Autonomous Underwater Vehicles (AUVs) provide a new prospect for data collection in underwater wireless sensor networks (UWSNs). For dynamic underwater environments, researchers typically apply deep reinforcement learning (DRL) to design multi-AUV collection schemes for UWSNs. However, these methods suffer from the following issues. 1)Overloaded observations. The importance of various observations for an AUV varies over time. Considering all observations equally complicates decision-making for subsequent actions. 2)Dynamic scale of AUVs and sensors. Once the number of AUVs or sensors is changed, traditional static neural networks require retraining, lacking scalability across diverse scenarios. To solve the above issues, we propose a Generalizable Attention-based Data collection scheme (GAMD) for Multi-AUV UWSNs, while enhancing AUVs’ collection efficiency. GAMD incorporates the attention mechanism with multi-agent DRL framework, which enables AUVs to prioritize observations more critical for action decisions. Moreover, we propose an adaptive information processing approach, enabling the AUV policy model to seamlessly adapt to various scenarios without retraining. Additionally, we develop a training paradigm with incremental complexity across different scale scenarios to simplify training process and accelerate convergence. Simulation results demonstrate that GAMD alleviates the training cost compared to the state-of-the-art methods, and simultaneously optimizes collection energy efficiency, collection time, and trajectory distance.

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and biased performance results. Moreover, novel UAN schemes increasingly integrate Artificial Intelligence (AI) techniques, yet existing simulators lack support for necessary AI frameworks, failing to train and evaluate these intelligent methods. On the other hand, these novel schemes consider more UAN characteristics involving more complex parameter configurations, which also challenge simulators in flexibility and fineness. To keep abreast of advances in UANs, we propose the Fourth Generation (FG) network simulator-3 (ns-3)-based simulator Aqua-Sim FG, enhancing the general and intelligent simulation ability. On the basis of retaining previous generations’ functions, we design a new general architecture, which is compatible with various programming languages, including MATLAB, C++, and Python. In this way, Aqua-Sim FG provides a general environment to simulate communication technologies, network protocols, and AI models simultaneously. In addition, we expand six new features from node and communication levels by considering the latest UAN methods’ requirements, which enhances the simulation flexibility and fineness of Aqua-Sim FG. Experimental results show that Aqua-Sim FG can simulate UANs’ performance realistically, reflect intelligent methods’ problems in real-ocean scenarios, and provide more effective troubleshooting and optimization for actual UANs. The basic simulator is available at https://github.com/JLU-smartocean/aqua-sim-fg.

To facilitate the exploration and exploitation of underwater resources, autonomous systems and underwater acoustic networks (UANs) are deployed for tasks unsuitable for direct human intervention and exchange information between devices. To keep the reliability of information exchanged, direct-sequence spread spectrum (DSSS) communication is commonly adopted for this scenario. To simplify the channel equalization process in DSSS communication, a minimum-mean-square-error (MMSE) equalizer is often utilized. However, the characteristics of underwater acoustic channels and acoustic modem, including long delay spreads and limited computational resources, lead to high computational complexity for MMSE equalization, thereby reducing decoding efficiency. To address this challenge, we propose a refined MMSE equalizer, termed the efficient MMSE equalizer (EME). Unlike conventional MMSE methods, the EME approach involves initially despreading the received chip sequence, followed by equalizing on the noisy symbols. By reducing the size of the correlation matrix in the core computational step of MMSE equalization, our method significantly improves computational efficiency. We assess the computational complexity of the proposed EME approach in comparison to conventional MMSE equalization and validate its performance through simulations and experimental studies. The results demonstrate that the EME achieves a bit error rate (BER) performance comparable to that of conventional MMSE equalization under high signal-to-noise ratio (SNR) conditions, while significantly enhancing computational efficiency.

Single beacon passive positioning is crucial for the covert and rapid navigation of autonomous underwater vehicles (AUVs). Traditional methods, which assume a fixed beacon position, known sound speed, and error-free AUV heading, limit the applicability and accuracy of passive positioning systems. In this article, we proposed an angle-based passive positioning algorithm, underwater moving bearing angle of arrival (U-MBAOA), to solve the above problems. First, we replace the bottom-anchored fixed beacon with a mobile surface beacon and develop a dynamic positioning model to accommodate its movement. Then, to enhance the positioning accuracy, we parameterized the sound speed and derived a weighted least squares method to estimate the sound speed. Finally, we constructed an error model for heading errors and positioning under a straight-line trajectory and combined this model with U-MBAOA, achieving the estimation of heading errors in passive positioning for the first time. Through simulation analysis and sea tests, we confirmed that our algorithms outperform the existing algorithms.

This paper proposes a novel multi-cluster network system for autonomous underwater vehicle (AUV) swarm communication and positioning, addressing challenges of low throughput, high end-to-end delay, and significant ranging errors in traditional asynchronous underwater acoustic networks. The proposed system introduces a novel hybrid Medium Access Control (MAC) protocol, which combines intra-cluster Time Division Multiple Access (TDMA) with inter-cluster Frequency Division Multiple Access (FDMA) to enhance channel utilization. Additionally, the system introduces an adaptive time-offset estimation method based on network data exchange and the Ultra-Short Baseline Moving Ranging (USBL-MR) algorithm, which significantly improves network throughput and reduces delay and improves positioning accuracy. Field trials demonstrate that the system achieves a throughput of 398.3 bps with an end-to-end delay of 21.2 s. Furthermore, the ranging Root Mean Square Error (RMSE) has been reduced from 7.87 to 2.42 m, marking a 14% improvement in positioning accuracy.

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput.

Autonomous Underwater Vehicle (AUV)-assisted Underwater Acoustic Networks (UANs) are promising for complex ocean applications. In essence, an AUV-assisted UAN is still dominated by fixed nodes, and Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have undisputed practicability in such fixed nodes-dominated UANs since they are simple and easy to deploy. However, AUV-assisted UANs may exist dynamic bidirectional data streams, while most existing protocols assume UANs have a unidirectional data stream, and their fixed scheduling sequence results in the long end-to-end delay in AUV-assisted UANs. In this paper, we first reveal a phenomenon between the data stream and the scheduling sequence, derived from real-world experiments: their consistent direction decreases the packet waiting delay but increases the slot length, and vice versa. To optimize the end-to-end delay, UANs with dynamic bidirectional data streams expect the MAC protocol to provide a flexible scheduling sequence. To this end, we propose a low-delay Adaptive Scheduling MAC protocol (AS-MAC) based on TDMA for AUV-assisted UANs. In AS-MAC, we analyze the relationship between scheduling sequence and data stream, extracting two significant factors: slot length and packet delay. Afterwards, we design Slot Length Model (SLM) and Packet Delay Model (PDM) to analyze the end-to-end delay of different data streams. Based on these two models, we present a Scheduling Sequence and Slot Length allocation Algorithm (SSSLA) to adaptively provide the minimum end-to-end delay for current bidirectional data streams. Extensive simulation results show that AS-MAC efficiently addresses severe queue congestion of the state-of-the-art protocols and reduces the end-to-end delay of different dynamic streams in various scenarios.

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs’ environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks’ demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation; (ii) greatly reduce the training time of AI algorithms; (iii) more rapidly obtain network status for multi-task scheduling at a low cost.

Increasing demands for versatile applications have spurred the rapid development of Unmanned Underwater Vehicle (UUV) networks. Nevertheless, multi-UUV movements exacerbates the spatial-temporal variability, leading to serious intermittent connectivity of underwater acoustic channel. Such phenomena challenge the identification of reliable paths for high-dynamic network routing. Existing routing protocols overlook the effects of UUV movements on forwarding path, typically selecting forwarders based solely on the current network state, which lead to instability in packet transmission. To address these challenges, we propose a Routing protocol based on Spatial-Temporal Graph model with Q-learning for multi-UUV networks (STGR), achieving high reliable and energy effective transmission. Specifically, a distributed Spatial-Temporal Graph model (STG) is proposed to depict the evolving variation characteristics (neighbor relationships, link quality, and connectivity duration) among underwater nodes over periodic intervals. Then we design a Q-learning-based forwarder selection algorithm integrated with STG to calculate reward function, ensuring adaptability to the ever-changing conditions. We have performed extensive simulations of STGR on the Aqua-Sim-tg platform and compared with the state-of-the-art routing protocols in terms of Packet Delivery Rate (PDR), latency, energy consumption and energy balance with different network settings. The results show that STGR yields 24.32 percent higher PDR on average than them in multi-UUV networks.

