As user equipments (UEs) and infrastructure evolve, the network architecture becomes increasingly heterogeneous, presenting challenges in managing diverse communication technologies and varied network demands. Recognizing these challenges, the integration of unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication emerges as an effective strategy to enhance network capacity and address the complexities of a heterogeneous network. In this context, mobile edge computing (MEC), facilitated by servers at the network edge, significantly reduces data processing latency and alleviates backhaul congestion. This paper proposes a UAV and D2D-enabled heterogeneous MEC framework that supports two offloading modes for UEs: the first is to offload tasks to UAVs equipped with computing servers, and the second is to offload tasks to nearby idle UEs via D2D links. Unlike existing schemes, the selection of offloading modes is determined by the network load, which improves resource utilization. To further reduce computing latency, we formulate an optimization problem that jointly considers UAV trajectory planning, resource management, and task offloading policy. Since the problem is non-convex and challenging to solve in polynomial time, we decompose it into a joint resource optimization and an offload scheme optimization. Based on this, an optimization algorithm based on block descent and potential game is proposed to obtain the optimal solution. Simulation results indicate that compared to typical UAV-based MEC without D2D, our solution can reduce latency by about 20%. In comparison to equivalent resource allocation, the proposed algorithm can reduce latency by about 25%.

Integrated sensing and communication (ISAC) technology is a promising contender for the future Internet of Underwater Things (IoUT). However, the complexity of underwater acoustic (UWA) channels and the randomness of ISAC signals may pose challenges to underwater communication and sensing. To address this issue, this paper investigates a novel communication-assisted bi-static sensing scheme capable of facilitating underwater multi-node collaboration using orthogonal frequency division multiplexing (OFDM), which refers to as UWA-OFDM-ISAC. Moreover, two efficient receivers are designed based on compressed sensing to enhance communication and sensing performance. In this paper, we first portray the UWA-OFDM-ISAC system model and emphasize that the Doppler and symbols estimated at the communication side are beneficial in enhancing the bi-static sensing performance. To estimate doubly spread UWA channels, an orthogonal matching pursuit-based interference cancellation channel estimation method is developed, which decouples Doppler and delay in OFDM signals and significantly reduces the parameter search dimension. Furthermore, we propose an enhanced detection algorithm based on matching pursuit, which can exploit sparse multipath information of echoes to improve target detection performance under doubly spread channels. The detection probability is improved by more than 30% at the 10⁻² bit error rate level compared with the energy detector. Finally, simulation results illustrate the effectiveness of the proposed UWA-OFDM-ISAC and demonstrate that the designed receivers have significant advantages relative to various existing algorithms.

With the development of underwater technology and the increasing demand for ocean development, more and more intelligent equipment is being applied to underwater scientific missions. Specifically, autonomous underwater vehicle (AUV) clusters are being used for their flexibility and the advantages of carrying communication and detection units, often performing underwater tasks in formation. In order to locate AUVs with high precision, we introduce an unmanned surface vehicle (USV) with global positioning system (GPS) and propose a USV–AUV network. Furthermore, we propose an ultra-short baseline (USBL) acoustic cooperative location scheme with an orthogonal array, which is based on underwater communication with sonar. Based on the derivation of the Fisher information matrix formula under Cartesian parameters, we analyze the positioning accuracy of AUVs in different positions under the USBL positioning mode to derive the optimal array of the AUV formation. In addition, we propose a USV path planning scheme based on Dubins path planning functions to assist in locating the AUV formation. The simulation results verify that the proposed scheme can ensure the positioning accuracy of the AUV formation and help underwater research missions.

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.

The deep-sea Internet of Things (DS-IoT) enhances massive data acquisition capabilities and augments the safety of industrial operations, serving as a powerful technology for exploiting rich and precious natural resources. However, the unique physical environment of the deep sea poses significant challenges in establishing a DS-IoT network. In this article, to improve the capabilities of deep-sea information collection, transmission, and processing, we conceive a six-layer system architecture for the future DS-IoT, which consists of layers for environment sensing, information collection, long-distance information transmission, control and information aggregation, fusion and processing, and application and service. Then, considering the complexity of the deep-sea environment that harms the stability of underwater networks, traditional networking techniques will falter, which motivates this article to propose machine learning-aided key techniques, focusing on communication, sensing, processing, and network aspects. Furthermore, we discuss the research on efficient trajectory planning and task scheduling for multi-autonomous underwater vehicles collaboration. The simulation results indicate that our method achieves reductions of 66.84% and 89.23% in scheduling route length and reasoning time, respectively, compared to traditional methods. Finally, we present the challenges faced by the DS-IoT and potential research trends.

Unexploded ordnance (UXO) buried in the seafloor poses a serious threat to the environment and human safety. Removing UXOs in the ocean presents a tricky challenge due to the greater difficulty in controlling the damage caused by explosions compared to those on land. Therefore, this study designs a method for detecting seafloor-buried UXO using the transient electromagnetic (TEM) approach and proposes a new forward model for UXO characteristic responses in the ocean by modeling the marine environments as a two-layer medium. We first numerically solve the time-harmonic equations of the primary magnetic field using Sommerfeld integrals and Hankel transforms. Then, we derive the characteristic responses of UXO in seawater based on the 3-D magnetic dipole model and the TEM method. Finally, we simulate the characteristic responses of six typical UXOs and two interfering targets, comparing them with those in free space and analyzing the effects of type, measured distance, and buried attitude on identification. The results show that the characteristic responses in seawater delay 2–12 ms with target size compared to free space. The errors of the characteristic response measurement depend not only on the measured distance but also on the buried attitude of the target. The errors reach the maximum when the target is vertical, the minimum when horizontal at the same distance, and disappear when the measured distance is longer than twice the target size. These findings establish a crucial foundation for accurately identifying the type, burial state, and location of UXO during marine demining.