Unmanned maritime surveillance systems (UMSS), consisting of Unmanned Surface Vessels (USVs) and smart buoys, as a typical application of the maritime Communication-Computing Converged Network (CCCN), play a crucial role in combating illegal fishing, smuggling, and piracy. However, the 2D images on which UMSS relies often lack spatial depth, and although 3D point cloud data can compensate for this limitation, complex and harsh weather degrades their quality and increases data volume. This increases the communication and computational burden, making efficient resource allocation far more complex, impairing UMSS’s responsiveness in dynamic maritime conditions. To address these issues, this paper proposes an edge intelligence-based real-time resource allocation framework for UMSS. Unlike existing works that only consider resource allocation, our framework also accounts for the impact of 3D point cloud data quality on system performance in harsh weather conditions. Specifically, it first leverages 3D point cloud data with a dehazing algorithm to mitigate haze-induced data expansion. Then we formulate an optimization problem to dynamically trade off throughput, latency, and Energy Consumption (EC) under varying maritime conditions. Finally, we utilize Deep Deterministic Policy Gradient (DDPG) methods to solve this problem. Additionally, we design task allocation strategies tailored to different maritime services. Simulation results show that our approach reduces average latency by 13%, lowers EC by 20%, and increases throughput by 10%, to guarantee the timely fulfillment of all tasks and improve the efficiency of UMSS under harsh weather conditions.

With the development of sixth-generation (6G) wire-less communication networks, the security challenges are becoming increasingly prominent, especially for mobile users (MUs). As a promising solution, physical layer security (PLS) technology leverages the inherent characteristics of wireless channels to provide security assurance. Particularly, stacked intelligent metasurface (SIM) directly manipulates electromagnetic waves through their multilayer structures, offering significant potential for enhancing PLS performance in an energy efficient manner. Thus, in this work, we investigate an SIM-assisted secure communication system for MUs under the threat of an eavesdropper, addressing practical challenges such as channel uncertainty in mobile environments, multiple MU interference, and residual hardware impairments. Consequently, we formulate a joint power and phase shift optimization problem (JPPSOP), aiming at maximizing the achievable secrecy rate (ASR) of all MUs. Given the non-convexity and dynamic nature of this optimization problem, we propose an enhanced proximal policy optimization algorithm with a bidirectional long short-term memory mechanism, an offpolicy data utilization mechanism, and a policy feedback mechanism (PPO-BOP). Through these mechanisms, the proposed algorithm can effectively capture short-term channel fading and long-term MU mobility, improve sample utilization efficiency, and enhance exploration capabilities. Extensive simulation results demonstrate that PPO-BOP significantly outperforms benchmark strategies and other deep reinforcement learning algorithms in terms of ASR.

The Internet of Things (IoT) is widely applied for forest monitoring, since the sensor nodes (SNs) in IoT network are low cost and have computing ability to process the monitoring data. To further improve the performance of forest monitoring, uncrewed aerial vehicles (UAVs) are employed as the data processors to enhance computing capability. However, efficient forest monitoring with limited energy budget and computing resource presents a significant challenge. For this purpose, this article formulates a multiobjective optimization framework to simultaneously consider three optimization objectives, which are minimizing the maximum computing delay, minimizing the total motion energy consumption, and minimizing the maximum computing resource, corresponding to efficient forest monitoring, energy consumption reduction, and computing resource control, respectively. Due to the hybrid solution space that consists of continuous and discrete solutions, we propose a diffusion-model-enhanced improved multiobjective grey wolf optimizer (IMOGWO) to solve the formulated framework. The simulation results show that the proposed IMOGWO outperforms other benchmarks for solving the formulated framework. Specifically, for a small-scale network with 6 UAVs and 50 SNs, compared to the suboptimal benchmark, IMOGWO reduces the motion energy consumption and the computing resource by 53.32% and 9.83%, respectively, while maintaining computing delay at the same level. Similarly, for a large-scale network with 8 UAVs and 100 SNs, IMOGWO achieves reductions of 41.81% in motion energy consumption and 7.93% in computing resource, with the computing delay also remaining comparable.

Devices within the marine Internet of Things (MIoT) can connect to low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) to facilitate low-latency data transmission and execution, as well as enhanced-capacity data storage. However, without proper traffic handling strategy, it is still difficult to effectively meet the low-latency requirements. In this paper, we consider a cooperative satellite-aerial-MIoT network (CSAMN) for maritime edge computing and maritime data storage to prioritize delay-sensitive (DS) tasks by employing mobile edge computing, while handling delay-tolerant (DT) tasks via the store-carry-forward method. Considering the delay constraints of DS tasks, we formulate a constrained joint optimization problem of maximizing satellite-collected data volume while minimizing system energy consumption by controlling four interdependent variables, including the transmit power of UAVs for DS tasks, the start time of DT tasks, computing resource allocation, and offloading ratio. To solve this non-convex and non-linear problem, we propose a joint computation offloading and resource management (JCORM) algorithm using the Dinkelbach method and linear programming. Our results show that the volume of data collected by the proposed JCORM algorithm can be increased by up to 41.5% compared to baselines. Moreover, JCORM algorithm achieves a dramatic reduction in computational time, from a maximum of 318.21 seconds down to just 0.16 seconds per experiment, making it highly suitable for real-time maritime applications.