Underwater Wireless Sensor Networks (UWSNs) represent a promising technology that enables diverse underwater applications through acoustic communication. However, it encounters significant challenges including harsh communication environments, limited energy supply, and restricted signal transmission. This paper aims to provide efficient and reliable communication in underwater networks with limited energy and communication resources by optimizing the scheduling of communication links and adjusting transmission parameters (e.g., transmit power and transmission rate). The efficient and reliable communication multi-objective optimization problem (ERCMOP) is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). ATraffic Load-AwareResourceManagement (TARM) strategy based on deep multi-agent reinforcement learning (MARL) is presented to address this problem. Specifically, a traffic load-aware mechanism that leverages the overhear information from neighboring nodes is designed to mitigate the disparity between partial observations and global states. Moreover, by incorporating a solution space optimization algorithm, the number of candidate solutions for the deep MARL-based decision-making model can be effectively reduced, thereby optimizing the computational complexity. Simulation results demonstrate the adaptability of TARM in various scenarios with different transmission demands and collision probabilities, while also validating the effectiveness of the proposed approach in supporting efficient and reliable communication in underwater networks with limited resources.

Underwater acoustic networks (UWANs) play a vital role in the Internet of Underwater Things (IoUT), enabling critical functions, such as communication, data collection, and navigation. Among the applications of the IoUT, localizing a mobile node (MN) via a UWAN is particularly promising. However, the existing localization algorithms are ineffective when faced with an insufficient number of time of arrival (TOA) measurements for an unsynchronized MN due to the presence of sparsely deployed anchor nodes and signal reception issues. Although tracking methods can offer position predictions, their accuracies are compromised over time due to the movement of MNs. To overcome these challenges, we propose a methodology that combines the departure time of a beacon signal (DOB) with limited TOA measurements. This methodology enables an MN to be localized using a TOA-based method, which typically requires fewer measurements than a time difference of arrival (TDOA)-based method. Based on this methodology, we introduce an algorithm called localization with insufficient TOA measurements (LITM), which comprises two subalgorithms: one for estimating and tracking the DOBs and the other for localizing MNs through a closed-form solution. Together, these subalgorithms provide accurate MN position estimates under the constraint of insufficient TOA measurements. To validate the performance of our proposed algorithm, we conduct both simulation studies and sea experiments. The results demonstrate the superior effectiveness and position estimation accuracy of our algorithm compared to those of the existing methods.

Localization is vital and fundamental for underwater acoustic sensor networks (UASNs), as it provides location information for UASNs to achieve various practical underwater tasks. Most existing localization methods assume small-scale scenarios without battery energy constraints, making it inapplicable to large-scale UASNs. In large-scale UASNs, localization suffers from the challenges of excessive energy consumption and large localization error because of harsh underwater conditions like node mobility and huge ranging errors. To this end, we propose an efficient localization scheme with velocity prediction (LSVP) to solve the above challenges for large-scale UASNs. LSVP considers node mobility, ranging errors, and energy balance in a unified framework, which is applicable to realistic and scalable UASNs. Specifically, we first design a Doppler-assisted velocity prediction (DVP) algorithm to decrease energy consumption, which can solve the excessive communications caused by node mobility under ocean currents. Then, a acrlong CIL algorithm is proposed to decrease the localization error, which can reduce location uncertainty and error propagation caused by ranging errors. Extensive simulation results indicate that LSVP can achieve accurate velocity prediction and high precision localization for large-scale UASNs.

Localization is a vital service in underwater acoustic sensor networks (UASNs). Autonomous underwater vehicles (AUVs), with their mobility and collaborations can provide accurate, extensive, and efficient localization service for large-scale UASNs. During localization, AUVs travel along the predefined paths and broadcast reference messages to aid sensor nodes in estimating locations. However, AUVs-aided localization faces the following two challenges: 1) complex localization path planning for multiple AUVs, which requires consideration of localization accuracy and optimization of travel path simultaneously and 2) harsh underwater localization conditions, such as unsynchronized clocks and stratification effects seriously affect the localization accuracy. To this end, an efficient AUVs-aided localization scheme (EAL) is proposed for large-scale UASNs, which jointly addresses the path planning and localization in an unified framework. Specifically, we propose a graph-based localization path planning mechanism, which considers the impact of path on localization and determines effective travel paths for AUVs. Furthermore, we design an iteration-based asynchronous localization mechanism, which could compensate the stratification effect and achieve accurate localization for the sensor nodes. Extensive simulation results show that the EAL can achieve efficient and high accuracy localization for the sensor nodes with the aid of multiple AUVs.

The automatic modulation classification for surface and underwater sensors in the perception layer is crucial in the Internet of Underwater Things (IoUT), where Deep Learning (DL) is becoming an important tool to improve classification accuracy. This work focuses on the radio environment in the perception layer. The biggest challenge in popular DL-based methods is deploying the algorithm in edge devices with limited computing power. Network pruning has been found to be a critical and effective method for network lightweight and the improvement of resources, thus mitigating potential interference. While not studied in previous work, this paper fills the hole in algorithm deployment’s criterion selection and accuracy loss. Specifically, we develop a Convolutional Neural Network (CNN) based lightweight framework on distinguishing modulated signals from generated datasets (which is named DLocean) in different Signal-to-Noise Ratios (SNR). The performance of the lightweight framework is tested on the edge device. The experiments demonstrate that the proposed model compensates for accuracy and can successfully classify the modulation schemes with 93.4% accuracy at the SNR=5 dB. Our results also show that the proposed framework can improve performance without exceeding the original network complexity on edge device deployment.

Underwater wireless sensor networks (UWSNs) can provide services to the ocean. The deployment is one of the key problems in UWSNs. Optimizing networks power consumption and coverage has always been a huge challenge in deployment. Existing deployment models do not consider the asymmetric-link phenomenon and power control. In this paper, a new multi-objective optimization deployment model with power control in UWSNs is proposed to obtain a deployment scheme, which reduces the power consumption, and the asymmetric-link phenomenon is considered. At the same time, due to the unsatisfactory optimization performance and stability of some current algorithms, this paper proposes an algorithm named Crow-Colony Search Optimization Algorithm (C-CSOA) to optimize the deployment scheme. In this algorithm, we adopt the framework of Colony Search Optimization Algorithm (CSOA). In addition, we combine the advantages of Crow Search Algorithm (CSA) and CSOA to improve the optimization performance of the algorithm. We conducted simulation experiments, and the results indicate that: First, the model we proposed is feasible. Second, an efficient deployment scheme can be obtained by C-CSOA. Third, when reaching the predetermined network coverage, the total power consumption of UWSNs is significantly reduced (nearly 23.93% in average). By comparing with various advanced optimization algorithms, it is showed that C-CSOA has advantages of the good optimization performance and the small standard deviation.

Addressing challenges like ocean currents, dynamic obstacles, collision avoidance, and computing limitations, a hybrid algorithm is proposed for multi-AUVs to search for random static or dynamic targets in a 3D marine environment and the environment is characterized by our Task Urgency Biological Neural Network (TUBNN) model, encapsulates factors such as currents, obstacles, and targets using neuronal activity representations. For optimizing task assignments in the face of unpredictable oceanic variables, a Double Layer Biological Self-organizing Map (DLBSOM) algorithm is proposed. Post assignment, the initial path, generated via DLBSOM, is refined by the end-to-end direct connection model to eliminate superfluous routes. Enhanced by the A$^\ast$algorithm and Fermat’s spiral curve, our approach ensures paths align with AUV navigational constraints. Integrating real-time sonar data, our Distance-Speed Comprehensive Evaluation (DSCE) strategy, combined with the local dynamic neural network, facilitates the avoidance of unforeseen barriers, ensuring safe AUV navigation. The correction redistribution mechanism, leveraging live data and assessing AUV and target statuses, refines task assignment. We holistically evaluate environmental, cooperative, and AUV-specific constraints, with our algorithm’s efficacy validated through rigorous theoretical analysis, simulations, and field tests.

This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessively on the rationality of other underwater nodes when guided by fully cooperative approaches, making it difficult to seek a trade-off between individual QoS and global fair-effective communications under imperfect conditions. Therefore, this paper presents aSEmi-COoperativePowerAllocation approach (SECOPA) that achieves fair-effective communication and robustness in IC-UASNs. The approach is distributed multi-agent reinforcement learning (MARL)-based, and the objectives are twofold. On the one hand, each intelligent node individually decides the transmission power to simultaneously optimize individual and global performance. On the other hand, advanced training algorithms are developed to provide imperfect environments for training robust models that can adapt to the time-varying acoustic channels and handle unexpected node failures in the network. Numerical results are presented to validate our proposed approach.

