The heterogeneous networks which collaborate among Space, Air, Ground, and Sea (SAGS) networks significantly promote the development of the Internet of Things (IoT). Billions of IoT devices connect to SAGS networks and generate massive data. We argue that it is crucial to construct a Collaboration Trust Interconnections System (CTIS) to provide the ubiquitous SAGS network accessibility and security. In this paper, a CTIS framework among the Unmanned Aerial Vehicles (UAV), Mobile Vehicles (MVs), and IoT devices is proposed to evaluate trust and select low-cost and high-trust participants to improve data quality. In this framework, MVs record the interaction information to form the verification chains, while the UAV is dispatched to collect baseline data to verify the data reported by MVs, thereby constructing global trust. Then, the hash values of baseline data are delivered to MVs to act as calibration baseline data, which provides verification certificate for interactions among MVs and constructs local interaction trust. Finally, a greedy-based winner recruitment strategy is proposed to achieve intelligent information control with maximum credibility and cost. The simulation results show CTIS framework reduces the cost by 5.62%, reduces false ratio and packet dropping rate by at least 32.49% and 44.8% compared with previous schems.

For the cloud computing based on Software Defined Networks (SDN), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer services response time due to the capacity-limited internet. To solve this problem, a novel services orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and services response delay. In SODA, the network is divided into three layers. (1) Data centers layer (DCL). Data centers release software with specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce services response delay. (2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along same routing path is higher to reduce redundancy data. (3) Vehicle network layer (VNL), mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation are conducted. The results illustrate that the proposed scheme has better performance compared with traditional scheme.

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.

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.

UAVs-enabled Mobile Crowdsensing (UMCS) has gained considerable attention recently, but it is challenging to meet the data collection needs of the entire city using only the UAV with limited energy. Furthermore, how to effectively minimize Age-of-Information (AoI) and ensure data quality has not been well solved in previous studies. Therefore, this paper proposes a hybrid optimization framework for AoI minimization, which recruits massive distributed workers as the main force for data collection, while the UAV acts as a data collection collaborator and is more inclined to fly to the SNs that cannot establish connections with workers, To mitigate the potential security threats incurred by dishonest workers of the MCS system, we first provide a Greedy-based Multi-worker Task Assignment (GMTA) strategy, aiming to assign more urgent data collection tasks to reliable workers under workload constraints. Then, we propose a Deep-Reinforcement-Learning-based Global AoI Minimization (DRL-GAM) strategy for the UAV path planning to find a set of optimal actions to minimize the global AoI. Based on the real dataset, our simulation experiments show that compared with traditional strategies, our DRL-GAM strategy can reduce the global AoI by an average of 6.49% $\sim$ 68.21% in various network sizes, and is more stable for the average standard deviation is only 51.75% of other strategies.