Publisher Copyright: © 2026 IEEE.

The rapid expansion of low earth orbit (LEO) satellite networks exposes critical limitations in conventional resource allocation schemes, which are unable to simultaneously optimize spectral utilization and adapt to heterogeneous traffic patterns under extreme mobility, necessitating a joint beam-frequency dynamic coordination framework. To address the challenges of multi-beam LEO systems, this paper introduces a hybrid framework that synergizes adaptive beam activation patterns and spectrum reuse optimization, enhanced by a deep reinforcement learning (DRL)-driven coordination mechanism for resource allocation in time. By dynamically adjusting beam activation patterns and frequency allocation, the framework optimizes spatial-temporal resource utilization while mitigating co-channel interference. Angular-constrained multi-criteria clustering achieves dynamic beam-user mapping with low-complexity adaptation for LEO mobility. The DRL-based component further coordinates multi-dimensional parameters, including transmit power and sub-channel assignment, to balance throughput and latency under time-varying channel conditions. Extensive simulations validate the framework’s capability to maintain high spectral efficiency and coverage performance across diverse scenarios, outperforming conventional static allocation methods. The results highlight its adaptability to dynamic traffic patterns and scalability for large-scale deployments, providing a robust foundation for next-generation LEO systems.

Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.

Multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) is a promising solution to achieve higher performances of dual functionalities. However, the existing cell-free/bistatic MIMO ISAC networks struggle to meet strict requirements for data-intensive communication and accuracy-sensitive radar positioning. To further achieve joint enhancement, we propose a novel network where two ISAC transmitters cooperatively perform communication and target positioning, fully leveraging the advantages of cell-free/bistatic principles in communication/radar systems, referred to as bistatic-enhancement MIMO ISAC. An optimization for joint beamforming design is established to maximize the sum data rate for communication users and minimize a novel positioning-enhanced Cramér-Rao lower bound (CRB) that evaluates positioning accuracy under their corresponding requirements. The established problem is solved under two schemes: cooperative block-level and symbol-level beamforming. The solution under the former scheme is derived by an iterative behavior. Under the latter one, inter-user interference is eliminated and co-channel interference is exploited for useful signal enhancement. The problem can be converted into a convex semi-definite problem (SDP) based on semi-definite relaxation (SDR). Experimental results substantiate the effectiveness of the proposed algorithms. More importantly, the proposed bistatic-enhancement network improves positioning accuracy by 32.5% ∼ 47.5% over the conventional bistatic-site one under different schemes.

The integration of intelligent reflective surfaces (IRS) on unmanned aerial vehicles (UAVs), termed UAV-IRS, to bolster wireless communications has emerged as a hotspot of academic research and industrial application. In this paper, we investigate the problem of secure communication in the harsh communication environment assisted by multiple UAV-IRSs, where the UAV-IRSs act as relays to assist the downlink secure communication between the base station and the users. To maximize the security sum rate between the base station and the users, the trajectory planning and phase shift design of multiple UAV-IRS needs to be jointly optimized. To solve this complex non-convex optimization problem, we introduce a distributed collaborative optimization scheme for multiple UAV-IRSs called credit-aware cooperative multi-agent reinforcement learning (MARL), which takes MARL as the base algorithm, and then solves the credit allocation problem among multiple UAV-IRSs by using cooperative game theory to facilitate exploration, and finally constrains non-cooperative behaviors among UAV-IRSs by using the primal-dual optimization algorithm to promote cooperation. Finally, the effectiveness and superiority of the proposed scheme is verified by comprehensive simulation experiments.

Deep Metric Learning (DML) aims at learning the representation of fine-grained data, which plays a vital role in fine-grained retrieval and classification applications. Previous studies have shown that cropping an object based on its bounding box (BBox) can significantly enhance performance. However, manually annotating the BBox is costly. In this paper, we propose to predict the BBox unsupervisedly, termed Towards Improved Deep Metric Learning via Unsupervised Object Location (DML-OL). DML-OL first proposes a BBoxNN to predict the BBox of the object. Building on this, DML-OL further introduces a CRNN, which crops and resizes the image based on the predicted BBox. Unlike the non-differentiable naive crop operator, the CRNN is designed to be fully differentiable. This differentiability property enables the model to be trained end-to-end using the pretext task without the BBox labels. We evaluate our proposed DML-OL on two strong baselines, and the results show that DML-OL outperforms the compared methods. Additionally, we demonstrate that DML-OL can predict the BBox accurately without the BBox labels.

