Satellite-enabled multi-access edge computing (MEC) networks have emerged as a promising solution for low-latency data processing in areas lacking infrastructure. However, these satellite MEC networks face significant security vulnerabilities and high communication latency due to the open-air interface and large-scale data transmission. To address these challenges, we propose a secrecy-aware adaptive federated learning (AFL) approach for a satellite MEC network. In this network, terrestrial devices perform local model training using their own data and periodically transmit updated model parameters to a satellite server in the presence of an eavesdropper. To secure both model uploading and downloading, idle devices act as friendly jammers, transmitting jamming signals to disrupt eavesdropping attempts. Our goal is to minimize the overall federated learning latency by jointly optimizing the number of quantization bits, the transmit power of MEC devices, the satellite’s transmit power, and the jammer selection strategy. To solve this problem, we first propose an AFL framework that minimizes the model uploading size while ensuring the required model accuracy. Building on this, the problem is divided into two subproblems of model uploading and model downloading, which are solved using a successive convex approximation (SCA)-based algorithm. Additionally, to improve secrecy performance, we introduce a low-complexity jammer selection strategy that significantly enhances the secrecy rate for both model uploading and downloading. Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods in terms of AFL convergence, secrecy performance, and overall latency.

Unmanned Aerial Vehicle base stations (UBSs) are able to assist a cellular IoT network to provide content delivery service for ground users. This paper studies the UBS deployment problem in a cache-enabled UBS-assisted cellular network. The UBS deployment problem is first formulated as a joint optimization problem with an objective to minimize the average content delivery delay of all users in a service area. We decompose the problem into three sub-problems: content caching deployment, position deployment, and BS association, and propose a multi-agent proximal policy optimization (MAPPO)-based UBS deployment algorithm to solve the sub-problems. Specifically, the proposed algorithm uses edge agents on UBSs and a two-layer MAPPO algorithm including a cache layer and a position layer to solve the content caching deployment sub-problem and the UBS position deployment sub-problem. Meanwhile, it uses a central agent in a core network and a proximal policy optimization (PPO) algorithm to solve the BS association sub-problem. Simulation results demonstrate that the proposed UBS deployment algorithm can significantly improve the network performance in terms of the average content delivery delay and the cache hit ratio of all users in the network.

Uncrewed Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight small language model (SLM)-based solution is developed using a pre-trained BERT backbone, enhanced UAV embeddings and contextual features, a geometry-aware path decoder, and ensemble inference to achieve low complexity, low latency, and high energy efficiency. Second, an Agentic AI-based framework is designed to exploit the reasoning and interactive capabilities of large language models (LLMs). It integrates four collaborative agents—Initializer, Actor, Critic, and Reflector—to form a closed loop of generation, optimization, evaluation, and reflection for iterative UAV path and energy optimization. Finally, simulations compare the SLM-, LLM-, and Agentic AI-based approaches.

In this paper, we study a movable antenna (MA) empowered secure transmission scheme for reconfigurable intelligent surface (RIS) aided cell-free symbiotic radio (SR) systems. Specifically, the MAs deployed at distributed access points (APs) work collaboratively with the RIS to establish high-quality propagation links for both primary and secondary transmissions, as well as suppressing the risk of eavesdropping on confidential primary information. We consider both continuous and discrete MA position cases and maximize the secrecy rate of primary transmission under the secondary transmission constraints, respectively. For the continuous position case, we propose a two-layer iterative optimization method based on differential evolution with one-in-one representation (DEO), to find a high-quality solution with relatively moderate computational complexity. For the discrete position case, we first extend the DEO based iterative framework by introducing the mapping and determination operations to handle the characteristic of discrete MA positions. To further reduce the computational complexity, we then design a single-layer iterative framework to solve all variables alternatively. In particular, we develop an efficient strategy to derive the sub-optimal solution for the discrete MA positions, superseding the DEO-based method. Numerical results validate the effectiveness of the proposed MA empowered secure transmission scheme along with its optimization algorithms.

Low-altitude economy networking (LAENet) aims to deploy various aerial vehicles to support diverse services, where data collection from edge devices via unmanned aerial vehicles (UAVs) is a critical task. The key challenge lies in jointly optimizing energy consumption and data freshness in spectrum-constrained and eavesdropping-prone low-altitude environments during the data collection process. Although deep reinforcement learning (DRL) has become a viable solution for UAV-assisted data collection, the RL agent still has limited ability to obtain and utilize informative feedback from complex low-altitude environments. In this paper, we propose a large language model (LLM)-enhanced DRL framework for secure data collection in the LAENet, where we leverage an LLM to process environmental feedback for the RL agent. Specifically, we employ the LLM as (i) a state processor to transform basic environmental observations into task-aligned representations, (ii) a reward designer to generate enriched reward signals that guide the agent's actions toward the optimization objective, and (iii) a simulator to construct a virtual LAENet environment for evaluating enhanced state-reward pairs before policy training. Theoretical analysis and numerical results demonstrate that the proposed LLM-enhanced DRL framework achieves faster convergence, improved training stability, and superior performance compared with state-of-the-art baselines.

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

Large Language Models (LLMs) are increasingly integrated into mobile services over wireless networks to support complex user requests. This trend has led to longer prompts, which improve LLMs' performance but increase data transmission costs and require more processing time, thereby reducing overall system efficiency and negatively impacting user experience. To address these challenges, we propose Joint Prompt and Power Optimization (JPPO), a framework that jointly optimizes prompt compression and wireless transmission power for mobile LLM services. JPPO leverages a Small Language Model (SLM) deployed at edge devices to perform lightweight prompt compression, reducing communication load before transmission to the cloud-based LLM. A Deep Reinforcement Learning (DRL) agent dynamically adjusts both the compression ratio and transmission power based on network conditions and service constraints, aiming to minimize service time while preserving response fidelity. We further extend the framework to JPPO++, which introduces a denoising-inspired compression scheme. This design performs iterative prompt refinement by progressively removing less informative tokens, allowing for more aggressive yet controlled compression. Experimental results show that JPPO++ reduces service time by 17% compared to the no-compression baseline while maintaining output quality. Under compression-prioritized settings, a reduction of up to$16\times$in prompt length can be achieved with an acceptable loss in accuracy. Specifically, JPPO with a$16\times$ratio reduces total service time by approximately 42.3%, and JPPO++ further improves this reduction to 46.5%.

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments show that this integrated GenAI framework consistently surpasses traditional interpolation and deep learning methods, especially under sparse sensing conditions. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.

Decentralized Federated Learning (DFL) enables collaborative model training without central coordination. However, DFL faces challenges in dynamic networks, where existing methods struggle to balance consensus rate and communication efficiency, while overlooking practical issues such as topology variation. This paper presents Dynamic AirComp-enabled DFL (DA-DFL), a novel framework that integrates over-the-air computation (AirComp) with the BASE-GRAPH consensus algorithm for efficient DFL over dynamic topologies. The convergence analysis for DA-DFL under dynamic settings is conducted to reveal the influence of the consensus period and communication errors. We define communication overhead metrics, and jointly optimize transceiver beamformers and dynamic topologies. A topology matching algorithm is developed to reduce communication overhead by aligning logical and physical topologies. Experiments show significant gains of DA-DFL in communication efficiency, e.g., reducing communication links and distances by up to 42% and 50%, respectively, compared to benchmarks.

This paper explores the application of movable antenna (MA), a cutting-edge technology with the capability of altering antenna positions, in a symbiotic radio (SR) system enabled by reconfigurable intelligent surface (RIS). The goal is to fully exploit the capabilities of both MA and RIS, constructing a better transmission environment for the co-existing primary and secondary transmission systems. For both parasitic SR (PSR) and commensal SR (CSR) scenarios with the channel uncertainties experienced by all transmission links, we design a robust transmission scheme with the goal of maximizing the primary rate while ensuring the secondary transmission quality. To address the maximization problem with thorny non-convex characteristics, we propose an alternating optimization framework that utilizes the general S-procedure, general sign-definiteness, successive convex approximation (SCA), and simulated annealing (SA) improved particle swarm optimization (SA-PSO) algorithms. Numerical results validate that the CSR scenario significantly outperforms the PSR scenario in terms of primary rate, and also show that compared to the fixed-position antenna scheme, the proposed MA scheme can increase the primary rate by 1.48 bps/Hz and 1.57 bps/Hz for the PSR and CSR scenarios, respectively.

Intelligent reflecting surface (IRS)-assisted integrated sensing and communications (ISAC) systems have been extensively studied to meet higher sensing requirements. For detection-oriented IRS-assisted ISAC problems, most studies have overlooked the detection interference caused by clutters and modeled simplified point-like targets. This paper investigates extended target detection in IRS-assisted ISAC systems within clutters. We present an optimal generalized likelihood ratio test detector and derive the corresponding probability of detection (PD) and probability of false alarm in closed form. Then, we jointly optimize the active and passive beamforming of the base station and IRS to maximize the PD under multi-user equipment (UE) communication rate constraints and the total transmit power constraint. We first simplify the complex objective function by proving the invariant property of a subspace projection matrix. We then present a novel alternating optimization (AO)-based algorithm to decouple the original problem into two subproblems, consequently convexified and solved using the semidefinite relaxation method. Simulations demonstrate the convergence of the proposed algorithm. The PD performance and the communication and sensing trade-off are significantly improved, compared to benchmarks.

This paper presents a novel approach for wireless Virtual Reality (VR) systems by integrating mobile crowdsensing with Deep Reinforcement Learning (DRL). In modern VR applications, ensuring optimal performance while managing system resources presents challenges including high bandwidth requirements, low Motion-to-Photon delay, and intensive computational demands. We address these challenges by proposing a DRL-based framework that jointly optimizes rendering strategies (local, remote, or collaborative) and user-SBS associations to maximize Quality of Experience while adhering to strict latency constraints. To overcome sparse feedback in complex wireless VR environments, we introduce a two-stage approach combining auction-based IoT-VR pairing with diffusion reasoning-enhanced DRL using the Diffusion Reasoning-based Reward Shaping Scheme (DRESS). Our energy model captures rendering-specific power consumption patterns across different strategies. Simulation results demonstrate that our diffusion-enhanced approach (PPO-RS+DF) achieves superior reward convergence and significantly lower latency compared to baseline methods. The diffusion mechanism effectively propagates sparse reward signals across the state-action space, enabling efficient learning from limited feedback and guiding the learning process toward latency-optimized policies for next-generation wireless VR applications.

The rapid advancement of AI-generated content (AIGC) has enhanced the Artificial Intelligence of Things (AIoT) by offering a novel approach to content generation and creation. However, the heterogeneity of AIGC services and the large scale of AIGC models present significant challenges for providing these services. In this paper, we propose an edge-cloud collaborative framework to facilitate the provisioning of heterogeneous AIGC services. In this framework, we focus on three kinds of representative AIGC services, including lightweight AIGC services, computation-intensive AIGC services, and preprocessing-based AIGC services. We jointly optimize resource allocation and AIGC model deployment at an edge server to minimize the service delay for AIoT devices. The delay minimization problem involves mixed-integer nonlinear programming, which is inherently complex. To address this issue, we propose a dual-layer optimization algorithm that decouples the problem into an inner-layer resource allocation subproblem and an outer-layer model deployment subproblem. These subproblems are then addressed using the Karush-Kuhn-Tucker conditions and a cross-entropy-based technique. Finally, simulation results demonstrate the effectiveness of our proposed joint optimization scheme, which achieves an average performance improvement of approximately 23.2%.

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user services. However, the growing longer prompt setting highlights the crucial issue of computational resource demands and huge communication load. To address this challenge, we propose Joint Power and Prompt Optimization (JPPO), a framework that combines Small Language Model (SLM)-based prompt compression with wireless power allocation optimization. By deploying SLM at user devices for prompt compression and employing Deep Reinforcement Learning for joint optimization of compression ratio and transmission power, JPPO effectively balances service quality with resource efficiency. Experimental results demonstrate that our framework achieves high service fidelity and low bit error rates while optimizing power usage in wireless LLM services. The system reduces response time by about 17 %, with the improvement varying based on the length of the original prompt.

The rapid adoption of cloud computing has introduced critical security challenges in the cloud, with evolving cyberattacks exposing vulnerabilities in conventional intrusion detection systems (IDS). Existing approaches often struggle with high false-positive rates, poor handling of imbalanced traffic, and computational overhead in dynamic cloud environments. To address these issues, we propose SLCAE-BiLSTM, a deep learning-based IDS which enhances feature extraction and sequential learning. The Single-Layer Contractive Autoencoder (SLCAE) ensures efficient data representation by minimizing redundancy while preserving critical attack patterns. Meanwhile, the Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies in network traffic, improving the detection of rare attacks. Experimental evaluations on two benchmark datasets demonstrate SLCAE-BiLSTM's superiority, achieving 99.91% and 99.87% accuracy in binary classification and 97.73% and 91.22% in multi-class classification, surpassing state-of-theart models such as SCAE-SVM, SAE-SVM, and SDAE-SVM. These high accuracy rates indicate a significant reduction in misclassification and improved detection of both common and rare cyber threats. Furthermore, its reduced computational overhead and faster inference time makes it an efficient solution for enhancing cloud security against emerging threats.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability.

With the proliferation of data sharing in the Internet of Things (IoT), protecting privacy-sensitive information and preventing unauthorized access have become paramount concerns. Existing centralized access control methods faces single-point-of-failure risks and lacks scalability for dynamic IoT systems. This paper proposes a fine-grained access control framework based on cross-blockchain technology for transparent and flexible IoT data sharing. In our framework, the cross-blockchain module is facilitated to eliminate data isolation across domains, the Interplanetary File System (IPFS) is used to mitigate centralized storage risks and reduce blockchain storage overhead, CP-ABE and symmetric encryption are integrated to enforce attribute-based, fine-grained access control with strong security guarantees. Meanwhile, the blockchain records data and key references to enforce secure access to shared data content. We further conduct security analysis and experimental evaluations, demonstrating the effectiveness and efficiency of the proposed scheme.

machine learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this article, a 5G aware real-time diabetes prediction framework is proposed using optimized bidirectional long short-term memory (Bi-LSTM) with deep reinforcement learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as blood glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of ten Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters, such as blood pressure and body weight. The proposed framework leverages a dataset of 1830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model’s future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient’s diabetic condition is categorized using the surveillance error grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly.

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as least privilege access, continuous verification, data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

The Internet of Things (IoT) plays a vital role in supply chain product traceability. However, traditional centralized traceability systems face challenges such as data tampering, lack of trust, and single points of failure. Blockchain technology offers a decentralized and tamper-resistant alternative, yet it still encounters issues related to low query efficiency, unbalanced storage demands, and unreliable data sources. To address these challenges, this paper proposes a new semantic multi-chain framework that dynamically allocates data to different semantic subchains. We also introduce an optimized query integrity verification strategy based on semantic aggregation, reducing the number of accessed blocks. We further refine the node allocation mechanism for subchains and establish a pre-blockchain data verification method to enhance data reliability. Experiments show that our solution reduces on-chain storage costs by 50%, improves query verification efficiency by 92% for 400,000 data records, and achieves an anomaly detection accuracy rate exceeding 90%, compared to its alternatives. This approach enables efficient and trustworthy traceability with reduced storage overhead and enhanced data reliability. The source code and datasets are available at https://github.com/orgs/Supply-Chain-Traceability-System/repositories.

Data traffic has grown exponentially with the rapid development of Narrowband Internet of Things (NB-IoT) technology. Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are essential technologies to enhance the performance of NB-IoT networks. This article proposes a new hybrid NOMA offloading strategy, allowing an Internet of Things (IoT) device to execute NOMA with other devices at different periods until the task offloading is completed. An optimization problem is established to minimize the overall offloading delay. To find the solution to the problem, we first use the proposed offloading strategy to determine the pairing method and offloading order of the IoT devices. Then, we transform the delay optimization problem into the link rate maximization problem. Finally, the closed-form solution of the optimal power allocation scheme for each IoT device is derived according to the theoretical analysis and the Karush-Kuhn–Tucker (KKT) condition. The simulation results show that the proposed offloading strategy and power allocation scheme effectively reduce the overall offloading delay under different device numbers and data lengths, which exceeds the orthogonal multiple access (OMA) schemes, the pure NOMA scheme, and the iterative multiuser NOMA scheme in lowering offloading delay.

Symbiotic radio (SR) presents an innovative wireless paradigm that simultaneously supports active primary and passive secondary transmissions. This technology significantly enhances spectrum and energy efficiency in network scenarios that support data transmission from a large number of Internet of Things (IoT) devices. Nonetheless, the received backscatter signal experiences attenuation due to the double path loss effect, thereby constraining the secondary network’s capacity to satisfy the data transmission requirements of IoT applications. To enhance the secondary network capacity with high energy efficiency in SR systems, we synergistically apply two promising technologies—wireless power transmission (WPT) and intelligent reflecting surfaces (IRS). Accordingly, this article explores the optimization of secondary network capacity in an SR system assisted by IRS and WPT, where high-density devices are organized into clusters. We adopt a hybrid access method that integrates time division multiple access (TDMA) for clusters accessing the Base Station (BS) and nonorthogonal multiple access (NOMA) for backscatter devices (BDs) communicating with each other in a cluster. By jointly optimizing active beamforming at the BS, passive beamforming at the IRS, and hybrid transmission time allocation, we maximize the sum data rate of the secondary links while ensuring that the communication requirements of primary links are met. To tackle this complex, high-dimensional, nonlinear problem, we propose a capacity optimization algorithm based on deep reinforcement learning (DRL). We conduct system performance evaluations, and the results validate the advantages of our proposed scheme in optimizing the secondary network capacity of SR systems compared to alternative approaches.

In computing-oriented communication scenarios, over-the-air computation (AirComp) directly computes results by leveraging the waveform superposition of wireless multiple access channels (MACs), eliminating the need to recover individual edge device (ED) signals. This approach enhances efficiency but faces challenges in maintaining low function distortion under deep fading. In this article, we propose a new retransmission-based AirComp framework that incorporates a channel prediction model to mitigate function distortion. Our framework introduces a performance analysis and optimization strategy involving cross-timeslot receiver combining. The optimal transmission coefficients and denoising factors are derived to minimize computing mean squared error (MSE) and reduce sum-power consumption. We design and analyze AirComp retransmission schemes that account for channel state information (CSI) estimation errors in the initial timeslot. Simulations validate the effectiveness of our framework, which markedly decreases the MSE and power consumption, e.g., by about 61% and 55%, compared to existing benchmarks, demonstrating its superior noise resilience and significantly alleviated the impact of imperfect CSI. These results highlight the potential of our framework to enable efficient and reliable AirComp in time-varying fading channels, particularly for resource-constrained Internet of Things (IoT) environments.

Mobility performance analyses of base station (BS) cooperation have an important role in the design of BS cooperation strategies for mobile UAVs in a cellular-connected UAV network (CCUN) for Internet of Things applications. This paper presents an in-depth study on the mobility performance analyses of a mobile UAV under BS cooperation in a CCUN. Performance models are derived for analyzing the mobility performance of a mobile UAV under BS cooperation in terms of the handover rate, handover probability, coverage probability, and average throughput, taking into account the occurrence of a handover. In deriving the performance models, modified 2-D and 3-D random waypoint (RWP) mobility models are used to describe the random or uncertain flight trajectories of a mobile UAV during a task execution. Based on the derived performance models, the impacts of main network parameters on the mobility performance of a mobile UAV under BS cooperation are investigated, which can help determine reference values for the network parameters in the design of a BS cooperation strategy for a mobile UAV. The difference between the mobility performance of a mobile UAV under a 2-D RWP model and that under a 3-D RWP model is investigated, which reveals several useful results for the design of BS cooperation strategies for mobile UAVs.

In the Internet of Everything (IoE) era, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) have emerged as crucial enabling technologies to support delay-sensitive and computation-intensive IoE services. Despite the potentials of RISs and MEC, achieving efficient service provisioning in IoE scenarios still faces significant challenges due to interdependencies among different types of resources. To address this issue, we propose a digital twin (DT)-empowered IoE framework that leverages real-time monitoring to virtually replicate network conditions, thereby assisting in decision-making in a physical IoE scenario. Specifically, the IoE scenario comprises a MEC server empowered by prestoring some service programs for task execution and a RIS that assists computation offloading. Taking into account deviations between DT and physical networks, we aim to minimize devices’ total task completion delay by jointly optimizing the service caching at the MEC server, the computation offloading of devices, the computing resource allocation at the MEC server, and the beamforming of the RIS. To handle the problem involving discrete and continuous factors, we develop a hybrid deep reinforcement learning (HDRL) algorithm that integrates the double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) approaches. In our HDRL algorithm, DDQN plays a crucial role in determining discrete variables representing service caching and computation offloading decisions, while DDPG focuses on optimizing resource allocation and RIS beamforming. We conduct simulations to evaluate the performance of the proposed scheme and compare it with several baselines. Simulation results demonstrate the superiority of our scheme in minimizing the task completion delay.

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this article, we specifically consider Internet of Electric Vehicles (IoEV) and we categorize GenAI for IoEV into four different layers, namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Uncrewed aerial vehicle base stations (UBSs) can be used to assist a cellular Internet of Things (IoT) network to provide content delivery service for ground users. This article studies the UBS deployment problem in a cellular IoT network for content delivery and formulates the problem as a joint mixed-integer linear programming problem with an objective to minimize the average content delivery delay of all users in a service area in a time frame. The formulated problem is decomposed into three subproblems: a content caching deployment problem, a UBS position deployment problem, and a BS association problem. A deep reinforcement learning-based UBS deployment (DRL-UD) algorithm is proposed to solve the problem. In the DRL-UD algorithm, an Informer-based user pattern prediction algorithm is introduced to predict the content request pattern and mobility pattern of users. Based on the prediction of user patterns, a two-layer proximal policy optimization (TLPPO)-based UBS deployment algorithm is introduced to solve the three subproblems using a cache layer, a position layer, and an implicit enumeration method, respectively. Simulation results show that the proposed DRL-UD algorithm can significantly reduce the average content delivery delay and increase the cache hit ratio of all users in the network.

The transceiver design for multi-hop multiple-input multiple-output (MIMO) relay is very challenging, and for a large scale network, it is not economical to send the signal through all possible links. Instead, we can find the best path from source-to-destination that gives the highest end-to-end signal-to-noise ratio (SNR). In this paper, we provide a linear minimum mean squared error (MMSE) based multi-hop multi-terminal MIMO non-regenerative half-duplex amplify-and-forward (AF) parallel relay design for a wireless sensor network (WSN) in an underground mines. The transceiver design of such a network becomes very complex. We can simplify a complex multi-terminal parallel relay system into a series of links using selection relaying, where transmission from the source to the relay, relay to relay, and finally relay to the destination will take place using the best relay that provides the best link performance among others. The best relay selection using the traditional technique in our case is not easy, and we need a strategy to find the best path from a large number of hidden paths. We first find the set of simplified series multi-hop MIMO best relays from source to destination using the optimum path selection technique found in the literature. Then we develop a joint optimum design of the source precoder, the relay amplifier, and the receiver matrices using the full channel diagonalizing technique followed by the Lagrange strong duality principle with known channel state information (CSI). Finally, simulation results show an excellent agreement with numerical analysis demonstrating the effectiveness of the proposed framework.