The proliferation of maritime activities has spurred the emergence of numerous computation-intensive and delay-sensitive marine applications and services. Given the inherent rationality, selfish nature, and limited computational abilities of marine devices, devising effective strategies to incentivize their participation in task processing become a critical challenge. In this article, we investigate the satellite-assisted marine multiaccess edge computing (MEC) and propose a two-tier task offloading scheme through a hybrid Stackelberg-Bargaining game approach to enhance offloading efficiency and maximize the utility of marine devices. Specifically, for the underwater acoustic communication, we consider the scenario where multiple autonomous underwater vehicles (AUVs), managed by maritime autonomous surface ships (MASSs), upload their collected data using nonorthogonal multiple access (NOMA) to optimize channel utilization. For the data transmission above the sea surface, we consider the scenario where a low-Earth orbit satellite (LEOS) functions as a space edge server to provide computing services, and MASS offloads workloads to LEOS through frequency division multiple access (FDMA) to prevent co-channel interference. we define the utility of AUVs, MASSs and LEOSs, and model the offloading process between AUVs and MASSs as a Stackelberg game, while representing the offloading interaction between MASSs and LEOSs as a Bargaining game. Additionally, we propose efficient algorithms to optimize AUV offloading strategies and MASS pricing strategies, while refining the bidding strategies for both MASSs and LEOSs. Simulation results demonstrate that the proposed algorithms significantly outperform benchmark schemes in achieving optimal solutions.

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this paper, we propose a multi-layer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.

Digital twin has been emerging as a promising paradigm that connects physical entities and digital space, and continuously evolves to optimize the physical systems. In this article, we focus on studying efficient communication and computation scheme when constructing the Marine Internet of Things (M-IoT)’s digital twin with secrecy provisioning. Specifically, the digital twin model is trained based on federated learning, in which all the unmanned surface vehicles deliver the trained models with nonorthogonal multiple access (NOMA) to the high-altitude platform (HAP) for global model aggregation. Considering the potential eavesdropping on the radio signals of HAP, we utilize the chaotic sequences to spread the model information before the global model broadcasting. In this framework, we aim to minimize the total energy consumption for constructing the digital twin of M-IoT by jointly optimizing the global accuracy, the local accuracy, the HAP’s transmission power and NOMA transmission duration, subject to the secrecy provisioning and latency constraint. An effective low-complexity algorithm is proposed to tackle this joint optimization problem with the use of a layered feature. Finally, numerical results are given to validate the performance gain of the proposed scheme, in comparison with the fixed accuracy scheme, the nonspread spectrum scheme and the time division multiple access transmission scheme.

In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in addition to internode distance, packet errors and collisions are also significant factors for point-to-point connectivity. Furthermore, when allocating node locations, deployment strategies focus on maximizing coverage, ignoring the tradeoff between coverage and network performance (reliability, latency, and energy efficiency). To this end, an efficient deployment scheme with network performance modeling (EDNPM) is proposed, to provide reliable data transmission in a time-aware and energy-efficient way for UWSNs. Specifically, we first explore sensor locations’ impact on communication and network factors, to improve the point-to-point connectivity and network performance. A network performance evaluation model (NPEM) is established to quantify performance metrics for guiding network deployment. Based on NPEM, network deployment is formulated as a multiobjective optimization problem, and we propose a novel network connection-constraint particle swarm optimization (NCPSO) algorithm to solve this problem. Notably, EDNPM is a unified network deployment framework for various underwater applications. Extensive experiments demonstrate that EDNPM outperforms other deployment algorithms in terms of network performance, and robustness with different network settings.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

With the rapid growth of marine services and applications for achieving smart oceans, advanced marine communication networks have attracted increasing interests. However, the limited resources constrain the applications in marine communication networks. In this paper, we investigate a two-tier computation offloading scheme for unmanned aerial vehicle (UAV) aided marine communication networks via game theory to improve offloading efficiency. Specifically, these underwater wireless sensors (UWSs) are deployed at the seafloor, which partially offloads their sensed information to unmanned surface vessels (USVs) for assist computing. USV acts as a relay to offload part of its data to UAVs. We formulate three optimization problems to optimize the utility of UWSs, USVs, and UAVs, respectively. To address the formulated problems, we propose efficient algorithms to derive the solutions, which can maximize the utility of each participant. Finally, simulations are conducted to validate the performance of the proposed algorithms, and the results show the efficiency and effectiveness of the proposed algorithms in comparison with the benchmark schemes.

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.

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this article, we investigate the unmanned aerial vehicle (UAV)-aided ISAC with mobile-edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a systemwise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict nonconvexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.

The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. In this article, we consider the UAV-assisted M-IoT network where USVs offload computation-intensive maritime tasks via non-orthogonal multiple access (NOMA) to the UAV equipped with the mobile-edge computing (MEC) server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation, as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of deep reinforcement learning (DRL), and the underlying algorithm is proposed to optimize the underlying multidomain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.