Medium Access Control (MAC) protocols are critical for scheduling resources to access multiple users without collisions in Underwater Acoustic Networks (UANs). Due to harsh marine environments and limited communication resources, UANs lack a standard MAC protocol to adapt to various scenarios. The specific UAN scenario suffers from how to analyze multiple basic MAC protocols’ performance boundaries and modify the most potential one. A practical solution is to evaluate MAC protocols’ performance by modeling data loss (collisions and packet errors) and service time. However, existing models provide inaccurate performance results, since they ignore the effects of unique UANs’ characteristics and MAC protocol diversity on data loss and service time. In this paper, we propose a MAC Performance Analysis Framework (MPAF) for UANs to consider both unique UANs’ characteristics and MAC protocols’ diversity. We design Successful Transmission Probability (STP) model and Packet Service Time (PST) model in MPAF to estimate nodal throughput, delay, and energy consumption. STP model analyzes data loss types of different MAC protocols by considering long propagation delay, half-duplex communication, and random backoff to achieve a superior STP result from a view of real underwater communication conditions. Based on STP model, we employ Markov chain to deduce the retransmission number in PST model. In this way, MPAF ensures effectiveness and applicability in real-ocean environments. Extensive simulation results show that MPAF can accurately evaluate different MAC protocols’ performance boundaries, select the most appropriate basic protocol, and provide modified suggestions for a specific UAN scenario.

Underwater wireless sensor networks (UWSNs) stand as promising technologies facilitating diverse underwater applications. However, the major design issues of the considered system are the severely limited energy supply and unexpected node malfunctions. This paper aims to provide fair, efficient, and reliable (FER) communication to the imperfect and energy-constrained UWSNs (IC-UWSNs). Therefore, we formulate a FER-communication optimization problem (FERCOP) and propose ICRL-JSA to solve the formulated problem. ICRL-JSA is a deep multi-agent reinforcement learning (MARL)-based optimizer for IC-UWSNs through joint link scheduling and power allocation, which automatically learns scheduling algorithms without human intervention. However, conventional RL methods are unable to address the challenges posed by underwater environments and IC-UWSNs. To construct ICRL-JSA, we integrate deep Q-network into IC-UWSNs and propose an advanced training mechanism to deal with complex acoustic channels, limited energy supplies, and unexpected node malfunctions. Simulation results demonstrate the superiority of the proposed ICRL-JSA scheme with an advanced training mechanism compared to various benchmark algorithms.

Performing a high-capacity Medium Access Control (MAC) protocol suffers from low bandwidth and long propagation delay in Underwater Acoustic Networks (UANs). Non-Orthogonal Multiple Access (NOMA) is a promising technology to assist MAC protocols in overcoming the above restrictions and improving UANs’ capacity. It enables multiple users to access the same frequency-time resource based on power or code differences of user classes. However, UANs lack MAC research employing NOMA’s physical advantages. Most existing NOMA-based MAC protocols are designed for terrestrial networks, which are inapplicable to UANs. In classifying users, they ignore the effects of harsh marine environments on acoustic channels and fail to decrease channels’ interference, resulting in conflicting communications. Moreover, such unreasonable classification results further affect resource allocation, leading to low transmission rate and high energy consumption in UANs. In this paper, we propose a Hybrid NOMA-based MAC protocol (HN-MAC) to achieve efficient concurrent communication for UANs. Specifically, HN-MAC combines power-domain and code-domain NOMA to classify users and allocate communication resources. For the user classification, we propose an Adaptive Clustering Algorithm (ACA), which dynamically determines the clusters’ number and classifies users based on channel gain and channel correlation under multipath conditions. In this way, HN-MAC decreases interference among multiple users in various ocean scenarios. During the resource allocation, we formulate a joint allocation problem of transmission power and codebook based on the clustering result to optimize transmission rate and energy consumption. Further, a genetic algorithm is proposed to solve the allocation problem by considering resource constraints. Simulation results show that HN-MAC provides more stable concurrent communications with less resource consumption than the state-of-the-art protocols in various UANs.

Providing qualified and fair communications for underwater wireless sensor networks (UWSNs) has garnered considerable interest in light of scarce resources and dynamic channel conditions. Fairness is critical in various situations, including emergency communications in resource-constrained underwater networks that balance load and energy among nodes to optimize network performance. Existing solutions for fair communications, on the other hand, frequently come at the expense of network capacity. This article focuses on the power-management strategy that jointly optimizes the reuse, fairness, and capacity of UWSNs while also proposing a new metric for fair spatial reuse in networks: the fair reuse index (FRI). We observe that the FRI for UWSNs is strongly reliant on the network density, channel conditions, and application requirements. As a result, UWSNs commonly exhibit load imbalance and struggle to meet required network lifetimes. Toward this end, we propose DMPM, a deep multiagent reinforcement learning-based power-management strategy for increasing the fair reuse of UWSNs. DMPM strives to maximize the network’s fair reuse while allowing for gentle network capacity decrease. In two representative communication scenarios, numerical results demonstrate that DMPM achieves a significantly better tradeoff between network capacity and fair reuse than baseline techniques. We also construct three reward functions for DMPM and discuss how different reward functions affect node behaviors. Delivery delays of different models are discussed. We hope that the work provided in this study will prove to be invaluable in the design and optimization of UWSNs.

Performing an effective media access control (MAC) protocol suffers from strong dependencies between Underwater Acoustic Networks’ upper and lower layers: 1) the network layer frequently uses geo-routing protocols, which do not provide the specific next-hop for MAC protocols, resulting in serious data collisions and 2) in such scenarios with the unknown next-hop, fixed orthogonal frequency-division multiplexing (OFDM) resource does not adapt to the changing environment, and degrades network performance (OFDM is a mature modulation technology in the physical layer). However, there is scant research on MAC protocols considering the network layer and the physical layer simultaneously, to solve data collisions and resource allocation. To this end, we present a cross-layer MAC protocol to integrate Geo-routing protocols and OFDM technology (GO-MAC) at the same time. GO-MAC employs a handshake scheme to allocate optimal communication resources and select the next-hop concurrently. First, we formulate the OFDM resource allocation as a joint optimization problem based on transmission mode, subcarrier spacing, guard interval, and transmission power, to decrease transmission delay and energy consumption. Then, a Karush–Kuhn–Tucker conditions-based Heuristic algorithm (KKT-H) is proposed to solve this problem. Finally, we consider node congestion and channel quality to assist geo-routing protocols with the next-hop selection, and decrease packet collisions. Simulation results show that our protocol matches geo-routing protocols and OFDM technology better than the state-of-the-art protocols, providing higher end-to-end reliability with lower costs.

Propagation delay and channel loss are two vital factors affecting reliability of Underwater Acoustic Networks (UANs). Different from land networks, UANs have long prop-agation delay and poor channel quality, which lead to serious data collision and high bit error rate, respectively. However, complex underwater environments impose great challenges to evaluate propagation delay and channel loss. As temperature is the most critical factor affecting them, in this paper, we propose to employ temperature to evaluate them. However, existing temperature prediction research are insufficient for accuracy or efficiency. This paper proposes a temperature prediction-assisted approach for evaluating propagation delay and channel loss, aiming to improve reliability and performance of underwater acoustic networks. We build a nonlinear autoregressive dynamic neural network-based temperature prediction model to improve prediction accuracy and reduce time complexity. Then, we evaluate propagation delay and channel loss considering different marine environments, including shallow and deep sea. Extensive simulation results show that our approach performs better than five advanced baselines.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine monitoring, military reconnaissance, hydrology surveys, etc. Their location information is an important apriori knowledge when they are carried out underwater. However, the complex underwater environments impose great challenges on location acquisition, especially for autonomous underwater vehicles (AUVs), because of their mobility and finite power. In UASNs, existing location and navigation methods can offer AUVs position information, but they may either need a doppler velocity log (DVL), which is inefficient due to the complex underwater environments, or they may require additional localization infrastructure to deploy underwater, which suffers from large communication latency among AUVs, and costs enormous power. In this article, an efficient velocity estimation and location prediction method (VELP) in UASNs is proposed to avoid the above restrictions. It only utilizes collaborations based on communication among AUVs to achieve higher precision location with lower cost. Specifically, we apply an AUV-assisted velocity estimation algorithm with Doppler shift estimation in the physical layer of UASNs to improve the velocity estimation accuracy instead of the DVL. Meanwhile, we build a belief propagation-neural network-based location prediction model, which decreases the communication requirements and obviates introducing modeling errors. Extensive experimental results show VELP achieves superior performance on both accuracy and efficiency, demonstrating its great advantage in offering AUVs’ location information.

Providing qualified and sustainable communications is one of the key challenges for the Internet of Underwater Things (IoUT) facing constrained energy supplements, nonstationary environments, and severe communication interference. Owing to spatial separation, several nodes can (and are often required to) make transmissions simultaneously to maximize network capacity. However, existing transmission solutions often face the dilemma between maximizing local capacity and global concurrency. We break this dilemma via UDARMF, an underwater distributed and adaptive resource management framework, which maximizes network capacity by supporting an increased number of communications in the network. It is a distributed deep multiagent reinforcement learning framework that uses an observation encoder and a local utility network to coordinate the collaboration among underwater nodes by adaptively tuning its transmit parameters. We designed experiments to compare UDARMF with baselines in network capacity, concurrency, and energy efficiency. Extensive experiments were conducted to find the appropriate hyperparameters to achieve the optimal network performances. We also analyze the performance of UDARMF and baselines over diverse communication and lifetime requirements, communication environment, and energy storage. Simple closed-form approximations of UDARMF are given to reveal that an energy-constrained network’s capacity increases with available energy, following a linear trend on the logarithmic scale. Experimental results demonstrate that compared to other methods, UDARMF achieves a much better tradeoff between network capacity and concurrency, at which the lifetime requirements are satisfied. The proposed framework and the closed-form approximations are likely to become valuable tools in designing and analyzing IoUT.

