Billions of devices are connected to the Internet of Things (IoT). These devices generate a large volume of data, which poses an enormous burden on conventional networking infrastructures. As an effective computing model, edge computing is rise to collaborative with cloud computing by moving part intensive computation and storage resources to edge devices, thus optimizing the network latency and energy consumption. Meanwhile, software defined networks (SDN) technology is promising in improving quality of service (QoS) for complex IoT-driven applications. However, building SDN-based computing platform faces great challenges, making it difficult for the current computing models to meet the low latency, high complexity and high reliability requirements of emerging applications. Therefore, a Cloud-MEC Collaborative Task Offloading scheme with Service Orchestration (CTOSO) is proposed in this paper. First, the CTOSO scheme models the computational consumption, communication consumption and latency of task offloading, and implements differentiated offloading decisions for tasks with different resource demand and delay sensitivity. What’s more, the CTOSO scheme introduces Orchestrating Data as Services (ODaS) mechanism based on SDN technology. The collected metadata are orchestrated as high quality services by MEC servers, which greatly reduces the network load caused by uploading resources to the cloud on the one hand, and on the other hand, the data processing is completed at the edge layer as much as possible, which achieves the load balancing and also reduces the risk of data leakage. Experimental results demonstrate that, compared to the random decision-based task offloading scheme and the maximum cache-based task offloading scheme, the CTOSO scheme reduces delay by approximately 73.82-74.34%, reduces energy consumption by 10.71%-13.73%.

Recently, rapid deployment on the fifth-generation (5G) networks has brought great opportunities for enabling data-intensive applications and brings an extending expectation on the developments of 6G. A basic requirement to develop 6G networks is to reach data with low latency, low cost, and high coverage in smart Internet of Things (IoT). Therefore, this article proposes a novel machine learning-based approach to collect data from multiple sensor devices by cooperation between vehicle and unmanned aerial vehicle (UAV) in IoT. First, a genetic algorithm is utilized to select vehicular collectors to collect massive data from sensor devices, which aims to maximize coverage ratio and to minimize employment cost. Second, we design a novel deep reinforcement learning (DRL)-based route policy to plan collection routes of UAVs with constrain energy, which simplifies the network model, accelerates training speeds, and realizes dynamic planning of flight paths. The optimal collection route of a UAV is a series of outputs based on the proposed DRL-based route policy. Finally, our extensive experiments demonstrate that the proposed scheme can comprehensively improve the coverage ratio of massive data collections and reduce collection costs in smart IoT for the future 6G networks.

Reconfigurable wireless network can flexibly provide efficient spectrum access service and keep stable operation in highly dynamic environment. In this paper, a primary-prioritized recurrent deep reinforcement learning algorithm for dynamic spectrum access based on cognitive radio (CR) technology is proposed. The spectrum Markov state is modeled to capture the evolution behavior to achieve the priority queuing of the primary users and the secondary users. According to the spectrum access strategies of the secondary users under different optimal criteria, we can obtain the best tradeoff benefits of spectrum access fairness and throughput. Furthermore, we proposed a learning-based algorithm for dynamic spectrum access, which allows the secondary users to modify their parameters to select the optimal access policy to maximize network throughput utilization. The Dueling Deep $Q$ -Network (Dueling DQN) with prioritized experience replay combined with recurrent neural network is used to improve the convergence speed. Extensive experimental results demonstrate that the proposed RDRL scheme outperforms the existing Dueling DQN and DQN schemes in terms of convergence speed and channel throughput.

Data aggregation is a widely adopted method to effectively reduce the data transmission volume and improve the lifetime of wireless sensor networks (WSNs). In the data aggregation networks, some parameters directly determine the delay of aggregation. In industrial applications, the data generated by different sensors have different requirements for delay or other QoS performance. In the previous study, a common strategy is that all kinds of data is aggregated into one frame when the condition is satisfied with a QoS requirement, which causes excessive energy consumption and severely impairs the lifetime of network. A Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and guarantee that the delay could be controlled within the corresponding QoS requirement constraint. The primary contributions of the DDAR scheme are the following: (a) The DDAR scheme makes data with different QoS requirement route to the sink along the different paths. The parameters of the aggregators in each path, such as aggregation deadline ($T_t$) and the aggregation threshold ($N_t$), are configured according to the QoS requirements. Accordingly, energy consumption can be reduced without degrading the performance of data transmission. (b) Based on DDAR scheme, an improved DDAR scheme is proposed to further improve performance through fully utilize the residual energy in the nodes which are far from the sink. The frequency of aggregation of these nodes increases by reducing the value of $T_t$ and $N_t$ so as to further improve the energy efficiency and reduce delay. Simulation results demonstrate that compared with the previous scheme, this scheme reduces the delay by 25.01%, improves the lifetime by 55.45%, and increases energy efficiency by 83.99%. The improved DDAR scheme improves the energy efficiency by 33.97% and service guarantee rate by 10.11%.

A large number of devices with communication and sensing capabilities are connected to the 5G network and facilitate distributed industrial Internet of Things (IIoT) applications. Those devices can update their program code by using software-defined technologies and upgrade functions without hardware replacement, thus, greatly facilitating the development of IIoT applications. Disseminating code safely to these devices is a pivotal issue. However, the existing methods rarely consider whether the code received by devices is integrated. Therefore, an unmanned aerial vehicle (UAV) assisted trustworthy code dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible mobile vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the code wait to be verified (CWV) directly from the selected devices (bedrock devices) as evidence. This scheme is a fundamental change from the previous passive, indirect and unverifiable trust evaluation schemes. In UTCD, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV as possible with the minimum cost. After that, based on the CWV, which is collected from bedrock devices, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible within the limited conditions of UAV. Finally, comprehensive experiments conduct on a real-life dataset demonstrate that the proposed scheme outperforms the existing schemes in terms of the efficiency and security of code dissemination.

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.