This paper proposes a multiple access control (MAC) protocol for integrated sensing and communication in underwater acoustic networks (ISAC-UANs) with dynamic target monitoring, localization, and tracking, where some ISAC nodes are deployed to collaboratively communicate and detect targets via integrated underwater acoustic signals. The proposed integrated underwater sensing and communication (USAC)-MAC protocol focuses on addressing the challenges of packet collisions and resource contention in the dual-function network. Specifically, the protocol is divided into three stages: target monitoring, target localization, and the target tracking phase. The target monitoring phase is carried out to initialize the network topology and search for detection targets. The conflict-free concurrent transmission constraints are established based on topological information to enable conflict-free and efficient data transmission. After detecting the target, the protocol transfers into the target localization phase, which localizes the target applying the time of arrival (TOA) method. We model the optimization problem to minimize the Cramér-Rao lower bound (CRLB) and employ the particle swarm optimization (PSO) algorithm to solve it under the constraint of the geometric position of nodes. During the target tracking phase, we enhance target detection accuracy by dynamically selecting detection node clusters based on predicted target trajectories. Concurrently, we schedule packet transmission times to minimize the communication frame length. Thereby, a joint optimization problem for the CRLB of localization and the frame length of communication is established and addressed by the simulated annealing (SA) algorithm, ensuring detection precision while reducing communication latency. Simulation results demonstrate that the proposed protocol exhibits superior performance with higher throughput, shorter frame length, shorter information update times, and more precise detection accuracy.

The integrated satellite-terrestrial network (ISTN) with inter-satellite free space optical (FSO) links and satellite-to-ground (S2G) radio frequency (RF) links is becoming an important enabler for the Internet of Things (IoT). However, investigating the performance of the ISTN remains several challenges, i.e., the high mobility and long propagation delays of S2G links, and the highly correlated line-of-sight S2G channels. To address these challenges, we propose a hybrid RF/FSO cooperative satellite-terrestrial communication system that integrates the space time block code with cooperative transmission to enhance the coverage probability and communication reliability of satellite downlink transmission. We model the inter-satellite FSO channels by considering pointing and tracking errors, and the S2G RF channels using the shadowed-Rician fading model. Subsequently, we derive the probability density function and cumulative distribution function for both RF/FSO signal-to-noise ratio (SNR) under channel estimation errors and the sum of two RF SNRs from the same distribution family. Finally, for the proposed system, closed-form expressions of the outage probability (OP) and the upper bound for the average bit error probability (BEP) are derived. The proposed system outperforms SISO and MISO systems by reducing average BEP, outage probability, and robustness to channel estimation errors.

In this work, we propose a multi-aircraft cooperative handover scheme for satellite-to-aircraft communication systems. Specifically, considering the dual characteristics of aircraft resource demands and three satellite states (normal, congested, and failed), multiple aircraft collaborate to make handover decisions while maintaining network stability and avoiding congestion. We formulate the cooperative handover problem as a multi-objective optimization problem to minimize communication latency and network congestion while maximizing connection stability. To solve this problem, we first model the handover scheme into the Markov decision process to facilitate seamless satellite-aircraft handover. Then we develop a multi-agent deep deterministic policy gradient (MADDPG) algorithm with centralized training and decentralized execution architecture. Due to the time-varying nature of the action space and the constraint that action selection is limited to currently visible and undamaged satellites, we implement an action mask approach to effectively filter out illegal actions instead of using conventional negative reward methods. The simulation results demonstrate that the proposed framework effectively reduces handover frequency, minimizes communication latency, and achieves better network load balancing, validating its feasibility and effectiveness in satellite-toaircraft communication systems.

