Millimeter-wave (mmWave) radar, with its high resolution, sensitivity to micro-vibrations, and adaptability to various environmental conditions, holds immense potential across multi-modal fusion sensing. Although there exist review papers on mmWave radar, there is a noticeable lack of comprehensive reviews focusing on its multi-modal fusion sensing capabilities. Addressing this gap, our review offers an extensive exploration of mmWave radar multi-modal fusion sensing, emphasizing its integration with other modalities. This review discusses the complex realm of millimeter-wave radar multi-modal fusion sensing, detailing its importance, hardware and software aspects, principles, and current potential and applications. It delves into data characteristics and datasets associated with mmWave radar, focusing on Doppler, point cloud, and multi-modal data formats. The review highlights how these data types enhance multi-modal fusion sensing and discusses methodologies, including signal processing and learning algorithms. Three categories of multi-modal fusion methodologies are proposed to optimally manage and interpret fused data. Various practical applications of mmWave radar multi-modal fusion sensing are illustrated, underlining the unique capabilities it provides when integrated with other sensors. The review concludes by identifying potential future research avenues, underscoring the immense potential of this field for further exploration and advancement.

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.

The emergence of coverage holes will dramatically degrade the quality of service of the industrial wireless sensor networks(IWSNs). Based on the novel confident information coverage(CIC) model, this work focuses on how to heal the confident information coverage holes in hybrid IWSNs containing both stationary nodes and mobile nodes. We pinpoint The confident information coverage hole healing(CICHH) problem with the goal of selecting some randomly scattered mobile nodes to be dispatched to the CIC holes detected by the stationary nodes such that the CIC holes can be repaired and the confident information coverage performance can be satisfied, and prove its NP-completeness. For handling the CICHH problem, we devise two energy-efficient heuristic solutions including a centralized confident information coverage hole healing algorithm and a distributed one. Both the proposed schemes aim at efficiently healing the CIC holes while minimizing the total moving energy consumption of the dispatched mobile nodes, or maximizing the mobile nodes' average remaining energy after movement, or minimizing the maximum mobile energy consumption of each dispatched mobile node. Experimental simulation results show the proposed schemes can energy-efficiently heal the CIC holes and outperform three peer algorithms in terms of the energy efficiency and coverage ratio.

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.

As a novel cyber-physical-social network paradigm, the internet of things(IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion and external attacks, the emergence of coverage holes would remarkably degrade the network performance and quality of service(QoS). For overcoming the drawbacks resulting from the coverage holes, this work focuses on how to locally detect coverage holes by exploiting onehop neighboring sensors’ cooperation based on the novel confident information coverage model(CIC), which is formulated as the Localized Confident Information Coverage Hole Detection(LCICHD) problem. For handling the CICHD problem, we devise a family of heuristic CIC holes detection schemes including the LCHD, LCHDRL, Random and RandomRL. Both the LCHD and LCHDRL schemes locally determine coverage status of each subregion and take the sensor communication ability into consideration. While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection. After acquiring the coverage status of each partitioned local subregion, the coverage hole boundary will be extracted by image processing techniques. For comparison, both the Random and RandomRL schemes arbitrarily select sensors within the sensing field to detect CIC holes, and the RandomRL scheme takes the sensors’ residual lifetime into consideration during the hole detection process. Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the LCHDRL algorithm is more practical and efficient compared to the other three peer solutions.