Accurate navigation is essential for underwater vehicles like AUVs, which often operate in deep or remote areas. However, complex ocean dynamics, cumulative inertial-measurement unit (IMU) drift, and diverse noise sources often result in erratic and unreliable position estimates. To overcome these challenges, we proposed a method that combines underwater acoustic signals with onboard motion sensor data to improve the underwater position tracking system. The proposed system uses a long baseline (LBL) acoustic array of surface buoys to capture the Time-difference-of-Arrival (TDoA) of a multi-pulse beacon. We extract arrival times using a superimposed-envelope-spectrum (SES) detector, which exploits the beacon’s periodic structure to stay reliable even in heavy noise. These acoustic measurements are fused with six-degree-of-freedom IMU data using a particle filter (PF). The filter suppresses IMU drift and reveals how long dead-reckoning remains reliable before an acoustic update becomes essential. Simulation results demonstrated that our PF-TDoA fusion method achieved up to 40% reduction in mean localization error compared to traditional fusion filters and optimization method. In the experiment, we compared the simulated IMU prediction with real-world acoustic measurements, and the resulting fused position estimated remained within 3 m of Global Positioning System (GPS)-reported trajectory, demonstrating robust performance under operational conditions. • We introduce a feature-based TDoA detector that reduces false alarms in heavy noise. • We fuse 6-DOF IMU and acoustic ranges in a PF to schedule pings only when needed. • We present a geometry-aware robust NLLS solver for accurate 3-D trilateration.

Underwater Acoustic Communication (UAC) has garnered significant attention due to its applications in marine science, defence, and exploration. With the vast majority of the Earth’s surface covered by water, there is a growing demand for reliable and effective communication techniques in underwater environments. However, the underwater acoustic channel is the most challenging channel due to its harsh characteristics, which significantly hinder the reliability and performance of UAC systems. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address high Doppler and high mobility scenarios. It has demonstrated tolerance to fast time-varying channel characteristics and enhanced resilience to Doppler shifts and multipath interference. These make OTFS suitable for the complex underwater environment. This study presents the first comprehensive review of the key challenges and state-of-the-art research and development of OTFS in underwater operations. It highlights the suitability and relevance of OTFS modulation for UAC. It also reviews recent advancements in channel estimation techniques tailored for OTFS-UAC systems. Potential future directions in sparse signal recovery for robust channel estimation and equalization, adaptive techniques, energy efficiency, and possible MAC protocols are also discussed to build a foundation for the next-generation UAC systems.

The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This paper introduces an Adaptive Guard band and Power control resource allocation scheme for mission critical applications (AGP-MCA), specifically designed to improve underwater communication. The AGP-MCA framework optimizes the acoustic spectrum based on the criticality of IoUT applications. AGP-MCA dynamically adjusts guard bands to effectively mitigate Doppler caused by mobile nodes and strategically manages transmission power to reduce power consumption significantly, and handles non-critical data through buffering. We formulate a comprehensive mathematical optimization model and employ a Whale Optimization Algorithm (WOA)-based meta heuristic approach to achieve near-optimal solutions while ensuring minimal computational complexity. Extensive simulations demonstrate that AGP-MCA enhances throughput, reduces both end-to-end delay and power consumption, and consistently outperforms existing protocols and configurations without adaptive guard bands. Further, it offers a robust and power-efficient solution for real-time mission-critical IoUT applications.

Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic Networks (UWAN) devices, coupled with the challenges associated with battery replacement or recharging, intensifies the challenges. Underwater acoustic communications are heavily constrained by available resources (e.g., very limited bandwidth, and limited energy storage). Consequently, the role of medium access control (MAC) protocol which distributes available resources among nodes is critical in maintaining a reliable underwater communication system. This study presents an extensive review of current research in MAC for UWAN. This study presents an extensive review of current research in MAC for UWAN. The paper explores the unique challenges and characteristics of UWAN, which are critical for the MAC protocol design. Subsequently, a diverse range of energy-efficient MAC techniques are categorized and reviewed. Potential future research avenues in energy-efficient MAC protocols are discussed, with a particular emphasis on the challenges to enable the broader implementation of the Green Internet of Underwater Things (GIoUT).

In Industrial Internet of Things (IIoT), the volume of generated traffic can be high, causing concerns over issues, such as high energy consumption and longer access delay. Caching IIoT content at the network nodes is a promising solution to address these concerns. However, cached IIoT content at the network nodes is vulnerable to malicious attacks, which can cause significant disruptions in industrial operations. In order to monitor and prevent such malicious activities, trusted systems/nodes with supervisory control can be placed at the network nodes. In this article, we introduce the concept of the trusted caching node (TCN) for IIoT. TCNs cache popular IIoT content and ensure robust security to the connected links. This article finds the optimum location for the TCNs and selects secured-routes for content distribution. We formulate a joint optimization problem and minimize the cost related to probable content breach and energy consumption. We also design a low-complexity heuristic algorithm to solve this problem in real-time. The simulation results demonstrate that our proposed solution achieves significant performance gain and ensures secured IIoT content delivery.

Proactive content caching at the network-edge is envisioned as a key technique to reduce backhaul congestion and latency in popular content delivery. Data storage units, however, require additional energy, which offers a challenge to researchers who intend to reduce energy consumption up to 90% in next generation networks. The concept of powering data storage units by renewable energy aligns with this energy reduction target. In this article, we introduce a proactive caching strategy, which caches content in helper nodes in a cooperative manner considering intermittent renewable energy and variable content load for various hours of the day. The hypothesis is that the amount of renewable energy available to various helper nodes varies depending on the node location and time of the day. The content load across helper nodes also varies with time. As such, if helper nodes work cooperatively by considering the available renewable energy and content load at each helper node, the aggregate non-renewable energy consumption for content caching can be further reduced. We model the research challenge as an optimization problem. Furthermore, we also present a heuristic solution. Our proposed scheme achieves a significant reduction in non-renewable energy consumption by 23% over the existing green solution.

