As an emerging network paradigm, the Internet of Things (IoT) which consists of a significant number of multi-functional and heterogeneous IoT nodes has attracted dramatic attentions from both academia and industry. With the merits of intelligent capacity, desirable scalability and high reliability, the IoT recently has been applied for smart ocean applications to provide protection for ocean environment monitoring and surveillance. Aiming to provide coverage service for ocean border environmental surveillance, this paper studies the barrier coverage problem which investigates how to select a collection of IoT nodes to obtain an IoT node chain and build barrier paths to detect intruders and trespassers crossing the border region of interest. To overcome the disadvantages in existing works on barrier coverage, we adopt a novel and widely adopted confident information coverage (CIC) model as the fundamental coverage model, and formulate the CIC barrier path construction problem (CICBC) with the goals of maximizing the number of barrier paths and minimizing the amount of IoT nodes in each barrier path. We propose a distributed CIC Barrier path construction approach based on Learning Automata (shorted as CBLA). The CBLA includes four crucial phases which are initialization phase, learning phase, monitoring phase and repairing phase. Each IoT node equips a learning automaton. CBLA selects optimal IoT node to construct the barrier path by learning. Simulation results show that the performance of CBLA algorithm outperforms two peer algorithms in terms of the number of barrier paths and the average number of nodes in each barrier path.

Social Internet-of-Things (SIoT), integrating the social networks and Internet-of-Things (IoT), leads to heterogeneous interactions of thing-to-thing, human-to-human, and human-to-thing, which in turn generates exploded information. Hence, as the soul of SIoT, information with its interaction and diffusion, records the track of humans and things and contains the hidden value for social administration and people’s lives. Therefore, how to characterize the interplay between behavior spreading and information diffusion in SIoT is essential to predict and management the information. Motivated by this, a more comprehensive understanding of the coupled modeling of social interaction and information diffusion processes in SIoT is conceived firstly. With the widespread adoption of cloud-edge computing, different nodes have different consciousness on information. Hence, a cloud-edge-aided information diffusion model is proposed for efficient interactions, which incorporates the role of edge in timely processing and feedback. On this basis, a blockchain based cloud-edge SIoT architecture is proposed for traceability and security of information diffusion. Furthermore, dynamical analysis of the coupled model in SIoT is provided, which illustrates the outbreak threshold, stability, and scale of information propagation. An interesting finding is that interactive behavior spreading only influences the final size of information propagation, not the spreading threshold. Extensive simulation results and detailed performance analysis verify the theoretical results, which are beneficial to provide traceable dissemination so as to find the most influential node and control the scale of information diffusion.

With the development of 6G, a series of ultra-reliable intelligent IoT applications have been promoted, which puts forward higher requirements for the security and reliability of networks. Coverage Holes (CHs) pose great challenges to the network coverage reliability, and it is essential to repair CHs quickly and effectively. In this paper, we focus on the problem of CHs recovery of 6G IoT based on Mobile Edge Computing (MEC) and Artificial Intelligence (AI) to ensure reliable coverage. A Confident Information Coverage Hole Recovery Algorithm (CICHRA) is proposed based on the fusion model of the disc model and the confident information model, which takes advantage of the spatial correlation. The mobile edge node uses the utility to guide its movement, and the CHs are recovered by repeated games based on Q-learning. Furthermore, an Improved Confident Information Coverage Hole Recovery Algorithm (I-CICHRA) is proposed to enhance recovery performance and reduce time complexity. The I-CICHRA makes edge nodes’ movement more directional and accurate, thus improving repair efficiency. A series of simulation comparisons with the Distributed Payoff-based Q-learning Algorithm (DPQA) indicate that the proposed algorithms have better performance in overall coverage, iteration times, energy consumption, and energy efficiency.

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this article, we define Confident Information Coverage Reliability ( CICR ) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluate CICR . The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.