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable range for global aggregations, benefiting FL from involving more local models with tolerable errors. The convergence of FL under the imperfect aggregation is analytically validated, where the influence of the local model quantization and modulation is captured through the translation of the SINR thresholds to the symbol error rates (SERs). Employing successive convex approximation and gradient descent, we jointly optimize the RIS configuration and the transmit powers of participating devices, thereby minimizing the convergence upper bound of FL under imperfect aggregation. Experimental results demonstrate that using SDPR, FL achieves superior convergence and accuracy by effectively utilizing model updates, even if they are received with errors. Moreover, more quantization bits do not necessarily offer better FL accuracy, and need to be tailored under specific SERs.

This paper proposes a new joint random caching and hierarchical transmission scheme for delivering multimedia content in cache-assisted heterogeneous networks. We use scalable video coding (SVC) and wireless edge caching to offer personalized video-watching services to end users. Resorting to stochastic geometry, we derive new expressions for successful transmission probabilities (STPs). Using the derived STPs, the achievable transmission rates (ATRs) and transmission delay experienced by multimedia users are obtained. The Gaussian-Chebyshev quadrature is employed to approximate the STPs and ATRs in closed form to improve mathematical tractability. We also attain the user satisfaction index (USI) concerning user’s delay experience. A transmission delay minimization problem is formulated and solved efficiently using the standard gradient projection approach to determine the optimal layer placement design under a random caching criterion. Simulation results confirm the correctness of the derived STPs, show the negligible approximation loss of Gaussian-Chebyshev quadratures, and manifest the performance superiority of the devised SVC-based caching design to the energy-optimal caching and the non-caching approach.

Federated learning (FL) contributes to data privacy by not disclosing raw data, but encounters challenges of privacy leakage from local gradient uploading. This paper introduces a novel over-the-air computation (AirComp)-based FL system that balances privacy and accuracy by leveraging the waveform superposition and channel propagation characteristics of AirComp. Specifically, we derive the privacy leakage metric to explicitly account for the effects of waveform aggregation and communication noise. We analyze the convergence upper bound to capture model update errors stemming from artificial and communication noise. We formulate a new joint privacy-accuracy optimization problem by incorporating privacy leakage in the model training objective, guiding the learning process towards enhanced privacy protection. We then employ convex optimization techniques to derive the optimal power scaling and artificial noise intensity. Simulations demonstrate up to 80% reduction in privacy leakage compared to baselines under stringent privacy constraints, while maintaining competitive learning performance. Our method exhibits enhanced robustness under low signal-to-noise ratios, achieving 40% lower privacy leakage under equivalent privacy budgets.

Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, roads, and pedestrians. From the systemwide perspective of intelligent transportation systems to a focused view on “vehicle-road-pedestrian”, this survey aims to provide a comprehensive review and summary of recent state estimation techniques for vehicle motion, road surface, and pedestrian motion. A thorough analysis of the reviewed literature, relevant datasets, evaluation metrics, and experimental platforms in this field is also conducted. Finally, existing challenges and future research directions about methods and performance evaluation are further discussed. This survey is expected to contribute to the advancement of research in dynamic state estimation of vehicle-road-pedestrian, thereby facilitating the development of efficient and safe intelligent transportation systems.

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communications. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it.In the first stage, the optimal multi-path traffic routing and the theoretical upper bound on the transmission rate of the network are derived.In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal multi-path traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of three unexpected situations on the system transmission rate.

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose ajointcomputation offloading,caching,communication resource allocation,computation resource allocation, and UAV trajectorycontrolapproach (J$\rm{C}^{5}$A). Specifically, J$\rm{C}^{5}$A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of J$\rm{C}^{5}$A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms.

Reconfigurable intelligent surface(RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact ofcarrier frequency offset(CFO) andphase noise(PN) resulting from hardware impairments on localization. This paper presents a novel,alternating optimization(AO)-based algorithm to jointly estimate the CFO, PN, anduser equipment(UE) position inorthogonal frequency division multiplexing(OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2m.

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

Allowing for energy sharing among base stations (BSs), we investigate a distributed BS system equipped with renewable power units and energy storage batteries. While offering significant benefits, this raises concerns about privacy protection. By leveraging the Laplace mechanism, our differential privacy (DP)-based method safeguards energy consumption data related to processing data tasks from different BSs. To address a long-term average cost minimization problem, we propose a distributed online algorithm for efficient energy sharing. We demonstrate that the boundary constraints of energy storage batteries can still be satisfied by choosing parameters appropriately. We provide a theoretical bound for the optimality gap and validate the effectiveness of our theoretical results. Numerical results indicate our algorithm can potentially reduce the average costs of BSs by up to 34%.

Low-delay high-gain optimal multi-hop routing path is crucial to guarantee both the latency and reliability requirements for infotainment services in the high mobility internet of vehicles (IoVs) subject to queue stability. The high mobility in multi-hop IoVs reduces reliability and energy efficiency, and becomes bottleneck for the optimal route solution using classical optimization methods. To a great extent, deep reinforcement learning (DRL)-based method is not applicable in IoVs environment because of the continuously changing topology and space complexity, which grows exponentially with the number of state variables as well as the relaying hops. Usually, in multi-hop scenario, network reliability and latency are affected by mobility as well as average hop count, which limit the vehicle-to-vehicle (V2V) link connectivity. To cope with this problem, in this paper, we formulate a minimum hop count delay-sensitive buffer-aided optimization problem in a dynamic complex multi-hop vehicular topology using a digital twin-enabled dynamic coordination graph (DCG). Particularly, for the first time, a DCG-based multi-agent deep deterministic policy gradient (DCG-MADDPG) decentralized algorithm is proposed that combines the advantage of DCG and MADDPG to model continuously changing topology and find the optimal routing solutions by cooperative learning in the aforementioned communications. The proposed DCG-MADDPG coordinated learning trains each agent towards highly reliable and low latency optimal decision-making path solutions while maintaining queue stability and convergence on the way to a desired state. Experimental results reveal that the proposed coordinated learning algorithm outperforms the existing learning in terms of energy consumption and latency at less computational complexity.

Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3-D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human–avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to roadside units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this article, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and multiagent deep reinforcement learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed-integer programming problem. To tackle this problem efficiently, the problem is transformed into a partially observable Markov decision process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).

The progressing development of Internet of Things (IoT) has accelerated the emergence of resource-intensive and latency-sensitive mobile applications, which throws out a great challenge to the battery-powered wireless devices (WDs) with low-computing capabilities. To solve this intractable issue, we investigate an intelligent reflecting surface (IRS)-assisted multiantenna wireless-powered mobile edge computing (WP-MEC) system, in which WDs first harvest wireless energy emitted by a hybrid access point (HAP), then offload their tasks to the edge server, and finally download the results. In consideration of the practical scenarios, the finite computing capability of edge server and the nonlinear end-to-end power conversion of energy harvesting (EH) circuits at WDs are considered. In addition, an IRS is deployed to improve the efficiency of wireless power transfer (WPT) and the rate of data transmission between HAP and WDs. Under this setup, both space division multiple access (SDMA) and time division multiple access (TDMA) protocols are exploited and evaluated for data transmission. For each protocol, we maximize the computational rate by jointly optimizing time allocation, beamforming designs of HAP and IRS, as well as offloading strategies of WDs. To solve the problem formulated under the SDMA protocol, we propose an efficient alternating optimization (AO) algorithm. For the problem under the TDMA protocol, an AO algorithm with low complexity is proposed. Numerical results demonstrate the high effectiveness of the proposed algorithms and the superiority of the SDMA protocol over the TDMA protocol.

In this article, we propose a movable antenna (MA)-enhanced scheme for wireless-powered mobile-edge computing (WP-MEC) system, where the hybrid access point (HAP) equipped with multiple MAs first emits wireless energy to charge wireless devices (WDs), and then receives the offloaded tasks from the WDs for edge computing. The MAs deployed at the HAP enhance the spatial Degrees of Freedom (DoFs) by flexibly adjusting the positions of MAs within an available region, thereby improving the efficiency of both downlink wireless energy transfer (WPT) and uplink task offloading. To balance the performance enhancement against the implementation intricacy, we further propose three types of MA positioning configurations, i.e., dynamic MA positioning, semidynamic MA positioning, and static MA positioning. In addition, the nonlinear power conversion of energy harvesting (EH) circuits at the WDs and the finite computing capability at the edge server are taken into account. Our objective is to maximize the sum computational rate (SCR) by jointly optimizing the time allocation, positions of MAs, energy beamforming matrix, receive combing vectors, and offloading strategies of WDs. To solve the nonconvex problems, efficient alternating optimization (AO) frameworks are proposed. Moreover, we propose a hybrid algorithm of particle swarm optimization with variable local search (PSO-VLS) to solve the subproblem of MA positioning. Numerical results validate the superiority of exploiting MAs over the fixed-position antennas (FPAs) for enhancing the SCR performance of WP-MEC systems.

Generative diffusion models (GDMs) have emerged as potent tools for generating high-quality, creative content across various media, including audio, images, videos, and 3-D models. Their application in artificial intelligence-generated content (AIGC) marks a pivotal advancement in the evolution from the Internet of Things (IoT) to the Artificial Intelligence of Things (AIoT). Considering the inherent multiple-access nature of AIoT, training GDMs via federated learning and deploying them collaboratively is paramount. However, such approaches introduce considerable security risks and energy consumption challenges. To address these issues, we propose a comprehensive architecture for GDMs, encompassing both training and sampling stages. This architecture, termed secure and sustainable diffusion (SS-Diff), aims to thwart trigger-based security threats, such as backdoor attacks and trojan attacks, while simultaneously reducing energy consumption in multiple-access AIoT. The SS-Diff architecture incorporates a dynamic quantization mechanism within the training phase, significantly reducing communication overhead and thereby improving both spectrum and energy efficiency. During the sampling stage, a detection-based defense strategy is employed to identify and negate trigger inputs associated with malicious attacks. Through extensive simulations, we evaluate the performance of the SS-Diff architecture. The results demonstrate that the SS-Diff can effectively train GDMs and eliminate the impact of the attacks, compared with existing schemes.

Unmanned aerial vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection (DC) scheme. To address this, we propose DC- nonorthogonal multiple access (NOMA), a new DC scheme that combines machine learning clustering with NOMA. We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a tradeoff between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intracluster DC protocol. Experimental results show that DC- NOMA achieves balanced DC time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.

With the rapid development of ubiquitous networks and smart devices, artificial intelligence-based unmanned aerial vehicles (UAVs) are drawing more and more attention. The rise in popularity of deep neural networks (DNNs) has spawned a research effort to deploy various kinds of DNN models on vehicles. They have been used to accomplish complicated vehicular tasks and enable the construction of intelligent vehicular networks. Despite the promising prospects, how to train and run them on resource-limited and hardware-constrained UAVs faces huge challenges. Furthermore, the tradeoff between accuracy and latency needs to be considered while reducing the computational cost of DNN training.

This paper studies the potential of tightly coupling the Internet-of-Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on non-renewable energy by diversifying power supply. Rechargeable batteries at end devices are considered for holistic energy management of the system. We jointly optimize the transmit powers and battery (dis)charging decisions of the devices, the receive beamformer of the edge servers, and the dynamic generation of different energy types. The alternating direction method of multipliers (ADMM) is applied to support distributed optimization of (dis)charging decisions at individual devices. The Karush–Kuhn–Tucker (KKT) conditions are applied to deliver semi-closed-form power control of the devices. Simulations demonstrate significant improvement of the algorithm in renewable energy utilization and cost saving, compared to the existing techniques.

Periodic data collection from numerous vehicular on-board sensors is necessary for aiding decision making in complex navigation and autonomous driving applications. The temporal freshness of data, represented by the Age of Information (AoI), thus holds critical significance. Integrating Unmanned Aerial Vehicle (UAV) relays with Reconfigurable Intelligent Surface (RIS) emerges as a promising strategy to establish reliable communication links between vehicles and data processing centers. Despite this potential, the current body of literature on the integration of UAV relays and RIS is insufficient, particularly in studying AoI. This paper addresses this gap by achieving a comprehensive optimization of the phase shifts at the RIS, spectrum allocation, and the UAV trajectory. The objective is to minimize the average AoI while adhering to the constraints associated with UAV energy consumption. This joint optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. It is tackled using an approach based on Multi-Step Dueling Double Deep Q Network (MSD3QN). Extensive simulations conducted across diverse scenarios prove the effectiveness of our proposed approach and demonstrate its ability in improving the timeliness of making decisions, reducing average AoI, and enhancing network coverage.

The development of Internet of Things (IoT) systems has given rise to security concerns stemming from the exposure of wireless channels and the exponential growth of connected devices. The security challenges can be severer in the next-generation IoT systems that can disperse over large areas under a cell-free (CF) network setting. In this article, we propose a novel covert downlink transmission scheme that jointly optimizes beamforming and artificial noise (AN) vectors to obscure critical transmissions at an eavesdropper in CF IoT Networks. We classify access points (APs) as information APs (IAPs) and noise APs (NAPs) based on their proximity to the IoT devices. IAPs transmit information while NAPs generate AN to prevent eavesdropping. We derive a closed-form solution for the detection error probability. By using the Lagrangian dual algorithm, the complex logarithmic problem is transformed into sum-of-ratios form. Then, we use semidefinite relaxation (SDR) to maximize the covert transmit rate. Numerical results show that the proposed scheme outperforms the state of the art, i.e., the suboptimal Rand- AP scheme, by increasing the transmission rate by more than 23% while maintaining covertness and is better than the rest of the benchmark schemes.

This article proposes an aerial and ground base station cooperation (AG_CoMP) strategy for improving the downlink transmission performance of an aerial UAV user (AUE) in an unmanned aerial vehicle and cellular integrated network. The AG_CoMP strategy allows both a ground base station (GBS) and an aerial base station (ABS) to participate in BS cooperation, and seeks to reduce the negative impact of BS cooperation on the overall network performance. The GBS and ABS closest to an AUE are associated to cooperatively provide downlink transmission of the AUE and a blanking area is defined to disallow nearby GBSs to transmit during the transmission of the AUE. A combination of noncoherent joint transmission and dynamic point blanking is used to reduce the large signaling overhead for transmission of the AUE’s data between the cooperative BSs with the proposed strategy. Performance models are derived to analyze the downlink transmission performance of an AUE in terms of the coverage probability, achievable throughput, and SINR meta distribution of the AUE. Numerical results show that compared with three benchmark strategies, the proposed AG_CoMP strategy can ensure a relatively good downlink transmission performance, and achieve a better downlink transmission performance when an AUE is at an intermediate altitude between an ABS and a GBS.

With the advantages of super-high-transmission rate, anti-electromagnetic interference and good confidentiality, optical wireless communication (OWC) systems play an important role in satellite Internet of Things (IoT). In this article, we propose a new meta-learning-based channel estimation scheme (meta-CE) to address the challenges of atmospheric turbulence and phase distortion in satellite OWC links. A neural network-based channel estimator outputs channel parameters, rather than categories, and utilizes meta-learning to enhance its convergence speed and adaptability to new environments. The meta-CE exhibits superior estimation accuracy, fast convergence, and generalization, compared to baseline schemes, and even outperforms the minimum mean square error (MMSE) channel estimation, especially with short pilot symbols, low-signal-to-noise ratio (SNR) and severe turbulence. In a$4 \times 4$multiple-input–multiple-output (MIMO) scheme with 8-bit pilot symbols and 0-dB SNR, the mean square error of the meta-CE is about 35% lower than that of the MMSE in a strong Gamma-Gamma turbulence channel.

Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities at the terminals often hamper these connections, resulting in inefficient communications and frequent ping-pong handovers. This paper proposes a Distributed Collaborative Beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the LEO satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that jointly balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we formulate a long-term multi-objective optimization problem to optimize these goals simultaneously. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal Multi-Objective Markov Decision Process (MOMDP). Then, we propose an Evolutionary Multi-Objective Deep Reinforcement Learning (EMODRL) algorithm to obtain multiple policies, in which the low-value actions are masked to speed up the training process. Simulation results show that DCB enables terminals that cannot reach the uplink achievable rate threshold to achieve efficient direct uplink transmission. Moreover, the proposed algorithm outmatches various baselines and saves 30% handover frequency with a similar uplink achievable rate compared with the rate greedy method, which thus reveals that the proposed method is an effective solution for enabling direct ground-space communications.

In the intelligent logistics and warehouses, the autonomous mobile device (AMD) holds a key position as it is equipped with the ability to carry out functions like material transportation and inventory inspection. Nevertheless, the effective execution of these functions necessitates the location of the AMD. Given the increasing proliferation of networks like WiFi and 5G, leveraging these signals to achieve AMD localization is a desirable solution. Therefore, this paper proposes a channel state information (CSI) based system forthrough-the-wall (TTW) passive AMDdetection andlocalization, named T-DeLo. T-DeLo first establishes a reference channel and utilizes it to cancel the strong signal interference (SSI) and phase errors, ensuring that the reflections introduced by the AMD can be estimated. Built upon this core, it uses the proposed novel two-dimensional matrix pencil algorithm to estimate jointly the path length change rate (PLCR) and time of flight (ToF) of the AMD induced reflections, in the TTW scenario. Unlike existing algorithms, this algorithm aggregates multiple measurements to improve the estimation performance under conditions of low signal-to-noise ratio (SNR). Finally, leveraging the estimated ToF and PLCR, T-DeLo realizes TTW AMD detection and localization via statistical and geometric analysis, respectively. In the TTW glass and brick wall scenarios, the extensive experimental evaluation shows that the AMD detection accuracy of T-DeLo is 0.964 and 0.952, while the median localization errors are 1.65 m and 2.05 m, respectively, laying a solid foundation for practical and ubiquitous AMD passive detection and localization.

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency.

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregate them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

Metaverse can provide powerful human-centric interactive experiences for users and metaverse application content is the fundamental component that supports the metaverse. Considering that metaverse users are more sensitive to the delay of content delivery service, unmanned aerial vehicles (UAV) can be used as aerial base stations (BSs) to assist a cellular network to provide better content delivery service for delay-sensitive metaverse users as UAV base stations (UBSs) have big potential for line-of-sight (LoS) transmission and can be deployed closer to metaverse users than macro base stations (MBSs). This paper studies the content delivery performance analysis of a cache-enabled UBS-assisted cellular network for metaverse users. Analytical models are derived for investigating the content delivery performance of the network in terms of the content delivery success probability of the network and the average content delivery delay of a metaverse user. In deriving the analytical models, a more realistic repulsive point process is considered for modeling the location distribution of MBSs, and an air-to-ground (A2G) channel model with both a LoS link and a non-line-of-sight (NLoS) link is considered. Moreover, the cache hit probability of a UBS using a probabilistic caching strategy is also taken into consideration. A BS association strategy for delay-sensitive metaverse users based on the strongest average received power at a user and the cache hit probability of a UBS is proposed. In addition, the association probabilities with the association strategy are derived for different types of base stations. A lower bound of the content delivery success probability and an upper bound of the average content delivery delay are obtained based on the derived analytical models. The numerical results justify the effectiveness and advantage of the proposed BS association strategy and show that there exist an optimal UBS height and an optimal value of the number of UBSs, which result in the optimal content delivery performance. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network to provide better human-centric content delivery service for metaverse user.

The use of legitimate unmanned aerial vehicles (UAVs) to surveil and track misbehaved UAVs can serve a crucial role in public safety and security. This paper proposes a new deep reinforcement learning (DRL)-based online control scheme for visual-based UAV-on-UAV tracking and monitoring, where a solar-powered, fixed-wing UAV tracks a suspicious UAV target by having the target inside its effective visual range. The key idea is a new deep deterministic policy gradient (DDPG)-based model, which can cope with the continuous state and action spaces of the monitor and learn the optimal acceleration control policy adapting to the solar power availability and the target’s movement. The state space is designed to be the relative position of the monitor to the target, thereby preventing model infeasibility. Experiments show that the new algorithm can maintain a desired distance from the target, and outperform control-and optimization-based alternatives in terms of energy efficiency and tracking accuracy. An interesting finding is that our algorithm learns faster and better with a constraint of a minimum allowed battery energy reserve. The reason is that, without the constraint, the monitor is more likely to deplete its battery before the end of a surveillance mission.

The exploitation of radio channels’ inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and multipath propagation, vary in a complex manner that makes it difficult for external adversaries to predict or replicate. A challenge lies in accurately assessing the channel’s randomness to ensure the generated keys are both secure and consistent across the platooning vehicles, especially in vehicular environments with high mobility and the ever-changing urban landscape. This paper proposes a novel channel-based key generation (DRL-KeyAgree) technique to enhance communication security within vehicular platoons through combinatorial deep reinforcement learning (DRL). DRL-KeyAgree addresses key disagreement among platooning vehicles by training advantage Actor-Critic (A2C), which integrates policy- and value-based strategies to dynamically select optimal quantization intervals adapting to the random wireless channels. Further incorporation of Long Short-Term Memory (LSTM) allows DRL-KeyAgree to capture the characteristics of partially observable radio channels, significantly enhancing the key agreement rate among vehicles. DRL-KeyAgree is rigorously evaluated using the standard National Institute of Standards and Technology (NIST) test suite.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

Accurate estimation of the State of Charge (SoC) of Li-Ion batteries is crucial for secure and efficient energy consumption in electric vehicles (EVs). Traditional SoC estimation methods often require expert knowledge of battery chemistry and suffer from limited accuracy due to complex non-linear battery behaviour. Owing to the model-free nature and enhanced ability of non-linear regression in deep learning (DL), this paper proposes a hybrid DL model trained by a novel metaheuristic technique, namely the Hybrid Sine Cosine Firehawk Algorithm (HSCFHA). The proposed method utilises the Transductive Transfer Learning (TTL) technique to leverage the intrinsic relationship between different real-world datasets to estimate the SoC of batteries accurately. The evaluation analysis includes three diverse datasets of EV charging drive cycles: the Highway Fuel Economy Test Cycle (HWFET), Highway Driving Schedule (US06) and Urban Dynamometer Driving Schedule (UDDS), at various temperatures of$0^\circ$C,$10^\circ$C, and$25^\circ$C. The considered evaluation metrics, i.e., Normal Mean Squared Error (NMSE), Root Mean Squared Error (RMSE), and$R^2$, achieve values of 0.091%, 0.087%, and 99.51%, respectively. The TTL-HSCFHA-DNN effectively produces higher accuracy with a time-efficient convergence rate, compared to existing methods. The approach enables EV systems to operate more efficiently with improved battery life.

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment(OS2)framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). DeployingOS2on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.