Streaming services have become increasingly popular in recent years, causing an upsurge in video traffic. The continuing upsurge will put enormous stress on existing network infrastructures. This has motivated researchers to find innovative solutions to address this challenge. Proactive content caching at the network edge is considered one such innovative solution, which can reduce backhaul congestion during evening peak hours. Consequently, researchers have introduced numerous approaches to cache content at the network edge. While the focus so far is on reducing the congestion, security issues in relation to content breach at caching nodes have not been adequately addressed so far. Content storage units at the network edge are more vulnerable to malicious attacks, and the loss incurred in this process can be significant. In this paper, we introduce a security aware caching technique to reduce the amount of potential loss caused by security attacks. We investigate the trade-off between the savings achieved from the implementation of proactive caching at caching nodes and the probable loss caused by security attacks. We model the research question as an optimization problem, and provide an optimal solution, which selects the suitable content to be cached at the most suitable nodes so that the loss caused by content breach becomes marginal. Simulation results show that our proposed technique significantly minimizes the loss caused by security attacks.

While the concept of ultra-dense small cell networks (SCNs) has brought a number of significant opportunities for the telecommunication industry, it has also introduced a major challenge for researchers, who must develop techniques to reduce the sharp increase in power consumption that will be required to backhaul traffic from SCNs to the core network. In this research, we investigate the green backhauling challenge for a fifth generation (5G) wireless communication network that uses both the passive optical network (PON) and millimeter wave (mmWave) backhauling to support its diverse groups of customers and applications. Our approach is based on the fact that the energy efficiency figures for the PON and the mmWave technologies are different under a given load condition. The PON technology is more energy efficient under heavy load conditions, whereas the mmWave technology offers better energy efficiency under low load conditions. As such, in response to varying traffic loads during various hours of the day, a fixed backhauling strategy is insufficient to guarantee the minimum power consumption and the required data rates. We formulate an optimization problem that considers the estimated hourly traffic load and determines the most energy efficient backhauling strategy for various hours of the day. Considering the complexity of the optimization problem, we also propose an energy efficient heuristic solution to solve this problem. Simulation results indicate that the proposed solution provides up to 32 percent more energy savings than the existing solution.

The concept of heterogeneous networks and small cells is likely to be a key addition to the fifth generation (5G) wireless communication standard, the prospect of which has motivated 5G researchers to investigate the coverage and integration of various small cells. Heterogeneous networks with small cells offer key benefits such as high frequency reuse, huge data rates and low power consumption for mobile nodes. High frequency reuse is particularly important given the exponential growth of data rates and the looming frequency scarcity problem. Another major consideration for 5G is the energy efficiency as with the exponential growth of demand, power consumption in the information and communication technology (ICT) sector is also expected to significantly increase. While the concept of small cells in heterogeneous networks addresses the frequency scarcity problem to a great extent, unless otherwise carefully managed, a large number of uncoordinated and lightly loaded small cells significantly increase the overall power consumption, contrary to the green communication target of the 5G standard. In this paper, we focus on an energy efficient resource management and planning system that investigates the impact of growing number of small cells in a macro cell. We present an analytical model for calculating the optimum number of small cells that need to be kept awake at various hours of a day for meeting the quality of service demands from all users. Simulation results show significant power consumption improvement than an existing model.

The deployment of mobile small cells has been identified as an effective strategy in delivering high data rates and providing seamless connectivity to a group of vehicular users. Although this approach can facilitate high data rates and ubiquitous wireless access, it introduces a new set of challenges, for instance, frequent changes in the interference set, requirements for more spectrum, high energy consumption and frequent handover. In this paper, we present a multi-hop mobile small cell network (SCN) that can facilitate wireless access to mobile nodes that do have direct transmission links to small cell base stations. We then formulate a solution to the frequency allocation problem for a multi-hop mobile SCN based on an optimization model. Considering the complexity of the optimization solution, we present a backhaul- aware frequency allocation solution based on the time-varying graph coloring concept. Simulation results confirm that the proposed solution outperforms an existing greedy solution by a significant margin in terms of throughput, spectral efficiency and fairness index.

Using p-Cycles to protect against single span failures in Wavelength-Division Multiplexing (WDM) networks has been widely studied. p-Cycle retains not only the speed of ring-like restoration, but also achieves the capacity efficiency over mesh networks. However, in selecting an optimal set of p-cycles to achieve the minimum spare capacity and fast computational time is an NP-hard problem. To address this issue, we propose a heuristic approach to iteratively select and refine a set of p-cycles, which contains two algorithms: the Heuristic p-Cycle Selection (HPS) algorithm, and the Refine Selected Cycles (RSC) algorithm. Our simulation results show that the proposed approach is within 3.5% redundancy difference from the optimal solution with very fast computation time even for large networks.

We propose a joint optimization model for capacity design of networks with p-cycles. The model is based on a modified definition of network fundamental cycles and the available straddling links. Concepts about visible and hidden straddling links, which are essential components of our model are also introduced. This is the first ILP model for joint optimization of p-cycle network that can be solved without enumerating p-cycle candidates, and has the ability to achieve optimum solutions. In addition, the complexity of our proposed model is much smaller than any conventional models, particularly when applying to a planar network. This model is suitable large size networks and for shared risk link group networks or backbone networks protected by p-cycle schemes.