Uncrewed aerial vehicle (UAV)-enabled ISAC systems have received widespread attention due to the high mobility of UAVs with good line-of-sight (LoS) paths to ensure communication and sensing performance. However, the existing works on UAV-enabled ISAC mainly focus on optimizing communication performance (e.g., sum rate) and sensing performance, resulting in excessive energy consumption and reducing the flight endurance of the UAV. Motivated by this, we draw a trade-off between such performance and energy consumption to achieve robust and efficient UAV-enabled ISAC. In this work, we aim to maximize the worst-case energy efficiency in UAV-enabled ISAC by jointly designing the beamforming and the UAV trajectory, while ensuring the UAV energy constraints and the ISAC performance. Nevertheless, solving this problem is non-trivial due to its non-convex nature, and the high coupling of the transmit beamforming vectors and the UAV dynamics adds an additional layer of complexity. To effectively address this non-convex issue, we alternately optimize the transmit communication and sense beamforming, as well as the UAV dynamic variables to obtain a sub-optimal solution, and the algorithm complexity is lower than the existing algorithms. Experimental results show a trade-off between energy efficiency and average sum rate. Furthermore, they indicate the superiority of the proposed algorithm to enhance energy efficiency by significantly reducing energy consumption without causing excessive sum rate loss.

On-demand edge computing power networks with both stationary fog nodes co-located with cellular base stations (CFNs) and mobile fog nodes mounted on vehicles (VFNs) provide promising solutions for coping with high spatio-temporal, compute-intensive, and latency-sensitive applications. Joint scheduling and resource allocation in such a network is challenging due to the trade-off between quality of service (QoS) and energy consumption, limited onboard capacity of IoT devices and fog nodes, and urban obstructions that impede line-of-sight links. To address these issues, this work envisions a network assisted by reconfigurable intelligent surface (RIS) with a multi-layer scheme. The computation tasks are offloaded from IoT devices to VFNs and further to CFNs based on the computational demand and latency requirements, and the RIS assists with wireless communication on both links. We jointly optimized the allocation of the subcarriers, the power, the offloading task bits, the time slot, and the RIS beamforming vectors under the constraints of task input bits and computing capability, to minimize the average energy consumption. To address the non-convex issue, we first decompose it into three sub-problems, and then alternately optimize these sub-problems by adopting successive convex approximation (SCA) where a locally optimal solution can be obtained. Simulation results demonstrate the superiority of the proposed offloading strategy where RIS with a multi-layer scheme is introduced in the on-demand edge computing power networks. Also, the effectiveness, feasibility, scalability, and adaptability of the designed algorithm are verified.

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task; ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder.

Uncrewed aerial vehicle (UAV)-assisted integrated sensing, computation, and communication (ISCC) network enables the entire data analysis process for practical applications. The existing works of UAV-assisted ISCC merely consider a single data source, and there still exist gaps in the collection of environmental data via multiple sources. Motivated by this, we envision a novel UAV-assisted air-ground collaborative ISCC network that fully explores the cooperation between aerial UAVs and ubiquitous ground Internet of Things (IoT) devices. To achieve effective, efficient, and fair joint task scheduling and resource allocation, an optimization is established to minimize two novel indicators, i.e., computation offloading and sensing penalty indices, subject to constraints of boundary, anti-collision, and UAV energy consumption. To tackle this problem, a deep reinforcement learning (DRL) framework is proposed where three advanced DRL algorithms are included under centralized and decentralized control schemes. In former scheme, the central controller makes globally optimal decisions. In latter scheme, multiple agents decide independently based on local information. We demonstrate a forest fire monitoring use case simulated in a national forest park. Results show the mutually interfering, competitive, and beneficial relationships among triple functionalities. Besides, our solution outperforms three state-of-the-art baselines in terms of effectiveness and efficiency.