Driven by the increasing computational demand of real-time mobile applications, Unmanned Aerial Vehicle (UAV) assisted Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges and constructing high-throughput line-of-sight links for ground users. Most exsiting studies consider simplified scenarios, such as a single UAV, Service Provider (SP) or service type, and centralized UAV trajectory control. In order to be more in line with real-world cases, we intend to achieve distributed trajectory control of multiple UAVs in UAV-assisted MEC networks with multiple SPs providing differentiated services. Our objective is to minimize the short-term computational costs of ground users and the long-term computational cost of UAVs, simultaneously based on incomplete information. We first solve the formulated problem by reaching the Nash Equilibrium (NE) of the game among SPs based on complete information. We further formulate a Markov game model and propose a Deep Reinforcement Learning (DRL)-based UAV trajectory optimization algorithm, where only local observations of each UAV are required for each SP's flying action execution. Theoretical analysis and performance evaluation demonstrate the convergence, efficiency, scalability, and robustness of our algorithm compared with other representative algorithms.

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and meanwhile jointly exploits the pilot sequences in the channel estimation stage and user information in the transmission stage to assist target detection. We investigate the joint design of the pilot matrix, training duration, and transmit beamforming to maximize the probability of target detection, subject to the minimum achievable rate required by the user. However, designing the optimal pilot matrix is rather challenging since there is no closed-form expression of the detection probability with respect to the pilot matrix. To tackle this difficulty, we resort to designing the pilot matrix based on the information-theoretic criterion to maximize the mutual information (MI) between the received observations and BS-target channel coefficients for target detection. We first derive the optimal pilot matrix for both channel estimation and target detection, and then propose a unified pilot matrix structure to balance minimizing the channel estimation error (MSE) and maximizing MI. Based on the proposed structure, a low-complexity successive refinement algorithm is proposed. In addition, we rigorously analyze the impact of pilot length and pilot matrix on two fundamental tradeoffs, namely MSE-MI and Rate-MI. Simulation results demonstrate that the proposed pilot matrix structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the significant region improvement of our proposed design as compared to other benchmark schemes. Furthermore, it is unveiled that as the communication channel is more spatially correlated, the Rate-MI region can be further enlarged.

In this paper, we develop a hybrid multiple access (MA) protocol for an intelligent reflecting surface (IRS) aided uplink transmission network by incorporating the IRS-aided time-division MA (I-TDMA) protocol and the IRS-aided non-orthogonal MA (I-NOMA) protocol as special cases. Two typical communication scenarios, namely the transmit power limited case and the transmit energy limited case are considered, where the device’s rearranged order, time and power allocation, as well as dynamic IRS beamforming patterns over time are jointly optimized to minimize the sum transmission delay. To shed light on the superiority of the proposed IRS-aided hybrid MA (I-HMA) protocol over conventional protocols, the conditions under which I-HMA outperforms I-TDMA and I-NOMA are revealed by characterizing their corresponding optimal solution. Then, a computationally efficient algorithm is proposed to obtain the high-quality solution to the corresponding optimization problems. Simulation results validate our theoretical findings, demonstrate the superiority of the proposed design, and draw some useful insights. Specifically, it is found that the proposed protocol can significantly reduce the sum transmission delay by combining the additional gain of dynamic IRS beamforming with the high spectral efficiency of NOMA, which thus reveals that integrating IRS into the proposed HMA protocol is an effective solution for delay-aware optimization. Furthermore, it reveals that the proposed design reduces the time consumption not only from the system-centric view, but also from the device-centric view.

In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) nature of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a communication-efficient federated deep reinforcement learning (CE-FDRL) method to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communication resources caused by model transmission, we propose to prune and quantize the shared DRL models to reduce the number of transferred model parameters. The communication interval is increased and the communication round is reduced by periodic model aggregation. The global convergence and computational complexity of our proposed method are also analyzed. Simulation results verify that our proposed method can offer better performance in reducing user request delay and improving cache hit rate and the transmitted parameters of our proposed method can drop to 60% compared to the existing benchmark schemes. Our proposed method is also shown to have faster training speed and higher communication efficiency.

Cell-free wireless communications is a new paradigm within the future 6G networks towards implementation of the Internet of intelligence with connected people and things. In a cell-free network, a large number of distributed access points are connected to a central processing unit and serve a smaller number of users over the same time-frequency resources. The system has shown great potential in improving network performance in some perspectives compared to the co-located and conventional small-cell systems. The next-generation Internet-of-Things systems could be dispersed over large areas under a cell-free network setting. This has given rise to security concerns stemming from the exposure of wireless channels and the exponential growth of connected devices. In this talk, a novel covert downlink transmission scheme is presented that jointly optimizes beamforming and artificial noise vectors to obscure critical transmissions at an eavesdropper in CF IoT Networks.

Virtual reality (VR) applications are becoming increasingly popular in both entertainment and commercial industries. Improving the immersive experience is essential to meet the expectations of users. To achieve the highest quality of experience (QoE), various factors such as delay, refresh rate, and data rate must be considered. Streaming only the field of view (FoV) of users based on gaze movement is a common method to improve the performance of end-to-end (E2E) delay. In this paper, we propose a novel non-overlapped tile streaming approach with dynamic resource allocation in VR networks to enhance the E2E delay with efficient resource block (RB) utilization. Our approach exploits the existence of overlapping tiles in consecutive FoV requests for reducing the number of tiles that need to be transmitted to users. By combining the non-overlapped tile streaming scheme with the flexible resource allocation, our proposed approach provides VR users with an enhanced QoE, particularly in terms of shorter E2E delay.

As a technology intended to reduce cellular network congestion and enhance user service quality, computation offloading in Multi-access Edge Computing (MEC) networks highlights the crucial issue of privacy protection. This paper proposes a novel solution to the computation offloading and privacy protection problem in the MEC network using a Multi-agent Deep Deterministic Policy Gradient (MADDPG) framework. Our approach utilizes game theory to encourage computation offloading by modeling the interaction between cloudlets, Data Center Operator (DCO), and users as an auction game. We formulate the resource allocation and privacy protection as an auction game with multiple bidders and incomplete information and then use MADDPG to find an optimal solution. To ensure privacy protection, we design a Local Differential Privacy (LDP) method in the MADDPG algorithm. Theoretical analysis and simulation results demonstrate the effectiveness of our approach in satisfying differential privacy and converging to an equilibrium. The proposed solution holds significant promise in addressing the computation offloading and privacy protection challenges in MEC networks.

The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5%, 2.3%, and 1.4%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.

Unmanned aerial vehicles (UAVs) have been increasingly considered as aerial servers in mobile edge computing (MEC) to assist mission-critical computation tasks of edge ground nodes. The tasks are buffered at the ground node, while the task offloading is scheduled by the UAV. When one ground node in MEC is scheduled to offload its tasks, other unselected ground nodes’ tasks could expire and be cancelled. To maximize the offloaded tasks to the UAV, this paper proposes a new joint optimization of cruise control and task offloading scheduling, which synthetically takes into account the computation capacity and battery energy of the ground nodes, and the speed limit of the UAV. Given a large and unknown network state and action space, a new deep reinforcement learning (DRL) framework based on graph neural networks (GNN) is developed to train online the continuous cruise control of the UAV and the task offloading schedule. Particularly, GNN explores feature correlations of network states to supervise the action training of the UAV in DRL. We implement the proposed GNN-DRL framework on Google Tensorflow. Extensive numerical results show that GNN-DRL improves the task offloading rate by 43%, compared to the DRL solution without GNN.

Over the past few years, billions of unsecured Internet of Things (IoT) devices have been produced and released, and that number will only grow as wireless technology advances. As a result of their susceptibility to malware, effective methods have become necessary for identifying IoT malware. However, the low generalizability and the nonindependently and identically distributed data (non-IID) still pose a major challenge to achieving this goal. In this work, a new federated malware detection paradigm, termed FED-MAL, is introduced to collaboratively train multiple distributed edge devices to detect malware. In FED-MAL, the malware binaries are transformed into an image format to lessen the impact on non-IID, and then a compact convolutional model, named AM-NET, is proposed to learn the malware patterns as an image recognition task. The compact nature of AM-NET makes it an appropriate choice for deployment on resource-constrained IoT devices. Following, a refined edge-based adversarial training is given in FED-MAL to empower generalizability and resistibility by generating adversarial samples from various participating clients. Experimental evaluation on publicly available malware data sets shows that the FED-MAL is efficacious, reliable, expandable, generalizable, and communication efficient.

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

The proliferation of Internet of Things (IoT) applications has led to the interconnection of multiaccess edge computing (MEC) systems through metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, the uneven utilization of multidimensional resources can result in resource fragmentation, thereby reducing the utilization of limited edge resources. This article focuses on mitigating multidimensional resource fragmentation in virtual network embedding (VNE) to maximize the profit of the infrastructure provider (InP). The problem is converted into a bilevel optimization problem, taking into account the interdependence between virtual node embedding and virtual link embedding. To solve this problem, we propose a nested bilevel VNE approach named BiVNE. BiVNE leverages an ant colony system (ACS) algorithm for the upper layer problem and utilizes the Dijkstra algorithm and an exact-fit spectrum slot assignment method for the lower layer problem. Evaluation results demonstrate that BiVNE can greatly improve the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.

This article presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet of Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value.

Industrial Internet of Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This article considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

The sixth-generation (6G) wireless communication has called for higher bandwidth and massive connectivity of Internet of Things (IoT) devices. The increased connectivity also demands advanced levels of network security, which are critical to maintain due to severe signal attenuation at higher frequencies. Intelligent reflecting surface (IRS) is an increasingly popular, efficient, solution to cater to higher data rates, better coverage range, and reduced signal blockages. In this article, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A mathematical design of the system model is presented, and an optimization problem is formulated. The secrecy rate of the trusted devices is maximized while guaranteeing Quality of Service (QoS) to all the legitimate, trusted, and untrusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices owing to the complex and continuous nature of action and state spaces. The results confirm a maximum gain of 2–2.5 times in the sum secrecy rate of trusted devices under the proposed model, as compared to the benchmark cases. The results also ensure the throughput performance of all trusted and untrusted devices. The performance of the proposed DDPG model is evaluated under meticulously selected hyper-parameters.

In the Internet of Things (IoT), massive sensitive and confidential information is transmitted wirelessly, making security a serious concern. This is particularly true when technologies, such as nonorthogonal multiple access (NOMA), are used, making it possible for users to access each other’s data. This article studies secure communications in multiuser NOMA downlink systems, where each user is potentially an eavesdropper. Resource allocation is formulated to achieve the maximum sum secrecy rate, meanwhile satisfying the users’ data requirements and power constraint. We solve this nontrivial, mixed-integer nonlinear programming problem by decomposing it into power allocation with a closed-form solution, and user pairing obtained effectively using linear programming relaxation and barrier algorithm. These subproblems are solved iteratively until convergence, with the convergence rate rigorously analyzed. Simulations demonstrate that our approach outperforms its existing alternatives significantly in the sum secrecy rate and computational complexity.

This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.

Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.

Unmanned aerial vehicles (UAVs) play a critical role in radio surveillance to decipher malicious messages, thanks to their flexibility, mobility, and likely line-of-sight (LoS) to ground targets. Reconfigurable intelligent surfaces (RISs) can potentially create radio surveillance channels towards the UAVs by passively configuring the radio environments without raising suspicion. This paper presents a new deep reinforcement learning (DRL)-driven framework for radio surveillance, where a fixed-wing UAV is employed to acquire the radio fingerprint of a suspicious transmitter (Tx) with the aid of a benign RIS. A new Twin Delayed Deep Deterministic policy gradient (TD3) model is designed to allow the UAV to learn its trajectory and the RIS configuration based on its observed transmit rate of the suspicious Tx, eliminating the need for channel state information to and from the RIS. The novel contributions include the consideration of the fixed-wing UAV, and the action and reward designed to capture the mobility constraint of the UAV. Simulations demonstrate that the new approach offers the UAV monitor an exceptional and reliable radio surveillance capability, while keeping a desired distance from the UAV to the Tx. The use of the RIS allows for significant improvements of over 37% and 59% in the eavesdropping success probability and average eavesdropping rate, respectively.

Virtual reality-embedded digital twin (VR-DT) service integrates digital twin with virtual reality to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive, and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation, and block size adjustment as a mixed-integer nonlinear programming problem. A fully decentralized multiagent compound-action actor–critic algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

Security of human wearable and implantable devices is often discussed as a topic of concern for human body interface and control systems (HBICS). The use of physiological biometrics such as the timing between heartbeats, also known as the inter-pulse interval (IPI), has been well researched for mitigating threats to confidentiality and integrity; however, not quite so for the mitigation of threats to availability. The jamming of communication links to cause denial-of-service (DoS) is one such type of threats to availability. In this paper, we propose, simulate and analyze four alternative algorithms which use IPI to add another layer of protection to the traditional pseudorandom frequency hopping system, to mitigate jamming attacks on communication links. The results reveal the feasibility for some of the algorithms to be used. Potential improvements, such as the use of datasets with higher sampling rate, are also identified.

Autonomous vehicles in the Internet of Vehicles (IoV) have the ability to generate their mobility pattern in advance and share it with other vehicles or a central location. This information enables traffic systems to be aware of future traffic flow. However, the current classical traffic systems barely consider the spatial dependency of traffic data, and more advanced systems suffer from storage limitations. These problems will be exacerbated by the falsification of traffic data through corresponding data attacks, such as position forging attacks. This, in turn, negatively impacts the performance of traffic management systems. This article proposes a heuristic distributed scheme (HIDE) to validate the mobility pattern of vehicles by penalizing or rewarding vehicles based on the contacts’ conformation. HIDE enables every vehicle to consider the claimed mobility pattern of every neighboring vehicle that is exchanged, and assigns apenaltyorrewardto each received mobility pattern and share with cloudlets via roadside units (RSUs). These calculations are based on an efficient time-homogeneous semi-Markov process (THSMP) to predict the likelihood of the accuracy of mobility patterns. Cloudlets calculate a weight factor to determine if a vehicle is malicious. The validation results from THSMP reveal that a high correlation is achieved between the theoretical model and simulation. Also, the results show that the model fairly identifies the malicious vehicles and assigns a low weight of impact on them compared to normal vehicles.

The Internet of Things (IoT) is an emerging technology that has earned a lot of research attention and technical revolution in recent years. Significantly, IoT connects and integrates billions of devices and communication networks around the world for several real-time IoT applications. On the other hand, cybersecurity attacks on the IoT are growing at an alarming rate since these devices are vulnerable because of their limited battery life, global connectivity, resource-constrained nature, and mobility. When attacks on IoT networks go undetected within a speculated period, such security attacks may prompt severe threats and disruptive behavior inside the network and make the network unavailable to the end user. Hence, it is quintessential to design an intelligent and robust security approach that promptly detects potential attack surfaces in a dynamic IoT network. This article investigates a comprehensive survey of machine learning, deep learning, and reinforcement learning-based intelligent intrusion detection techniques for securing IoT. Also, this article thoroughly illustrates the implementation of various categories of security threats in IoT with a neat diagram. Significantly, we classify the threats into two broad categories: 1) wireless sensor networks (WSNs) inherited security attacks and 2) routing protocol for low power and lossy networks (RPL) specific security attacks in IoT. Finally, we present potential research opportunities and challenges in intelligent intrusion detection approaches in future IoT security.

Caching at the network edge can reap significant advantages to improve service quality by reducing the transmission cost and network congestion. Edge caching with device-to-device (D2D) communication helps offload cellular traffic during a surge in network traffic. This article considers the classic clustering problem for D2D users, followed by an incentive mechanism designed for successful D2D communication. The proposed clustering scheme merges D2D users with similar interests into one cluster. Specifically, a hierarchical agglomerative clustering algorithm is applied, where clusters are formed based on the similarity in users’ social interests. The cache hit probability is then optimized for each cluster, and the performance is examined for a varying number of clusters. We then propose a monetary incentive-based mechanism based on a points system to increase user participation for successful D2D communication by keeping track of a user’s content-providing history. Using this approach, devices with a good participation rate in D2D communication are identified and rewarded for their performance. Through simulations, we show that the hit rate improves by more than 40% after ensuring incentive-based methods in the D2D network.

This article puts forth an aerial edge Internet of Things (EdgeIoT) system, where an unmanned aerial vehicle (UAV) is employed as a mobile-edge server to process mission-critical computation tasks of ground Internet of Things (IoT) devices. When the UAV schedules an IoT device to offload its computation task, the tasks buffered at the other unselected devices could be outdated and have to be canceled. We investigate a new joint optimization of UAV cruise control and task offloading allocation, which maximizes tasks offloaded to the UAV, subject to the IoT device’s computation capacity and battery budget, and the UAV’s speed limit. Since the optimization contains a large solution space while the instantaneous network states are unknown to the UAV, we propose a new deep-graph-based reinforcement learning framework. An advantage actor–critic (A2C) structure is developed to train the real-time continuous actions of the UAV in terms of the flight speed, heading, and the offloading schedule of the IoT device. By exploring hidden representations resulting from the network feature correlation, our framework takes advantage of graph neural networks (GNNs) to supervise the training of UAV’s actions in A2C. The proposed graph neural network-enabled A2C (GNN-A2C) framework is implemented with Google Tensorflow. The performance analysis shows that GNN-A2C achieves fast convergence and reduces considerably the task missing rate in aerial EdgeIoT.

Integrating simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technologies into Internet-of-Things (IoT) networks, named SWIPT-enabled cognitive IoT networks, has become an effective approach to resolve the short lifetime of battery-constrained IoT Devices (IoDs) and spectrum scarcity. In this type of networks, IoDs are regarded as secondary users (SUs) being charged with wireless power. To improve the sum throughput of IoDs, we allow IoDs to switch among spectrum sensing, SWIPT and information transmission adaptively. Correspondingly, three-dimensional resources, i.e., time (for performing the three actions), power (including power transmitted from an IoT controller to each IoD and power for receiving information and charging at each IoD) and spectrum, are jointly and adaptively allocated to maximize the sum throughput of IoDs. Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. Simulation results show our adaptive design yields superior performance in terms of the sum throughput of IoDs.

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.

This paper investigates the computation offloading problem in a high mobility internet of vehicles (IoVs) environment, aiming to guarantee latency, energy consumption, and payment cost requirements. Both moving and parked vehicles are utilized as fog nodes. Vehicles in high mobility environments need collaborative interactions in a decentralized manner for better network performances, where agent action space grows exponentially with the number of vehicles. The vehicular mobility introduces additional dynamicity in the network, and the learning agent requires a joint cooperative behavior for establishing convergence. The traditional deep reinforcement learning (DRL)-based offloading in IoV ignores other agent’s actions during the training process as an independent learner, which makes a lack of robustness against the high mobility environment. To overcome it, we develop a cooperative three-layer, more generic decentralized vehicle-assisted multi-access edge computing (VMEC) network, where vehicles in associated RSU and neighbor RSUs are in the bottom fog layer, MEC servers are in the middle cloudlet layer, and cloud in the top layer. Then multi-agent DRL-based Hungarian algorithm (MADRLHA) in the bipartite graph maximum matching problem is applied to solve dynamic task offloading in VMEC. Extensive experimental results and comprehensive comparisons are conducted to illustrate the superiority of our proposed method.

Virtual reality (VR) over wireless has emerged as an important application in future mobile networks. However, it is difficult for the existing mobile networks to meet the requirements of massive data transmissions and ultra-low latency for wireless VR. Multi-access edge computing (MEC) network, providing caching and computing capacities at network edge, emerges as a promising method to support wireless VR. However, mobile VR users' quality of experience (QoE) may be degraded by frequent handoffs. In this paper, we propose a proactive caching, computing and communication (3C) resource allocation method to provide smooth VR videos to handoff users. Specifically, the expected 3-dimensional (3D) video or 2D video for rendering is cached at a target base station (BS) ahead of time, and the size and quality of the video file are decided according to the 3C resources at the BS. Then, we model the the proactive 3C resource allocation as a Markov decision process and an effective allocation policy is obtained by a model-free algorithm based on deep reinforcement learning. Numerical results show that the proposed method can provide VR users with high QoE when they are moving between BSs.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

The idea of co-operative caching in a cache-enabled wireless network has gained much interest due to its services in terms of short service delay and improved transmission rate at the user end. In this article, we consider a co-operative caching mechanism for a fog-enabled Internet of Things (IoT) network. We propose a delay-minimizing policy for fog nodes (FNs), where the goal is to reduce the service delay for the IoT nodes, also known as terminal nodes (TNs). To this end, a novel smart clustering mechanism is proposed, aiming to efficiently assign FNs to the TNs while improving the network benefit by finding a tradeoff between the delay and the network's energy consumption. We perform mathematical analysis and extensive simulations to highlight the potential gain and the proposed policy.

Aiding cellular networks by unmanned aerial vehicles (UAVs) is a leap forward to address the ever-rising, multifarious, and dynamic traffic demands. The UAV utilization in wireless communication presents essential advantages, such as position control and appealing Line-of-Sight (LoS) components. In this article, we consider a UAV deployed as an aerial base station (ABS) to assist a terrestrial base station (TBS), serving several users in a hotspot area, via user offloading. Considering unavailable knowledge of the user's locations, the ABS is assumed to hover at the cell center (geometric center) above the TBS as a best-effort location for all the users. Taking into account the mutual interference between the aerial and terrestrial communication links due to spectrum reuse, we study the impact of the ABS altitude and transmit power, as well as the offload portion, on the user's downlink sum rate under the settings of the LoS channel. The results show that the optimal values for the design variables are either the maximum or the minimum boundaries. Also, the results demonstrate that our method outperforms benchmark schemes with appreciable amounts of gain. We also investigate the method's behavior with the ABS horizontal position optimization, probabilistic LoS channel, uplink communication, and orthogonal spectrum allocation.

Unmanned aerial vehicle (UAV) is a key enabler for communication systems beyond the fifth generation due to its applications in almost every field, including mobile communications and vertical industries. However, there exist many challenges in 3-D UAV placement, such as resource and power allocation, trajectory optimization, and user association. This problem becomes even more complex as UAV changes its height, which in turn varies the channel conditions and reduces the coverage on account of high co-channel interference. To maximize the user coverage in uplink transmission, we propose to jointly optimize the 3-D UAV placement and path-loss compensation factor. Moreover, we also optimize the latter for various UAV deployment heights in the suburban environment. Simulation results have demonstrated that the joint optimization of the UAV height and path-loss compensation factor results in better coverage and throughput performance as compared to the baseline scheme.

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.

Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security. It is non-trivial to optimize the trajectory of a monitor while conceiving its monitoring intention, due to typically non-convex propulsion and thrust power functions. This article presents a novel framework to jointly optimize the propulsion and thrust powers, as well as the 3D trajectory of a solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking and surveillance. A multi-objective problem is formulated to minimize the energy consumption of the monitor and maximize a weighted sum of distance keeping and altitude changing, which measures the disguising of the monitor. Based on the practical power models of the UAV propulsion, thrust and hovering, and the model of the harvested solar power, the problem is non-convex and intangible for existing solvers. We convexify the propulsion power by variable substitution, and linearize the solar power. With successive convex approximation, the resultant problem is then transformed with tightened constraints and efficiently solved by the proximal difference-of-convex algorithm with extrapolation in polynomial time. The proposed scheme can be also applied online. Extensive simulations corroborate the merits of the scheme, as compared to baseline schemes with partial or no disguising.