Low earth orbit (LEO) satellite communication systems have become the indispensable part of sixth generation (6 G) communications. However, since the LEO and geostationary earth orbit (GEO) satellite systems will inevitably share limited frequency resources, the communication signal from LEO satellites have the possibility to cause harmful interference to the GEO systems. To address this issue, this paper proposes an adaptive beamforming strategy to mitigate the interference while improving the system spectral efficiency (SE). In specific, we formulate the problem as the nonlinear mixed integer programming (NMIP) optimization problem, and apply the weighted minimum mean square error (WMMSE) and alternative optimization algorithm to obtain the closed-form solutions. Furthermore, to reduce the high complexity of beamforming when the LEO system serves a massive number of ground users (GU), we propose a channelaware user clustering scheme utilizing the channel correlation between GUs so that all GUs within the same cluster share the same precoding vector. Extensive simulations show that the proposed scheme effectively mitigates the interference.

In this paper, we investigate the multiple autonomous underwater vehicles (AUVs) collaborative data collection and tra- jectory planning scheme in integrated sensing and communication for underwater acoustic networks (ISAC-UANs). To collect data efficiently, AUVs traverse the overlapping communication region of sensor nodes instead of accessing every sensor node. In addition, the sensing function is required to enable obstacle avoidance. To this end, we first propose the time division multiple access (TDMA)-based communication and mono-static sensing strategy for ISAC-UANs. Secondly, we formulate the data collection and trajectory design problem into a min-max problem to minimize energy consumption and enhance the network throughput. To solve this problem, we decouple it into three sub-problems: the sensor node clustering problem, the cluster traversal problem, and the trajectory planning problem. The first sub-problem is to determine the AUV traversal area to collect data, which is solved by an overlapping communication regions based clustering algorithm. The second sub-problem is to determine the AUVs' cluster traversal sequence minimizing the traversal length, which is solved by an min-max ant colony optimization (ACO)-based algorithm. The third sub-problem involves planning the optimal trajectory for AUVs to reach the data collection area while avoiding obstacles, which is solved by the soft actor-critic (SAC)-based online trajectory planning algorithm. Extensive simulations demonstrate that the proposed scheme outperforms benchmarks in terms of trajectory length, energy consumption, and network throughput.

In this paper, we investigate the coverage path planning (CPP) scheme for autonomous underwater vehicles (AUVs) cooperative environment detection in integrated underwater acoustic communication and detection networks (UCDNs), where multiple AUVs detect unexplored oceanic environments and avoid obstacles. Firstly, we present the detection range prediction model related to oceanic environmental parameters and propose the detection and communication scheme in UCDNs. Secondly, to conduct the cooperative environment detection mission, we formulate the CPP problem as a mixed combinatorial and sequential quadratic optimization problem to maximize the coverage ratio and minimize the path length of AUVs. To solve this problem, we investigate the multi-agent proximal policy optimization (MAPPO)-based CPP scheme. In specific, the CPP problem is modeled as a partially observable Markov decision process (POMDP). Since the path planning of the AUVs is not only related to the local information but also the other AUVs' information, the information should be shared among AUVs based on the UCDNs. Furthermore, we introduce the MAPPO-based algorithm under the centralized training with decentralized execution (CTDE) architecture. Extensive simulations are carried out to demonstrate the strength of the proposed scheme.

The distributed multi-object tracking (DMOT) technologies have attracted much attentions, where multiple distributed sensor nodes sense objects individually and fuse information cooperatively to track multiple objects. However, the multi-node information fusion for DMOT faces the label mismatching problem and different limited fields-of-view (FoVs) problem. To address these problem, in this paper, we propose a distributed multi-object tracking scheme called consistent fusion by associated groups (CFAG) algorithm, including two stages: 1) Distributed track association, which firstly identifies node pairs with overlapping FoVs and then match the tracks by node pairs based on the matching history, and finally the associated track is formed into groups. This stage can reduce the computation complexity when the number of nodes and objects is large. 2) Intra-group fusion, which firstly fuse state estimates based on the arithmetic average within each group, and then label the fused state estimate according to the matching history. For different groups, intra-group fusion can be conducted in parallel. Simulation results indicate that the proposed algorithm performs better than the benchmark in terms of tracking accuracy and label consistency.