As one of the most promising networks, the space–air–ground–aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, and unbalanced network load. In this article, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply the edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Second, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Third, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a preprocessing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This article presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.

For an autonomous underwater vehicle (AUV), navigation is a key functionality. Dead-reckoning (DR) navigation is an important class among all the AUV navigation methods. In DR, the measurement errors of inertial sensors (such as gyroscopes and accelerometers) lead to accumulated errors with time, which affect navigation accuracy significantly. Especially, accumulated errors in fast-changing environments, such as waves near or on the surface, are tough to handle. In this article, we propose a neural-network-based AUV navigation method for fast-changing environments, called NN-DR. NN-DR employs the neural network to predict pitch angles accurately, which is our core contribution. In NN-DR, we smoothly integrate the Kalman filter, neural network, and velocity compensation to reduce accumulated errors. Extensive simulation experiments are conducted to test the correctness and stability of NN-DR, and the results show that NN-DR is very effective in lowering accumulated errors. For instance, at time 300 s, NN-DR achieves superior performance on accuracy for navigation, about 160 times than the state-of-the-art DR methods, demonstrating great advantage on AUV navigation for fast-changing environments.

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Cognitive acoustic (CA) is emerging as a promising technique for environment-friendly and spectrum-efficient underwater communications. Due to the unique features of underwater acoustic networks (UANs), traditional spectrum management systems designed for cognitive radio (CR) need an overhaul to work efficiently in underwater environments. In this paper, we propose a receiver-initiated spectrum management (RISM) system for underwater cognitive acoustic networks (UCANs). RISM seeks to improve the performance of UCANs through a collaboration of physical layer and medium access control (MAC) layer. It aims to provide efficient spectrum utilization and data transmissions with a small collision probability for CA nodes, while avoiding harmful interference with both “natural acoustic systems”, such as marine mammals, and “artificial acoustic systems”, like sonars and other UCANs. In addition, to solve the unique challenge of deciding when receivers start to retrieve data from their neighbors, we propose to use a traffic predictor on each receiver to forecast the traffic loads on surrounding nodes. This allows each receiver to dynamically adjust its polling frequency according to the variation of a network traffic. Simulation results show that the performance of RISM with smart polling scheme outperforms the conventional sender-initiated approach in terms of throughput, hop-by-hop delay, and energy efficiency.

In recent years, the underwater cognitive acoustic network (UCAN) has been advocated as an efficient technique to enhance the utilization of acoustic channel, while not interrupting the activity of marine mammals, sonars and other acoustic users. In cognitive radios, the common control channel (CCC) based media access control (MAC) protocols are very popular for their high reliability, easy implementation and low overhead. However, due to the severe frequency-dependent attenuation of acoustic waves, a UCAN may not have enough bandwidth for CCC. How to prevent the control channel from congesting in a UCAN with heavy traffic should be investigated carefully. With this in mind, we propose a dynamic control channel MAC (DCC-MAC) for distributed UCANs. Nodes in DCC-MAC could adjust the bandwidth of their control channel adaptively based on the situation of network traffic. Whenever acoustic nodes detected the congestion of CCC, they could flexibly select proper data channels to extend the bandwidth of their control channel, and return excessive frequency bands back when the control channel becomes idle. Simulation results show that DCC-MAC could reduce the collision probability among control messages significantly, thereby providing a better network performance in terms of throughput and energy efficiency than conventional cognitive MAC protocols.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate the time synchronization and localization problems in underwater sensor networks, where more challenges are introduced because of the unique characteristics of the water environment. These challenges include long propagation delay and transmission delay, low bandwidth, energy constraint, mobility, etc. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm interactive multiple model (IMM) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

Abstract Recent surges in the development of underwater acoustic networks (UANs) have lead to a rapid acceptance of this technology in scientific, commercial, and military applications. However, limited work has been performed on developing secure communication mechanisms and techniques to protect these networks. Security mechanisms are wildly studied in terrestrial networks, and various defense mechanisms have been developed as safeguards. Because of the difference in communication mediums and physical environments, the existing solutions for terrestrial networks cannot be directly applied for UANs. In this paper, we study the effects of denial‐of‐service jamming attacks on UANs using real‐world field tests. We develop our own jammer hardware and signals in order to analyze the characteristics of different jamming attack models on a network. Our tests are performed on multiple commercial brand acoustic modems and an orthogonal frequency division multiplexing modem prototype. We show that UANs can be easily jammed using carefully timed attacks, which are energy efficient. Copyright © 2012 John Wiley & Sons, Ltd.

Recently, various medium access control (MAC) protocols have been proposed for underwater acoustic networks (UANs). These protocols have significantly improved the performance of MAC layer in theory. However, two critical characteristics, low transmission rates and long preambles, found in the commercial modem-based real systems, severely degrade the performance of existing MAC protocols in the real world. Thus, a new practical MAC design is demanded. Toward an efficient approach, this paper analyzes the impact of these two modem characteristics on the random access-based MAC and handshake-based MAC, which are two major categories of MAC protocols for UANs. We further develop the nodal throughput and collision probability models for representative solutions of these two MAC protocol categories. Based on the analyses, we believe time sharing-based MAC is very promising. Along this line, we propose a time sharing-based MAC and analyze its nodal throughput. Both analytical and simulation results show that the time sharing-based solution can achieve significantly better performance.

This brief presents a system design technique for improving the energy utilization of radio frequency (RF) circuits powered by renewable energy sources. Different from conventional systems, the operation of self-powered RF circuits is largely constrained by two factors: time-varying channel conditions and nondeterministic renewable energy levels. The proposed technique dynamically adjusts the modulation scheme to deal with these two factors in a coherent manner. This is the effective way to maximize the data rate of RF circuits while maintaining the required performance under unstable energy supplies. A detailed VLSI implementation is developed with negligible energy overheads. Simulation results demonstrate that the proposed technique outperforms conventional RF circuits based on the fixed modulation scheme under variable channel and energy conditions.

Reliable broadcast is a critical service for Underwater Acoustic Networks (UANs). In this paper, we propose a Two-phase Broadcast Scheme (TBS) for UANs. TBS includes two phases: Fast Spreading phase and Data Recovery phase. It does not require topology or neighbor information. In the Fast Spreading phase, which is a best effort phase, opportunistic overhearing and network coding are combined to accumulate encoded packets. A probability based forwarder selection scheme is employed to alleviate the broadcast storm problem. A rebroadcast scheduling algorithm is also proposed to reduce collisions. The Data Recovery phase is to guarantee reliability if a node fails to complete data decoding in the Fast Spreading phase. Through delayed request sending, the Data Recovery phase at a node will not interfere with the Fast Spreading phase at other nodes. Through simulations, we demonstrate the advantages of TBS in terms of efficiency and reliability.

In underwater acoustic networks (UANs), due to the unique characteristics of acoustic modems such as long preambles and extremely low transmission rates, contentions are usually costly. As a result, both random access and handshake based MAC protocols do not perform as well as expected. A collision-free approach is therefore considered to more likely achieve a better performance. Following this principle, we propose a collision-free scheduling-based MAC protocol, called DOS, for UANs. DOS is a cluster-based protocol, where each cluster head independently generates on-demand and collision-free schedule for its cluster members using local information. Through pure scheduling and cooperation among neighboring clusters, DOS guarantees collision-free transmissions of both control and data packets. Compared with existing collision-free scheduling based MAC protocols, DOS is distributed and on-demand, i.e., it schedules according to nodes' dynamic transmission requests. Further, DOS does not require CDMA or power adjustment for collision resolution. Extensive simulation results show that DOS far outperforms random access and handshake based MAC protocols. It also achieves a comparable throughput to a centralized scheduling algorithm, and this is a big accomplishment for DOS, a distributed solution.

Underwater Acoustic Networks (UANs) are a transformative technology that is helping expand military, commercial and scientific applications in deep sea environments. The nature of network deployment in these deep sea environments has spurred a new paradigm in network routing known as pressure routing. Pressure routing is based on geographic routing but only uses limited location information, namely depth information, to route data from the sea floor to the surface. Recent work has shown that existing pressure routing protocols are vulnerable to malicious intrusions. In this paper we propose a resilient pressure routing protocol that seeks to reduce the effectiveness of malicious attackers, such as spoofing attacks. We evaluate our proposed protocol in a simulation environment and show that it reduces the effectiveness of spoofing attacks and maintains routing performance with minimal trade-off.