Intelligent Transportation Systems (ITS) urgently require real-time data collection with minimized Age of Information (AoI), yet face critical challenges from high-dynamic traffic environments and unstable wireless channels. By virtue of the low deployment cost and the high-speed mobility, Uncrewed Aerial Vehicle (UAV) bring us a feasible approach to the aforementioned problem. Nevertheless, such a problem is far from trivial due to lot of factors ranging from the highly dynamic communication environment, the dimension-varying input/output for each UAV, to the extremely large solution space for all the UAVs as a whole in a distributed collaborative manner. Although existing Multi-Agent Reinforcement Learning (MARL) solutions are widely used to address the above challenges, they all rely on fixed-dimensional input/output processing (e.g., padding/truncation strategies), leading to redundancy or loss of information due to dimensionality-changing scenarios. To address this gap, we proposed an improvement scheme based on attention-driven MARL, which redesigns the policy and critic network based on the attention mechanism to help UAVs extract critical information from dimension-varying data in an accurate and efficient manner. Finally, we verify the superiority and robustness of the proposed scheme through multiple sets of experiments with multiple different aspects. The simulation results show that the proposed scheme is scalable and efficient, and the weighted average AoI under different scenarios is lower than the existing state-of-the-art schemes by$13.1\%\sim 56.2\%$.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

Joint task scheduling and resource allocation for unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) in emergency rescue activities has become an essential and challenging problem. However, the existing works have only considered such a problem for standalone UAV networks without considering the cooperation between UAVs and ground base stations (BSs), nor have they considered the uncertainty in terms of the availability of BSs due to damage/reconstruction in disaster events. In this paper, we consider a novel post-disaster UAV-assisted ISAC system where the UAVs are used to supplement the networking capacity of out-of-service ground BSs while using their radio signals for sensing. We apply transfer learning with deep reinforcement learning (DRL) to learn task scheduling and resource allocation strategies that can rapidly adapt to uncertainty in the environment. Experimental results show that the proposed algorithm outperforms the state-of-the-art in both communication and sensing performance and convergence speed. Moreover, the transfer learning-based DRL shows faster convergence and better robustness when the availability of BSs suddenly changes.

Integrated sensing and communications (ISAC) has received considerable attention from both industry and academia. By sharing the spectrum and hardware platform, ISAC significantly reduces costs and improves spectral, energy, and hardware efficiencies. To support the large number of communication users (CUs) and sensing targets (STs), the design of multiple access (MA) is a fundamental issue in ISAC. MA techniques in ISAC are expected to avoid mutual interference between sensing and communicating functions under the critical constraints of both functions. In this article, we present an overview on approaches of MA for ISAC, from orthogonal transmission strategies to nonorthogonal ones, realized in time, frequency, code, spatial, delay-Doppler, power, and/or multiple domains. We discuss their individual implementation schemes and corresponding resource allocation strategies, as well as highlight future research opportunities.

Mobile edge computing (MEC) has attracted widespread attention due to its ability to effectively alleviate the cloud computing load and significantly reduce latency. However, the potential eavesdroppers challenge the security of the MEC systems and the rapid development of artificial intelligence (AI) has made this security situation more severe. In most existing studies, the eavesdroppers are non-intelligent and it is assumed that they are fixed or move in a simple manner. Obviously, there is a gap from such an assumption to the real conditions that the eavesdropping unmanned aerial vehicles (UAVs) may adjust their flight paths intelligently. To better reflect real-world scenarios, we consider a multi-UAV-assisted MEC system in the presence of intelligent eavesdroppers and propose an adversarial multi-agent reinforcement learning (MARL)-based scheme for secure computational offloading and resource allocation. With this scheme, we aim to solve the zero-sum game between the legitimate UAVs and the eavesdropping UAVs, in which the two types of UAVs take turns acting as the agents of MARL to alternately optimize their respective opposing objectives. The simulation experimental results indicate that the proposed scheme significantly outperforms the existing baseline methods in dealing with the intelligent eavesdropping UAVs, and ensures high energy efficiency of Internet of Things (IoT) devices even in the worst-case scenario when dealing with potential eavesdropping threats.

