The advancement of nanotechnology has enabled the deployment of medical nanonetworks within the human body, further accelerated by progress in terahertz communication technologies and nanonetwork routing protocols. However, nanonodes may move in dynamic environments due to environmental influences or self-propulsion mechanisms, posing significant challenges to routing protocol design. To address this, we propose a dynamic routing protocol for nanonetworks that integrates real-time velocity vectors to account for time-varying node positions. During relay node selection, key factors such as candidate nodes' velocity vectors are comprehensively evaluated to determine the optimal relay for next-hop transmission. To ensure efficient data exchange among multiple nodes, we design a time-division multiple access (TDMA)-based media access control (MAC) protocol to prevent packet collisions and losses. The protocol assigns distinct transmission and reception time slots to different node types and implements a countdown mechanism to manage channel access and eliminate conflicts. Numerical and simulation results demonstrate that the proposed protocol significantly outperforms benchmark protocols, achieving notable improvements in both time and energy efficiency.

As a privacy-preserving solution, federated learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in near-field communication (NFC)-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages the superposition property of wireless multiple-access channels, enabling faster model training and accommodating more clients, even in bandwidth-constrained scenarios like NFC. However, due to its analog-integrated nature, the FL performance is also affected by other factors, such as channel noise. These motivate us to consider how the selected client set and channel noise affect FL performance. To explore this concern, in this article, we consider an over-the-air FL system with analog gradient aggregation and analyze the impact of the selected client set and channel noise on FL training performance. The theoretical analysis effectively shows the importance of the clients’ number and the power scaling factor to the FL training performance. Based on the theoretical analysis, we transform the global optimization problem into the client selection problem and propose a dynamic client selection scheme to optimize the training performance under the aggregation error constraint. Experimental results demonstrate that our proposed scheme can boost FL by speeding up the convergence of the global model (at least 35%) and saving energy consumption.

In recent years, reliable and timely data collection from underwater acoustic sensor networks (UASNs) has attracted widespread attention in academia. For this proposal, we study the autonomous underwater vehicle (AUV)-based real-time mobile relaying network in UASNs. Relay placement determines the reliability of communication in the relay network. We first formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate the FRA with a three-dimensional ellipsoid mathematical formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the different-sized FRA constraints. To this end, we propose a nearest-community (N-C) trajectory planning algorithm and further propose a member grouping method to form communities. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the proposed N-C can successfully receive more packets per minute and consume fewer sensors' energy than other algorithms.

Relaying is an effective method to achieve reliable and timely data collection, which is one of the most important parts of underwater acoustic sensor networks (UASNs). Considering that the relay position determines the reliability of relay communication, we study the problem of relay placement and place an autonomous underwater vehicle (AUV) to mobile relay data transmission and realize the reliable, low-latency, and low-energy data collection in UASNs. First, we formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate FRA with a three-dimensional geometry formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the FRA constraint. To this end, we propose a nearest-community (N-C) trajectory planning algorithm to design the AUV relay trajectory. A member grouping method and the necessity of position (NoP) concept are proposed to further reduce the relay positions and trajectory length of the AUV. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the N-C using NoP-based member grouping can successfully receive more packets per minute and consume fewer sensors’ energy than other algorithms.

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.

Ocean big data is becoming a future trend of the Internet of Underwater Things (IoUT). Underwater wireless sensor networks (UWSNs) technique is a promising method to realize ocean big data. However, the limited energy and the low location accuracy of sensor nodes make the data collection of UWSNs difficult. To reduce the energy consumption of sensor nodes, we consider an autonomous underwater vehicle (AUV) based data collection, where the AUV moves close to the sensors to collect data using short-range high-rate communications. Particularly, we propose a grouping-based dynamic trajectory planning (GDTP) for the AUV. GDTP does not require the position information of sensor nodes. It dynamically detects the existence and directions of sensor nodes, based on which the detected sensor nodes are grouped by a proposed common communication area model. The cruising direction of the AUV is dynamically determined with the maximum expected payoff that considers the data collection and energy consumption in inaccurate detection. Simulation results show that the proposed GDTP collects more data packets with less energy compared to the existing schemes.