The explosively increasing development of marine communication networks will improve the quality of service (QoS) of marine applications (e.g., ocean farm and marine tourism), which has attracted much attention from both academia and industrial in recent years. However, real-time data processing for diverse marine tasks (especially those computing-intensive and latency-sensitive tasks) is still challenging due to the limited marine communication and computing resources. Mobile edge computing (MEC) driven by powerful computing capability is envisioned as a promising solution to address the issue for resource-constrained marine services. In this paper, we propose a hybrid offshore and aerial-based multi-access edge computing scheme in marine communication networks to improve the QoS of marine applications. Specifically, we consider a scenario that both offshore base-station and unmanned aerial vehicles (UAVs) are equipped with edge-servers, and the computation workloads of unmanned surface vehicle (USV) can be simultaneously offloaded to offshore base-station and UAVs via multi-access manner. To minimize the latency of completing USV’s workloads and reduce USV’s energy consumption, we formulate a joint optimization problem to optimize the offloading decision, transmission time, and computing-rate allocation, with the objective ofMinimizing theMaximumWorkloadsLatency (MMWL). Exploiting the features of the formulated problem, we present a layered structure approach and decompose it into three subproblems. We propose efficient algorithms to obtain the optimal solutions and validate the optimality of the proposed algorithms. Finally, we provide simulation results and analysis to demonstrate the effectiveness and efficiency of the proposed scheme and algorithms in comparison with benchmark algorithms.

Benefiting from high mobility, unmanned aerial vehicles (UAVs) can reconstruct wireless connections for affected areas. Most of the existing work has usually ignored the influence of limited airborne energy on the dependability of UAV data transmission. Accordingly, this article proposes an UAV-assisted wireless powered system to achieve dependable data collections in Internet of Things (IoT). Specifically, an UAV leverages energy beamforming to transfer energy to ground users (GUs) in downlink subtimeslot, while the GUs transmit data to the UAV with the harvested energy in uplink subtimeslot. For this system, a joint optimization problem of subtimeslot allocation and UAV route planning is investigated to maximize the system energy efficiency subject to UAV dynamics, time slot duration, and GUs' rate threshold. To tackle the nonconvexity of the formulated problem, a low-complexity alternating iterative algorithm is proposed. The first subproblem optimizes subtimeslot allocation by using the bisection method and Lagrange multiplier method for the fixed UAV route, while the second optimizes the UAV route for the periodic and single flight modes with the given subtimeslot allocation. Then, the two subproblems are alternatively solved until convergence. The simulation results demonstrate that the proposed algorithm can not only optimize the UAV route, but also achieve a good compromise between system throughput and UAV propulsion energy consumption.

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

With the continuous increase of maritime activities and services, it is imperative to establish the “Smart Ocean” architecture, to promote more efficient and safe marine information dissemination with high quality of service provisioning. In this paper, we propose a digital twin (DT) assisted maritime networking architecture based on a comprehensive review of the maritime networks, where the roles and superiority of DT are emphasized with the consideration of the network heterogeneity and dynamics. We utilize the interaction between virtual and physical entities to improve the flexibility and adaptability of the network, aiming at more efficient maritime network resource management and optimization. We then discuss the main challenges and some potential research issues in DT-assisted marine network design. The proposed maritime networking architecture based on DT can provide a good reference for network planning and deployment of maritime networking architecture to adapt to different marine environments.

Digital twin (DT), which integrates physical networks and digital space by using advanced technologies of sensing, communication and computation, has been envisioned as a promising paradigm for improving the quality of service in physical systems. In this paper, we propose a federated learning (FL)-enabled DT system consisting of the physical layer and DT layer. With FL, all wireless devices (WDs) can collaborate to update a universal DT model, after the DT server cluster (DSC) aggregates all the local models sent by the WDs with non-orthogonal multiple access (NOMA). Moreover, an action model based on the DT system is also updated to optimize the operations of WDs. To increase the energy efficiency, we formulate a problem to minimize the cost of the total energy consumption of the system by optimizing the time allocation of local training, uploading the local models, generating the action model as well as broadcasting the action model and DT model. The numerical results validate the effectiveness and efficiency of our proposed algorithm.

To deal with the ever increasing wireless traffic, we have recently designed a vehicular cognitive capability harvesting network (V-CCHN) architecture to leverage vehicles as an alternative “transmission medium” (i.e., an opportunistic data carrier), besides the wireless spectrum, to effectively transport data from the location where it is collected to the place where it is consumed or utilized in a smart city environment. In the V-CCHN, cognitive radio technologies are utilized so that a large amount of data can be exchanged between vehicles and roadside infrastructure through short-range high-speed transmissions. Considering the limited contact duration and the uncertain activities of primary users, how to facilitate efficient data exchange between vehicles and roadside infrastructure is very challenging. This problem is further complicated by the fact that the mobility of vehicles might not be accurately predicted. In this paper, we propose a probabilistic data prefetching (PDP) scheme for the V-CCHN to address these challenges. By considering the conditional value at risk, we formulate the PDP schematic design as an optimization problem which allows us to obtain the corresponding PDP scheme. Finally, we have conducted extensive study to evaluate the performance of the obtained PDP scheme under various parameter settings.