Mobile-edge computing (MEC) as a promising technology enables it to satisfy ever-increasing demands for low-latency and ultrareliable services. However, due to the limitations of computing capability and the dynamic network environment, it is challenging to process massive data with low latency. In this article, we consider a dynamic MEC network with a high-performance edge server, multiple time-varying channels, and multiple mobile devices. We aim to find a policy that can maximize the processing success rate of computational tasks and the fairness index of the system while minimizing the process delays. To this end, we propose a heuristic-assisted multiagent reinforcement learning (RL)-based framework to realize the joint optimization of computation offloading and resource allocation. On the one hand, heuristic search is introduced in this framework to find a better resource allocation policy in edge servers and further assist the multiagent RL algorithm to determine offloading policy in mobile devices. On the other hand, a novel parameterized multiagent RL algorithm based on soft actor–critic (SAC) is also proposed to broaden the effectiveness and availability of the proposed framework. Simulation results of the average cumulative reward, success rate, processing delay, and fairness index fully verify the superiority of the proposed framework and algorithm for supporting this problem.

In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate with on-ground target users. To optimize the communication and sensing performance, we formulate a joint user association, UAV trajectory planning and power allocation problem to maximize the minimum weighted spectral efficiency among UAVs. This paper exploits the centralized and the decentralized deep reinforcement learning (DRL) solutions to solve the sequential decision-making problem. On one hand, we first introduce the centralized soft actor-critic (SAC) algorithm. Then, we explore the equivalent transformation of the optimization objective based on symmetric group, propose the random and the adaptive data augmentation schemes to design the replay memory buffer of SAC, and accordingly propose SAC algorithms assisted by data augmentation to tackle the transformed problem. On the other hand, the multi-agent soft actor-critic (MASAC), a decentralized solution, is also introduced to solve this sequential decision-making problem. The experiment results reveal the effectiveness of the centralized and the decentralized solutions in considered scenarios. Specifically, the SAC assisted by the adaptive scheme significantly outperforms other centralized solutions in the training speed and the weighted spectral efficiency. Meanwhile, the decentralized MASAC algorithm behaves best in the early training speed.

Optimizing the trajectory of Unmanned Aerial Vehicle (UAV) Base Station (BS) is an important operational task to improve the Quality of Service (QoS) for remote areas. However, existing works mainly neglect the fair coverage and dynamic Ground Users (GUs). In this paper, we propose a novel coverage fairness index (CFI) to measure whether dynamic GUs are served as fairly as possible. Then, we formulate the problem as a constrained problem with the objective of maximizing fair coverage and minimizing energy consumption while satisfying the bound constraints. An accurate and efficient Soft-Actor-Critic (SAC)-based UAV trajectory optimization algorithm is proposed to solve the complex constrained problem based on deep reinforcement learning. Experiments are executed to prove the feasibility and efficiency of the proposed algorithm. The results manifest that the performance of the proposed algorithm is better than that of the two existing baseline methods.

Unmanned Aerial Vehicle (UAV) Base Station (BS) placement optimization is an essential operational task to improve the Quality of Service (QoS) in UAV-aided wireless cellular networks. The existing approaches are almost zeroth order methods, and the few first order methods mainly ignore the allocation fairness, computational efficiency, and backhaul constraints. In this paper, we formulate the UAV placement problem as a constrained optimization problem, with the objective of maximizing the fair coverage versus energy consumption while satisfying the backhaul constraints at different time nodes. To guarantee fair QoS allocation, we introduce a novel fairness index to ensure fair communication opportunity and the novel region coverage ratio to avoid excess QoS on covered spots. An accurate and efficient proximal stochastic gradient descent based alternating algorithm that iteratively executes two optimization steps is proposed to optimize the UAV locations, which enables the fast single point-based first order methods to solve the complex problems with constraints. Experiment results manifest that the proposed algorithm performs well both in synthetic data scenario and in real city scenario. Furthermore, the proposed first order algorithm is more efficient than the existing zeroth order algorithm, typically referring to the meta-heuristic method.

