Scheduling-based Medium Access Control (MAC) protocols significantly augment the ability for simultaneous transmissions while mitigating signal interference, thereby elevating the bandwidth utilization efficiency crucial for data-collection-oriented underwater acoustic sensor networks (UASNs). However, existing scheduling-based MAC protocols for UASNs primarily analyze the interference between pairwise links or nodes, neglecting the cumulative interference effect fostered by the concurrent transmissions of multiple nodes. Besides, the existing multi-node interference models used in Radio Frequency (RF) based wireless networks only describe the total strength of cumulative interference signals and do not consider scenarios in which the interference from multiple nodes does not align at the receiver. This paper introduces an innovative interference model to extensively quantify the non-aligned overlapping interference from multiple packets on a single valid packet in segments. Based on this segmented interference model, we establish a transmission time constraint that can avoid excessive interference in high-interference segments and utilize low-interference segments for parallel reception, thereby enhancing the degree of time reuse. The optimization of transmission slot scheduling is modeled as a sequential decision-making process, wherein the action space is delineated by considering the constraints related to multi-packet interference. Furthermore, the Reinforced Packet Level Slot Scheduling (R-PLSS) algorithm leveraging the principles of approximate dynamic programming is proposed to allocate transmission slots within each frame. The simulation results demonstrate that the R-PLSS algorithm avoids the practical packet demodulation failure caused by excessive interference from multiple packets and can comprehensively improve the channel utilization efficiency.

The dynamic production environments and stringent quality of service (QoS) requirements in discrete manufacturing smart factories pose significant challenges to deploying edge intelligence networks. These networks must simultaneously satisfy critical QoS metrics while maintaining adaptability to fluctuations in resource availability and task priorities. To address the industrial demands for real-time responsiveness, lightweight design, and flexible deployment, this paper proposes an adaptive deployment optimization strategy for edge intelligent networks based on an improved K-means particle swarm optimization (IK-PSO) algorithm. The strategy incorporates dynamic clustering and weight adjustment mechanisms to optimize multiple performance metrics, including latency, throughput, reliability, and interference mitigation. Experimental results validate that the IK-PSO-based deployment optimization strategy rapidly converges to high-quality solutions across different scenarios and various factory complexities, significantly improving network performance. This study provides a practical and efficient solution for network deployment in smart factories, contributing to the ongoing development of intelligent production and resource management.

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.

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.