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms ofuncertaintask delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop atruthfulbudgetedutilitymaximizationauction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

Most of existing works about data offloading do not consider the participation of mobile subscribers (MSs) when designing the budget allocation, such that the fairness performance is challenging. For a mobile data offloading system consisting of a base station run by a service provider (SP), multiple MSs, and several third-party WiFi access points (APs), in this paper we study how the SP allocates the budget among APs and arranges the offloading data rate for MSs such that the fairness of each MS’s profit is guaranteed. By jointly considering the preferences of MSs for different APs and budget limit, we propose a two-phase participatory budget and rate allocation (PBRA) scheme where a core solution is designed for the budget allocation in the first phase to guarantee the fairness of all MSs, and an optimal rate allocation based on the core solution is designed to maximize the expected amount of data offloading in the second phase. Simulation results demonstrate the efficacy of our proposed PBRA scheme. Specifically, our proposed scheme can achieve the fairness among all MSs with a less performance loss in terms of the expected amount of data offloading by comparing with three benchmark schemes.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this article, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother ship (MS)-assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the MS location, channel allocation, and transmit power are jointly optimized to minimize the mean-square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED's offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED's offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED's latency-requirement and the selected WDs' required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs' delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD's optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.

The industrial Internet of Things (IIoTs) are well deployed to monitor pollutant emissions or device statuses in the industrial factory, especially in the chemical plants. To give a quick response of monitoring results, the industrial big data generated by IIoTs containing various tasks need to be processed as soon as possible. However, the priority constraints among the tasks generated by sensors are not considered in the existing architectures, which may result in the delayed response. Thus, in this article, we propose a mobile edge computing (MEC)-enabled architecture considering the priority constraints among tasks with the objective to minimize the response time. The tasks can be executed in MEC servers or cloud servers based on task complexity. Traditional methods search the optimal task allocation strategy through a set of initial strategies and a optimizer without the consideration of relationship among tasks. Thus, we propose a Bayesian network based evolutionary algorithm (BNEA) for optimizing a task allocation strategy. To fully consider the priority among tasks, the BNEA studies a Bayesian network based decomposition strategy in which the tasks are decomposed based on the relationship reflected by the learned Bayesian network structure. The BNEA searches the optimal task allocation strategy with the help of decomposed tasks cooperatively. Moreover, we propose a probability-based update strategy for particles to avoid draping into local optima. The experimental results verify that the BNEA can achieve the best response time through the corresponding task allocation strategy, which means that the data generated from the IIoTs can be transmitted to the destination in the shortest time.

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

The unmanned surface vehicle (USV) cluster, during marine task execution, works in a challenging communication environment. The cluster's network topology and states of wireless channel change rapidly with time. And the computing resources of fog nodes may be shared by several task requests simultaneously. Therefore, it's necessary to find an effective load balancing mechanism to cope with the ever-changing adverse factors. The load balancing problems of vehicular fog computing of USV cluster are investigated in this work. And furthermore, the corresponding mathematical models, including marine vehicular fog computing networks, wireless channels, and several typical scheduling mechanisms, are established. The analytical models and simulation results show that the proposed scheduling algorithm based on minimum response time performs better than other selected algorithms and can significantly reduce the response time and blocking probability of task requests.

Mobile edge computing (MEC) has been considered as a promising approach for enabling computation-intensive Internet services in future wireless systems. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted MEC, in which edge-computing users (EUs) adopt NOMA to simultaneously offload part of their computation-workloads to the edge-server (ES). To improve the spectrum-efficiency, we consider a paradigm of underlaying device-to-device (D2D) communications, namely, the EUs reuse a cellular user's (CU's) licensed channel for offloading transmission. We firstly characterize the transmit-powers of EUs and CU in this D2D approach, and then formulate a joint optimization of the EUs' computation- workloads offloading and the ES's computation-resource allocation, with the objective of minimizing the latency in completing the EUs' tasks. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose an efficient algorithm for computing the optimal solution. Numerical results are provided to validate the effectiveness and efficiency of our proposed NOMA assisted MEC via the D2D sharing1.

Energy harvesting is a promising solution to enable energy sustainable operation of IoT devices. Especially for under-water IoT network as it is difficult and costly for underwater IoT devices to replace the battery. Unlike traditional power supply, energy harvesting from green sources is a random process and is dependent on the charging environment, which poses new challenges for provisioning quality of services of IoT networks. Due to the high cost for low-powered IoT devices to update its energy status with the scheduler, distributed transmission protocol is more desirable for the IoT networks. In this work, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. Each IoT device can contend for channel access only when it is ready, i.e., it has a data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by tuning the transmission probability. Given that the wireless environment is unknown at different IoT devices, e.g., total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.

In recent years, maritime accidents increase with the frequent maritime activities. Real-time transmission of search and rescue video can help decision makers make better search and rescue arrangements. To improve the efficiency of the maritime search and rescue tasks, we propose a Q-learning based delay sensitive routing protocol (QDSR) for maritime search and rescue networks. QDSR can dynamically adapt to the changes of the topology of maritime communication networks and optimize the end-to-end delay and packet delivery ratio through iterative learning. Simulation results demonstrate that the proposed QDSR can select an optimal path, thereby improve the reliability and timeliness of maritime emergency video transmission.