Swarms of autonomous underwater vehicles (AUVs) forming mobile underwater networks often operate in moving currents, which introduce severe turbulence that interferes with coordinated and stealthy navigation of fleet. Therefore, individual AUV must adjust their heading whenever needed to ensure it can reach a pre-determined destination. To achieve accurate navigation, AUVs must maintain precise knowledge of their locations. This paper develops the “Suave” (Swarm underwater autonomous vehicle localization) algorithm to localize swarms of AUVs operating in rough waters. The purpose of Suave is to ensure that all AUVs arrive at their destinations by preserving localization throughout the entire mission. Suave lowers the probability that an AUV swarm is detected by reducing the number of occasions that vehicles must surface to obtain accurate location information from external sources such as satellites. The Suave algorithm also achieves better energy conservation through improved control of localization reference messages. Simulations show Suave significantly improves localization accuracy, lowers energy consumption, and the probability of swarm detection.

Optimal deployment of multi-surface gateways is a challenging task that has been shown to improve the performance of underwater sensor networks (UWSN). However, the dynamics of both the surface gateways and the underlying sensor deployments can impair the benefits of an initial deployment. In this paper we opt to tackle the dynamics of the underwater environment by adaptive redeployment of the surface gateways. Surface-gateways decide on the timing of the redeployment by keeping track of the transmission power used by nodes during communications. They coordinate their redeployment plan based on the outcome of an optimization algorithm to find the best global solution to minimize the overall transmission power level used by all the nodes in the network. We have used greedy heuristics to solve the optimization problem to cope with processing limitations of UWSN nodes. Our results show that our strategy results in a reduction in the overall energy consumption and the average end to end delay.

Underwater Sensor Networks are associated with a number of applications that require real-time delivery of large data. Examples include: still frame images or full-motion videos in seismic monitoring setups, oil pipe leak detection, and capturing sea activities. However, the transmission of large data relying only on acoustic communication is challenging due to the unique characteristics of aquatic channels, mainly, limited available bandwidth and long propagation delays. In this paper, we advocate a new system architecture called Underwater Sensing and Processing Network (USPN). USPN adopts efficient data processing algorithms to reduce data size and hence improve channel utilization and reduce end-to-end delay. We develop an analytical model for two network scenarios. The first scenario includes a single processing node communicates with a gateway while in the second scenario data are communicated via relay nodes. The objective of our model is to maximize the performance gain in terms of reducing the overall end-to-end delay and power consumption. In addition, we analyze the tradeoff between different objective parameters and the obtained gain. Our results show USPN can maximize the performance gain dramatically.

Finding the position of a device within an underwater network, known as localization, has been critical for the advancement of autonomous underwater vehicle (AUV) research. Many of the current methods of underwater localization rely on a modem's ability to accurately estimate the time of flight for a given message transmission. This ability is also utilized by a modem's built-in ranging function. To advance the research in underwater localization, a comparison study was done on the ranging function of three commercially available underwater acoustic modems, all of which utilize different modulation schemes. A pair of AquaSeNT (OFDM), Benthos (MFSK), and LinkQuest (DSSS) acoustic modems were tested at several positions in both a pool and lake environment. It was found that the AquaSeNT was the most robust, as in it always calculated a distance, at the expense of precision. The precision of the AquaSeNT was effected by a consistent 8-m bias. The LinkQuest was found to be the most accurate and precise, but had issues communicating in short channels (large delay spread), and would occasionally produce erroneous measurements. The Benthos had a balance between communication success as well as precision and accuracy.

Underwater images are useful for researching the characteristics of the environment and its inhabitants. Using the underwater acoustic modems along with an autonomous underwater vehicle (AUV), images can be transmitted from a camera mounted on the AUV to a modem. A real-time implementation was done to transmit and receive images underwater using the AquaSENT acoustic modems. An image was extracted from a streaming web cam, resized, and split into individual packets which were capable of being transmitted by a modem. Once these packets were received by another modem, they were reconstructed to display the original image. The packet sizes that were transmitted were varied in a controlled test bed with shallow water. The preliminary results show that packets were generally fully received, and the transmitted image was identical to the received image.

Acoustic communications are the dominant method for underwater information transmission in underwater environments where radio is heavily absorbed underwater. Due to the vast discrepancy in propagation delay between acoustic and optical communication, short range low cost optical systems have the potential to compliment the capabilities of underwater acoustic networks.In this paper we evaluate several applications in which ultralow cost, short range optical transmitter-receiver pairs can be applied to improve the performance and utility of underwater acoustic sensor networks. Applications evaluated include time synchronization between nodes and TCP communication.Next, Broadcast MAC, a prototypical media access control protocol which performs well underwater is simulated in Aquasim. Results of this simulation show that schemes which focus on using optical links to reduce the total amount of data sent over the acoustic network are more worthwhile those which focus on reducing the overhead associated with adding additional nodes.Finally a prototype, low cost IR transmitter/receiver pair is constructed from parts which can be purchased for a few dollars. A parts list, circuit diagram, and details on setting up the linux time synchronization server and establishing a TCP/IP link over the serial connection are included. While a blue-green LED would have provided much greater range underwater ( 100 ft vs 1 ft), the inherent simplicity of this device should help to save a great deal of time in the implementation of other more advanced underwater optical communication systems.

Cognitive acoustic (CA) is emerging as a promising technique for environment-friendly and spectrum-efficient underwater acoustic networks (UANs). Due to the unique features of UANs, traditional spectrum management systems used for radio networks need an overhaul to work efficiently in underwater environments. In this paper, we propose a receiver-initiated spectrum management (RISM) system for underwater cognitive acoustic networks (UCANs). RISM seeks to significantly improve the performance of UANs through a collaboration of the physical layer and medium access control (MAC) layer. This system features collaborative spectrum sensing, efficient spectrum sharing and advanced spectrum decision algorithms. It aims to provide collision-free data transmissions and efficient spectrum utilization for CA users, while avoiding harmful interference with both “natural acoustic systems”, such as marine mammals, and “artificial acoustic systems”, like sonar users and other UANs. Simulation results show that, RISM can effectively operate in both tree topology and partially connected mesh topology networks and achieve collision-free data transmissions.

Coastal regions are vital to human beings. However, the marine habitats distributed along the 159,000km U.S. shoreline are subject to increasing stressors from both landside and seaward directions. To better understand these problems, new techniques and methods are needed to build large-scale, long-term, high-resolution, and self-sustained in-situ 3-D coastal monitoring systems. This paper discusses three key techniques: integrated microsensors for low-power and high-resolution monitoring of water quality parameters, biomass-based underwater energy harvesting for self-sustained sensor operation, and power management schemes between renewable energy sources and sensor devices. The synergism of these techniques is able to transform the way in which we observe and understand the sustainability vulnerable coastal environments and the fragile ecosystems they host.

Time synchronization plays a critical role in distributed network systems. In this paper, we investigate the time synchronization problem in the context of underwater sensor networks (UWSNs). Although many time-synchronization protocols have been proposed for terrestrial wireless sensor networks, none of them can be directly applied to UWSNs. This is because most of these protocols do not consider long propagation delays and sensor node mobility, which are important attributes in UWSNs. In addition, UWSNs usually have high requirements in energy efficiency. To solve these new challenges, innovative time synchronization solutions are demanded. In this paper, we propose a pairwise, cross-layer, time-synchronization scheme for mobile underwater sensor networks, called DA-Sync. The scheme proposes a framework to estimate the doppler shift caused by mobility, more precisely through accounting the impact of the skew. To refine the relative velocity estimation, and consequently to enhance the synchronization accuracy, the Kalman filter is employed. Further, the clock skew and offset are calibrated by two runs of linear regression. Simulation results show that DA-Sync outperforms the existing synchronization schemes in both accuracy and energy efficiency.

Recently, various Medium Access Control (MAC) protocols have been proposed for underwater acoustic networks. These protocols have significantly improved the performance of MAC layer in theory. However, two critical characteristics, low transmission rates and long preambles, found in the commercial modem-based real systems, drastically degrade the performance of existing MAC protocols in the real world. A new practical MAC design is demanded. Toward a proper approach, this paper analyzes the impact of the two newly found modem characteristics on the random access-based MAC and handshakebased MAC, which are two major types of MAC protocols for underwater acoustic networks. We further develop the nodal throughput and collision probability models for representative solutions of these two MAC protocol types. Based on the analysis, we believe time sharing-based MAC is very promising. Along this line, we propose a time sharing-based MAC and analyze its nodal throughput. Both analytical and simulation results show that the time sharing-based solution can achieve significantly better performance.

