay while maintaining the advantages of directional antenna with a higher charging efficiency. In our MDMC, an MC determines its charging points to visit and the directions of its beams at each charging point. The charging points can be determined in consideration of the distribution of the devices and the remaining energy. After selecting charging points, the travel paths are determined by considering the remaining energy of the IoT devices. In addition, to relax the problem complexity, we propose an efficient two-stage multiple-directional beam selection (MDBS) algorithm. In the first stage, the directions of beams are determined. The second stage calculates the charging time of each beam for minimizing delay. The simulation results show that the MDMC outperforms the existing single-directional antenna-based charging schemes in terms of the energy efficiency and charging delay approximately 15% and 25%, respectively.

Internet of Things (IoT) systems are characterized by highly automated operating environments, which comprise several IoT end devices (IDs) that generate vast amounts of data with strict real-time communication and high data rate requirements. Edge computing facilities are an alternative to traditional cloud computing and support massive data processing in IoT systems while reducing the burden on data centers. In this paper, we consider an edge-based IoT system that comprises an edge server (ES), edge gateways (EGs), and IDs that communicate wirelessly. The EGs reduce the load on the ES by preprocessing data received from ID. However, it may not be possible for a few EGs to accommodate a sheer number of IDs, given the limited computing power and communication coverage of the EGs. Therefore, it is necessary for a few IDs to directly connect to the ES without the support of EGs. Thus, we propose a resource orchestration scheme between EGs and ES and/or among EGs based on a Lagrangian and the Karush-Kuhn-Tucker condition. The scheme allocates optimal resources by considering the computing capacities of EGs and ES and manages interference among the EGs to maximize the efficiency of IoT systems. The performance evaluation indicates that the proposed scheme outperforms the existing schemes in terms of aggregate throughput, latency, data reception rate, and workload fairness among EGs by 42%, 59%, 37%, and 40%, respectively.

In industrial Internet of Things (IIoT) applications for smart manufacturing system, efficient allocation of the carrier and computing resources is crucial. However, existing resource assignment schemes in smart manufacturing system cannot provide timely provision of resources to the inherently dynamic and bursty user demands. To reflect real-time supply and demand for smart manufacturing resources, several research results on auction-style resource assignments have been introduced; however, security, privacy, and trust computing related issues are not actively discussed in the results. The resources should be assigned to devices according to the system policy, which depends on the information provided by IIoT devices. If there are any resource demanding devices, they can report manipulated malicious information for their own interest to obtain more resources. That is, the smart manufacturing system may be vulnerable due to selfish smart manufacturing devices’ behaviors. This reduces the efficiency of the entire system and moreover ceases the plant-wide process. While many research contributions related to the trust computing aim at detecting malicious nodes, this paper presents a novel view of trust computing by showing why devices inside the smart manufacturing system have to act honestly. In this paper, a Vickrey–Clarke–Groves auction-based hierarchical trust computing algorithm is proposed for: 1) computing carrier resources required for wireless communication between IIoT devices and gateways and 2) distributing CPU resources for processing data at central processing controller. Last, simulation results demonstrate that the utilities of each participant are maximized when the IIoT devices and gateways are trustful.

Internet of Things (IoT) technology has accelerated various industries through digital transformation. In an edge computing-based smart factory, a significant number of IoT devices generate large volumes of real-time data. This big data requires efficient routing among edge gateways (EGs) and an edge server for real-time data processing. Existing industrial wireless communication systems provide relatively low data rates and network capacity for real-time sensor data and control information over a wireless channel. This calls for the use of the very large bandwidth available at the mmWave spectrum for real-time data transmission. Existing data routing techniques for the mmWave band are based on traditional mobile ad hoc routing techniques and do not reduce the transmission delay for real-time sensory data in smart manufacturing systems. Therefore, to alleviate the real-time data processing requirement, we propose a new directional routing and link scheduling algorithm based on maximum weight independent set (MWIS). The proposed algorithm solves complicated MWIS problems efficiently and computes backhaul link scheduling results in a relatively short time by lowering the deafness problem among EGs. For transmission fairness, we used a Jain's fairness index method with numerical analysis of the transmission fairness constraint. We measured the efficiency of our proposed scheme in terms of throughput, delay, packet loss rate, and transmission fairness. Our simulation results show that the proposed scheme outperforms existing mmWave routing techniques. Moreover, we investigated the performance difference between the proposed algorithm and the optimal solution.

The Internet of Things (IoT) is expected to play an important role in the construction of next generation mobile communication services, and is currently used in various services. However, the power-hungry battery significantly limits the lifetime of IoT devices. Among the various lifetime extension techniques, this paper discusses mobile charging, which enables wireless power transfer based on radio frequency with mobile chargers (MCs). MCs function as traveling target IoT networks that provide energy to battery-operated IoT devices. However, MCs with an energy-constrained battery result in limitation of travel-time. This paper formulates a problem to minimize energy consumption for charging IoT devices by determining the path of motion of an MC and efficient charging points, and proves that the problem is NP-hard. An efficient algorithm, named best charging efficiency (BCE), is proposed to solve the problem and the upper bound of the BCE algorithm is guaranteed using the duality of linear programming. In addition, an improved BCE algorithm called branching second best efficiency algorithm with additional searching techniques is introduced. Finally, this paper analyzes the difference in performance among the proposed algorithms, optimal solutions, and the existing algorithm and concludes that the performance of the proposed algorithm is near optimal, within 1% of difference ratio in terms of charging efficiency and delay.

The next-generation mobile network anticipates integrated heterogeneous fronthaul and backhaul technologies referred to as a unified crosshaul architecture. The crosshaul enables a flexible and cost-efficient infrastructure for handling mobile data tsunami from dense Internet of things (IoT). However, stabilization, energy efficiency, and latency have not been jointly considered in the optimization of crosshaul performance. To overcome these issues, we propose an orchestration scheme referred to as the stabilized green crosshaul orchestration (SGCO). SGCO utilizes a Lyapunov-theory-based drift-plus-penalty policy to determine the optimal amount of offloaded data that should be processed either at the eastbound or westbound computing platforms to minimize energy consumption. To achieve system stability, the cache buffer is considered as the main constraint in developing the optimization process. Moreover, the amount of offloaded data transmitted via crosshaul links is selected by adopting the binary min-knapsack problem. Accordingly, a lightweight heuristic algorithm is proposed. As the cache buffer is stabilized and the computations are controlled, the SGCO ensures adjustable computing latency threshold for various IoT services. The performance analysis shows that the proposed SGCO scheme exposes effective energy consumption compared to other existing schemes while maintaining system stability considering latency.

Most previous spectrum sensing techniques use omni-directional antennas. Unlike omni-directional antennas, the use of directional antennas for spectrum sensing is a promising technique that can realize fine-grained sensing for the primary user (PU) with a longer sensing range. In this paper, we propose a centralized cooperative directional sensing technique for cognitive radio networks. We assume that one secondary coordinator called the fusion center (FC), gathers sensing results from secondary nodes. Using the reported information, the FC optimizes the sensing period, sensing power, and sensing beams per secondary node. For optimization, we use a modified gradient descent method with numerical methods to solve the nonlinear optimization problem. The simulation results show that our directional spectrum sensing technique is well suited for the existing cognitive radio environment. The optimal scheme shows proposed here better performance in all simulation factors than the non-optimized scheme.