Pre-caching popular contents in advance at the network edge such as base stations is a promising method to improve the service quality by reducing the transmission cost and network congestion. In this paper, by jointly considering the mobile users' rating on different contents into the incentive mechanism design, we proposed a rating-aware incentive mechanism for efficiently selecting some BSs to pre-cache the popular contents. The proposed mechanism can achieve higher performance compared with other existing methods in terms of the social welfare. In specific, the proposed mechanism can improve the achieved social welfare by 11.75% and 9.97% compared with the mechanism of random caching and the caching based on bid price, respectively. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

The Internet of Things (IoT) for smart ocean is a promising paradigm that will support emerging applications in the areas of maritime transport, emergency search and rescue, security and border surveillance, environmental protection, etc. There has been a surging amount of data acquired from different maritime terminals, such as vessels, buoys, and offshore platforms. As a result, the demand for high-speed, ultrareliable, and low-latency maritime communications and data processing is proliferating. In this context, transmission and processing of maritime data have become a research hotspot. IoT technologies are expected to dramatically enhance the capacity, safety, and efficiency of connected vessels and other maritime terminals. Meanwhile, the unique characteristics of smart ocean applications create heterogeneous challenges in achieving viable, reliable, and secure communications and data processing. Addressing the challenges calls for novel approaches and consideration for the deployment of next-generation maritime communication networks. Therefore, it is essential to pursue research on new theories, architecture, and technologies to fully exploit the capability that is delivered by IoT for smart ocean to form efficient and intelligent maritime communication systems. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the applications of IoT for the smart ocean.

Proactive caching in mobile-edge computing (MEC) networks is promising to handle the ever-increasing demand for wireless video services, and transcoding at MEC servers further improves the flexibility of video content delivery. However, how to effectively conduct caching for adaptive bitrate streaming poses great challenges due to the uncertainty of user preferences. The caching decisions also have a profound impact on the system energy efficiency since they may change the video delivery modes. In this article, by integrating caching, transcoding, and backhaul retrieving in a MEC-enabled adaptive streaming system, we propose a holistic solution to jointly determine the caching of bitrate-aware files and the scheduling of video requests in an energy-efficient manner. Specifically, we leverage a data-driven approach to characterize the uncertainty of real request arrivals. Based on the uncertainty model, we formulate a data-driven risk-averse optimization to derive a robust strategy for caching and delivery scheduling, which is a two-stage stochastic mixed-integer programming (SMIP) with the goal of minimizing the total expected energy consumption. We also develop feasible solutions and conduct extensive simulations on real-world data sets. The results validate the effectiveness of the proposed scheme in both the energy efficiency and the cache hit ratio.

This article proposes a novel orthogonal frequency-division multiplexing (OFDM) autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperforming the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this treatise also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning (DL) with the philosophy of super-resolution reconstruction, which uses dense convolutional neural networks (Dense-Nets) to reconstruct low-resolution pilot information images into high-resolution full-channel impulse responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. The simulation results show that under slow fading, the proposed channel estimator (CE) can estimate the CIRs perfectly. Under fast fading, the proposed CE outperforms the existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful CE are potentially attractive approaches for the Internet of Vessels (IoV).

At present, the proliferation of the online connected devices conceives the Internet of Things (IoT), in which many wireless sensors, smart devices are implemented. However, the nature of openness rooted in IoT makes itself vulnerable to be attacked. One of the pioneer countermeasures is the moving target defense (MTD), which encourages an active and dynamic defense in IoT. In this article, a macroscopic research in MTD is carried out. The existing macroscopic studies take advantage of a traditional game theory. Consequently, protected IoT devices need extra operations to dominate the game. In this article, we take a dramatically different approach where a player can dominate the game without extra operation. Our approach benefits from the power of the zero-determinant (ZD) strategy, in which the player who adopts ZD can unilaterally set the expected payoff of the adversary or itself. Aware of such a powerful strategy, both players may want to employ it for dominating the confrontation. In this case, two fundamental questions need to be answered: who should take the ZD strategy? And to what extent can the ZD player dominate the game? To solve these problems, we model the interactions between the IoT devices and the malicious attackers as a Markov game. Besides, we obtain the conditions to adopt ZD, based on which we deduce the effectiveness of the ZD player. To the best of our knowledge, we are the first to employ the ZD strategy theory to enhance a better counterattack performance in IoT.

Recent advances in device-free wireless sensing have created the emerging technique of device-free human gesture recognition (DFHGR), which could recognize human gestures by analyzing their shadowing effect on surrounding wireless signals. DFHGR has many potential applications in the fields of human-machine interaction, smart home, intelligent space, etc. State-of-the-art work has achieved satisfactory recognition accuracy when there are a sufficient number of training samples. However, it is time consuming and labor intensive to collect samples, thus how to realize DFHGR under a small training sample set becomes an urgent problem to solve. Motivated by the excellent ability of the generative adversarial network in synthesizing samples, in this article, we explore and exploit the idea of leveraging it to realize virtual samples augmentation. Specifically, we first design a single scenario network with new architecture and better-designed loss function to generate virtual samples using a few number of real samples. Then, we further develop a scenario transferring network to generate virtual samples by utilizing the real samples not only from the current scenario but also from another available scenario as well, which could improve the quality of synthesized samples with the extra knowledge learned from another scenario. We design an mmWave-based DFHGR testbed to test the proposed networks, extensive experimental results demonstrate that the augmented virtual samples are of high quality and facilitate DFHGR systems to achieve better accuracy.