In this paper, we consider a Fog-IoT network, comprising multiple terminal nodes (TNs) as well as fog nodes (FNs), in which each TN first determines the proper FN to be associated with different quality of service (QoS) and quality of transmission (QoT) constraints, such as energy efficiency, processing delay, and bandwidth requirements; then, it sends its tasks to the associated FN. The main objective of this paper is to maximize the transaction throughput of FNs, i.e., the number of tasks that FNs can process, while there exists a throughput fairness as well as an energy consumption fairness among all FNs in the network. To this end, two algorithms, namely TF-TRADE and QL-TRADE, are proposed. The TF-TRADE aims to ensure throughput fairness and at the same time optimize the total transaction throughput of the network. The QL-TRADE not only follows the same objective, but also tries to balance the energy consumption among all FNs. Analysis and simulation results reveal that the proposed algorithms could alleviate the network performance in terms of the transaction throughput as well as the energy consumption balance among all FNs.

Optimal content caching has been an important topic in dense small cell networks. Due to spatial and temporal variation in the popularity of data, most content requests cannot be directly served by the lower tiers of the network, increasing the chances of congestion at the core network. This raises the issues of what to cache and where to cache, especially for content with different popularity patterns in a given region. In this work, we focus on the issue of redundant caching of popular files in a cluster when designing a content allocation scheme. We formulate the considered problem as a stable matching theory problem, where the preferences of each cache entity are sent to the Macro Base Station (MBS) for stable matching. The caches share their request lists with the MBS, which subsequently uses Irving One-Sided matching algorithm to generate a unique preference list for each caching entity such that every preference list is a representative of the popular data in that region. The algorithm achieves the desired goal of efficient caching with few but smartly planned repetitions of the popular files. Results show that our proposed scheme provides better performance in terms of cache hit ratio with increasing number of requests as compared to a popularity based scheme.

Most Geo-routing protocols of Vehicular ad-hoc Network (VANET) follow a junction selection process. But none have considered the average lifetime of the vehicles under an RSU for junction selection. For delay intolerant networks like VANET, calculating only the number of vehicles is not enough for junction selection. An inclusion of the average lifetime of the vehicles per RSU will give a real-time idea to project vehicles duration in a selected junction. Our paper proposes an efficient junction selection mechanism which incorporates real-time traffic update. Moreover, the most important factor in a vehicular network is security. Our proposed protocol also ensures security. RSU will actively count the duration of the vehicles and contact with trusted authority if any irregular event takes place. This protocol provides an algorithm for identifying unusual incidents and act accordingly. In conclusion, the proposed protocol will efficiently route the packets without compromising security.

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

The wireless communication field is getting closer to the integration of the unmanned aerial vehicle (UAV) and cellular network for further enhanced service quality. In this paper, we study a wireless communication system with coexisting aerial and terrestrial base stations, which respectively serve their associated users. By taking into account the mutual interference between the aerial and terrestrial communication links, we study the impact of the aerial base station (ABS) altitude and transmit power on the system's downlink and uplink data rates. In many situations, the results show that the optimal ABS altitude and transmit power are either the maximum or the minimum possible values depending on, for instance, the relative proximity between the ABS, the ABS user (AU), and the terrestrial base station user (TU).

In this paper, we develop and analyze a smart digital stethoscope - SkopEdge - to provide reliable remote e-health monitoring with a minimum delay while enhancing overall network performance. SkopEdge initially records the heart sounds from individuals and then senses the quality of the network. Depending on the network traffic, SkopEdge converts the audio clip into an appropriate format before transferring it to remote locations for estimating the number of heartbeats and storage. Towards this, we formulate the link quality along with SkopEdge's current configuration as a Markov Decision Process (MDP) with actions as conversion format selection. The remote server then returns the result, which SkopEdge displays on its screen. Real-time implementations show that SkopEdge works efficiently in all network conditions. Further, audio conversions usually degrade the quality of sound, but our proposed system does not change its primary components. Although SkopEdge exhibits an increase in energy consumption by 79% while converting to lower-quality formats, it also reduces the energy consumption by 99% while transmitting the same, which subsequently results in energy savings. Further, we provide an analysis of the estimated heartbeats in an audio clip by SkopEdge.

With the advancement in transportation system, vehicle to vehicle and vehicle to infrastructure communication has drawn much attention. For delay intolerant networks like VANET, efficient routing mechanism is one of the major concerns. Geo-routing protocols provide the best services in this regard because of their simplicity and for not having route discovery process. Though coordinator vehicles are preferable for forwarding a packet in most of the existing geo-routing protocols, there is a lack of research on coordinator selection method. A coordinator is a vehicle which has the maximum number of neighbours around it and thus helps to forward the packet more efficiently. The main objective of this research is to introduce an efficient and durable score-based coordinator selection method for the geo-routing protocol of VANET. The results show that the selected coordinator is stable over a considerable period of time. As a result, less number of control packets are exchanged between the nodes and network faces lesser delay.

The routing protocol for low-power and lossy networks (RPLs) is a standard routing protocol for resource-constrained devices in the Internet of Things (IoT) networks. Primarily, RPL can support a dynamic range of mobility among the nodes in the network, which becomes a great demand now for real-time applications. At the same time, RPL is much vulnerable to various security attacks because of its resource-constrained nature. Such security attacks might cause severe threats and destructive behavior inside the network. In this article, we primarily focus on the Sybil attack, where an attacker claims multiple illegitimate identities either by fabricating or compromising the nodes. Also, in this type of attack, a single adversary is required to control multiple legitimate nodes in the network, and thereby, the adversary node saves the physical resources. In this article, we propose a novel artificial bee colony (ABC)-inspired mobile Sybil attack modeling and lightweight intrusion detection algorithm for the Sybil attack in mobile RPL. Moreover, we considered three different categories of Sybil attack based on its behavior, and we analyze the performance of the RPL under the Sybil attack in terms of packet delivery ratio, control traffic overhead, and energy consumption. Also, we examine the performance of the proposed algorithm in terms of accuracy, sensitivity, and specificity.

Software-defined networking (SDN) is a cutting-edge technology, featuring a centralized control that facilitates, e.g., flexible deployment of unmanned aerial vehicles (UAVs). On the other hand, UAV-enabled communication is a promising technology due to its numerous traits such as the ability of on-demand deployment and the high likelihood of strong line-of-sight (LoS) communication links. However, UAV-enabled communication suffers non-perpetual nodes and intermittent communication links. Fortunately, SDN provisions an unparalleled global vision on such dynamic network architecture to overcome the challenges of loose links and disappearing nodes. On the other hand, there are catastrophic or remote regions where a UAV-mounted base station (BS) potentially functions without a terrestrial BS, albeit a WiFi access point (AP) may still exist. Therefore, this paper considers software-defined coexisting UAV-mounted BS (UBS) and WiFi AP, and investigates the queuing delay behavior via the UBS positioning and the AP traffic offloading. The subscribers (users) are divided into cellular subscribers (CSs) and WiFi subscribers (WSs). A CS is connected to the UBS and is possibly granted simultaneous access to the AP, leading to WiFi traffic offloading. Leveraging the software-defined global view, the objective of a software-defined controller (SDC) is to minimize the average M/M/1 queuing delay of the CSs, while guaranteeing the delay performance for the WSs, via optimizing the spectrum allocation, the UBS position, the CS association to the AP, and the CS traffic offloading. The optimization problem is non-convex, comprising binary variables, for which we use the block coordinate descent and successive convex approximation methods to find a high-quality solution. Numerical results verify that our solution achieves significant performance gains over benchmark schemes.

The wireless communication field is approaching the realization of the integrated unmanned aerial vehicle (UAV) and cellular network for further improved communication performance. In this paper, we consider a cellular network with a UAV-mounted aerial base station (ABS) coexisting with multiple terrestrial base stations (TBSs), where each base station (BS) is serving multiple users. Under the probabilistic channel based environment, we design the three-dimensional (3D) positioning for the ABS and the transmit power allocation for all the nodes in the uplink (UL), downlink (DL), and combined UL and DL operations. Considering the ABS user (AU) and the TBS user (TU) as contending parties, our objective is to maximize the weighted sum of the minimum data rate of the AUs and the minimum data rate of the TUs. The whole optimization problem is non-convex and difficult to be directly solved, for which we employ the block coordinate descent (BCD), the successive convex approximation (SCA), the particle swarm optimization (PSO), and the discrete search algorithm (DSA) methods. Numerical results shed some light on interesting observations regarding the comparison between our solution and benchmark schemes, as well as the optimal 3D position of the ABS in UL, DL, and combined UL and DL operations.

Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

Small base stations (BSs) equipped with caching units are potential to provide improved quality of service (QoS) for multimedia service in ultra-dense cellular networks (UDCNs). In addition, the Coordinated MultiPoint (CoMP) transmission method, allowing multiple BSs to jointly serve users, is proposed to increase the throughput of cell-edge mobile terminals (MTs). Yet, the combination of content caching and CoMP in UDCNs is still not well explored for future networks. In this paper, we focus on the application of caching in CoMP-integrated UDCNs, where the cache-enabled BSs can collaboratively serve each MT using either joint transmission or single transmission. We propose a multidimensional cooperative caching (MDCC) scheme, supporting storage-dimension and transmission-dimension cooperations for the content placement. In particular, we analyze the delivery delay based on request patterns, transmission method, and the proposed cooperation strategy. Then the content placement problem is formulated as a problem of minimizing the overall expected delay. The problem is a mixed binary integer linear programming (BILP) problem, which is NP-hard. Therefore, we address the problem with approximation and substitution, and design a genetic algorithm (GA) based method to solve it. Simulation results demonstrate that the proposed MDCC scheme contributes performance gain in terms of content delivery delay in both cell-core and cell-edge areas.

This paper presents a new Multi-hop Device-to-Device (MD2D) routing protocol, referred to as SMDRP (SDN-based Multi-hop D2D Routing Protocol), for SDN-based wireless networks. Our proposed protocol can be considered as a semi-distributed routing protocol, where an SDN controller manages and controls part of the overall MD2D routing functionality to increase scalability while enabling network operators to control and maintain the out-of-band packet forwarding network. This paper also extends prior work on the Hybrid SDN Architecture for Wireless Distributed Networks (HSAW) [1] and is adapted to the framework presented in this paper. In HSAW, since all link state information is flooded by the controller to the nodes, the network will experience scalability problem. In our approach, this problem is overcome by only passing the next hop for each active route to the mobile nodes. To investigate this, we performed a theoretical and simulation studies comparing HSAW with SMDRP. From our result, it can be seen that for larger density populated networks, SMDRP shows better scalability than HSAW. In addition, mobile nodes need less memory and energy for their communications.

With rapid increase in the use of traffic-intensive applications, approaches that improve users experience by reducing delay are recently receiving enormous attention. Caching in Device-to-Device (D2D) networks, in particular, is considered as an effective technique to improve the service quality of the network. In this paper, an agglomerative hierarchical clustering algorithm is proposed for a cache-assisted D2D communication network. The algorithm considers users preferences and groups them into the same cluster based on the similarity of their requested content. An optimal caching strategy has been applied and the cache hit probability has further being optimized within each cluster. Performance of the algorithm has been examined in different clusters, considering both sparse and dense user environments. Simulation results show that the cache hit probability within each cluster is higher for higher Zipf parameter, denser domains, and larger number of participating devices. The D2D cache hit probability has also been examined with changing number of clusters under a base station. In this scenario, the results show that the clustering based D2D cache hit probability is higher than the non-clustered case, and the cache hit probability increases with increasing number of clusters.

Minimizing energy consumption while providing quality of service (QoS) is of paramount importance for energy-constrained networks, such as Internet of Things (IoT). The emergence of fog computing in IoT has the great potential to reduce the energy consumption of the IoT nodes, which also known as terminal nodes (TNs), and also minimizing the task delays. However, this in general comes at the price of the higher energy consumption of the fog nodes (FNs). This paper aims to study the energy consumption tradeoff between the TNs and FNs in Fog-IoT system. To this end, we first propose a new protocol, where the TNs immediately broadcast their data with certain transmission rate to potentially all FNs without the need of firstly determining which FN to associate with. Upon receiving the data, FNs determine whether to process the data locally or forward to the cloud center, depending on whether they are overloaded or not. By considering the fading channels between TNs and FNs, we mathematically characterize the energy consumption tradeoff between TNs and FNs by varying the broadcasting rate of the TNs.

Integrating backscatter communication into wireless powered communication networks (WPCNs) allows for more efficient utilization of the resources and improves the network performance. This paper studies a multiuser backscatter-assisted wireless powered communication network (BS-WPCN), where a set of energy-constrained users use the energy signal of the power source (PS) for two purposes: energy harvesting and backscattering. Particularly, a harvest/backscatter-then-transmit (HBTT) protocol is presented, where the users first harvest energy and backscatter their modulated information to the access point (AP), and then transmit information to the AP by regular wireless information transmission using their harvested energy. We maximize the overall network throughput by finding the optimal time allocation for the energy transfer (ET) of the PS as well as the backscattering and information transmission (IT) of the users, under the energy storage constraint at the users. The effectiveness of the proposed scheme and the impact of related network parameters on the performance are explored via numerical simulations.

Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks.

In this paper, we propose a new energy-aware algorithm, called energy balancing algorithm (EBA), for a three-tier fog-Internet of Things (IoT) network. The EBA comprises of two optimization models to reduce the energy consumption and delay of the network, while guaranteeing the energy balancing among all fog nodes (FNs). The first optimization model, called best transmission power and transmission rate (BTPR), finds the optimal transmission power and transmission rate of terminal nodes (TNs), such that the request loss is prevented. Then, the topology potential between each TN and FN is defined in the best FN (BFN) model to find the BFN for serving the TN, while the energy balancing among all FNs is guaranteed. Simulation results reveal that the proposed EBA can reduce total energy consumption and delay of the network and yield efficient energy balancing among all FNs.

IPv6 routing protocol for low-power and lossy networks (RPL) is a standardized routing protocol for the energy-constrained networks in the Internet of Things networks. Nowadays, many real-time applications demand mobility support among the nodes in RPL with a wide range of speed. But, supporting mobility in RPL becomes a critically challenging issue because the devices are resource constrained and the communication links are lossy to establish a stable network. Moreover, control packet overhead increases rapidly over mobility, which in turn drains out energy and affects the overall lifetime of the network. In this paper, we propose a mobility-aware energy efficient parent selection algorithm that supports random mobility of the nodes in RPL and chooses the best parent from the preferred parent list based on the metrics, namely, expected transmission count, expected lifetime, received signal strength indicator, and Euclidean distance (dij) between the mobile node and the parent node under selection. We also propose a dynamic trickle algorithm for Trickle Timer to solve long listen only period and allocates timer dynamically based on the random set of neighbor nodes under mobility. Specifically, this paper analyzes the performance of the proposed algorithm against previous algorithms, namely, original RPL, corona-RPL, enhanced trickle, reverse Trickle Timer mechanism, and mobility-enhanced RPL in terms of packet delivery ratio, energy consumption, and average end-to-end delay under different scenarios.

Wireless powered communication (WPC), where the required energy for communication is obtained via radio frequency (RF) energy harvesting, is a promising technology for the upcoming self-sustainable Internet of Things (IoT) networks. In this paper, a multiuser IoT network is considered, where an energy-constrained relay assists the information transmission of a number of IoT devices to the access point (AP) using WPC. In particular, the relay acquires the energy needed for information forwarding from the RF energy transfer of the AP, which serves as a dedicated energy transmitter for the relay. The objective is to maximize the total network throughput by jointly optimizing the wireless energy transfer (WET) duration and the relay's energy expenditure in each time slot, subject to the energy causality constraint, for both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. We show that the optimization problems for both the AF- and DF-based systems are convex and thus can be solved using convex optimization techniques. Our analysis shows that the solution of the sum-throughput maximization problems depends on the scheduling order of the IoT devices and the optimal solution is derived for different ordering scenarios. Finally, numerical simulations are presented to corroborate our analysis.

In this paper, we propose an adaptive flow-rule placement scheme, FlowStat, in a software-defined network (SDN) with an aim to provide per-flow statistics to SDN controller while enhancing overall network performance. The proposed scheme consists of three phases-forwarding path selection, flow-rule placement, and rule redistribution. In the first phase, we formulate a max-flow-min-cost optimization problem to determine optimal forwarding paths while considering multi-commodity flows with heterogeneous requirements. In the second phase, an integer linear programming problem is formulated to decide forwarding rules for paths computed in the first phase, so that the total number of exact-match is minimized. As finding optimal solution to the problems is NP-hard, we propose two greedy heuristic approaches to solve the problems in polynomial time. Finally, we propose a rule redistribution scheme on detecting rule congestion at a switch, in order to accommodate new flows in the network. Extensive experimental results show that the proposed scheme, FlowStat, is capable of providing per-flow statistics to the SDN controller while enhancing the network performance compared to existing schemes-ReWiFlow and ExactMatch. In particular, FlowStat is capable of reducing end-to-end delay and QoS violation by 46% and 75% (approx.), respectively, compared with the ReWiFlow and ExactMatch schemes, while providing 85% accurate per-flow statistics to the SDN controller.

The unprecedented growth in cellular traffic forced researchers towards extending the Long Term Evolution (LTE) cellular network to unlicensed bands, which is known as U-LTE. However, this extension is by no means straightforward, because centralized control impeded in traditional LTE BSs challenges the performance and radio resources management of today's cellular networks. To cope with the increasing scarcity of today's cellular architecture and radio resources management, for the first time, we propose Break-Even Point (BEP) as a novel approach for solving the radio resources management problem. In this case, the BEP is modelling where the total remaining throughput of Wi-Fi or Small cell Programmable Base Station (SPBS) equals to the total consuming throughput by Users Equipment (UEs). Therefore, we can identify if Wi-Fi and Femtocell or SPBS are working above or below BEP. By using proposed BEP scheme, we guarantee cellular network utilizations with optimal precision, as well as, performance evaluation indicates that optimal spectrum access and fair distribution by using BEP scheme.

Heterogeneous network is considered as one of the promising solutions for the next-generation cellular network to manage the ever increasing demand of network capacity, and to provide desirable data rate for ensuring user satisfaction. Traffic steering (TS) is recently reported as an emerging technique for diverse objectives in the heterogeneous networks, which can steer the user's traffic to the suitable network entities. In this paper, we propose a universal TS framework, which considers the current status of the networks as well as the current condition and demand of the users to fulfill the desirable objective of the TS policy. The network monitoring capability and global view of the network are introduced by incorporating software-defined networking (SDN) into cellular network to make the dynamic decision for steering the user's traffic to the optimal access point (AP). On the top of the proposed TS framework, an operator defined steering policy assists the TS algorithm. Based on the numerical analysis, it is shown that the proposed TS framework can effectively steer the user's traffic to the optimal destination to achieve the objective of the policy. Our Proposed TS framework can be an effective management tool for the next-generation heterogeneous cellular networks.

Underwater Wireless Sensor Networks are characterized by their harsh channel conditions, lack of access to terrestrial services such as GPS for localization, limited battery life and low memory and processing capabilities. Furthermore, the effect of passive node mobility produces dynamic network topology and decreases localization accuracy. For terrestrial networks, access to GPS signals can provide accurate location information which can be used to ensure efficient network reconfiguration. Such a luxury does not exist in the underwater environment. In this paper, we propose a cluster-based network that can reconfigure as the network spatially evolves. The clustering algorithm is based on the swarming patterns of birds, fish and insects called murmurations. We reduce the murmuration behavior into two variables: orientation and opacity. The orientation data determines which nodes should be clustered together, and opacity data determines the optimal cluster head. Our results show that our murmuration inspired clustering algorithm manages network topology changes caused by underwater currents effectively and is shown to improve network lifetime by decreasing the number of cluster redefinitions and cluster head selections, thereby reducing unnecessary overhead in an already energy constrained environment. Furthermore, our results show that the decrease in network coverage due to node death is minimized by moving energy consumption to nodes with overlapping coverage regions.

In this article we propose a harmonious licensed-unlicensed spectrum management framework for LTE and WiFi coexisting in a software defined environment. Two key resource dimensions are employed, bandwidth and Virtual Queues (VQs), in conjunction with a third resource dimension, i.e. time. The full potential of this novel approach lies in the replacement of inter-technology carrier sensing with centrally managed resource access via leveraging the global view of Software Defined Networking (SDN). Results illustrate that the proposed paradigm addresses together selectivity and fairness for LTE and WiFi, as well as demands for both high and low priority traffic. The outcomes also show holistic superiority for our framework when compared with two other benchmark schemes.

The technology of Internet of Things (IoT) has been gaining great popularity in recent years, as it provides an effective and immediate bridge between the physical world and the virtual objects in the cyber space, which can lead to innovative applications and services with high efficiency and productivity. However, IoT is just at the beginning stage of a longer journey. In-depth research and development efforts on systems, networks and architectures of IoT for efficient large-scale deployments are still required to fill the gaps between the current performance and service requirements, particularly with the predicted importance of IoT in the upcoming years, improved connectivity and communication among numerous devices will become necessary and critical.

Device-to-device (D2D) communications are set to form an integral part of future 5G wireless networks. D2D communications have a number of benefits such as improving energy efficiency and spectrum utilization. Until now much of the D2D research in LTE and 5G-type network scenarios have focused on direct (one-hop) communications between two adjacent mobile devices. In this paper, we propose a new routing framework called virtual ad hoc routing protocol (VARP). This framework introduces significant advantages such as better security, lower routing overheads, and higher scalability, when compared to conventional ad hoc routing protocols. It also reduces traffic overhead in LTE networks using multi-hop D2D communications under management of a software defined networking (SDN)controller. Further, it enables the development of various types of routing protocols for different networking scenarios. To this end, a source-routing based protocol was developed on top of VARP, referred to as VARP-S. We present a detailed analytical study of routing overhead in the VARP-S protocol, as compared to overhead analysis of our previous proposed hybrid SDN architecture for wireless distributed networks (HSAW) Our results show that VARP-S, compared to HSAW, achieves higher network scalability and lower power consumption for mobile nodes.

Wireless Body Area Networks (WBANs) are seen as the enabling technology for developing new generations of medical applications, such as remote health monitoring. As such it is expected that WBANs will predominantly transport mission-critical and delay sensitive data. A key strategy towards building a reliable WBAN is to ensure such networks are highly immune to interference. To achieve this, new and intelligent wireless spectrum allocation strategies are required not only to avoid interference, but also to make best-use of the limited available spectrum. This article presents a new spectrum allocation scheme referred to as Smart Channel Assignment (SCA), which maximizes the resource usage and transmission speed by deploying a partially-orthogonal channel assignment scheme between coexisting WBANs as well as offering a convenient tradeoff among spectral reuse efficiency, transmission rate, and outage. Detailed analytical studies verify that the proposed SCA strategy is robust to variations in channel conditions, increase in sensor node-density within each WBAN, and an increase in number of coexisting WBANs.