This paper intends to study the energy efficiency in caching based UAV networks, where fog radio access network (FRAN) and non-orthogonal multiple access (NOMA) are considered meanwhile. Taking full account of the impact of caching, subchannel assignment, and power allocation in UAV enabled wireless networks, we formulate the problem of maximizing energy efficiency. In order to better solve the proposed non-convex problem, we propose a subchannel assignment algorithm and a power allocation algorithm applying alternating direction method of multipliers (ADMM). The final simulation section verifies the fast convergence of the algorithm and compares the advantages with existing algorithms.

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

In the current researches, artificial intelligence (AI) plays a crucial role in resource management for the next generation wireless communication network. However, traditional RL cannot solve the continuous and high dimensional problems. To handle these problems, the concept of deep neural network (DNN) is introduced into RL to solve high dimensional problems. In this paper, we first construct an information interaction model among primary user (PU), secondary user (SU) and wireless sensors in a cognitive radio system. In the model, the SU is unable to get the power allocation information of the PU, and needs to use the received signal strengths (RSSs) of the wireless sensors to adjust its own power. The PU allocates transmit power relying on its power control scheme. We propose an asynchronous advantage actor critic (A3C)-based power control of SU that is a parallel actor-learners framework with root mean square prop (RMSProp) optimization. Multiple SUs learn power control scheme simultaneously on different CPU threads, reducing neural network gradient update interdependence. To further improve the efficiency of spectrum sharing, the distributed proximal policy optimization (DPPO)-based power control is proposed which is an asynchronous variant of actor-critic with adaptive moment (Adam) optimization. It enables the network to converge quickly. After several power adjustments, the PU and the SU meet quality of service (QoS) requirements and achieve spectrum sharing.

NOMA is a key technology for future wireless communication, which can improve the spectral efficiency (SE) of mobile network. In this paper, a subchannel assignment algorithm is applied to maximize the energy efficiency of downlink heterogeneous NOMA network. Different from previous works, the subchannel assignment problems and power allocation problem are formulated as non-convex problem, then we transform the original problems to convex optimization problems and solve it using difference of convex functions (DC) programming. The simulation results confirm that the applied scheme not only enhance the sum rate of heterogeneous NOMA network but also energy efficiency (EE) of small cell base station (SBS).

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.

Millimeter wave (mmWave) communication technology has become an attractive solution to meet exponential growth demand for mobile data services. In this paper, we propose a deep neural networks (DNN) based algorithm for user association and power optimization problem in mmWave heterogeneous network on the basis of gradient iterative algorithm. We jointly design the user association and power optimization to maximize energy efficiency (EE) utilizing Lagrange dual decomposition and then approximate it by DNN models. In addition, an asynchronous distributed DNN based scheme is proposed, which divides the large network model into small distributed networks for distributed data processing on each small base station side to reduce computational time. Simulation results show that the proposed scheme can achieve a high EE with low computation time.

Network coverage and capacity optimization is an important operational task in cellular networks. The network coverage maximization by adjusting azimuths and tilts of antennas is focused and the existing approaches are mainly gradient-free methods. A standard gradient descent algorithm and its improved version, namely a Stochastic Gradient Descent (SGD) algorithm are proposed on the basis of a novel coverage indicator, named as the soft coverage indicator, to approximate the hard version of the original coverage indicator. We prove that the gradient vector is sparse, which accelerates gradient calculation, due to the number limitation of base stations within a specific distance from a given sampling point even if there are many decision variables of azimuths and tilts. Also, the SGD algorithm only requires a small amount of computation based on cheap estimates of the gradients, and thus is applicable to large-scale networks in an efficient manner. The experiments show that the proposed approaches perform well both in their near-optimal solutions and in their computation efficiency compared with the meta-heuristic algorithms. The extensibility and practicality of the proposed algorithms are also discussed.