A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

Vehicle-based crowdsourcing is becoming a powerful paradigm that can outsource intensive tasks to vehicles by exploiting their on-board resources. In this paper, we focus on the problem of motivating vehicles to join the crowdsourcing system. Considering the various delay demands of tasks in smart cities, we design a delay-aware incentive mechanism to employ vehicles based on reverse auction. Specifically, by taking task delay into consideration, we model the utility of service requester as a function closely related to when its released tasks would be completed. In our mechanism, the participating vehicles bid for their preferred tasks by submitting not only the bidding prices, but also the estimated time of completion (ETC). To maximize the utility of the service requester under a budget constraint, the proposed delay-aware mechanism is cast as a nonmonotone submodular maximization problem with a knapsack constraint. Due to the NP-hardness of the formulated problem, we develop an approximate algorithm for bid selection and payment determination, which guarantees truthfulness, budget feasibility, individual rationality, profitability, and computational efficiency. Simulation results demonstrate the effectiveness of our proposed incentive mechanism.

Internet of Vehicle (IoV) is an important paradigm to realize the intelligent transportation system. However, with the increasing number of vehicular applications, how to satisfy the ubiquitous requirements of communication and computation is challenging. Fog computing provides the real-time transportation services to local users timely through close-proximity data processing, rather than routing data to a remote central data center in the cloud. More importantly, the fog computing will facilitate the autonomous driving (AD) revolutionarily. This paper investigates the problem of optimal deployment and dimensioning (ODD) of fog computing-based IoV infrastructure for AD. For the ODD problem, we present two diverse architecture modes, i.e., the coupling mode (CRF) and the decoupling mode (DRF), and formulate the ODD problem into two integer linear programming formulations with the objective of minimizing the deployment cost. A heuristic algorithm is also proposed to achieve the suboptimal deployment solution for large-scale fog computing-based IoV. Numerical results show that the DRF is more cost-effective and flexible than the CRF for deployment in practice.

Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.

Nowadays, the accidents of oil spill become more and more frequent, causing pollution to the natural resources, marine environment and lives in the sea. As a result, the detection of oil spill draws more and more attentions. One of the most popular region-based active contour models proposed by Chan and Vese, is widely used to image segmentation. But it can't segment hyperspectral oil spill image well, which has blurry boundaries, low distinction, and noise and so on. In order to segment oil spill region from the hyperspectral oil spill image accurately, we improved the region-based active contour model in this paper. For the energy functional, we firstly bring the thought of Fisher criterion into the fitting term to get a better classification result faster. Secondly, a new stop function based on gradient of spectral angle measurement is added into the length term, so as to take advantage of the edge information fully even it is blurry. At last, the model is extended to be able to segment desired material from the complex image with several classes in it. We take some experiments on synthetic and real hyperspectral images to verify the effectiveness of our model, and apply it to the airborne hyperspectral oil spill image. Results of the proposed model on synthetic and testing hyperspectral images show that it outperforms the CV model greatly, and does better than several other segmentation and classification algorithms. Results on hyperspectral oil spill images show that it improves the ability of distinguishing oil spills from sea water, even there are boats and flats in the image.

In this paper, a backstepping based adaptive fuzzy control algorithem is presented for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. By introducing the "minimal learning parameter (MLP)" technique, the proposed scheme is able to circumvent the problem of "curse of dimension" for high-dimensional systems. Meanwhile, all the virtual control laws and actual control law in the system are updated by a novel actual adaptive update law, thus the number of parameters updated online for whole system is only by one. Takagi-Sugeno (T-S) fuzzy systems are used to approximate the unknown system functions. It is shown via Lyapunov theory that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is employed to illustrate the effectiveness and advantages of the proposed scheme.

C-RAN, i.e., Cloud-Radio Access Network, is a new cellular network architecture for the future mobile network infrastructure. It is proposed to provide a possible solution for operators to construct mobile access networks in a cost-effective manner. Different from traditional cellular network architectures that are built with many stand-alone base stations (BSs), C-RAN is now viewed as an architecture evolution based on distributed BSs. C-RAN has drawn extensive attentions from the operators due to its “4C” characteristics, i.e., Clean, Centralized processing, Collaborative radio, and real-time Cloud radio access network. In this paper, we focus on the Infrastructure Deployment and Layout Planning (IDLP) problem under the C-RAN architecture. The IDLP problem is formulated as a generic integer linear programming (ILP) model which can optimally: (i) minimize the network deploying cost, (ii) identify the locations of Remote Radio Units (RRUs) and Wavelength Division Multiplexers (WDMs), (iii) identify the association relations between RRUs and WDMs, (iv) satisfy the mobile coverage requirements so as to allow the mobile user access through RRU. We solve the model using Gurobi, which is the newest ILP solver by now. A series of case studies are conducted to validate the optimization framework and demonstrate the solvability and scalability of the ILP model. Computational results show the significant performance benefits of CoMP in C-RAN in terms of lower cost, larger capacity and higher reliability.