The performance of Underwater Sensor Networks (UWSNs) can be severely affected by the dynamics of underwater environment. A surface sink is usually deployed at a pre-specified location to maximize one or more performance metrics. However, when the network is dynamic, a redeployment of surface sink should be considered to reduce the effect of mobility on the network performance. Redeployment can be done periodically, at times based on a mobility prediction models, or adaptively based on performance degradation. Unnecessary redeployment can result from using the periodic or prediction based redeployment. In this paper we present an adaptive dynamic sink redeployment strategy that enforces redeployment only if a reduction in energy consumption is guaranteed. The redeployment decision is based on routing information collected at the surface sink throughout network operation. We use a location unaware routing protocol “adaptive power controlled routing protocol” as the underlying routing strategy. When the mobility of the network is not severe, nodes tend to use a fixed power level to communicate with neighboring nodes or surface sink. However, if more nodes are switching to use higher power levels for communication and the energy consumption is increased a sink redeployment procedure is started. Surface sink then triggers localization and finds the optimal new location of surface sink to minimize total energy consumption. Simulation results show that adaptive sink redeployment achieves a considerable reduction in energy consumption.

Cooperative communication has been studied extensively as a promising technique for improving the performance of terrestrial wireless networks. However, in underwater cooperative acoustic networks, long propagation delays and complex acoustic channels make the conventional relay selection schemes designed for terrestrial wireless networks inefficient. In this paper, we develop a new best relay selection criterion, called COoperative Best Relay Assessment (COBRA), for underwater cooperative acoustic networks to minimize the one-way packet transmission time. The new criterion takes into account both the spectral efficiency and the underwater long propagation delay to improve the overall throughput performance of the network with energy constraint. A best relay selection algorithm is also proposed based on COBRA criterion. This algorithm only requires the channel statistical information instead of the instantaneous channel state. Our simulation results show a significant decrease on one-way packet transmission time with COBRA. The throughput and delivery ratio performance improvement further verifies the advantages of our proposed criterion over the conventional channel state based algorithms.

Time synchronization is an important requirement for many services provided by distributed networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless Sensor Networks (WSNs). However, none of them can be directly applied to Underwater Sensor Networks (UWSNs). A synchronization algorithm for UWSNs must consider additional factors such as long propagation delays from the use of acoustic communication and sensor node mobility. These unique challenges make the accuracy of synchronization procedures for UWSNs even more critical. Time synchronization solutions specifically designed for UWSNs are needed to satisfy these new requirements. This paper proposes Mobi-Sync, a novel time synchronization scheme for mobile underwater sensor networks. Mobi-Sync distinguishes itself from previous approaches for terrestrial WSN by considering spatial correlation among the mobility patterns of neighboring UWSNs nodes. This enables Mobi-Sync to accurately estimate the long dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency.

A mobile underwater wireless sensor network is a system of mobile underwater sensors which are acoustically networked. Existing ocean studies using independent mobile sensors have led to many interesting discoveries. Therefore, the networking capability will extend the capability of existing approaches and enable novel applications because it provides more control and coordination of the sensors. However, network mobility and environment uncertainty create numerous challenging issues which need to be addressed before such a paradigm becomes reality. Network deployment is among the most important problems because it has a significant impact on other research areas. In this paper, we consider the reuse-based deployment scheme in which a mobile underwater sensor network operates in a bounded area and a sensor, when reaching beyond the area, is deployed back into it. We propose a deployment framework to study the connectivity and coverage of such networks and show its effectiveness through two case studies. Our framework is based on the idea that the reuse of network nodes stabilizes these network metrics. By modeling them, we can estimate the number of nodes needed to achieve a requirement on network coverage or network connectivity only by solving algebraic equations rather than running more simulations.

In this paper, we investigate reliable data transfer for multi-hop underwater acoustic networks. Motivated by experiences from real-world field tests, we propose a Practical Coding-based Multi-hop Reliable Data Transfer (PCMRDT) protocol. For the per-hop reliable data transfer, PCMRDT combines random linear coding and selective repeat to achieve high reliability and efficiency. We analyze the data recovery capability for random linear coding so that we can set an appropriate coding rate. In addition, PCMRDT utilizes a multi-hop coordination mechanism to eliminate collisions and decreases average end-to-end delay over multiple hops. Simulation results show that PCMRDT can significantly reduce the network delay with high energy efficiency.

In this paper, we investigate multi-hop reliable data transfer for underwater acoustic networks. We propose a new protocol, called FOuntain Code based Adaptive multi-hop Reliable data transfer (FOCAR). FOCAR is essentially a hybrid ARQ scheme which integrates Fountain codes with hop-by-hop retransmission-upon-failure. It considers the half duplex nature of the underwater acoustic modems and adapts the block size of each hop to optimize the end-to-end delay for the multi-hop network scenario. Extensive simulation results show that FOCAR can achieve high reliability with low end-to-end delay and high energy efficiency.

In underwater wireless sensor networks (UWSNs), one efficient way to alleviate the burdens of high propagation delay and high error probability during transmission is to deploy surface-level gateways, which utilize radio waves to forward information to a control station. Usually, deployment of the gateways is considered as an optimization problem with the objective to best satisfy certain parameters. In this work, we propose a prediction assisted dynamic surface gateway placement algorithm for mobile underwater sensor networks, called “PADP”, which intends to maximize the coverage within a specific period of time. PADP applies a tracking scheme “IMM” to predict sensor nodes' positions, adopts branch-and-cut to solve the optimization problem, and employs a disjoint-set data structure to handle connectivity. Simulation results show that PADP outperforms the existing static gateway deployment scheme.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate time synchronization and localization problems in underwater sensor networks. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm IMM (interactive multiple model) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

In this paper, we investigate the multichannel MAC problem in underwater acoustic sensor networks. To reduce hardware cost, only one acoustic transceiver is often preferred on every node. In a single-transceiver multichannel long-delay underwater network, new hidden terminal problems, namely, multichannel hidden terminal and long-delay hidden terminal (together with the traditional multihop hidden terminal problem, we refer to them as "triple hidden terminal problems”), are identified and studied in this paper. Based on our findings, we propose a new MAC protocol, called CUMAC, for long-delay multichannel underwater sensor networks. CUMAC utilizes the cooperation of neighboring nodes for collision detection, and a simple tone device is designed for distributed collision notification, providing better system efficiency while keeping overall cost low. Analytical and simulation results show that CUMAC can greatly improve the system throughput and energy efficiency by effectively solving the complicated triple hidden terminal problems.

In this paper, we propose a practical scheme, Non-Binary Joint Network-Channel Coding (NB-JNCC), for reliable multi-path multi-hop communication in arbitrary large-scale wireless networks. NB-JNCC seamlessly couples channel coding and network coding to effectively combat the detrimental effect of fading of wireless channels. Specifically, NB-JNCC combines non-binary irregular low-density parity-check (LDPC) channel coding and random linear network coding through iterative joint decoding, which helps to fully exploit the spatial diversity and redundancy residing in both channel codes and network codes. In addition, since it operates over a high order Galois field, NB-JNCC can be directly combined with high order modulation without the need of any bit-to-symbol conversion nor its inverse. Through both analysis and simulation, we demonstrate the significant performance improvement of NB-JNCC over other schemes.

Time synchronization plays a critical role in distributed network systems. In this paper, we investigate the time synchronization problem in the context of underwater sensor networks (UWSNs). We propose a pairwise, cross-layer, time synchronization scheme for mobile underwater sensor networks, called TSMU. Facilitated by the Kalman Filter, the proposed method greatly improves the dynamic propagation delay estimation by exploring the Doppler effect. Simulation results show that TSMU outperforms existing synchronization schemes in both accuracy and energy efficiency.

Due to harsh aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on the predicted mobility pattern. Anchor nodes with known locations in the network will control the localization process in order to balance the trade-off between localization accuracy, localization coverage, and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.

Due to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for time-critical applications in an energy-efficient way. On the one hand, traditional retransmission upon failure usually introduces very large end-to-end delay and is thus not proper for time-critical services. On the other hand, common approaches without retransmission consume lots of energy. In this paper, we propose a new multipath power-control transmission (MPT) scheme, which can guarantee certain end-to-end packet error rate while achieving a good balance between the overall energy efficiency and the end-to-end packet delay. MPT smartly combines power control with multipath routing and packet combining at the destination. With carefully designed power-control strategies, MPT consumes much less energy than the conventional one-path transmission scheme without retransmission. Besides, since no hop-by-hop retransmission is allowed, MPT introduces much shorter delays than the traditional one-path scheme with retransmission. We conduct extensive simulations to evaluate the performance of MPT. Our results show that MPT is highly energy-efficient with low end-to-end packet delays.

One challenge in delay tolerant networks (DTNs) is efficient routing, as the lack of contemporaneous end-to-end paths makes conventional routing schemes inapplicable. Many existing DTN routing protocols adopt multi-copy replication and/or are incognizant of mobility models. Hence they are not suitable for networks with extremely stringent resources and time-varying mobility models such as underwater sensor networks. In this paper, we propose a generic prediction assisted single-copy routing (PASR) scheme that can be instantiated for different mobility models in underwater sensor networks. PASR employs an effective greedy algorithm which captures the features of network mobility patterns, and provides guidance on how to use historical information. We demonstrate the superior performance of PASR through simulation.