The characteristics of volatile ocean environments and complex acoustic communication channels have posed great difficulties to the design of real-time data transmission in underwater wireless sensor networks (UWSNs). In this paper, we develop a centralized cross-layer protocol that mitigates network interference and maximizes concurrent transmissions to reduce end-to-end delay. Instead of optimizing individual layers separately, we blend the traditional layered architecture and combine the physical layer, medium access control (MAC) layer, and network layer functions together. Specifically, we optimize the power control in the physical layer, link scheduling in the MAC layer, and routing in the network layer jointly to achieve a global optimization of end-to-end delay. Firstly, the joint design problem is formulated as a mixed integer linear programming (MILP) problem, which is an NP-hard problem and hard to solve mathematically. Then, we propose a bio-inspired-algorithm-based solution, namely discrete improved artificial bee colony (DIABC) algorithm, aiming at finding an approximate optimal cross-layer scheduling scheme. To further reduce end-to-end delay, we optimize the uplink frame structure and routing metric in the centralized cross-layer framework. The simulation results show that the proposed protocol achieves network performance improvement in terms of end-to-end delay, service rate, and energy consumption.

The Internet of Underwater Things (IoUT) is an emerging field of the Internet of Things (IoT) that extends IoT technologies to ocean environment. Recently, underwater wireless sensor networks (UWSNs) have shown great potential in IoUT and are envisioned to facilitate various IoUT applications. Considering the broad categories of IoUT applications, the routing protocol in UWSNs is expected to provide data transmission with different requirements. However, due to potential conflicts between different requirements of IoUT applications, most existing work only takes a certain requirement into account to optimize the corresponding transmission objective, which is often achieved at the cost of other requirements. To address this problem, a multi-objective routing (MOR) protocol is proposed in this paper to cater to the diverse demands of IoUT applications simultaneously. Specifically, the MOR protocol considers three key requirements in IoUT applications enabled by UWSNs, i.e., energy consumption, end-to-end delay, and link quality, to formulate the multi-objective routing optimization problem. Additionally, this paper also considers link congestion as an optimization objective to avoid frequent use of the local optimal communication links. Then, to solve the multi-objective routing optimization problem, a heuristic scheme based on the non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Simulation results have shown the feasibility and effectiveness of the proposed MOR protocol and verified the necessity of multi-objective optimization in IoUT.

In this paper, we propose a value of information (VoI)-based packet scheduling scheme (VBPS) in autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect data from areas not accessible to static nodes and then relay data via static nodes. VoI is a performance metric to measure the importance of data packets with different levels of urgency. The proposed scheme aims to avoid collision with the ongoing packet transmission of static nodes without their accurate global information. In specific, the static node localization stage and the topology construction stage are carried out to obtain the local information. Furthermore, the transmission scheduling stage is implemented to avoid packet collision and formulates a combinatorial optimization problem maximizing VoI under the constraint of packet collision avoidance. To solve this complicated problem, a low-complexity distributed search algorithm is proposed, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. In addition, a collaborative search algorithm is proposed to avoid packet collision among different AUVs by enabling collaboration among AUVs. Extensive simulation results under various scenarios demonstrate the superior performance of the proposed scheme.

In this paper, we propose the multi-autonomous underwater vehicle (AUV) collaborative data collection in integrated underwater acoustic communication and detection networks (UCDNs). Specifically, multiple AUVs collaboratively traverse the sensor nodes to collect data while detecting the environment to avoid obstacles along the trajectory. We first propose a time division multiple access (TDMA)-based packet transmission and active bistatic sonar detection strategy for UCDNs to transmit the sensor data and detect the unknown environment. Furthermore, we formulate the collaborative data collection problem as a mixed combinatorial and sequential quadratic optimization problem to minimize the trajectory length of multiple AUVs. To solve this problem, we decouple it into two subproblems, i.e., the node traversal subproblem and the trajectory planning subproblem. The former subproblem is converted into the multi-traveling salesman problem (MTSP), which is solved by the Q-learning-based algorithm to improve the robustness. The latter subproblem is optimally planning each AUV's trajectory while avoiding obstacles, which is solved by the soft actor-critic (SAC) algorithm to online make continuous trajectory decisions. Simulation results demonstrate that the proposed scheme outperforms benchmarks in terms of energy consumption and overall trajectory length.