Cascaded Passive Optical Network (PON) has been reported as an effective approach for achieving flexible deployment of optical network units (ONUs) in metropolitan areas and possibly a great cost reduction from the operator perspectives. It serves as a promising technique to support low-cost deployment of long-reach Passive Optical Networks (LRPONs), which is one of the keys to enable a fiber to the premises (FTTP) service provisioning scenario. Motivated by its future-proving importance, this paper investigates a dimensioning task which revisits the cascaded splitter topology for the LRPON new scenario. We formulate and solve the splitter topology and placement optimization problem with the objective of deployment cost minimization using Integer linear programming (ILP). Two different schemes with or without cascade splitting topology are developed and implemented via case studies. The case study results show that the cascaded splitter topology is way more cost-effective, economical, and suitable in the deployment of LRPON for FTTP in practice.

ABSTRACT This paper addresses the issues of Base station—User Association and Resources Allocation (BUA‐RA) in OFDM‐TDMA based broadband wireless access (BWA) networks under passive optical networks (PON)‐WiMAX integration. With the powerful coordination capability at the optical line terminal (OLT), a key technology of inter‐cell cooperative transmission (CT) is incorporated in the integrated network architecture, which is called cooperative PON‐WiMAX network (CPWN). To achieve an efficient integration and inter‐cell cooperative transmission in the CPWNs, the BUA‐RA scheme is critical to the Quality of Service (QoS) provisioning for each user. In order to minimize the network resource usage, we provide three new BUA‐RA schemes which first time employ the cooperative transmission in a multi‐cell BWA network. The three schemes are designed for three kinds of subscribers with different moving types, and can be adaptively applied based on the network load. Simulations are conducted to verify the proposed BUA‐RA schemes by comparing with those without cooperative transmission technology. Our results demonstrate the efficiency of our proposed schemes, which are based on mathematical formulations and linearization. Copyright © 2010 John Wiley & Sons, Ltd.

To satisfy the stringent requirement of capacity enhancement in wireless networks, cooperative relaying is envisioned as one of the most effective solutions. In this paper, we study the capacity enhancement problem by way of Relay Stations (RSs) placement to achieve an efficient and scalable design in broadband wireless access networks. To fully exploit the performance benefits of cooperative relaying, we develop an optimization framework to maximize the capacity as well as to meet the minimal traffic demand by each Subscriber Station (SS). In specific, the problem of joint RS placement and bandwidth allocation is formulated into a mixed-integer nonlinear program. We reformulate it into an integer linear program which is solvable by CPLEX. To avoid exponential computation time, a heuristic algorithm is proposed to efficiently solve the formulated problem. Numerical analysis is conducted through case studies to demonstrate the performance gain of cooperative relaying and the comparison between the proposed heuristic algorithm against the optimal solutions.

A unified approach to the steady-state mean square error (MSE) and tracking performance analyses for real and complex adaptive filtes with error nonlinearities is developed. Some general clofied-form analytical expressions for the steady-state performances are given. Our analyses are based on Taylor series expansion and and so-called complex Brandwood-form series expansion (BSE). Under these general explicit expressions, some well-known adaptive filters can be viewed as special cases. In addition, the closed-form analytical expressions for the steady-state performance for real and complex least-mean p-power (LMP) algorithm with different choices of parameter p are also given. A mass of simulations show the accuration of our analyses.

To satisfy the stringent requirement of capacity enhancement in wireless networks, cooperative relaying is envisioned as one of the most effective solutions. In this paper, we focus on the problem of capacity enhancement by way of relay stations (RSs) placement, which is a critical task of network planning and deployment to achieve an efficient and scalable network design. To fully exploit the performance benefits of cooperative relaying, we develop an optimization framework to maximize the capacity as well as meet the minimal traffic demand for each subscriber station (SS). The problem of joint RS placement and bandwidth allocation is formulated into a mixed integer nonlinear program and solved through a heuristic approach based on genetic algorithm (GA). Moreover, an upper bound on the capacity is derived to assist the estimation of system performance given a network configuration. Numerical results are presented to demonstrate the effectiveness of the solution approach and the performance benefits due to RS placement and optimal bandwidth allocation through cooperative relaying.

Based on series expansion, the steady-state mean-square error (MSE) analysis for real and complex least mean$p$-order (LMP) algorithm is developed, and some closed-form analytical expressions for the steady-state MSE and the corresponding restrictive conditions for step-size are given. Moreover, in Gaussian noise environments, its steady-state performance is also investigated. The analyses for some well-known algorithms and the computer simulation validate the accuracy of the results.

This paper studies the problem of network dimensioning and location planning (DLP) in multi-hop wireless networks by incorporating recent advances in wireless multilevel cooperative relaying (CR), which has been recognized as an effective design paradigm for achieving throughput/capacity enhancement in modern metropolitan area networks. The paper is committed to develop an optimization framework and a suite of decent solution approaches which can manipulatively capture the nature of the DLP problem and precisely characterize the behavior of multi-level cooperative relaying. For this purpose, the tasks of dimensioning, relay placement, relay allocation, and signal relay sequence design are jointly considered and accommodated into a unified framework. To make the solution of the optimization problem computationally tractable, a heuristic two-phase algorithm is developed. Simulation and case studies are conducted to verify the proposed optimization framework, and the results demonstrate the significant cost reduction and achievable rate improvement due to multi-level CR.