Time synchronization is a critical service for distributed networks. In this paper, we investigate this problem in underwater sensor networks (UWSNs). We propose a novel time synchronization scheme, called ``Mobi-Sync''. Mobi-Sync effectively utilizes the spatial correlation of underwater mobile sensor nodes to estimate the long and dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency.

A SEA Swarm (Sensor Equipped Aquatic Swarm) is a sensor "cloud" that drifts with water currents and enables 4D (space and time) monitoring of local underwater events such as contaminants, marine life and intruders. The swarm is escorted at the surface by drifting sonobuoys that collect the data from underwater sensors via acoustic modems and report it in real-time via radio to a monitoring center. The goal of this study is to design an efficient anycast routing algorithm for reliable underwater sensor event reporting to any one of the surface sonobuoys. Major challenges are the ocean current and the limited resources (bandwidth and energy). In this paper, we address these challenges and propose HydroCast, a hydraulic pressure based anycast routing protocol that exploits the measured pressure levels to route data to surface buoys. The paper makes the following contributions: a novel opportunistic routing mechanism to select the subset of forwarders that maximizes greedy progress yet limiting co-channel interference; and an efficient underwater "dead end" recovery method that outperforms recently proposed approaches. The proposed routing protocols are validated via extensive simulations.

In this paper, we investigate the multi-channel MAC problem in underwater acoustic sensor networks. To reduce hardware cost, only one acoustic transceiver is often preferred on every node. In a single-transceiver multi-channel long-delay underwater network, new hidden terminal problems, namely multi-channel hidden terminal and long-delay hidden terminal (together with the traditional multi-hop hidden terminal problem, we refer to them as "triple hidden terminal problems"), are identified and studied in this paper. Based on our findings, we propose a new MAC protocol, called CUMAC, for long delay multi-channel underwater sensor networks. CUMAC utilizes the cooperation of neighboring nodes for collision detection, and a simple tone device is designed for distributed collision notification, providing better system efficiency while keeping overall cost low. Analytical and simulation results show that CUMAC can greatly improve the system throughput and energy efficiency via effectively solving the complicated triple hidden terminal problems.

Network lifetime is often a crucial measure of the cost-effectiveness of underwater wireless sensor networks and a guiding factor of their deployment. In this paper, we propose a placement strategy for surface gateway nodes in order to maximize network lifetime under a given set of functional requirements. We formulate the problem as an optimization problem and solve it for sample networks. Results show a trade-off between lifetime and other performance metrics, such as average end-to-end delay. We propose a multi-objective reformulation of the problem to strike the required balance between performance and lifetime.

Deploying radio-capable gateways at the sea surface can mitigate the limitations of acoustic communications in underwater sensor networks (UWSN). Finding the best placement for gateways is formulated as an ILP problem. The choice of gateway candidate locations can affect both the feasibility and the quality of the solution. In this paper, we show the limitation of using a regular mesh of candidate locations, and present a novel algorithm for defining candidate locations that both reduces the complexity of the optimization problem and enhances the feasibility and quality of the solution.

In this paper, we propose a practical scheme, called Non-Binary Joint Network-Channel Decoding (NB-JNCD) for reliable communication in wireless networks. It seamlessly couples channel coding and network coding, and can effectively combat the detrimental effect of fading of wireless channels, especially in large networks. On a high order Galois field, NB-JNCD combines non-binary LDPC channel coding and random linear network coding through iterative joint decoding, which helps fully exploit the spatial diversity and redundancy residing in both codes. Furthermore, the scheme can unify non-binary source coding and high order modulation without the need of any bit-to-symbol conversion and its inverse. Through analysis and simulation, we demonstrate the significant performance improvement of NB-JNCD against other schemes.

The AS-level Internet topology has shown significant clustering features. We propose a new set of clustering metrics and conduct extensive measurement on the AS- level Internet topology. We give a thorough characterization on the clustering features and their evolution. We also study the clustering features of different topological structures by comparing the Internet with various topology models. Due to the limitation of existing topology models on capturing clustering features, we design a new topology model based on clustering. Through extensive evaluations, we claim that our model can closely capture the clustering features as well as other common topological properties.

Due to adverse aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with mobility prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on its predicted mobility pattern. Anchor nodes with known locations in the network will control the whole localization process in order to balance the tradeoff between localization accuracy, localization coverage and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.

Deploying multiple surface-level radio-capable gateways enhances the performance of underwater acoustic sensor network. The locations of gateways have to be carefully selected to maximize the benefit in a cost-effective way. In this paper, we show how to efficiently solve the surface gateway deployment optimization problem, using heuristic approaches. The results of applying these proposed algorithms to a variety of practical deployment scenarios suggest that these heuristics are nearly optimal for practical cases.

Due to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for time-critical applications in an energy-efficient way. On the one hand, traditional retransmission-upon-failure usually introduces very large end-to-end delay, thus is not proper for time-critical services. On the other hand, common approaches without retransmission consume lots of energy. In this paper, we propose a new multi-path power-control transmission (MPT) scheme, which can guarantee certain end-to-end packet error rate while achieving a good balance between the overall energy efficiency and the end-to-end packet delay. MPT smartly combines power control with multi-path routing and packet combining at the destination. With carefully designed power control strategies, MPT consumes much less energy than the conventional one-path transmission scheme without retransmission. Besides, since no hop-by-hop retransmission is allowed, MPT introduces much shorter delay than the traditional one-path scheme with retransmission. We conduct extensive simulations to evaluate the performance of MPT. Our results show that MPT is highly energy efficient with low end-to-end packet delays.

Cooperative communication with single relay selection is a simple but effective communication scheme for energy-constrained networks. In this paper, we propose a novel selective single-relay cooperative scheme, combining selective-relay cooperative communication with physical-layer power control. Based on the MAC-layer RTS-CTS signaling, a set of potential relays compute individually the required transmission power to participate in the cooperative communication, and compete within a window of fixed length. The "best" relay is selected in a distributed fashion with minimum signaling overhead. We derive power-control solutions corresponding to two policies on relay selection: one is to minimize the energy consumption per data packet, and the other is to maximize the network lifetime. Our numerical and simulation results confirm that the proposed scheme achieves significant energy savings and prolongs the network lifetime considerably.

Abstract This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communication using physical waves as the carrier among nodes in an underwater sensor network (UWSN). The physical waves under discussion include sound, radio, and light. We first present the fundamental physics of different waves; then we discuss and compare the pros and cons for adopting different communication carriers (acoustic, radio, and optical) based on the fundamental first principles of physics and engineering practice. The discussions are mainly targeted at underwater sensor networks (UWSNs) with densely deployed nodes. Based on the comparison study, we make recommendations for the selection of communication carriers for UWSNs with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. Copyright © 2008 John Wiley & Sons, Ltd.

A good P2P file sharing system is usually expected to achieve the following design goals: scalability, routing efficiency and complex query support. In this paper, we propose such a system, called PSON, which can satisfy all the three requirements. PSON is essentially a semantic overlay network of logical nodes, in which queries are routed on the basis of semantics. A logical node is formed by a cluster of peers that are close to each other in the physical network. Each cluster selects a powerful peer as super peer to support routing in the overlay network. To facilitate routing, all the super peers (or logical nodes) are organized in the form of a balanced tree. By exploiting the concepts of hierarchy and semantics, PSON can support complex queries in a scalable and efficient way. In this paper, we will describe the system architecture, and examine the key component of PSON design, i.e., semantic overlay construction and routing. We also conduct simulations, and show that the query routing in PSON is very efficient (O(log(n)) in the case of exact query).

In this paper, we investigate the reliable data transport problem in underwater sensor networks. Underwater sensor networks are significantly different from terrestrial sensor networks in two aspects: acoustic channels are used for communication and most sensor nodes are mobile due to water current. These distinctions feature underwater sensor networks with low bandwidth capacity, large propagation delay, high error probability, half-duplex channels, and highly dynamic topology, which pose many new challenges for reliable data transport in underwater sensor networks. In this paper, we propose a protocol, called segmented data reliable transport (SDRT), to achieve reliable data transfer in underwater sensor networks. SDRT is essentially a hybrid approach of ARQ and FEC. It adopts efficient erasure codes (so-called SVT codes in this paper), transferring encoded packets block by block and hop by hop. Compared with other existing reliable data transport approaches for underwater networks, SDRT can reduce the total number of transmitted packets, improve channel utilization, and simplify protocol management. In addition, we develop a mathematic model to estimate the expected number of packets actually needed. Based on this model, we can set the block size appropriately for SDRT, as helps to address the node mobility issue. We conduct simulations to evaluate our model and SDRT. The results show that our model can closely predict the number of packets actually needed, and SDRT is energy efficient and can achieve high channel utilization.