Clock synchronization is critical for many WSNs due to the need of inter-node coordination and collaborative information processing. Existing protocols based on message passing achieve satisfactory clock synchronization accuracy, however, incur prohibitively high overhead especially in large-scale networks. In this paper, we propose a new clock synchronization approach called ROCS which exploits the radio data system (RDS) from FM radio stations. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40km2. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

The design of a wireless sensor network is introduced for the metallurgical gas monitoring, in which the sensor node support a slot-based configurable gas sensor array both for metallurgical-specific and general purposes at the factory or far regions. The key technologies to compress the multisensor vector data is discussed with an amendatory LBG vector quantization and Huffman coding algorithms. Both the proposed network design and data compression schemes are verified with the hardware prototype and practical data. To the best of our knowledge, it is a novel exploration and practice for the applications of wireless sensor networks in the metallurgical industries, especially for the metallurgical gas monitoring.

Wireless sensor networks usually are deployed in the complex environments and take a long time to run without human intervention. In addition, the sensor nodes have limited resources and use unstable wireless link for communication, which cause wireless sensor networks various problems in the actual operation. Therefore, the real-time monitoring tools are needed to maintain the operation of wireless sensor networks, which are capable of monitoring network operating conditions, assessing network performance, detecting network failure and optimizing network operation. A network monitoring and packet sniffing tool for wireless sensor networks (NSSN) is presented and implemented in this paper. As a kind of real-time monitoring tools based sniffers, NSSN can capture the radio packets from the normal nodes using NSSNer nodes, so that it can monitor network status, find network problems and optimize network configuration without any interference in the normal operation of wireless sensor networks. The functions which NSSN has implemented include network monitoring, protocol parsing and display, network diagnosis and performance measurement, data mining and statistical analysis. According to the actual deployment, NSSN has been verified good monitoring performance.

Clock synchronization is critical for Wireless Sensor Networks (WSNs) due to the need of inter-node coordination and collaborative information processing. Although many message passing protocols can achieve satisfactory clock synchronization accuracy, they incur prohibitively high overhead when the network scales to more than tens of nodes. An alternative approach is to take advantage of the global time reference induced by existing infrastructures including GPS, timekeeping radio stations, or power grid. However, high power consumption and geographic constraints present them from being widely adopted in WSNs. In this paper, we propose ROCS, a new clock synchronization approach exploiting the Radio Data System (RDS) of FM radios. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40 $km^2$. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS intelligently predicts the time error due to drift, and adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

(1) Institute of Software, Chinese Academy of Sciences, China; (2) Graduate University, Chinese Academy of Sciences, China; (3) Department of Computer Science and Engineering, Michigan State University, United States

Wireless sensor networks consist of a large number of low-cost and micro sensor nodes, which form the networks in wireless and ad hoc way to achieve the tasks that contain gathering, processing and transferring data in the deployment region. Sensor network management is to monitor and control the operational status of sensor networks, enabling them to provide effective, reliable, safe and economical services. With the increase of WSNs applications. WSNs management has become more and more important. A network management system for large-scale wireless sensor networks (WSNMS) is presented and implemented in this paper, which is designed and implemented with the characteristics of WSNs. The functions which WSNMS has implemented include four categories: configuration management, performance management, fault management and accounting management. According to the results of actual experiments which consist of 215 nodes as managed objects and a gateway, WSNMS is able to undertake the large-scale network management functions and has good performance.

The NISAT testbed consists of a center server and many test units. The test units probe the internal interconnected signals inside the motes with extra hardware sniffers. The server gathers, parses and analyzes all data from test units to obtain the information on the network behavior. By adopting the passive sniffing mechanism, NISAT has no side effect on the normal behavior of sensor networks, and it is transparent to the software running on motes. NISAT offers accurate and precise test data for the studies on sensor networks, especially for the high-precision performance measurements and black-box tests without source codes.