This paper considers a dual-hop wireless powered communication network (DH-WPCN), where the communication between a hybrid access point (HAP) and a number of users is assisted by energy-limited relays. In order to power relays for assisting the communication, the HAP broadcasts a dedicated energy signal in the downlink. Each relay harvests energy from this signal and utilizes the harvested energy in forwarding its corresponding user's data to the HAP with an amplify-and-forward (AF) relaying approach. Under this setup, we study the problem of maximizing the minimum throughput among network users. The objective is to optimize time allocations for energy and information transfer so as to ensure throughput fairness and at the same time optimize the sum-throughput. Simulation results confirm the effectiveness of our proposed fairness enhancement algorithm and reveal the trade-off between sum-throughput and fairness.

Underwater wireless sensor networks are garnering a greater research interest as human's endeavor to investigate deeper into the depths of the ocean. However, infrastructure is currently lacking in underwater communications. Acoustic communications have been long the preferred method, however, as applications become more resource intensive, the low bit rates and high latency of acoustic communications require the use of optical or electromagnetic communications. In this research, we propose a surface-to-floor optical link capable of carrying higher bit rates and with a much improved latency. However, much research in underwater optical communications does not take into account passive node mobility. Passive node mobility is caused by water currents moving nodes in jet streams and vortices. We therefore introduced a mobility model to our research to better simulate the underwater communications channel. Passive node mobility can cause transmitter and receiver misalignment, a problem for free space optical communications. This misalignment can also introduce a greater temporal spread in received photons further degrading the links performance. It was found that when both the transmitter and receiver exhibited passive node mobility, the temporal spread increased greatly and Inter Symbol Interference which could produce higher bit error rates than previously suggested in literature.

The main problems of current cellular networks illustrating high delay and traffic congestion relate to their network inflexibility and incorporated control and data planes. However, the prerequisites of 5G cellular systems are low delay, high throughput, low congestion at the core cellular networks, and seamless mobility and fast handover. To alleviate aforementioned problems we propose a 3-Tier Software-Defined Networking (SDN) architecture combined with a novel OpenFlow (OF) switch, which includes a User Rate-Perceived (URP) protocol and a Macrocell/Femtocell Information Base (MFIB). Our simulations prove that proposed mechanisms bring numerous benefits such as low delay, high throughput, low cost, and seamless mobility and fast handover to 5G cellular networks and high Dense Networks, which are called DenseNets.

Conventional planning and optimization of cellular networks for supporting the peak-time user demand leads to substantial wastage of electrical energy. Therefore, we propose an energy-aware dynamic network provisioning framework for realizing green mobile cellular systems by reducing energy consumption in access networks. Proposed mechanism allows base stations (BSs) to offload their entire traffic to the neighbors and thus allows some BSs to switch into sleep mode for saving energy. Four different traffic offloading schemes for redistributing users among the neighboring BSs are proposed and investigated. Flexible resource allocation is integrated in the system for maintaining quality of service (QoS) throughout the network. Furthermore, for avoiding the high complexity of the formulated generalized energy optimization problem, a heuristically guided algorithmic framework is developed. System performance is evaluated using extensive simulations demonstrating a substantial energy savings. Impact of the proposed network provisioning framework on the bandwidth utilization is also analyzed.

Smart meter (SM) is a key component of the smart grid, which reports power consumption to a centralized control center at regular intervals. The long term evolution (LTE) network has gained much attention as an attractive communication platform for SM traffic due to its high bandwidth, low latency, and coverage. However, when a large number of SMs simultaneously access the LTE network, it is very likely to result in preamble congestion. This eventually becomes a contributing factor for prolonged delays at the eNode-B, which would eventually degrade the performance of the LTE network. Hence, a random access mechanism is desirable for preamble signatures to occupy resource blocks for SMs. In this paper, we introduce a novel technique that combines contention- and noncontention-based random access methods. Our proposed method does not require a backoff time and regeneration of a new preamble if the first attempt is unsuccessful. Although SMs have fixed periodic communications, under the proposed random access mechanism preamble signatures do not need to be reserved. We analyze the proposed mechanism's efficiency in terms of metrics collision probability and packet delay. The simulation results are validated against the 3GPP standard.

To make full use of space/air resources, a Space-Air Integrated Network (SAIN) which is a hierarchical network with satellites, airships and hovering Unmanned Aerial Vehicles (UAVs) is constructed. Nowadays, Free Space Optical (FSO) links have been widely applied in SAINs since they provide high-rate and large-capacity data transmission. Unfortunately, the FSO links across the atmosphere would perform badly in adverse weather. Besides, the limited number of transceivers brings bottlenecks of link capacity and node energy. To solve these problems, in this paper, we first propose an adaptive RF/FSO switching mechanism based on predictions of atmosphere conditions, so that the high- rate and large-capacity data transmission can be achieved while overcoming the negative influences of bad weathers. Moreover, under the limited number of transceivers, we design a Dynamic Energy & Traffic Balance (DETB) topology control algorithm. Except for transmit power, both residual bandwidth and energy are taken into account for obtaining an optimal topology dynamically. Finally, a hierarchical routing policy combined with our DETB algorithm is proposed. It has been proved that our method extends the network lifetime, and the network throughput remains stable.

Mobility management in cellular networks, such as the Long Term Evolution (LTE) System, relies on a centralized anchor, which depends on both the control plane and data plane. Therefore, mobility management suffers from many limitations such as low scalability and a single point of failure. Distributed Mobility Management (DMM) overcomes previous limitations and can be applied for HetNets or DenseNets. In this paper, we propose Three-Tiered Software Defined Networking (SDN)-based DMM to solve mobility problems and handover in DenseNets such as femtocells, in addition to macrocells. Our proposal reduces overhead signaling through the core network. As a result, it decreases delay and increases throughput over other cellular networks.

Coordinated multi-point (CoMP) transmission introduced by long-term evolution-advanced (LTE-A) cellular systems has shown great promise for improving network spectral efficiency (SE). On the other hand, the ever increasing energy consumption in cellular systems has emerged as a major concern for the network operators. This paper proposes a novel CoMP technique for the downlink of two-tier heterogeneous LTE-A cellular networks and investigates its energy efficiency (EE) performance. The proposed CoMP technique combines both dynamic point selection (DPS) and joint transmission (JT) CoMP schemes for improving network SE. Thus, under the proposed CoMP technique, multiple transmitting base stations (BSs) selected dynamically from the available high-power macrocells and low-power small cells are coordinated for jointly serving a user. Various spatial distributions of small cells in the macrocell coverage area are considered. A Poisson distributed hard core point process (HCPP) is also used for maintaining a minimum distance between any two of the small cells. Extensive simulations are carried out for evaluating the EE performance of cellular networks deployed integrating the proposed CoMP technique. Network performance is also compared with that of non-COMP, only JT and only DPS transmission scheme based cellular networks.

Femtocells, which is one of the methods followed by 4/5G cellular system, are used for handling increasing data traffic in heterogeneous (HetNets) and dense networks (DenseNets). In this case, traditional mobility and session management approaches become obsolete and more appropriate methods need to be developed. Therefore, we propose a Distributed Mobility Management (DMM) and a User Rate-Perceived (URP) algorithms over a 3-Tier Software Defined Network architecture. Delay will decrease, throughput will increase, fast handover, reduce overhead signaling and avoid bottleneck by implementing these algorithms to solve the problems of mobility and session management, and capacity in DenseNets over the existing centralized mobility management in Long Term Evolution (LTE).

Acoustic, wave based underwater wireless sensor networks (UWSNs), commonly used for underwater surveillance and explorations, fail to meet the growing demand for fast detection and higher data rates. Electromagnetic (EM) wave based underwater networks; on the other hand, have great potential for supporting such stringent requirements for various applications. Therefore, this paper proposes and investigates several three dimensional (3D) cluster-based UWSN architectures for detecting underwater intruders. The proposed architectures consisting of sensor nodes (SNs) and cluster heads (CHs) detect an intruder within the network area and forward the necessary information to an onshore base station (BS) for estimating the 3D location of the intruder. All communications from SNs to BSs are of EM wave based. Information of received EM signal power, and the location of SNs and CHs are utilized for estimating the locations of intruders. Extensive simulations are carried out for evaluating the proposed network performance in a salty seawater environment. Localization accuracy is compared in terms of error in distance and direction estimations demonstrating performance gaps among the architectures.

One of the critical components of the Smart Grid (SG) is the Smart Meter (SM), which reports power consumption to a centralized control center at regular intervals. The Long Term Evolution (LTE) network has received much attention as a promising platform for SG communications due to its high bandwidth and low latency over a large coverage area. Nevertheless, when a huge number of SMs access the LTE network, the preamble collision becomes an issue. The negative effects of preamble collision are packet loss, delay, reestablishment attempts, and physical channel utilization, which degrade the performance of the LTE network. Hence, a Random Access (RA) mechanism is required for preamble signature to occupy Resource Blocks (RBs) for SMs. In this paper, we introduce a hybrid procedure that combines contention and non-contention based random access methods. The significance of this method is that, although SMs have fixed periodic communications, preamble signatures do not need to be reserved. The simulation results are compared with LTE standard. Packet loss is reduced by approximately 43%. SM latency is decreased by 11 ms at 90% of probability delay. Re-establishment attempts are lower than standard by proximately 76%. Finally, physical channel utilizations are reduced by around 3%.

We propose a novel communication system for an emergency fire response team, which provides Internet service on the way to and in the disaster area. The system is composed of a multi-interface mobile ad-hoc network (MANET) router, a Global Positioning System (GPS) receiver, and two Wi-Fi interfaces with directional antennas, which can be easily attached to the roof of a vehicle. The front-side Wi-Fi interface of the vehicle is operated in the infrastructure mode, and the rear-side interface is operated in the access point mode. Different channels are assigned to each AP interface of the vehicles. Infrastructure-mode Wi-Fi interfaces automatically scan and connect to an appropriate AP interface and create MANET links. Some experiments using this wireless system with the WINDS satellite mobile earth station and nine fire engines were conducted in Ebetsu, Hokkaido. We measured the TCP throughput and confirmed that a throughput of more than 10 Mbps was able to be obtained by most of the node pairs. In addition, high-vision video streaming was able to be successfully transmitted to the streaming server on the Internet through MANET and satellite communication links while they were platooning.

Cooperative communication significantly improves the performance of wireless systems. Transmission of signal in such a system can be accomplished by the help of intermediate relay nodes. However, due to the selfish nature of network nodes, an incentive mechanism is required to stimulate relay nodes to cooperate. On the other hand, the authors assume that the source nodes are ill‐informed about channel conditions of the relay nodes, which may result in asymmetry of information. In this study, the authors propose a distributed power allocation and price assignment algorithm over cooperative wireless networks. The proposed solution aims to achieve optimum power allocation to the source nodes and best price of power at the relay nodes, in the presence of asymmetric channel state information. To this end, the authors combine contract theory and auction mechanism in order to provide the highest possible utility for both the source and the relay nodes. The proposed distributed approach benefits the source nodes by preventing the relay nodes from cheating behaviour. Additionally, it favours the relay nodes by letting them assign the final price of power. Finally, the authors present simulation results in order to demonstrate efficiency of the proposed distributed multi‐user algorithm.

Abstract Authentication is one of the most important security processes in Worldwide Interoperability for Microwave Access (WiMAX) networks. This process allows the receiver (whether base station or subscriber station) of a packet to be confident of the identity of the sender and the integrity of the message. Most attacks will attempt to violate the authentication process. If such attacks become successful, an attacker will gain an illegitimate access to network resources, thus increasing the probability of future malicious attacks. A number of algorithms and methods have been proposed to improve the security level of authentication in WiMAX networks. However, the evaluations of these algorithms and methods have always been based on a particular aspect, such as reducing overhead, protecting unauthenticated management messages, preventing WiMAX from external attacks, and protection during the handover process. Motivated by these variations, we review the state of art of WiMAX authentication mechanisms, namely, PKMv1, PKMv2, and the recent authentication protocols. We discuss their mechanisms, strengths, weaknesses, and potential countermeasures against each other. Our discussion is based on four categories, namely, enhancements, differences, advantages, and limitations. Copyright © 2016 John Wiley & Sons, Ltd.

An Intersection Assistance System aim to assist road users in avoiding collisions at intersections, either by warning the driver or by triggering automated actions. Such a system can be realized based on passive scanning only (e.g., using LiDAR) or supported by active Inter-Vehicle Communication (IVC). The main reason to use Inter-Vehicle Communication (IVC) is its ability to provide situation awareness even when a possible crash candidate is not yet in visual range. The IVC research community has identified beaconing, i.e., one-hop broadcast, as the primary communication primitive for vehicular safety applications. Recently, adaptive beaconing approaches have been studied and different congestion control mechanisms have been proposed to cope with the diverse demands of vehicular networks. In this paper, we show that current state-of-the-art congestion control mechanisms are not able to support IAS adequately. Specifically, current approaches fail due to their inherent fairness postulation, i.e., they lack fine grained prioritization. We propose a solution that extends congestion control mechanisms by allowing temporary exceptions for vehicles in dangerous situations, that is, situation-based rate adaptation. We show the applicability for two state-of-the-art congestion control mechanisms, namely Transmit Rate Control (TRC) and Dynamic Beaconing (DynB), in two different vehicular environments, rural and downtown.

Abstract Accuracy is still the greatest challenge in the wireless sensor network localization efforts. Several diverse factors can give rise to localization errors. Modeling such diverse influencing factors to deliver a single, reasonably simple and practical solution is a difficult task. In order to address the problem of location inaccuracy, we propose a comparatively simple and ingenious approach, which is the simultaneous perturbation stochastic approximation (SPSA) localization engine. SPSA bypasses tedious modeling of the influencing factors where some of them are yet to be explored and random in nature. SPSA‐based localization estimates the non‐anchor node locations through minimizing the summation of estimated errors of all neighbors. However, the downside of SPSA is that it incurs errors in some specific relative neighborhood configurations often referred to as flip ambiguity. So, we further propose a solution to the flip ambiguity problem by implementing a constrained optimization with a penalty function method on the identified flip nodes. Most importantly, error propagation of the iterative localization algorithm is managed by incorporating a neighbor confidence matrix. We name this modified SPSA engine as simultaneous perturbation stochastic approximation by neighbor confidence (SPSA‐NC). Experimental results show that SPSA‐NC offers significantly better localization accuracy than its state‐of‐the‐art competitors, namely, simulated annealing and the ordinary SPSA. The SPSA‐NC program is available for downloading at http://www.dnagroup.org/SPSANC . Copyright © 2015 John Wiley & Sons, Ltd.

In this paper we introduce a novel analytic approach in clustering wireless nodes on ground fields through the assistance of an aerial base station, forming an aerial backhauled network. On the ground, some of the deployed nodes act as cluster heads, and aggregate and relay the terrestrial data towards the aerial platform. We present a novel cluster-head selection algorithm based on Matern hard-core point process, and then obtain the analytical formulation to optimize the spacing between cluster heads to minimize the overall energy consumption. The formulated problem utilizes stochastic geometry to capture the random nature of the locations of the deployed nodes, leading to a tractable analysis of the expected network metrics. Furthermore, we compare the performance of the proposed approach with other clustering algorithms, and show the improvement in energy efficiency.

In this paper, we propose a prediction algorithm for dynamic channel allocation amongst coexisting Wireless body area networks (WBANs). Variations in channel assignment due to mobility scenarios within each WBAN as well as the movement of WBANs towards each other is investigated. The proposed scheme is further optimized to allocate the optimum transmission time with synchronous and parallel transmissions such that interference is fully avoided. This reduces the number of interfering nodes and leads to better usage of the scarce limitation of resources in these networks, larger network lifetime, higher energy savings and higher throughput. In fact, the aim of this protocol is to mitigate interference along with maintaining minimum power consumption in order to maximize network lifetime and increase the spatial reuse and throughput of each WBAN. Simulation results show that our approach achieves a much higher spatial reuse using the smart spectrum allocation scheme for interference mitigation in collocated WBANs. We conduct extensive simulations for coexistence prediction in different mobility scenarios using the NS-2 simulator. Consequently, we demonstrate the efficiency of the proposed protocol in providing interference-free channel assignments and higher energy savings.

An integrated space/air information network is a convergence of satellite communication networks in space regions and aircraft communication networks in air regions. It supports direct internal information interactions. Along with the extensive application of the integrated space/air information network especially in military fields, its routing problem becomes a key point of research. However, the existing routing algorithms are mainly designed for the space or air region separately and there is little study on the generalized routing for the integrated space/air information network. In this paper, we propose a Hybrid time-space Graph based Hierarchical Routing (HGHR) scheme for this integrated network. The hybrid time-space graph includes two subgraphs: a deterministic one and a semi-deterministic one. As satellite orbits are pre- known in the space region, we introduce a deterministic time-space subgraph. While each aircraft has a cyclic movement with the predictable contact probability and contact time in the air region, we construct a semi-deterministic time-space subgraph according to the prediction results of a discrete time homogeneous semi-Markov model. Simulation results show that HGHR has good performance in terms of data delivery ratio and end- to-end delay.

The research on smart grid security has led to several important results. Nevertheless, previous works neglect to consider the time-varying properties of smart grid, which in many attack strategies play a pivotal role. Mainly because of the time-varying collaboration among communication agents and the time-varying confrontation between defenders and attackers, smart grid communication network should not be considered as a static entity. Understanding the smart grid resilience through time-varying analysis is thus crucial to both the grid protection and to the design of new countermeasures against cyber threats. In this work we attempt to bring time dimension into smart grid security analysis. In each time step, attackers determine the attack targets based on a 2-state Markov process. Defenders (smart meters) are empowered with two defense actions: either to monitor their neighbors which requires more resources but yields timely discovery of attacks, or not to monitor their neighbors which saves resources but leads to slow response to attacks. Defenders choose between the two by estimating the attack probability based on previous time slots. The objective of defenders is to minimize their defense cost. Simulation results are provided to demonstrate the superior performance of the proposed scheme over the conventional methods.

The Long-Term Evolution (LTE) network has become an attractive candidate for Smart Grid (SG) communications. In spite of LTE's growing popularity as a means of transporting SG data, it was primarily designed for high speed and high capacity mobile data traffic such as video streaming and Internet. Typically there are more than 10,000 Smart Meters (SMs) per eNode-B, bombarding it with billions of tiny fixed-size packets containing power consumption data. All SMs need to make a connection to transmit an SM packet and they need a scheduling and Resource Block (RB) request that follows the LTE standard. In this paper, we introduce SM data transmission via LTE control signaling in order to conserve resources at the eNode-B (physical channels, scheduling and RBs). With our proposed mechanism, the results show that SM packets can be sent via a control signal with no scheduling or RB usage required at the eNode-B. Simulation results show that usage of the Physical Uplink Share CHannel (PUSCH) usage is decreased 40 percentage points. Physical Downlink Share CHannel (PDSCH) and Physical Downlink Control CHannel (PDCCH) usage are reduced by 2 percentage points when compared with LTE standard.

Cooperative transmission between Primary licensed Users (PUs) and Secondary unlicensed Users (SUs) in cognitive radio networks aims to improve the probability of transmission opportunities. This paper investigates the problem of finding the best match for each PU among a set of available SUs and allocating optimum power to them. The proposed method in this paper works only with local information, due to the unavailability of global Channel State Information (CSI). Using a graph theoretical approach and applying principle of contract theory lead us to a distributed solution that works under asymmetry of information. Simulation results are carried out to evaluate the network throughput versus different number of PUs and SUs. We finally compare the performance results with a benchmark scenario.

There is growing interest in new physical-layer transmission methods based on known-interference cancellation (KIC). These KIC-based methods share the common idea that the interference can be cancelled when the bit-sequence of it is known, which can improve the efficiency of wireless data communications. Existing work on KIC mainly focuses on single-hop or two-hop networks, with physical-layer network coding (PNC) and full-duplex (FD) communications as typical examples. This paper extends the idea of KIC to multi-hop networks, and proposes a bidirectional end-to-end KIC (BE2E-KIC) transmission method for the scenario where two nodes intend to exchange packets through multiple intermediate nodes. With BE2E-KIC, the involved nodes can simultaneously transmit and receive on the same channel. We first discuss the procedure of BE2E-KIC and provide a theoretical analysis on its feasibility and effectiveness. Then, we propose a medium access control (MAC) scheme that supports BE2E-KIC, which schedules packet transmissions in more realistic cases with the presence of packet-loss. Simulation results illustrate that BE2E-KIC can improve the network throughput and reduce the end-to-end delay compared with other existing transmission methods.

As the Smart Grid matures, the number of User Equipment (UE) required for monitory and control applications will continue to rise. This will increase the traffic in an already burdened telecommunications network. This research aims to optimally allocate radio resources to UEs such that the Smart Grid Quality of Service requirements are satisfied, with minimal effect on pre-existing telecommunication traffic caused by the imposition of Smart Grid UEs. To address this resource allocation problem, a Lotka-Volterra based resource allocation algorithm and scheduler was developed due to its ability to easily adapt to the dynamics of a telecommunications environment. Each `species' in the Lotka-Volterra scheme is modelled as either Data, Voice or Smart Grid traffic class in a Rayleigh-Fading channel. Unlike previous resource allocation algorithms, the Lotka-Volterra scheme allocated telecommunications resources to each class as a function of its growth rate. Subcarrier power allocation is non-uniform and the total power allocated to each class is based on the size of its queued traffic. By doing so, the Quality of Service requirements of the Smart Grid are satisfied, with minimal effect on pre-existing traffic. Class queue latencies are reduced by intelligent scheduling of periodic traffic and forward allocation of resources. Results also proved that for high traffic situations, the Lotka-Volterra resource allocation algorithm and scheduler provided a high degree of fairness between traffic classes, whilst ensuring minimum latency requirements of Smart Grid data.

This paper presents a novel primitive for self-organization amongst multiple coexisting Wireless Body Area Networks (WBANs). It follows a biologically inspired approach based on the theory of pulse-coupled oscillators. Our proposal allows for coexisting WBANs to use delayed information from previous transmissions to adjust to a collision-free TDMA schedule amongst each other for future communications. Most importantly, it does not require a global coordinator as all nodes achieve synchronization in a completely self-organized manner. Simulation results show that our protocol achieves a significantly fast convergence time despite little information from its coexisting networks. Moreover, the proposed approach is shown to be robust to variations in channel conditions, density of sensor nodes within each network and the number of coexisting WBANs. We conduct extensive simulations to evaluate the efficiency of the proposed protocol using the NS-2 simulator.

Resource allocation is an important topic with a wide range of applications. In many practical cases, users and resource suppliers are players in the market. As a result, much effort has been made in applying market mechanisms (such as auction and game-theoretic results) to resource allocation. The conventional approach in such studies is to consider cases where users' resource demands are fixed. However, in practice, resource demands are often elastic, which can be related to the quality of experience (QoE) that the user receives. We consider elastic resource demands in this paper, and propose a double auction and negotiation (DAN) scheme, which includes a conventional auction stage as well as a negotiation stage, where the latter allows users to dynamically adjust their demands. The proposed DAN scheme not only allows more users to get access to some amount of resource (thereby avoiding users becoming completely disconnected), but also increases the payoff of resource suppliers, as is confirmed by simulations. We also discuss the conditions of having Nash equilibrium in the users' resource demands and suppliers' pricing in the negotiation stage.