In this paper, we propose a two-tier overlay multicast architecture (TOMA) to provide scalable and efficient multicast support for various group communication applications. In TOMA, multicast service overlay network (MSON) is advocated as the backbone service domain, while end users in access domains form a number of small clusters, in which an application-layer multicast protocol is used for the communication between the clustered end users. TOMA is able to provide efficient resource utilization with less control overhead, especially for large-scale applications. It also alleviates the state scalability problem and simplifies multicast tree construction and maintenance when there are large numbers of groups in the network. To help MSON providers efficiently plan backbone service overlay, we suggest several provisioning algorithms to locate proxies, select overlay links, and allocate link bandwidth. Extensive simulation studies demonstrate the promising performance of TOMA

Multicast solutions have been evolving from "bottom" to "top", i.e., from IP layer (called IP multicast) to application layer (referred to as application layer multicast). Recently, there are some new proposals (named as overlay multicast) using certain "infrastructure" (composed of proxies) in the middle. Although it is well accepted that application layer multicast and overlay multicast are easier to deploy while sacrificing bandwidth efficiency compared with IP multicast, little research has been done to systematically evaluate and compare their performance. In this paper, we conduct a comparative study of different types of multicast routing protocols. We first present a qualitative comparison of three types of protocols, and then we provide a quantitative study of four representative protocols, namely, PIM-SSM, NARADA, NICE, and POM by extensive simulations. Our studies will help to answer some of the most important questions, such as which way to go: top, bottom, or in the middle?.

Traditional IP multicast faces a serious state scalability problem when there are a large number of groups in the network. Recently, a novel approach called aggregated multicast was proposed [6], in which multiple groups share one delivery tree. A key problem in aggregated multicast is group-tree matching (i.e., assigning groups to trees). In this paper, we formally study the dynamic version of the group-tree matching problem. We propose a generic dynamic on-line algorithm (GDOA) and provide an approach to determine the upper bound on its performance. We quantitatively compare the performance of GDOA and other existing on-line heuristics. Extensive simulations demonstrate that GDOA is a very practical solution with promising performance and reasonable computation requirement.

Network clustering is an important technique widely used in efficient hierarchical routing protocol design, network modelling and performance evaluation, etc. In this paper, we discuss the important clustering criteria, such as node connectivity, cluster diameter, number of orphan nodes. Our main contribution is a novel clustering algorithm SACA based on an accurate clustering measure called SCM. SACA adaptively forms clusters to incrementally improve the clustering quality, taking node connectivity into consideration. It can control the cluster size effectively and limit the number of orphan nodes. Our simulation study indicates that SACA is more accurate than MCL, a well accepted scalable and efficient clustering scheme, while requiring comparable running time for power law topologies and grid topologies, and significantly less running time for random topologies.

Recently, there has been extensive research on integrating cellular networks and ad hoc networks to overcome the limitations of cellular networks. Although several schemes have been proposed to use such hybrid networks to improve the performance of individual multicast groups, they do not address the quality of service (QoS) issues when multiple groups are present in the networks. Our work, on the other hand, considers an interesting scenario of hybrid networks when an ad hoc network cannot accommodate all the groups and a base station has to select a subset of groups to optimize its bandwidth savings and maximize the utilization of the ad hoc network, while providing QoS support for multicast users. In this work, we develop a network model for multicast admission control which takes wireless interference into account, formulate the group selection problem as a multidimensional knapsack problem, and propose an integer linear programming (ILP) formulation and a polynomial-time dynamic algorithm. We also examine a distributed implementation of the dynamic algorithm in real systems. Simulation studies demonstrate that the dynamic algorithm is able to achieve very competitive performance under various conditions, in comparison with the optimal solution computed by the ILP approach.

IP multicast confronts a severe scalability problem when there are large numbers of multicast groups in the network due to state explosion and control explosion. In backbone networks, this state scalability problem is exacerbated, since there are potentially enormous multicast groups crossing backbone domains, in this paper, we propose a scalable protocol, called BEAM (bi-directional aggregate multicast), which uses the concept of aggregated multicast. BEAM is a distributed protocol using bi-directional trees. It is simple and easy to implement. Through simulations, we show that BEAM can greatly improve state scalability with very low overhead: up to 98% state and tree setup and maintenance overhead reduction with less than 0.14 bandwidth waste in our experiments.

In this paper, we measure and model the distribution of multicast group members. Multicast research has traditionally been plagued by a lack of real data and an absence of a systematic simulation methodology. Although temporal group properties have received some attention, the location of group members has not been measured and modelled. However, the placement of members can have significant impact on the design and evaluation of multicast schemes and protocols as shown in previous studies. In our work, we identify properties of members that reflect their spatial clustering and the correlation among them (such as participation probability, and pairwise correlation). Then, we obtain values for these properties by monitoring the membership of network games and large audio-video broadcasts from IETF and NASA. Finally, we provide a comprehensive model that can generate realistic groups. We evaluate our model against the measured data with excellent results. A realistic group membership model can help us improve the effectiveness of simulations and guide the design of group-communication protocols.

IP multicast suffers from scalability problems as the number of concurrent active multicast groups increases, since it requires a router to keep a forwarding state for every multicast tree passing through it. In QoS multicast provisioning, the problem is exacerbated, since not only the forwarding state but also the resource requirement of a multicast group must be kept at the router. To provide scalable QoS multicast support, in this paper, we propose a novel architecture, called Aggregated QoS Multicast (AQoSM). Using the concept of aggregated multicast, AQoSM can support QoS multicast scalably and efficiently in DiffServ-supported MPLS networks. In this paper, we develop the framework for the architecture and provide a feasibility check from an implementation point of view. The architecture is flexible and can be customized to the needs and the existing protocols of a domain. Our simulations indicate that the architecture performs well in several common scenarios. It achieves smaller blocking of users with strong QoS requirements because of its load balancing capability. It also achieves up to 85% reduction in state with a modest 10% of bandwidth overhead.

Source specific multicast (SSM) is a viable solution for current multicast applications, since the driving applications to date are one to many, including Internet TV, distance learning, file distribution, streaming media, etc. It brings many benefits in billing, address allocation, and security. However, SSM still confronts the serious state scalability problem when there are a large number of simultaneous on-going multicast groups in the network. We propose a protocol to improve the state scalability of source specific multicast, which is called aggregated source specific multicast (ASSM). We design the detailed ASSM protocol and show that our solution can obtain significant multicast state and tree management overhead reduction while achieving transparency to end-users, compatibility with existing multicast technologies and low overhead.

IP multicast suffers from a scalability problem with the number of concurrently active multicast groups because it requires a router to keep the forwarding state for every multicast tree passing through it and the number of forwarding entries grows with the number of groups. In this paper, we propose an approach to reduce the multicast forwarding state. In our approach, multiple groups are forced to share a single delivery tree. We discuss the advantages and some implementation issues of our approach, and conclude that it is feasible and promising. We then propose metrics to quantify state reduction and analyze the bounds on state reduction of our approach. Finally, we use simulations to verify our analytical bounds and quantify the state reduction. These initial simulation results suggest that our method can reduce multicast state significantly.

To protect against possible network node or link failure and achieve high reliability of communications, pre-planned failure recovery schemes are needed in modern high-speed communication networks. A couple of schemes have been previously reported for multicast communications. We present a scheme based on a "dual-tree" structure in which a secondary tree for fault-tolerance purpose is built as a complement to a primary multicast tree. The secondary tree provides alternative delivery paths that can be activated when link or node failure is detected in the primary multicast tree. Simulation experiments show that this scheme has shorter restoration time and cause less multicast tree cost increase after restoration than some schemes proposed previously.

Previous work has demonstrated that long-range dependence (LRD) and multiple time-scales are important characteristics of realistic traffic. LRD traffic that exhibits multiple time scales poses a significant challenge to performance guarantee. We examine the impact of LRD in traffic, and address issues concerning how to compute and guarantee loss performance for LRD multiple time-scale traffic under realistic networking conditions.

Conventional analysis techniques of queueing systems typically assume that the stochastic process under study is already in steady state. This assumption is, however, not valid if the life cycle of the process is not large enough. Previous work in transient analysis of queueing systems usually focuses on Markov models. This paper, in contrast, presents an analysis technique for investigating transient performance of queueing systems that are not necessarily Markovian.

Performance-oriented studies typically rely on the assumption that the stochastic process modeling the phenomenon of interest is already in steady state. This assumption is, however, not valid if the life cycle of the phenomenon under study is not large enough, since usually a stochastic process cannot reach steady state unless time evolves towards infinity. Therefore, it is important to address performance issues in transient state.Previous work in transient analysis of queueing systems usually focuses on Markov models. This paper, in contrast, presents an analysis of transient loss performance for a class of finite buffer queueing systems that are not necessarily Markovian. We obtain closed-form transient loss performance measures. Based on the loss measures, we compare transient loss performance against steady-state loss performance and examine how different assumptions on the arrival process will affect transient loss behavior of the queueing system. We also discuss how to guarantee transient loss performance. The analysis is illustrated with numerical results.