This paper studies autonomous underwater vehicles (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect information from areas not accessible to static nodes and then relay data via static nodes. Due to the difficulty of obtaining the accurate global information of all the static nodes, we propose a novel packet scheduling scheme by utilizing local information obtained by AUVs. In the proposed scheme, the localization of static nodes stage and the topology construction stage are carried out beforehand to obtain the local information, based on which the transmission scheduling stage is implemented. Furthermore, in the transmission scheduling stage, we design a value of information (VoI)-based packet transmission scheduling (VBPS) strategy to avoid packet collision. Specifically, we introduce a performance metric, i.e., VoI, to measure the importance of data packets with different levels of urgency. Then, we formulate a combinatorial optimization problem to maximize VoI taking packet collision avoidance into consideration. A low-complexity distributed search algorithm is proposed to solve the problem, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. Extensive simulations under various scenarios are carried out to evaluate the performance of the proposed algorithm.

In this paper, we propose an imaging scheme for unmanned aerial vehicle (UAV)-Enabled integrated sensing and communication (ISAC), where the UAV serves as a flexible communication auxiliary and a versatile sensing platform with the cooperation of a ground base station (GBS). To guarantee the performance of both sensing and communication, the proposed imaging scheme is based on the orthogonal frequency division modulation (OFDM) ISAC waveform and bistatic communication-assisted sensing strategy, which can be divided into three steps. Firstly, the UAV transmits OFDM ISAC signal, which contains the UAV position information to enable the communication-assisted sensing strategy. Secondly, the GBS receives the line-of-sight (LoS) ISAC signal from the UAV and the reflected ISAC signal from targets, in which the bistatic sensing architecture is designed to process data frequently and bypass the self-interference problem. Thirdly, the GBS preprocesses the received signal and reconstructs the image based on polar format algorithm (PFA) with the knowledge of UAV positions to relax the constraint of UAV trajectory. Numerical simulations are carried out to validate and evaluate the proposed UAV-enabled ISAC imaging scheme.

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

With the development of the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have become an enabling system to support real-time and continuous data transmission. Existing scheduling-based medium access control (MAC) protocols ignored the limitations between the generation time and the transmission time of forwarding packets. This results in unavailability of some scheduled slots, and makes packets endure more waiting time on relay nodes and thereafter longer end-to-end delay. To reduce transmission latency, this article develops a novel scheduling-based MAC protocol, which schedules slots in the packet level. All packets to be generated and transmitted in a frame are predicted and scheduled. Considering our defined packet collision constraint and traffic-flow constraint, we formulate the slot scheduling problem in a frame into a combinatorial optimization problem, which ensures that all source packets transmitted in a frame can be delivered to the sink node with the minimum average end-to-end delay within the same frame. To solve this problem, two algorithms are proposed, namely, an optimal packet-level slot scheduling (PLSS) algorithm and a heuristic approximate PLSS (PLSS-A) algorithm. The performances of our proposed protocol using both algorithms are evaluated with different network scales, packet lengths, and offered traffic loads. Numerical results demonstrate that both PLSS and PLSS-A perform well in terms of average end-to-end delay and service fairness, and have an advantage in the network throughput in large-scale networks. Our proposed PLSS MAC protocol is predicted to be promising in large-scale UASNs with demand for real-time and long-term monitoring.

With the development of the Internet of Underwater Things (IoUT), two critical problems have been prominent, i.e., the energy constraint of underwater devices and large demand for data collection. In this article, we introduce an autonomous underwater vehicle (AUV)-aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. First, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Second, the clustering algorithm is proposed. Third, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intracluster nodes, as well as among intercluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely, AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. The simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long-term monitoring, and high data traffic load.