Integrated Sensing and Communication Driven Digital Twin for Intelligent Machine Network

Intelligent machines (IMs), including industrial machines, unmanned aerial vehicles (UAVs), and unmanned vehicles, etc., could perform effective cooperation in complex environment when they form IM network. The efficient environment sensing and communication are crucial for IM network, enabling the real-time and stable control of IMs. With the emergence of integrated sensing and communication (ISAC) technology, IM network is empowered with ubiquitous sensing capabilities, which is helpful in improving the efficiency of communication and sensing with the mutual benefit of them. However, the massive amount of sensing information brings challenges for the processing, storage and application of sensing information. In this article, ISAC driven digital twin (DT) is proposed for IM network, and the architecture and enabling technologies are revealed. ISAC driven DT structurally stores the sensing information, which is further applied to optimize communication, networking and control schemes of IMs, promoting the widespread applications of IMs.

Sensing-assisted Accurate and Fast Beam Management for Cellular-connected mmWave UAV Network

Beam management, including initial access (IA) and beam tracking, is essential to the millimeter-wave Unmanned Aerial Vehicle (UAV) network. However, conventional communication-only and feedback-based schemes suffer a high delay and low accuracy of beam alignment since they only enable the receiver to passively hear the information of the transmitter from the radio domain. This paper presents a novel sensing-assisted beam management approach, the first solution that fully utilizes the information from the visual domain to improve communication performance. We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction. Besides, we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments. Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay, association accuracy, tracking error, and communication performance.

Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer

As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches.

Game Theory and Coverage Optimization Based Multihop Routing Protocol for Network Lifetime in Wireless Sensor Networks

Wireless sensor networks (WSNs) are self-organizing monitoring networks with a large number of randomly deployed microsensor nodes to collect various physical information to realize tasks such as intelligent perception, efficient control, and decision-making. However, WSN nodes are powered by batteries, so they will run out of energy after a certain time. This energy limitation will greatly constrain the network performance like network lifetime and energy efficiency. In this study, to prolong the network lifetime, we proposed a multi-hop routing protocol based on game theory and coverage optimization (MRP-GTCO). Briefly, in the stage of setup, two innovational strategies including a clustering game with penalty function and cluster head coverage set were designed to realize the uniformity of cluster head distribution and improve the rationality of cluster head election. In the data transmission stage, we first derived the applicable conditions theorem of inter-cluster multi-hop routing. Based on this, a novel multi-hop path selection algorithm related to residual energy and node degree was proposed to provide an energy-efficient data transmission path. The simulation results showed that the MRP-GTCO protocol can effectively reduce the network energy consumption and extend the network lifetime by 159.22%, 50.76%, and 16.46% compared with LGCA, RLEACH, and ECAGT protocols.

Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

Wireless sensor networks (WSNs), one of the fundamental technologies of the Internet of Things (IoT), can provide sensing and communication services efficiently for IoT-based applications, especially energy-limited applications. Clustering routing protocol plays an important role in reducing energy consumption and prolonging network lifetime. The cluster formation and cluster head selection are the key to improving the performance of the clustering routing protocol. An energy-efficient routing protocol based on multi-threshold segmentation (EERPMS) was proposed in this paper to improve the rationality of cluster formation and cluster head selection. In the stage of cluster formation, inspired by multi-threshold image segmentation, an innovative node clustering algorithm was developed. In the stage of cluster head selection, aiming at minimizing the network energy consumption, a calculation theory of the optimal number and location of cluster heads was established. Furthermore, a novel cluster head selection algorithm was constructed based on the residual energy and optimal location of cluster heads. Simulation results show that EERPMS can improve the distribution uniformity of cluster heads, prolong the network lifetime and save up to 64.50%, 58.60%, and 56.15% network energy as compared to RLEACH, CRPFCM, and FIGWO protocols respectively.

Discrete Army Ant Search Optimizer-Based Target Coverage Enhancement in Directional Sensor Networks

Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.

Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive Q-Learning Approach

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANET routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive Q-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAV nodes via the queuing theory, and then the closed-form solutions of neighbors' change rate (NCR) and neighbors' change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive Q-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time at a low cost. The simulation results verify the accuracy of the topology dynamic analysis model and also prove that our TARRAQ outperforms the Q-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Toward Intelligent and Efficient 6G Networks: JCSC Enabled On-Purpose Machine Communications

Driven by the vision of "intelligent connection of everything" toward 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches. In this article, we propose an on-purpose machine communication framework enabled by joint communication, sensing, and computation (JCSC) technology, which employs machine semantics as the interactive information flow. Naturally, there are potential technical barriers to be solved before the widespread adoption of on-purpose communications, including the conception of machine purpose, fast and concise networking strategy, and semantics-aware information exchange mechanism during the process of task-oriented cooperation. Hence, we discuss enabling technologies complemented by a range of open challenges. The simulation result shows that the proposed framework can significantly reduce networking overhead and improve communication efficiency.

Toward Trusted and Swift UAV Communication: ISAC-Enabled Dual Identity Mapping

The UAV network has recently emerged as a capable carrier for ubiquitous wireless intelligent communication in the B5G/6G era. Nevertheless, the separation of dual identity raises challenges from the perspective of communication efficiency and security, including tedious communication feedback and malicious Sybil attacks. Meanwhile, thanks to the emerging integrated sensing and communication (ISAC) technology, the sensing ability incorporated in communication advances crucial opportunities for accurately and efficiently mapping identity from dual domains. This tutorial discusses the exciting intersection of ISAC and the future intelligent and efficient UAV network. We first describe the motivation scenario and present the framework of the proposed novel ISAC-enabled dual identity solution. The detailed modules of identity production, mapping, management, and authentication are discussed. By endowing UAVs with an advanced capability: opening their eyes when communicating with each other, we detail three typical applications and the advantages of our proposal. Finally, a series of key enabling techniques, open challenges, and potential solutions for ISAC-enabled dual-domain identity are discussed. This tutorial for the intelligent and efficient UAV network brings new insight on providing dual-domain identity via ISAC technology, with an eye on trusted and swift communication research tailored for the 6G UAV network.

Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (D-ID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.