Emerging vehicular applications and their implementation are still research challenges due to highly dynamic nature of vehicular networks. Nevertheless, asynchronous communication provides support for successful implementation in such unique characteristic networks. In this paper we propose an approach that integrates the application middleware operations with multicast routing to develop a dynamic dissemination framework. The method aggregates content-based subscriptions in the form of a compact data format using Binary Decision Diagram and extends Spatio-temporal Multicast Routing Protocol to route the events in the network. Simulation results present a comparative performance of the filtering techniques applied in the framework in terms of the network overhead and delivery ratio. Results also present the comparison of the matching time of subscription queries with the event publication messages.

Wireless body area networks (WBANs) are intelligent wireless monitoring systems, consisting of wearable, and implantable computing devices on or in the human body. They are used to support a variety of personalized, advanced, and integrated applications in the field of medical, fitness, sports, military, and consumer electronics. In a WBAN, network longevity is a major challenge due to the limitation of the availability of energy supply in body nodes. Therefore, routing protocols can play a key role towards making such networks energy efficient. In this work, we exhibit that a routing protocol together with an effective body node coordinator (BNC) deployment strategy can influence the network lifetime eminently. Our initial work shows that the variation in the placement of a BNC within a WBAN could significantly vary the overall network lifetime. This motivated us to work on an effective node placement strategy for a BNC, within a WBAN; and thus we propose three different BNC placement algorithms considering different features of available energy efficient routing protocols in a WBAN. Our simulation results show that these algorithms along with an appropriate routing protocol can prolong the network lifetime by up to 47.45%.

The conventional approach for collecting data in sensor networks is the combination of automatic repeat-request techniques with a collection tree scheme in which all routes end up in the sink node. A high number of acknowledgments and retransmissions is the main drawback of this scheme. Erasure-correcting codes, in particular Fountain codes, can be employed to reduce the number of retransmissions. In the collection tree scheme, it is common for multiple routes to share a bottleneck. In such scenarios, to reach the optimal network throughput, it is necessary to combine the Fountain codes and Network Coding (NC) technique. Y-networks can be considered as an abstraction to this scenario. This article proposes a new algorithm, namely, Adaptive Distributed LT code (ADLT), for combining LT codes with NC in Y-networks. The ADLT algorithm enables belief propagation decoding by employing a novel technique called degree distribution updating, to preserve Robust Soliton degree distribution at destination. Unlike previously proposed algorithms in the literature, the ADLT algorithm has the flexibility to handle any number of sources with different block sizes and transmission rates, where sources perform standard LT coding. Simulation results confirm that the performance of the ADLT algorithm is close to that of standard LT code.

Energy efficiency is one of the top priorities for future cellular networks, which could be accomplished by implementing cooperative mechanisms. In this paper, we propose three eNB-centric energy saving cooperation techniques for LTE systems. These techniques named as intra-network, inter-network and joint cooperation, involve traffic-aware intelligent cooperation among eNBs belong to the same or different networks. Our proposed techniques dynamically reconfigure LTE access networks in real-time utilizing less number of active eNBs and save energy. In addition, these techniques are distributed and self-organizing in nature. Analytical models for evaluating switching dynamics of eNBs under these cooperation mechanisms are also formulated. We thoroughly investigate the proposed system under different number of cooperating networks, traffic scenarios, eNB power profiles and their switching thresholds. Optimal energy savings while maintaining QoS is also evaluated. Results indicate a significant reduction in network energy consumption. System performance in terms of network capacity utilization, switching statistics, additional transmit power and eNB sleeping patterns is also investigated. Finally, a comprehensive comparison with other works is provided for further validation. Index Terms Energy efficiency; cooperative cellular networks; self-organizing networks; LTE networks I.

The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.

This paper proposes a novel adaptive internetwork interference mitigation scheme, namely AIM, for environments with multiple coexisting Wireless Body Area Networks (WBANs). The proposed scheme, operating on a nodes' traffic priority, packet length, signal strength and density of sensors in a WBAN, makes three major contributions as compared to the current literature. Firstly, it considers node-level interference for internetwork interference mitigation rather than considering each WBAN as a whole. Secondly, it allocates synchronous and parallel transmission intervals for interference avoidance in an optimal manner. Finally, it significantly reduces the number of orthogonal channels assigned to achieve a higher throughput as well as better usage of the scarce limitation of resources in WBANs. Simulation results show that our protocol achieves a significantly higher spatial reuse compared to existing approaches for interference mitigation in WBANs.

Cognitive radio is a promising paradigm to improve utilization of the radio frequency spectrum. In this work, we study a contract-based mechanism for sharing spectrum among primary licensed user (PU) and secondary unlicensed users (SUs). We assume that SUs cooperate in PU signal transmission. However, there are some challenges that the PU should overcome. First, considering the fact that nodes are selfish, brings the necessity of a reimbursement method to remunerate the helping SUs. Second, SU hides its private channel state information from other nodes, which results in asymmetry of information. The proposed contract-theory based framework provides a solution for spectrum allocation under asymmetric channel information. We compare the performance results with a benchmark scenario and we show through simulation results that both the PU and the SU gain positive revenue.

This paper investigates the problem of linear spatial collaboration for distributed estimation in wireless sensor networks. In this context, the sensors share their local noisy (and potentially spatially correlated) observations with each other through error-free, low cost links based on a pattern defined by an adjacency matrix. Each sensor connected to a central entity, known as the fusion center (FC), forms a linear combination of the observations to which it has access and sends the resulting signal to the FC through an orthogonal fading channel. The FC combines these received signals to find the best linear unbiased estimator of the vector of unknown signals observed by individual sensors. The main novelty of this paper is the derivation of an optimal power-allocation scheme in which the coefficients used to form linear combinations of noisy observations at the sensors connected to the FC are optimized. Through this optimization, the total estimation distortion at the FC is minimized, given a constraint on the maximum cumulative transmit power in the entire network. Numerical results show that even with a moderate connectivity across the network, spatial collaboration among sensors significantly reduces the estimation distortion at the FC.

Performance evaluation of communication protocols is usually carried out using typical network metrics as delay, jitter, or goodput. However, recent studies in the context of Inter-Vehicle Communication (IVC) have shown that using these metrics is not sufficient for evaluating vehicular safety applications. To highlight the importance of safety metrics and their applicability, we extended our existing simulation framework Veins to visualize these metrics live while the road traffic and network simulation are running in parallel. In particular, we demonstrate the impact of communication on intersection assistance applications. To simulate different intersection approaches, we implemented a simulation model that resembles different kinds of driver behavior and enables crashes at intersections. The resulting situations are displayed in the road traffic simulator and give the visitor insights on the current state of endangered vehicles. Furthermore, an autonomous controller has been implemented which tries to avoid accidents and hence shows the real-world impact, i.e., accidents can be avoided using advanced beaconing techniques. To increase interactivity of the demo, visitors will have the possibility to interact with and take control over endangered vehicles.

Cellular networks are becoming heterogeneous due to the deployment of various low-powered base stations (BSs) within the macro cells. This introduces different classes of BSs and increases the BS density which creates complexity in interference analysis. Recent heterogeneous cellular network models consider Poisson Point Process (PPP) distribution for all BSs within the network for performance analysis. Although this type of consideration can lead to unrealistically close deployment of BSs, it is argued to still provide a close lower bound with a realistic scenario. In this work, we consider a grid-based deployment for macro BSs to ensure a minimum separation between them and randomly distributed low-powered BSs within the macro cells. We analyze the co-channel interference in such network for a macro cell user and evaluate coverage performance for downlink transmission under closed access. Instead of considering the actual distance of every user for network performance evaluation, we use a average user distance based on the user distribution. This simplifies the analysis while mostly providing a higher coverage probability than the PPP distributed models.

Wireless Body Area Network (WBAN) is an intelligent wireless network of wearable computing devices, supporting a variety of personalized, advanced and integrated applications in the field of medical, fitness, sports, military and consumer electronics. In WBAN, network longevity is a major challenge due to the limitation in the availability of energy supply in those devices, where routing protocol plays a key role towards making such networks energy efficient. In this paper, we have proposed a probabilistic routing protocol for heterogeneous WBANs, dubbed as probabilistic energy-aware routing protocol (PER), which enables a selective utilization of relay node, in an energy effective manner, to prolong the network lifetime. The delivery predictability of PER is estimated based on the available energy, path loss and traffic congestion of a node. The comparative analysis of our simulation based results also show the prospective consistency of PER, in term of network longevity.

Cooperation in wireless systems requires providing proper incentives. Furthermore, relay nodes may not be willing to reveal their channel state information to others, which will result in asymmetry of information. This paper integrates contract theory and auction mechanism to stimulate relays to cooperate and maximize utility of both the source node and the relay node. In the presence of asymmetric information, a novel power allocation and price assignment algorithm is proposed which takes into account the individual power constraint of nodes. The proposed solution is distributed and easy to implement with negligible signaling overhead. Simulation results are presented to quantify the performance of the proposed algorithm.

Emerging vehicular applications and its efficient dissemination is still a research challenge due to highly dynamic nature of these networks. Nevertheless, asynchronous communication can support such unique characteristic. The proposed approach in this paper combines publish/subscribe semantics with multicast routing to develop a dissemination framework for vehicular networks. The method aggregates content-based subscriptions in the form of a compact data format using Binary Decision Diagram and extends Spatio-temporal Multicast Routing Protocol to create a dynamic dissemination mesh. It optimizes routing using advertisements semantic to enhance the scalability of the framework. Similar subscriptions are grouped using clustering to form multicast groups. The framework has been evaluated in terms of the delivery ratio and network overhead metrics in different networking conditions. The performance of the proposed approach is also compared with subscription semantics based routing. The results show that advertisement based optimization shows considerable increase in scalability when compared to the use of subscriptions semantics.

Since their invention Luby Transform (LT) codes have been regarded as an efficient capacity achieving channel code over binary erasure channels. Majority of the previous research is focused on designing the degree distribution of LT codes which are asymptotically optimal. Nevertheless, deigning the optimal degree distribution of LT codes for finite and small message lengths remains as an open problem. This work addresses the degree distribution optimization of LT codes over binary erasure channel. Based on AND-OR tree analysis of Belief Propagation (BP) algorithm, a new formulation of the problem in the form of standard semidefinite programming is presented. The new formulation is free of any approximation. The obtained semidefinite program is reduced to a linear program where its numerical solution is feasible for reasonable message lengths. Simulations confirm that the optimized degree distributions outperform Robust Soliton distribution, both in terms of overhead and Encoding/Decoding complexity.

Despite the complex cellular network models, when it comes to analytical techniques for modeling intercell interference, researchers often resort to simpler network models. The conventional network models such as the simple Wyner model including its modifications have been widely used for this purpose. However, all these network models including the popular grid based one-dimensional model have always considered users to be uniformly distributed within cells for simplicity. However, in a real cellular network users are not always uniformly distributed specially due to hot-spot regions and capacity centric deployment of base stations. Unfortunately, these popular network models are not capable of considering such non-uniform user distribution scenarios. As the performance evaluation of a cellular network will greatly depend on the cell's user distribution, we propose a relay network model for downlink transmission which considers both uniform and non-uniform distribution of users. We compare the coverage performance of our proposed model with the complex grid model for a non-uniform user distribution scenario. We also show that using relays opportunistically near the cell boundary can improve coverage performance of a cell edge user to a great extent.

Self-Organizing Network (SON) is a key feature of the Long Term Evolution (LTE) system. SON introduces several Self-Optimizing features for the LTE's Evolved Universal Terrestrial Radio Access Network (EUTRAN), thereby improving performance, flexibility and lowering costs through network intelligence, automation and network management. Mobility Load Balancing (MLB) is one such feature that has recently attracted much interest. According to the definition of the third Generation Partnership Project (3GPP), a successful MLB mechanism must be capable of optimizing the radio, hardware and transport network resource of an eNodeB. Thus far, the existing proposals have only addressed the radio resource aspect. Therefore, this paper proposes an MLB mechanism fully capable of optimizing both radio and transport network loads of an eNodeB. The proposed MLB mechanism is based on an ecologically inspired load optimization algorithm developed for equitable load distribution in a multi-resource environment. Optimal load conditions required for the stability of an eNodeB could be accurately estimated for both radio and transport interfaces by the proposed MLB algorithm. Analytical proof and simulation results are provided for supporting the above proposed model.

Cooperation in complex networks is an interdisciplinary topic of recent interest. Vehicular ad hoc networks (VANETs), a class of such complex self organizing networks have recently emerged as a platform to develop cooperative communication systems. The evolution of cooperation in these networks depend on the influence from its neighbors. Hence the impact of node neighborhood in terms of degree correlation is important for the evolution of cooperation in such networks. This paper formulates a Public Goods Game group interactions model for packet forwarding in vehicular networks and analyze the impact of degree correlations on the evolution of cooperation. Simulation results show that in highly assortative vehicular networks, cooperation appears to evolve around cliques of similar degree patterns. However, diffusion of cooperation is higher in the neighborhood of higher degree nodes as they can easily influence the strategy of their neighbors. On the other hand, cooperation rarely evolves among low and medium degree nodes as their strategies cannot be sustained over time.

The establishment of a previously non-existent data class known as the Smart Grid will pose many difficulties on current and future communication infrastructure. It is imperative that the Smart Grid, as the reactionary and monitory arm of the Power Grid, be able to communicate effectively between grid controllers and individual UEs. Like most wireless sensor networks (WSN), the data sent by individual UEs has limited usefulness and precision. Collection of a large amount of data produces information that is useful to the system and which can be acted upon. However, this increases the communication traffic in an environment where communication traffic from other mobile users is already high. By ensuring effective communications between Distributed Generators and the Smart Grid, renewable resources that are subject to large fluctuations can be utilized more effectively and efficiently. This research proposes that a Proportional Fairness Algorithm, when combined with Lotka-Volterra Population Theory, will ensure fair bandwidth allocation for all User Equipment, whilst guaranteeing Smart Grid operating constraints such as minimal latency. Furthermore, the optimization of the bandwidth allocation maximizes Smart Grid Quality of Service, while also minimizing the decrease in Non-Smart Grid UE Quality of Experience.

Selection of relays is central to efficient utilization of cooperative diversity gains when multiple relays are available in the network. Such selection is generally based on some form of channel state information (CSI), which is always imperfect in practice. The effects of using imperfect CSI in a relay selection process have been generally considered in existing literature without the account for spatial distribution of relays, while current works on relay selection in random networks mainly assume perfect CSI. In this paper, we analyze the outage performance of a single source–destination pair communicating through a decode-and-forward relay, chosen from a Poisson point process (PPP) of candidate relays using perfect and imperfect CSI. We derive exact outage probability expressions for the selection cooperation strategy. Closed-form expressions are provided for special cases, and asymptotic analysis is conducted to highlight the high-SNR system behavior.

This paper proposes a novel middleware for service selection in mobile ad-hoc networks with a particular focus on scenarios immediately and subsequently afterwards an emergency. The proposed middleware, operating on a mobile user's hand-held device and collecting the user's contexts through machine-to-machine connectivity, has three major contributions as compared to the current literature. The middleware, based on the collected contexts, firstly classifies a service request, e.g., safety-related or comfort service. Then, it initiates the required network connectivity for service discovery, i.e., the ad-hoc connectivity when an infrastructure-based network is inaccessible. Finally, the middleware selects the service based on a method specifically proposed for that service category, including pre-defined realistic user contexts, to access it. The simulation results show that the middleware achieves up to 12 percent higher success rate compared to the minimum hop count service selection method. Compared with the same method, it has a response time up to 50 percent lower for the safety-related but 80 percent higher for the comfort services. Yet, the middleware attains up to 96 percent user satisfaction rate for both service classes.

A global trend toward large scale emergencies has placed an emphasis on the achievement of a ubiquitous public safety network. Such a network may be realized over a mobile ad hoc network formed by the handheld mobile devices. Therefore, traffic in the network can be user generated and thus semantic. Unfortunately, none of the traffic management techniques proposed for the underlying network considers the semantic properties of the generated traffic. Therefore, in this paper, we propose a semantic traffic management framework which has two modules: traffic monitoring unit and traffic reduction unit. Although the first module analyzes the semantic traffic to detect an emergency, the latter module removes redundant semantic information for traffic reduction. We have supported the feasibility of the proposed semantic framework through simulation. Simulation results suggest that the framework is capable of accurate and early detection of an emergency as well as traffic reduction while keeping sufficient information to report the emergency. Copyright © 2012 John Wiley & Sons, Ltd.

Precise resource allocation such as best relay selection or optimum power allocation improves performance in cooperative communications. In this paper, we propose a strategy to select the best relay nodes in decode-and-forward parallel cooperative relay networks.We apply a contract theoretic framework over the wireless system to formulate an incentive compatible contract. The designed contract would not just stimulate relays to cooperate, but also incite relays to reveal their channel state information in a trustworthy manner. The proposed relay selection scheme is based upon minimizing the cost for the source node to send data to the destination node. However, the source node wishes to maintain a certain transmission rate to the destination node, although there is a constraint imposed on each relay node's power. Simulation results show that the proposed scheme can improve utility of the source node, compared with the nearest neighbor relay selection algorithm.

Network modeling gives an insight into the performance of the main parameters of a system. One of the important performance metrics in cellular networks is outage probability. The conventional model that has been widely used to analyze cellular networks is the Wyner model. This model became popular due to its simplicity which included fixed user locations and deterministic, homogeneous interference intensity. However, in a real cellular network, the user locations are random and interference level varies throughout a cell. Based on these aspects, the Wyner model was extended to incorporate fading, shadowing etc. Recently, these models are compared with a grid model which considers random user locations, path loss, fading etc. It is shown that the Wyner model and its modifications cannot precisely demonstrate the performance metrics of a cellular system as compared to the grid model. However, the grid model is quite complex as it considers all the parameters for every user locations within a cell as well as adjacent cells. In this paper, we show that using a simple averaging in the complex grid model can provide a good approximated result, better than that of the Wyner model, yet much less complex than the grid model.

Outage performance of network topology-aware opportunistic relay selection is studied using stochastic geometry techniques with the focus on the impact of different levels of channel state information (CSI) available at relays. Specifically, two scenarios with either (a) exact instantaneous or (b) only statistical CSI are compared with an explicit account for both small-scale Rayleigh fading and path loss due to random node locations. Analytical and simulation results suggest that although similar diversity order can be achieved in both cases, the lack of precise CSI to support relay selection translates into significant increase in the power required to achieve the same level of QoS. In addition, when only statistical CSI is available, achieving high diversity order is possible by employing multiple relays at the cost of performance degradation at low SNR due to splitting of system resources.

In an effort for reducing infrastructure configuration, operations and maintenance costs, the 3rd Generation Partnership Project (3GPP) has standardized a set of functions called Self Organizing Networks (SON) from Releases 8 onwards. Mobility Load Balancing Optimization (MLBO) is one such SON feature that has recently attracted much interest. According to 3GPP, a successful MLBO algorithm must be capable of optimizing the radio and transport network loads. Unfortunately, the existing proposals have not been able to successfully address this requirement. Therefore, for the first time, this paper proposes an MLBO algorithm fully capable of optimizing both radio and transport network loads of an eNodeB. The underlying algorithm uses an ecologically inspired graphical theory for equitable resource/load distribution in a multi-resource, multi-class environment. Optimal load levels required for the stability of the eNodeB could be accurately estimated by the proposed eco-inspired graphical theory. Analytical proof and simulation results are provided for supporting the above argument.

Energy efficiency is one of the central concerns for future cellular networks, which could be accomplished by adaptively reconfiguring the networks with the changing traffic environment. In this paper, we propose and analyze a dynamic sectorization scheme of evolved node Bs (eNBs) in long term evolution (LTE) systems for saving energy at radio access network (RAN) level. Under the proposal, based on the instantaneous traffic, each eNB dynamically reconfigures itself in real-time utilizing minimum number of sectors, while maintaining the quality of service (QoS). The proposed technique is implemented in each eNB requiring no correspondence with other eNBs, while no human intervention is required as well. Thus, the proposed technique is of distributed and self-organizing in nature. We thoroughly investigate the performance of the proposed system under a wide range of traffic scenarios, eNB power profiles, user distributions and QoS requirements. Simulation results indicate a significant reduction in network energy consumption. Furthermore, the proposed technique is simple, but effective and easy to implement. Comparison with other research is also presented, which proves the superiority of our proposal.

The number of emergencies has been observed to increase exponentially in recent years. Mobile devices, being ubiquitously present with users, often found to be a preferred way of communication in these emergencies. Since realistic prediction of user movements in emergencies is still an open issue, recently a machine to machine (M2M)-based movement prediction framework has been proposed. The proposed framework considers user's behavioral changes as well as simple geographical constraints initiated by emergencies. Nevertheless, these considerations are not sufficient to present a practical emergency environment and thus realistically predict emergency-affected user movements. Consequently, this paper proposes a modified M2M-based framework for movement prediction. The modified framework considers not only user's behavioral changes but also following factors: a realistic pre-emergency mobility model, arbitrarily-shaped obstacles, a signal propagation model and a practical personal belief computation. Simulation results have been used to verify effectiveness of the proposed framework.

Physical-layer network coding (PNC) is a promising approach for wireless networks. It allows nodes to transmit simultaneously. Due to the difficulties of scheduling simultaneous transmissions, existing works on PNC are based on simplified medium access control (MAC) protocols, which are not applicable to general multihop wireless networks, to the best of our knowledge. In this paper, we propose a distributed MAC protocol that supports PNC in multihop wireless networks. The proposed MAC protocol is based on the carrier sense multiple access (CSMA) strategy and can be regarded as an extension to the IEEE 802.11 MAC protocol. In the proposed protocol, each node collects information on the queue status of its neighboring nodes. When a node finds that there is an opportunity for some of its neighbors to perform PNC, it notifies its corresponding neighboring nodes and initiates the process of packet exchange using PNC, with the node itself as a relay. During the packet exchange process, the relay also works as a coordinator which coordinates the transmission of source nodes. Meanwhile, the proposed protocol is compatible with conventional network coding and conventional transmission schemes. Simulation results show that the proposed protocol is advantageous in various scenarios of wireless applications.

We propose a distributed cooperative framework among base stations (BS) with load balancing (dubbed as inter-BS for simplicity) for improving energy efficiency of OFDMA-based cellular access networks. Proposed inter-BS cooperation is formulated following the principle of ecological self-organization. Based on the network traffic, BSs mutually cooperate for distributing traffic among themselves and thus, the number of active BSs is dynamically adjusted for energy savings. For reducing the number of inter-BS communications, a three-step measure is taken by using estimated load factor (LF), initializing the algorithm with only the active BSs and differentiating neighboring BSs according to their operating modes for distributing traffic. An exponentially weighted moving average (EWMA)-based technique is proposed for estimating the LF in advance based on the historical data. Various selection schemes for finding the best BSs to distribute traffic are also explored. Furthermore, we present an analytical formulation for modeling the dynamic switching of BSs. A thorough investigation under a wide range of network settings is carried out in the context of an LTE system. Results demonstrate a significant enhancement in network energy efficiency yielding a much higher savings than the compared schemes. Moreover, frequency of inter-BS correspondences can be reduced by over 80%.