Aiming at the periodical data gathering application of wireless sensor network, the transmission delay and the network traffic loads are analyzed for the route-based data aggregation algorithm. It is then investigated how to minimize the network traffic within the data delay bound. An optimization problem is deduced based on the traffic aggregation model. Inspired by the hill-climbing algorithm, a centralized algorithm named as ldquoEffectiveness based Aggregation Time Allocation (EATA)rdquo is introduced and evaluated. In our simulation experiments, EATA can lead the network to a maximal traffic aggregation performance and good adaptability compared with other existing algorithms.

Aiming at the periodical data gathering application of Wireless Sensor Network, this paper first analyzed the network traffic flow and transmission delay in typical aggregation model. Then it investigated how to properly allocate aggregation time to minimize total network traffic within certain delay bound. An optimization problem was deduced and numerical solutions for regular network are calculated. Inspired by the numerical results, this paper introduced the concept of the critical aggregation level and presented an algorithm called Triangle-shaped Hierarchy aggregation Time Allocation (THTA). In our simulation experiments, THTA can lead the network to the maximum aggregation performance and fine adaptability compared with other existing algorithms.

In this paper we firstly improve a virtual network buffer model (VB), based on which we propose a joint sender/receiver rate control algorithm for scalable video streaming. A transmission rate is determined by a rate control algorithm at the sender which employs the program clock reference (PCR) embedded in the video streams to work in a refined way. An over-boundary playback rate adjustment mechanism based on proportional-integral (PI) controller is performed at the receiver to maximize the visual quality of the displayed video according to the receiver buffer occupancy. Simulation results show that our proposed algorithm can reduce the overflow and underflow of the sender and receiver buffer, and achieve better video quality and quality smoothness than traditional rate control algorithms.

In this paper we present a video communication scheme that integrates rate control and buffer management at the source. A transmission rate is obtained via a rate control algorithm, which employs the Program Clock References (PCR) embedded in the video streams to regulate the transmission rate in a refined way and thus reduce the client buffer requirement. The server side also maintains multiple buffers to trade off random loss for controlled loss of visually less important data. We test our system with Standard Definition Television (SDTV) and High Definition Television (HDTV) traces, and find that the proposed scheme serves best for the transmission by slowing down the transmission rate without higher buffer requirement in both cases.

In a wireless sensor network for data-gathering applications, if all network data congregate to a stationary sink node hop by hop, the sensor nodes near the sink have to consume more energy on forwarding data for other nodes, which probably causes the early function loss of the sensor network. Employing a mobile sink can alleviate the hotspot problem and balance the energy consumption among the sensor nodes. In this paper, we propose two autonomous moving schemes for the mobile sink. In our schemes, the sink makes moving decisions without complete knowledge of network topology and the energy distribution of all sensor nodes. We evaluated the performance of our moving schemes by simulation and the results show that both the two schemes can extend the network lifetime prominently.

In this paper, we present a practical end-to-end video transmission system with refined rate allocation at the server and adaptive playback adjustment at the client that enables High Definition (HD) video streaming via bandwidth-constraint IP network. A transmission rate is determined by a rate control algorithm which employs the Program Clock References (PCR) embedded in the video streams to regulate the transmission rate in a refined way and thus reduce the client buffer requirement. An over-boundary playback adjustment mechanism based on Proportional-Integra (PI) controller is performed at the receiver to maximize the visual quality of the displayed video according to the receiver buffer occupancy. We test our system with Standard Definition Television (SDTV) and High Definition Television (HDTV) traces, and find that our proposed algorithm can reduce overflow and underflow of sender and receiver buffer, and achieve better video quality and quality smoothness than traditional rate control algorithms.

In this paper, a novel non-saturation analytical model for priority based IEEE 802.11 network is introduced. Unlike previous work that is focused on MAC backoff for saturation stations, this model uses Markov and M/ M/1/K theories to predict MAC and queuing service time and loss. Then a performance prediction based enhancement scheme is proposed. By dynamic tuning of protocol options, this proposed scheme limits end-to-end delay and loss rate of real-time traffic and maximizes throughput. Consequently, call admission control is taken to protect existing traffics when the channel is saturated. Simulations validate this model and the comparison with IEEE802.11e EDCA shows that our mechanism can guarantee quality of service more efficiently.