In this paper, we propose an efficient privacy-preserving scheme for secure packet transmission at wireless link layer. The proposed scheme is constructed by using hash values in reverse hash chains as interface identifiers. It can successfully and efficiently resist the Media Access Control (MAC) address based attacks, such as flow tracking and traffic analysis, which are launched by either outside or even inside attackers. In addition, some optimization techniques are also introduced to further improve the efficiency of the proposed scheme. The extensive analysis and simulations demonstrate the enhanced security and efficiency of the proposed scheme.

This paper tackles a fundamental problem of network planning and dimensioning under EPON-WiMAX integration for next-generation wireless metropolitan-area broadband access. Due to the powerful coordination capability of the optical line terminal (OLT), inter-cell collaboration through physical layer cooperative transmission (CT) among the optical network unit-base stations (ONU-BSs) can be initiated. In order to achieve the most efficient deployment of network infrastructure and meet the long-term performance requirements, the proposed planning and dimensioning model jointly considers the problems of ONU-BS placement, BS-User (BU) association, and resource breakdown assignment (RBA), which are further formulated into a combinatorial optimization problem. To linearize the problem formulation, an approach based on decomposition and Special Ordered Set of Type 1 (SOS1) remodeling method is developed such that the reformulated problem can simply be solved by CPLEX. Case studies are conducted to demonstrate the performance gain in terms of total infrastructure cost and spectra efficiency against the case without considering collaboration among the ONU-BSs and optimal RBA.

Cooperative relaying is one of the most effective techniques in coverage extension and capacity enhancement by virtue of spatial diversity. To fully explore the benefits of adopting relay stations (RSs), a vital issue is the placement of RSs by jointly considering an advanced coding scheme. In this paper, we aim to provide a general framework for solving the minimum cost RS placement problem in 802.16J Mobile Multi-hop Relay (MMR) networks. We first introduce a novel dual-relay architecture, where all the users, i.e., the mobile stations (MSs) and the fixed subscriber stations (SSs), are connected to the BS via two active RSs through decoded-and-forwarding scheme. We will then demonstrate that the most significant advantages of the dual-relay architecture lie in the ability of achieving high throughput for the systems. In addition, the users can be subject to better fault tolerance, robustness, and power saving. We formulate the dual- relay RS placement problem, and solve it through a two-phase algorithm to deal with the NP-hardness. Numerical analysis is conducted to evaluate the performance gain due to cooperative RS placement in the proposed framework, and demonstrate that the proposed approach can lead to a well acceptable solution compared with that by exhaustively searching.

Without appealing to the circularity assumption of the a priori estimation error in , two closed-form analytical expressions for the steady-state excess mean-square error (EMSE) in a noise-free environment are derived again based on Taylor series expansion for real and complex Bussgang algorithms, respectively; and the restrictive conditions of these two expressions for the steady-state EMSE are also given.

To make the WiMAX Point-to-Multi-Point (PMP) systems more competitive and applicable to the future metropolitan area networking scenarios, deploying relay stations (RSs) as defined in IEEE 802.16j has been considered a promising solution that can replace the 802.16e mesh mode for coverage extension and throughput enhancement. In this paper, we are committed to tackle the task of RS placement and relay time allocation in IEEE 802.16j Mobile Multi-hop Relay (MMR) networks, in order to meet the uneven distributed traffic demand of each subscriber station (SS) as well as the thirst for system capacity. By incorporating advanced cooperative relaying technology such as Decode-Forward (D-F) or Compress-Forward (C-F), the task of RS placement and relay time allocation is formulated into an optimization problem, aiming at finding the optimal location of a single RS and the resource allocation for all the SSs. Numerical analysis is conducted through a number of case studies to demonstrate the performance gain by using the proposed approach for relay placement and relay time allocation.

Due to the nature of wireless ad hoc networks such as dynamic infrastructure and non-centralized management, the routing process has a huge exposure to malicious hacking and intrusions. This fact results in a likelihood of node compromise, leading to a disruption of the legitimate network functions/services. Most reported studies in coping with the problem have focused on the effort of protection on route discovery and data transmission against various attacks. In this paper, we solve the problem from a different perspective by targeting the node compromise revocation, i.e., isolating and breaking off the misbehaving nodes. To mitigate the security breaches from internal compromised nodes and eventually eliminate compromised nodes from the wireless ad hoc networks, we propose an energy efficient malicious node removal mechanism. Further, a new attack on routing service called entrap attack is introduced, where an innocent node is incriminated as a malicious node.

Connection reliability is one important service level agreement (SLA) parameter for a customer and should be carefully considered in survivable WDM networks. A sound scheme should guarantee customers' reliability and simultaneously benefit a service provider in resource efficiency. Under the SLA constraints and the assumption of shared risk link group (SRLG) failures, a novel dynamic differentiated shared path-protection algorithm (DDSP) in WDM mesh networks is proposed. Based on the basic ideas of the K-shortest path algorithm and partial SRLGdisjoint protection, DDSP can provide differentiated services for customers according to their SLA parameters while optimizing resource utilization. Simulation results show that DDSP not only can efficiently guarantee the specific SLA requirements of customers, but also can achieve significant performance gain and lead to remarkable reduction in blocking probability.