Recently, physical-layer network coding (PNC) attracts much attention due to its ability to improve throughput in relay-aided communications. However, the implementation of PNC is still a work in progress, and synchronization is a significant and difficult issue. This paper investigates the feasibility of synchronous PNC with M-ary quadrature amplitude modulation (M-QAM). We first propose a synchronization scheme for PNC. Then, we analyze the synchronization errors and overhead of potential synchronization techniques, which includes phase-locked loop (PLL) and maximum likelihood estimation (MLE) based synchronization schemes. Their effects on the average symbol error rate and the goodput are subsequently discussed. Based on the analysis, we perform numerical evaluations and reveal that synchronous PNC can outperform conventional network coding (CNC) even when taking synchronization errors and overhead into account. The theoretical throughput gain of PNC over CNC can be approached when using the MLE based synchronization method with optimized training sequence length. The results in this paper provide some insights and benchmarks for the implementation of synchronous PNC.

Count-of-beacon (COB) is a new fundamental radiolocation technique which is resistant to the nonexistence of line-of-sight path. However, the way that cells are populated has a direct impact on its performance. This paper studies the statistical behavior of the COB-based distance measurement error in a randomly populated cellular mobile radio system for a variety of distances. The attributes of the distribution are then characterized in view of the density of nodes and the real distance value. The closed-form expression of the Cramer-Rao lower bound for the COB technique is also obtained using the acquired statistical description of the distance error. Finally, numerical examples are presented to provide an insight into the performance of the COB technique.

Energy efficiency and self-organizing network architectures are the top issues for future cellular systems. In this paper, we propose an energy saving self-organizing access network architecture for long term evolution (LTE) cellular systems. Self-organizing nature is attained through intelligent cooperation among the evolved node Bs (eNBs) communicating via X2 interface in the LTE evolved UMTS terrestrial access network (E-UTRAN). Using the proposed coordination and cooperation, the E-UTRAN is dynamically reconfigured in real-time utilizing minimum number of active eNBs and switching the redundant eNBs into sleep mode. System performance under different network scenarios, cell layouts and eNB power consumption profiles is evaluated. Simulation results indicate a substantial drop in the network's energy consumption. In addition, the network becomes more efficient in utilization of its available capacity.

Physical-layer network coding (PNC) brings throughput improvement for wireless networks. However, its synchronization requirement is widely recognized as an obstacle to its implementation. In this paper, we focus on phase-level synchronization and propose a time-slotted carrier synchronization scheme for PNC. We then analyze the phase error tolerance of PNC under different bit error rate (BER) requirements, and the synchronization overhead for obtaining synchronous signals below the phase error margin. We also consider the impact of different hardware (in particular, the phase-locked loop) parameters on the overhead in our analysis. Afterwards, we evaluate the performance of the proposed synchronization scheme with simulations. The results show that the proposed scheme is feasible with some typical hardware parameters. The throughput gain of PNC when using the proposed scheme is only slightly lower than the theoretical gain.

The denoise-and-forward (DNF) method of physical-layer network coding (PNC) is a promising approach for wireless relaying networks. In this paper, we consider DNF-based PNC with M-ary quadrature amplitude modulation (M-QAM) and propose a mapping scheme that maps the superposed M-QAM signal to coded symbols. The mapping scheme supports both square and non-square M-QAM modulations, with various original constellation mappings (e.g. binary-coded or Gray-coded). Subsequently, we evaluate the symbol error rate and bit error rate (BER) of M-QAM modulated PNC that uses the proposed mapping scheme. Afterwards, as an application, a rate adaptation scheme for the DNF method of PNC is proposed. Simulation results show that the rate-adaptive PNC is advantageous in various scenarios.

The presence of non-line-of-sight (NLOS) propagation poses significant problems in positioning a mobile terminal within cellular communication systems. Count-of-Beacon (COB) is a new radiolocation technique where the existence of NLOS propagation does not degrade the precision of the estimated position. However, non-uniformity of subscribers' distribution negatively affects the performance of the COB technique. The Density_Map of each cell can be used to lighten the impact of nonuniform distribution of subscribers. However, the accuracy of the Density_Map is highly reliant on the propagation conditions of the wireless environment. This paper analyzes the sensitivity of the Density_Map to the deviation of the attenuation that signals experience in shadow fading environments. This analysis is then used to redefine the Density_Map. Simulation results are used to verify the validity of the new defined map when the environment is suffering from high shadowing.

In this paper, we propose a cooperative and self-organizing energy efficient access network framework for heterogeneous cellular systems. Intranetwork and internetwork cooperation are jointly employed for optimizing the energy savings. In intranetwork cooperation, based on the instantaneous traffic, base transceiver stations (BTSs) in a network cooperatively and intelligently swap traffic, switch operating modes, and thus conserve energy. While, using internetwork cooperation, multiple networks are engaged cooperatively to share their BTSs for further optimizing energy savings. Comprehensive simulations using realistic traffic patterns are carried out for different schemes and network scenarios. Results identify that the proposed joint cooperation can achieve significant energy savings, which is much higher than if the intranetwork and the internetwork cooperation are applied separately. Moreover, the proposed framework and the algorithms are sufficiently self-organizing, i.e., they are autonomous in decision making and implementation process.

Recent theoretical studies of physical-layer network coding (PNC) show much interest on high-level modulation, such as M-ary quadrature amplitude modulation (M-QAM), and most related works are based on the assumption of phase synchrony. The possible presence of synchronization error and channel estimation error highlight the demand of analyzing the symbol error rate (SER) performance of PNC under different phase errors. Assuming synchronization and a general constellation mapping method, which maps the superposed signal into a set of M coded symbols, in this paper, we analytically derive the SER for M-QAM modulated PNC under different phase errors. We obtain an approximation of SER for general M-QAM modulations, as well as exact SER for quadrature phase-shift keying (QPSK), i.e. 4-QAM. Afterwards, theoretical results are verified by Monte Carlo simulations. The results in this paper can be used as benchmarks for designing practical systems supporting PNC.

In this paper an algorithm for distributed data storage in large-scale sensor networks has been proposed. The main objective is to distribute the generated information throughout the network to increase its lifetime. At the same time it is desired that the original data can be recovered later by collecting the contents of a limited number of sensor nodes. In the scenario considered here the sensor nodes have limited energy and memory and each sensor saves only one encoded packet. Also sensors do not hold any routing table and no global information regarding network topology is available. The algorithm proposed here is based on Raptor codes and it inherits their linear time encoding and decoding complexity. Random walks are used for disseminating data amongst sensor nodes. Major benefits of the algorithm presented here is that it utilizes the online decoding property of Raptor codes by achieving the desired code degree distribution and for estimating the required global information regarding network topology, no excessive random walks and transmissions are used.

Of late, Vehicular Networks (VANETs) applications is capturing major importance in the research field. Efficient dissemination becomes demanding when information explosion overloads the network. Development of an efficient event disseminating framework is necessary to support such highly dynamic, self-organizing networks. Event broker middleware provides seamless messaging in heterogeneous environments making it most suitable for VANETs. In light of this, the paper proposes an approach that accumulates content-based subscriptions in a compressed structure using Binary Decision Diagrams (BDDs) encoding and extends VANET's Spatio-temporal Multicast Routing Protocol (SMRP) to use the framework from middleware tier to build a most favourable dissemination mesh.

The ever-growing popularity of handheld mobile devices has been accelerated by their ubiquitous presence to support user mobility. In mobile environments, a prior prediction of user movements can improve both network and application-level performances. In the existing literature, a major research on mobility has focused on user behavior to predict their movements. Although emergency-affected users are more likely to deviate from their usual behavior, the influence of an emergency on user behavior and thus user movements has not been investigated until now. However, an accurate user movement prediction in emergencies is often more important due to post-emergency resource constrains in the network. Being closely carried by their users, mobile devices can be conveniently employed to monitor user behavior in emergencies and consequently predict user movements. Therefore, this work proposes a movement prediction framework for mobile users in emergencies using a machine-to-machine (M2M) network of mobile devices. Simulation results exhibit that the prediction accuracy reaches up to 100%, i.e, predicted movements by the proposed framework are exact to the real movements.

Cooperative communication when other mobile nodes could act as potential relays, poses significant challenge in designing relay selection algorithms due to the information asymmetry between the source and the potential relays. The potential relays in this case are better informed about their channel conditions than the source. Therefore, the source must provide them with incentive to reveal their information and hence for their participation in relaying. In this paper, we study the problem of relay selection under a budget constraint when there is such an information asymmetry. By leaving the bargaining power only to the source, we propose a simple relay selection mechanism that requires limited interaction with potential relays and their participation is also voluntary. We will first use contract theory to formulate incentive compatible contracts that can be broadcasted to nearby mobile nodes which can act as potential relays. Once these mobile nodes notify the contracts they are willing to accept, source then optimally selects a set of relays based on this information. We analyze the performance of this mechanism by simulations and discuss the effect of information asymmetry.

In this paper, by exploiting the availability of multiple cellular radio access networks (RANs), we propose a novel two-level cooperative access network framework for superior energy efficiency. Geographically co-located RANs mutually cooperate each other through the combined use of base transceiver station (BTS) level intranetwork cooperation and RAN level internetwork cooperation. By jointly applying the two types of cooperation, BTSs within individual RAN as well as BTSs of several RANs communicate and intelligently cooperate for dynamically minimizing the number of active BTSs and thus reduce energy utilization at the access network level. Extensive simulations are carried out for exploring the degree of energy savings and sleeping patterns of BTSs of each cooperating RANs as well as that of the combined network. Simulation results show that through the proposed joint cooperation, each cooperating RAN gains substantial economical benefits by significantly reducing its energy expenditure. Furthermore, comparison with the cases of intranetwork and internetwork cooperation acting alone reveals that by participating in the joint cooperation, each RAN can achieve much higher energy savings.

The core of the Next Generation Network (NGN) is a converging point of various classes of session flows. As a result, despite the presence of admission control techniques, a competition for resources becomes inevitable. In such an environment, certain flows may be disadvantaged due to others consuming or withholding access to a resource that is limited in availability. Therefore, it is extremely important to explore a solution for equitable resource distribution, which does not disadvantage either class of session flow. In light of this, this paper develops an ecologically inspired graphical theory for equitable resource distribution in a multi-resource, multi-class environment. Optimal resource supply levels required for a stable coexistence could be accurately predicted by the proposed graphical theory. Analytical proof and simulation results are provided for supporting the above argument.

Of late, an easy access to the Web 2.0 applications through the hand-held mobile devices has provided the consumers with the opportunity to express opinions on their invoked services conveniently. These opinions, also known as the Internet word of mouth, often impact a potential consumer's service selection notably in service rich urban terrains. Access to these opinions involves tariffs since they are available over the Internet. In addition, individual analysis of such opinions is inconvenient due to mobile devices' inherent limitations. However, a `pervasive cooperative consumer network' can be employed using consumers' connectivity enabled mobile devices to share and acquire service related opinions at no monetary cost. Yet, this network, realized over an underlying mobile ad hoc network, does not consider inclusion of the opinions for service selection. Therefore, we propose a service selection framework to integrate opinions with the widely used minimum hop count method for service selection. The opinions are first clustered using the Wordnet-based K-means clustering algorithm and then quantified using the Sentiwordnet scores to be automatically included in the framework. The proposal is further supported by the simulation results.

In a Cognitive Radio (CR) enabled network, Secondary Users (SUs) have an impact on the Quality-of-Service (QoS), and ultimately the revenue of the PU Primary User (PU) license holders. An important metric of an investor's economic welfare is the ratio of mean returns to the volatility of returns (Sharpe ratio). Numerous CR access schemes have been proposed that fail to account for the economic welfare of PUs when SUs access their spectrum. In this paper we consider the PU's spectrum license as an investment and analyze the impact of CR activity on the PU's economic welfare, in order to derive a cost of production of spectrum access rights such that the PU's economic welfare is not degraded. This creates an incentive for PUs to permit CR access in their spectrum bands, by ensuring that the characteristics of returns on the substantial investment made in spectrum licenses are preserved. However, under any instantaneous or spot pricing scheme, the risk of price volatility is introduced. We also propose a framework to alleviate the risk to SUs from a volatile CR rights spot price, by introducing the concept of forward pricing and hedging of CR rights. This reduces the variability of cash-flow associated with the purchase of CR rights, ensuring that the QoS provided by an SU network remains unaffected by a high CR access price. We also illustrate the leverage effect, where a PU will achieve a higher mean return- on-investment with SU operation, albeit with a higher variance of returns.

Drastic propagation conditions in wireless environments, specifically the presence of non-line-of-sight (non-LOS) propagation in urban areas, pose a major challenge to the acquisition of location information. Count-of-Beacon (COB) is a new radiolocation technique where the absence of a LOS path does not degrade the precision of the estimated position. In this technique, the distance between a mobile terminal and a base station can be derived by means of the geometry of overhearing. The geometry of overhearing models the correlation between the position and the proportion of overheard power-controlled beacon packets. However, the deviation of the attenuation that signals experience, mainly due to shadow fading, has not been considered in this model. This paper reformulates the geometry of overhearing by focusing on the effects of shadow fading. Simulation results are used to verify the validity of the new derived model at different levels of shadowing. The closed-form expression of the Cramér-Rao lower bound (CRLB) is also derived to benchmark the accuracy of the COB technique. In addition, the major source of error affecting the precision of the COB technique is identified and its statistical description is provided.

The aim of this paper is to study the effect of cooperation on system delay, quantified as the number of retransmissions required to deliver a broadcast message to all intended receivers. Unlike existing works on broadcast scenarios, where distance between nodes is not explicitly considered, we examine the joint effect of small scale fading and propagation path loss. Also, we study cooperation in application to finite networks, i.e. when the number of cooperating nodes is small. Stochastic geometry and order statistics are used to develop analytical models that tightly match the simulation results for non-cooperative scenario and provide a lower bound for delay in a cooperative setting. We demonstrate that even for a simple flooding scenario, cooperative broadcast achieves significantly lower system delay.

Abstract In wireless mesh networks (WMNs), real time communications (e.g., Voice over IP (VoIP) and interactive video communications) may often be interrupted as packets are frequently lost or delayed excessively. This usually happens due to the unreliability of wireless links or buffer overflows along the routing paths. The mesh connectivity within the WMN enables the capability to enhance reliability and reduce delay for such applications by using multiple paths for routing their packets. The vital components in multi‐path routing for achieving this are the pre‐determined formation of paths and the technique that the paths are deployed for packet traversal. Therefore, we propose a novel multi‐path routing protocol by introducing a new multi‐path organization and a traffic assignment technique. The designed technique dubbed as FLASH (Fast and reLiAble meSH routing protocol) discovers one primary path between a pair of source and destination based on a new proposed metric, and thereafter selects mini‐paths, which connect pairs of intermediate nodes along the primary path. The primary path and mini‐paths are concurrently deployed, as multiple copies of packets are routed through. This technique compensates for possible outage at intermediate wireless nodes or their corresponding wireless links along the primary path. Routing along mini‐paths is performed in such a way that redundant copies do not cause an excessive congestion on the network. The effectiveness of the proposed scheme is evaluated analytically and through extensive simulations under various load conditions. The results demonstrate the superiority of the proposed multi‐path organization in terms of reliability and satisfactory achievements of the protocol in enhancing delay and throughput compared to the existing routing protocols, especially for long distances and in congested conditions. Copyright © 2010 John Wiley & Sons, Ltd.

Because of the popularity of smart mobile devices with the capabilities to capture location information by using global positioning system (GPS), numerous novel geographical applications and services have been introduced. Among these applications, geocast service has recently gained a lot of attractions, since it has great potential to achieve multicast-like geo-applications such as location-based shopping advertising and geographic emergency notification. Unfortunately, the network intermittent connectivity can be suffered by mobile users, especially when they employ low cost wireless connectivities to form mobile ad hoc networks (MANETs). Accordingly, a novel opportunistic geocast routing scheme (OGRS) has been proposed in the literature to facility geocast service over intermittently connected mobile ad hoc networks (ICMANs). In OGRS, the expected visiting rate to the destined region of a message is proposed for an intermediate node to choose the next-hop with larger visiting likelihood. However, the performance still can get worse in an ICMAN with large geographical scale, since the system requires extensive time to collect all necessary geographical information. As a result, this paper introduces an enhanced OGRS for a large scale ICMAN. It will employ an approximate region for choosing the potential carriers, if the destined area of a geocasting message is out of knowledge, or too far to be reached. Such approximate region has a larger initial size, but its radius will be exponentially decayed to the original radius of the destined region as time elapses. Simulation results demonstrate the enhanced delivery performance offered by the proposed OGRS with exponential decay for large geographical areas.

Efficient transmission of variable-bit rate (VBR) video traffic in Broadband Wireless Access (BWA) networks is currently an active research topic. Due to the dynamic changes in bandwidth requirements of VBR video and the limited network bandwidth, dynamic bandwidth allocation (DBA) is required. Traffic prediction is a promising approach to improve the effectiveness of DBA in BWA networks. In this paper, we propose DEEP (Density Estimation basEd Predictor), a novel prediction scheme for MPEG traffic. In DEEP, the density of probability of MPEG frames is estimated through kernel density estimation method. This density is then used in forecasting the future bit rate of I, P, and B video frames. Simulation results show that DEEP is able to predict the bit rate of MPEG traffic more accurately than the conventional Least Mean Squares (LMS) algorithm, and is less sensitive to traffic variation. We provide application guidelines of this predictor in the IEEE 802.16 standard.

Location estimation of mobile terminals under strictly non-line-of-sight propagation conditions is still an open issue. Count-of-Beacon (COB) is a new radiolocation technique where the presence of non-line-of-sight propagation does not affect the accuracy of the estimated position. In this technique, a mobile terminal's location is realized by counting the total number of beacon packets that it can overhear. However, non-uniform distribution of mobile terminals within cells reduces the accuracy of the estimation. This paper proposes the modified version of the COB technique in which the impact of non-uniform distribution is mitigated by using the Density_Map of each cell. The introduced Density_Map divides the entire area of each cell into some regions. The estimated density-coefficients of these regions are then used to adjust the total number of overheard packets. The adjusted value closely matches the total number of packets that would likely to be overheard if cells were uniformly populated. Simulation results are used to verify the effectiveness of the proposed technique.

Disruption-tolerant networks (DTNs) have gained great attractions recently by addressing intermittent network connectivity suffered due to short radio transmission range, high node mobility, sporadic node densities, etc. Data communication across DTN is achieved over store-carry-forward (SCF) paradigm by exploiting temporary wireless links and connections opportunistically arising from mobility nature of nodes. In the literature, multi-copy-based routing approaches are widely considered to overcome mobility randomness and subsequently the uncertainty of future network states, while routing information such as mobility statistics and context is employed to control the number of message copies. However, none of the existing approaches can be directly implemented for recent emerging geocast services. This is because instead of specifying any particular node as the destination in advance, the message generated by a geocast service falls into a certain geographical area addressed by the location descriptions. As a result, this paper introduces a novel opportunistic geocasting message delivery scheme for DTNs. To improve the efficiency, the expected visiting rate to the destined region of a message is proposed for an intermediate node to choose a next-hop with larger visiting likelihood. Simulation results demonstrate the enhanced delivery performance offered by the proposed opportunistic geocasting message delivery scheme.

Estimating the location of mobile terminals in dense urban environments is still an open issue. This paper proposes a new radiolocation technique which is robust to severe Non-Line-of-Sight and multipath propagation conditions that are typical of urban microcellular and picocellular systems. In the proposed technique, three-dimensional location estimation can be achieved by counting the number of overheard beacon packets that power-controlled mobile terminals transmit periodically. The overhearing model in 3D space is also introduced to model the correlation between the position and the number of overheard packets. Simulation results are used to verify the effectiveness of the proposed technique.

Solving Fastest Distributed Consensus (FDC) averaging problem over sensor networks with different topologies has received some attention recently and one of the well known topologies in this issue is star-mesh hybrid topology. Here in this work we present analytical solution for the problem of FDC algorithm by means of stratification and semidefinite programming, for the Star-Mesh Hybrid network with K-partite core (SMHK) which has rich symmetric properties. Also the variations of asymptotic and per step convergence rate of SMHK network versus its topological parameters have been studied numerically.

Data communication over intermittently connected mobile ad hoc networks (ICMANs) can be carried out by opportunistically employment of mobility nature of nodes and the storage space of all nodes. In order to overcome mobility randomness and subsequently uncertainty of future network state, multi-copy-based routing approaches are widely considered. However, scalability is still an issue, even extra routing information such as mobility statistics and context is employed to control the number of message copies. This is because communicating overhead to exchange all such information still exponentially increases as network size increases. Therefore, this paper introduces an opportunistic virtual backbone (VBB) constructing algorithm to form a hierarchical architecture over ICMANs. Once VBB is formed, all routing information and multiple message copies are limited among the VBB members, and thereby routing overhead can be reduced dramatically. Different from the conventional unit disk graph (UDG) based solutions for MANETs, the proposed algorithm utilizes a weighted graph derived from mobility statistics to model underlying long-term stable topology of ICMANs. To reduce constructing overhead, only partial two-hop mobility statistics are collected by each node. Moreover, intermittent connectivity and delayed message transmission are also addressed in the paper. The simulation results indicate that the proposed algorithm is highly scalable as network size increases and the existing SCF routing protocols can work well over ICMANs where VBBs are formed.

In this paper analytical solution of fastest mixing Markov chain problem over a sensor network with K-partite topology is provided. The solution procedure consists of Stratification of sensor network's connectivity graph and semidefinite programming. The studied topologies are evaluated in terms of asymptotic and per step convergence rates. The obtained optimal transition probabilities have been compared with those obtained from Metropolis-Hasting method by comparing mixing time improvements numerically.

In a heterogeneous wireless network (HWN), users from multiple networks with various QoS requirements compete with each other for finite resources. As this competition becomes intense, the distribution of resources may become extremely haphazard, thus making users of certain classes to be disadvantaged over others. This may destabilize the HWNs and even lead certain classes of users requiring relatively higher resources into complete extinction. Hence, the key for a sustainable HWN is a resource management scheme, which fairly allocates all network resources. Therefore, in this paper, first we propose an analytical model to characterize such unfairness and instability in HWNs. This approach is based on the theory of ecological multi-species multi-resource competition (MSMRC). Next, to counter the aforementioned instability and unfairness, we propose a new joint radio resource management (JRRM) scheme, MSMRC scheme, which is capable of ensuring the HWN's stability and maintain the coexistence of users of widely diverse requirements, even under the extreme situations. Results indicate that the MSMRC scheme also supports the sustainability of the HWN by helping it to withstand sudden disruptions.

The continuing trend of large scale emergencies in last decades have emphasized on ubiquitous public safety services. The growth of mobile handheld devices have provided people with wide scale flexibility of on demand services and enjoyment of ubiquity. In addition, currently people are not only service consumers but also information providers to realize ubiquitous public safety networks. During and right after an emergency, people seek safety and more likely to explode the underlying mobile ad hoc network with numerous reports on the emergency. These human originated reports may not be syntactically identical so the traditional traffic management schemes are not adequate to handle such explosion. Yet, these reports are either semantically or conceptually similar. Therefore, we propose a semantic agglomerative traffic management framework to efficiently remove the aforementioned semantic information redundancy by employing the Agglomerative Clustering and Latent Semantic Analysis techniques. Simulation results are presented to further establish the worthiness of the proposed framework.

Improving energy efficiency, reducing carbon footprint and self-sustainability are key concerns in the design and development of future green communication networks. Therefore, in this paper, a novel energy efficient cellular access network architecture based on the principle of ecological proto-cooperation is proposed. Furthermore, for the first time, the wake-up technology is introduced to cellular access networks for implementing the proposed cooperative architecture. According to our proposal, base transceiver stations (BTSs) cooperatively and dynamically make intelligent decisions for switching between different power modes depending on network traffic conditions. Next, an extensive simulation process under different traffic patterns is carried out for identifying network parameters corresponding to optimal energy savings. The analysis of results reveals that the proposed architecture is capable of substantially reducing the energy consumption. In addition, as a secondary result, the proposed architecture offers an additional level of sustainability to the cellular access network infrastructure.

Cooperation among mobile nodes during establishment of a cellular connection can improve the performance of the network by lowering the transmission power and increasing throughput of the end nodes. Multipath routing is one way for such cooperation by using multiple parallel paths between source and destination nodes, where the main data stream is split into streams of lower data rates and routed to the destination through cellular and ad-hoc connections. Proxy nodes are defined as the cooperating nodes in the split stream paths. An efficient proxy discovery algorithm is essential for selecting the best set of proxy nodes among all nodes that are volunteering for cooperation. A resource allocation algorithm is also equally important for assigning transmission power and data rates to each of the selected proxies. In this paper, efficient algorithms for proxy discovery and resource allocation are proposed that can minimize the overall network power consumption.

The use of licensed spectrum by Cognitive Radio (CR) enabled secondary users (SUs) impacts the Quality-of-Service (QoS) and ultimately, the revenue earned by Primary User (PU) license holders. It is therefore important to find appropriate pricing models for pricing spectrum for access by CR-based networks. Pricing of CR access rights can be modeled as an investment problem, which raises the issue of PU economic welfare preservation in the context of returns on its investment in spectrum licenses. By analyzing the impact of CR activity on the reward-to-variability ratio (Sharpe ratio) of returns on the spectrum investment, the degradation in economic welfare experienced by the PU as a result of SU activity can be quantified. In this paper, we examine the impact of a multiple PU environment and variable traffic characteristics on the dynamics of the Sharpe Ratio based pricing strategy. Numerical results indicate that with multiple spectrum license holders, the Sharpe Ratio based pricing framework will continue to maintain PU economic welfare but results in a reduced access price for SUs.

This issue highlights the current and future trends of aerospace communications and networking, including satellites, high-altitude platforms (HAPs), optical deep-space communications, interplanetary communications, aircraft communications, with a look at those "classical" services that still lead the mass market of aerospace applications and will do so into the future.

Abstract The heterogeneous nature of network traffic in next generation networks (NGNs) may impose scalability issue to traffic monitoring applications. While this issue can be well addressed by existing sampling approaches, owing to their inherent ‘lossy’ characteristic and data reduction principle, traditional sampling techniques suffer from incomplete traffic statistics, which can lead to inaccurate inferences of the network traffic. By focusing on two distinct traffic monitoring applications, namely, anomaly detection and traffic measurement, we highlight the possibility of addressing the accuracy of both applications without having to sacrifice one for the sake of the other. In light of this, we propose a generic sampling framework, which is capable of providing creditable network traffic statistics for accurate anomaly detection in the NGN, while at the same time preserves the principal purpose of sampling (i.e., to sample dominant traffic flows for accurate traffic measurement), and thus addressing the accuracy of both applications concurrently. With the emphasize on the accuracy of anomaly detection and the scalability of monitoring devices, the performance evaluation over real network traces demonstrates the superiority of the proposed framework over traditional sampling techniques. Copyright © 2010 John Wiley & Sons, Ltd.

One of the most important problems studied in data harvesting wireless sensor networks (WSNs) is the optimization of the tradeoff between the accuracy of the reconstructed field data and the resource consumption. In order to optimize the resource consumption, whilst not compromising the accuracy of the reconstructed field data, an ecologically inspired marginal value theorem strategy (MVTS) is proposed for a mobile agent for choosing the next sensor node to be visited in the data acquisition process. The proposed MVTS can adaptively gain new knowledge during the process of collecting observations from a WSN comprising of static sensor nodes. Therefore, only the relatively important sensor observations will be colleted by the agent according to the variety of the background environmental data. This is thought as an efficient way to reserve the resources, such as energy and bandwidth, because only the important observations are collected. Illustrated analytical and simulation results confirm the above achievements.

In Wireless Mesh Networks (WMNs), packets are frequently lost or excessively delayed due to the failure of links and nodes, or the existence of bottlenecks along their routing paths. This often causes an outage or performance degradation for the clients. A mesh fashion topology of WMNs enables the capability to relieve this issue by using multi-path routing as a possible solution. Therefore, we propose a novel multi-path routing protocol that utilizes the mesh connectivity of WMNs in order to enhance the delay and reliability. The designed protocol discovers one primary path and multiple mini-paths between a source and a destination. Whilst the former connects the source to the destination, the latter connects pairs of intermediate nodes along the primary path. Multiple copies of packets are simultaneously routed through the mini-paths to compensate for possible outage at intermediate nodes along the primary path or their corresponding links. Routing along these mini-paths is performed in a way that redundant copies do not cause an excessive congestion on the network. The designed protocol is specifically advantageous for applications which are sensitive to delay and throughput. For evaluation, extensive simulation is carried out using the OMNET discrete event simulator and subsequent results validate the performance of our proposed protocol.

In this paper, inspirations from behavioral ecology are applied for mobile agent assisted data collection in a Wireless Sensor Networks (WSNs). With the help of the marginal value theorem based strategy (MVTS), each observation (Λ), which is gathered by a given sensor node, is considered as a marginal information source with a relative entropy H(Λ). The mobile agent exploits the correlation and chooses the next sensor node to be visited, which is deemed that the information contribution of the contained observation is not smaller than a predefined threshold (TH). Therefore, understanding the correlation models can benefit the WSNs system from two aspects. On one hand, the resource consumption could be reduced during the acquisition process of the observation; on the other hand, the accuracy of the reconstructed field data is least compromised, due to relatively critical observations being collected by the mobile agent over a dynamically changing environmental. The resource consumption such as energy and bandwidth, is proportional to the number of visited sensors. With MVTS, the resource consumption is optimized through bypassing the sensors with relatively unimportant observations. Illustrated analytical and simulation results confirm the above achievements.

Due to widespread deployments of Vehicular Area Networks (VANs) and the increasing affordability of mobile data and smart mobile devices, there is an increasing demand for accessing applications and services on-the-go. As a result, group mobility scenarios have emerged, especially over VANs on public transport networks (e.g., buses, trains, aircraft, and so on). Therefore, when a group of users roam as a single unit, the NGMN must be equipped with efficient route optimization and group mobility management support. In light of this, the Internet Engineering Task Force (IETF) has defined the NEtwork MObility (NEMO) basic support protocol for location transition management for a group of members moving between two networks. Nevertheless, IETF's group mobility management solution is limited to homogeneous networks. Therefore, this paper proposes the integration of NEMO support for NGMN architectures for enabling group mobility management among multiple heterogeneous networks. Further, according to the evaluation and results, it is also capable of successfully handling handoff and mobility management for complex nested mobility scenarios.

Future cellular systems target the provision of high-speed multimedia and data services. However, support of such new services intensifies traffic burstiness and poses an increase of threat of congestion. This paper proposes a cooperative cellular architecture through which load balancing is achieved by exploiting the broadcast nature of radio waves and using minimum overhead. Unlike other existing architectures, in this scheme, neither a routing algorithm nor an additional frequency band is required. Both analytical and simulation results are used to evaluate the effectiveness of the proposed architecture. In addition, it is shown that nodes selfish behavior does not significantly affect the performance of the system when only mobile terminals are used as cooperative nodes.

This paper proposes a security control framework for mitigating epidemic and pandemic network attacks in Next Generation Mobile Networks (NGMN) by adopting several interesting principles from the human immune system (HIS) and the field of epidemiology. In particular, the proposed framework incorporates two key components: namely, the attack localization process and the attack recovery process. While the former administers the autonomous attack mitigation process and the security updates procedure, the latter accommodates a last line of defense (in the form of quarantine and rate limiting) for preventing repeated attacks on the NGMN. Performance evaluation indicates that the proposed security control framework is efficient in restricting both epidemic and pandemic attacks, and effective in alleviating the impact of the attacks on the network, thereby enhancing the survivability of the NGMN.

With the expansion of the Next Generation Mobile Network (NGMN), ubiquitous service access is increasingly becoming a reality. As a result, an increasing number of users are accessing applications and services while travelling, especially on public transportation systems, thus giving rise to group mobility scenarios. Therefore, it is essential for the NGMN to be equipped with group mobility management support for achieving seamless session handoffs for a group of users or devices moving together. For a group of members moving between two networks, location transition management was initially addressed in the Internet Engineering Task Force's (IETF) NEtwork MObility (NEMO) basic support protocol. However, the two networks in this case are homogeneous in nature. This paper proposes the integration of NEMO support for an NGMN architecture for enabling group mobility management between multiple heterogeneous networks. Results and analysis illustrate that by integrating NEMO support to an NGMN reduces handoff latency, transient packet loss, jitter, and signaling cost for both end users and service providers.

Today, with the rapid advance in broadband technology, streaming technology is applied to many applications, such as content delivery systems and web conference systems. On the other hand, we must implement digital rights management (DRM) to control content spreading and to avoid unintended content use. Traitor tracing is one of the key technologies that constructs DRM systems, and enables content distributors to observe and control content reception. General methods make use of watermarking to provide users' individual information unique to each user. However, these methods need to produce many individual contents. Especially, this is not realistic for real-time streaming systems. Furthermore, watermarking, which is a key technology adopted by contemporary methods, has known limitations and attacks against it. This is why the authors have proposed a method to monitor the content stream using traffic patterns constructed from only traffic volume information obtained from routers. The proposed method can determine who is watching the streaming content and whether or not a secondary content delivery exists. This information can be also used with general methods to construct a more practical traitor-tracing system. A method to cope with random errors and burst errors has also been investigated. Finally, the results of simulation and practical experiment are provided demonstrating the effectiveness of the proposed approach.

The demand for anytime, anywhere, anyhow communications in future generation networks necessitates a paradigm shift from independent network services into a more harmonized system. This vision can be accomplished by integrating the existing and emerging access networks via a common Internet Protocol (IP) based platform. Nevertheless, owing to the inter-worked infrastructure, a malicious security threat in such a heterogeneous network is no more confined to its originating network domain, but can easily be propagated to other access networks. To address these security concerns, this paper proposes a biologically inspired security framework that governs the cooperation among network entities to identify security attacks, to perform security updates, and to inhibit attacks propagation in the heterogeneous network. The proposed framework incorporates two principal security components, in the form of anomaly detection framework and security control framework. Several plausible principles from two fields of biology, in particular the human immune system (HIS) and epidemiology have been adopted into the proposed security framework. Performance evaluation demonstrates the efficiency of the proposed biologically inspired security framework in detecting malicious anomalies such as denial-of-service (DoS), distributed DoS (DDoS), and worms, as well as restricting their propagations in the heterogeneous network.

For an infrastructure-less wireless environment, content dissemination among mobile users can be facilitated by self-organizing mobile ad hoc networks (MANETs) through low cost wireless connections. However, due to limited radio transmission range, sporadic node densities and power limitations, MANETs can become intermittently connected. As a result, some proposed solutions exploit mobility and storage space of the nodes to distribute contents even if a route never exists. In all these mechanisms, whenever two mobile nodes encounter each other, each node only focuses on making independent disseminating decisions to improve the overall performance. With this in mind, this paper presents a cooperative cache-based content dissemination framework (CCCDF) to carry out the cooperative soliciting and caching strategies for the two encountering nodes. Two cooperative strategies are investigated: CCCDF (Optimal) is to maximize the overall content delivery performance while CCCDF (Max-Min) is to share the limited network resources among the contents in a Max-Min fairness manner. Simulation results demonstrated the enhanced delivery performance offered by the proposed CCCDF over existing schemes.

Guaranteeing Quality of Service (QoS) in Cognitive Radio (CR) networks is a challenging task due to the random nature of radio channel conditions and primary user traffic. In this paper we analyze the recently proposed mean-variance based QoS and resource management methods, and introduce the concept of mean-variance evaluation of QoS and resource management techniques. Inspired by financial Portfolio Selection theory, mean-variance based resource management techniques for CR networks are statistical approaches and do not require instantaneous knowledge of channel state and Primary User (PU) activity, and enable the option to tradeoff between risk (QoS variance) and reward (QoS mean). Using throughput as a measure of QoS, we present a derivation of the theoretical throughput mean-variance characteristics of a CR-OFDM system employing a mean-variance based QoS management strategy. We conduct an analysis of existing Portfolio Selection based strategies in the mean-variance domain and compare them to the theoretical performance. Finally, we present a further enhancement of the approach by explicitly considering constraints on individual channel power allocation in the mean-variance optimization problem. Simulation results illustrate the effect of our enhancements in improving the risk-reward profile of the mean-variance based QoS management strategy, by moving it closer to the theoretical characteristic.

This paper proposes the inclusion of two traffic forecasting frameworks in traffic sampling paradigm. The proposed frameworks: namely, the pattern forecasting and the attack forecasting, predicts the occurrence of traffic deviation and examines the existence of malicious attack in the traffic deviation, respectively. While the former utilizes the ARAR model to forecast the network traffic, the latter exploits the statistical likelihood function to determine whether any malicious attack is the origin of the traffic deviation. In addition, a dynamic weight assignment strategy is proposed to further improve the efficiency of the sampling strategy. Performance evaluation indicates that the inclusion of both forecasting frameworks and dynamic weight assignment in the sampling strategy can improve the accuracy and scalability of the anomaly detection.

Providing the required quality of service (QoS) for clients in a wireless mesh network (WMN) is a challenging task. This is due to the complex architecture of the WMN and unpredictable behavior of the wireless links and the intermediate nodes. This can be alleviated by introducing a cognitive mechanism within the WMN. In this paper, a WMN is developed, where the data traffic is forwarded in a cognitive manner via multiple paths. The aims are to maximize the client's achievable performance (e.g., data rate, delay) and minimize the imposed congestion in the network. The proposed mechanism constantly perceives the network performance through feedback loops, learns and predicts the performance of the paths, and takes appropriate decisions. The decision includes a set of routing paths and their corresponding data rates. The resulted network is capable of adapting itself with the changes, whilst improving its performance. The proposed mechanism is simulated using OMNET discrete event simulator and subsequently validated.

Abstract-Recently, popularity of multimedia content sharing among the Internet users and development of wireless mobile devices have promoted a trend of deploying Peer-to-Peer (P2P) networks over mobile ad hoc networks (MANETs) for mobile content distribution. However, due to limited radio transmission range, sporadic node densities and power limitations, MANETs may become intermittently connected thereby disrupting such mobile content distribution. Even though, various content delivery schemes have been proposed in the literature to resolve such underlying intermittent connectivity, another key issue for P2P content distribution networks i.e., how to search or lookup the interested contents in intermittently connected MANETs (ICMANs) context, is not investigated. With this in mind, this paper deploys a fully decentralized unstructured P2P network over ICMANs and accordingly introduces epidemic content search mechanism to discover the interested contents based on the related keywords. Note that the mobility and storage space of the mobile peers are exploited here to diffuse the content search requests. Moreover, to improve the efficiency under the restricted storage space conditions, a utility-based buffer management policy is presented. By evaluating each possible search request based on the relevant information such as mobility statistics, the requests with lower evaluated values are accordingly discarded when the buffer is full. Simulation results demonstrate the enhanced service performance offered by epidemic P2P content search scheme with utility-based buffer management policy.

This paper proposes an adaptive sampling strategy to address the accuracy and scalability issues of anomaly detection at high-speed backbone side of next generation mobile network (NGMN). The proposed sampling strategy is formulated based on the network traffic condition. It is constituted by two important functions namely the traffic identification and the sampling decision. While the former utilizes spectral analysis to identify the severity status of the traffic flows, the latter exploits both the flow status and flow size to compute the optimal sampling rate. In addition, a renormalization process is proposed to address the scalability issue in the network. Our analysis demonstrates that the proposed technique is efficient in providing adequate statistics for detecting anomaly traffic and scales well to the high speed traffic of NGMN.

Over the last few years, the popularity of multimedia content sharing among the Internet users has promoted a new kind of networking where the content dissemination is determined by the interests of users rather than pre-specified destinations. Development of wireless mobile devices has further enhanced the trend of content exchange among the mobile users. However, such mobile nodes based content dissemination may suffer the underlying intermittent connectivity due to the inherent limitations such as short radio transmission range, sporadic node densities and constrained power. To this effect, some solutions have been proposed in the literature that exploit the mobility and storage space of the nodes to distribute contents even if a route never exists. In all these mechanisms, whenever two mobile nodes encounter each other, each node only focuses on making independent disseminating decisions to improve the overall performance. However, even though each node can get its own optimized decisions, it may not be an optimized decision between the two encountering nodes. With this in mind, this paper presents a cooperative content dissemination framework (CCDF) with an analytical model to select and store the contents that maximizes the overall content delivery probability in the future. Simulation results demonstrate the enhanced delivery performance offered by the proposed CCDF over existing schemes.

Traditional data aggregation methods use data originating from all sensor nodes of the wireless sensor network (WSN). Unfortunately, the use of all available sensors is not a resource efficient approach for such environments. Therefore, in this paper, we propose a novel aggregation procedure based on a hypothesis testing, where a subset of reporting nodes is intermittently selected. Neyman-Pearson lemma is used to select reporting nodes and the critical value, which is used in this aggregation procedure. Furthermore, a performance evaluation is discussed to illustrate how the proposed aggregation procedure outperforms the existing detection methods, and the dependency of the aggregation procedure and network attributes.

This paper presents a customizable framework for employing Cognitive Radio in Intermittently Connected Wireless Networks, which aims to maximize transmission opportunities that arise when fully connected network partitions are formed. Various layer 3 routing and information dissemination protocols would benefit from increased opportunity utility. We propose to use Cognitive Radio to permit multiple flows to occur in close proximity while minimizing spectrum wastage. We provide an example implementation of the proposed MAC framework which could increase the MAC throughput of networks exhibiting intermittent connectivity and network partition.

Efficient methods for analyzing vertical handoffs for IP based data sessions are essential for emerging heterogeneous data networks. This is mainly due to the high frequency of vertical handoffs experienced by roaming users in such internetworked environments. This paper presents an analytical approach for evaluating vertical session handoffs in such an environment where the packet arrivals follow a Pareto distribution. The reason behind this assumption is due to the fact that probability distributions with long tails have proven to be better suited for modeling packet inter-arrival times for Internet based data traffic. The analysis and evaluation are applied for a framework previously designed by the authors' for interworking between heterogeneous data networks. Finally, the results obtained from this analysis are compared against the results obtained from a classical queuing model where Poisson arrivals and exponential service times are assumed.

Abstract In this paper, we propose a signature‐based hierarchical email worm detection (SHEWD) system to detect e‐mail worms in large‐scale networks. The proposed system detects novel worms and instantly generates their signatures. This feature helps to check the spread of any kind of worm—knownorunknown. We envision a two‐layer hierarchical architecture comprising local security managers (LSMs), metropolitan security managers (MSM), and a global security manager (GSM). Local managers collectsuspiciousflows and hand them to metropolitan managers. Metropolitan managers then use cluster analysis to sort worms from the suspicious flows. The sorted worms are used to generate the worm signature which is relayed to the global manager and then to all the collaborating networks. A separate scheme is proposed to automatically select suitable values of the system parameters. This parameter selection procedure takes into account the current network state and thethreat levelof the ongoing attack. The performance of the whole system is investigated using real network traffic with traces of worms. Experimental results demonstrate that the proposed scheme is capable to accurately detect email worms during the early phase of their propagations. Copyright © 2008 John Wiley & Sons, Ltd.

Changes in network dynamics (e.g., link failure, congestion, buffer overflow and so on) may render ubiquitous service access untenable in the Next Generation Mobile Network (NGMN). Since the commercial viability of a network in a competitive market depends on the perceived user satisfaction, to atone for the loss of the guaranteed service access, it is desirable to compensate the users either with future quality-of-service (QoS) enhancements and/or price reductions. Focusing on the price reduction aspect, this paper proposes a non-cooperative game theory based compensation algorithm that derives the best outage compensation (i.e., price reduction for the outage period t) over different service types. Taking into account all-IP based applications in the future, the service types are categorized into different classes such as flat rate based (i.e., cents for the entire session(s)), time based (i.e., cents per minute), volume based (i.e., cents per MB), etc., whereupon the compensation algorithm is translated into an n-player game, based on the current subscription profiles. With step sized cost reductions, the selection of the outage compensation is governed by the Nash equilibrium points and fairly allocates cost reduction among the ongoing service types.

This paper proposes an architecture for interworking heterogeneous all-IP networks with an in-depth analysis of its performance. The novelty of this framework is that it freely enables any 3G cellular technology, such as the Universal Mobile Telecommunications System (UMTS) or the CDMA 2000 system, to interwork with a given broadband wireless access (BWA) system, such as the Worldwide interoperability for microwave access (WiMAX) network or the wireless local area network (WLAN) via a common signaling plane. As a universal coupling mediator for real-time session negotiation and management between these dissimilar networks, the IP multimedia subsystem (IMS) has been exploited. The analytical evaluation investigates the behavior of handoff delay, transient packet loss, jitter, and signaling cost during a vertical handoff for the given framework. Finally, an OPNET based simulation platform has been introduced for the verification of the analytical model and results.

Abstract Optimized routing (from source to sink) in wireless sensor networks (WSN) constitutes one of the key design issues in prolonging the lifetime of battery‐limited sensor nodes. In this paper, we explore this optimization problem by considering different cost functions such as distance, remaining battery power, and link usage in selecting the next hop node among multiple candidates. Optimized selection is carried out through fuzzy inference system (FIS). Two differing algorithms are presented, namely optimized forwarding by fuzzy inference systems (OFFIS), and two‐layer OFFIS (2L‐OFFIS), that have been developed for flat and hierarchical networks, respectively. The proposed algorithms are compared with popular routing protocols that are considered as the closest counterparts such as minimum transmit energy (MTE) and low energy adaptive clustering hierarchy (LEACH). Simulation results demonstrate the superiority of the proposed algorithms in extending the WSN lifetime. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Due to limited radio transmission range, sporadic node densities and power limitations, mobilead hocnetworks (MANETs) can usually become intermittently connected, thereby data routing process is disrupted in the absence of an end‐to‐end routing path. To this effect, various ‘store‐carry‐forward (SCF)’ routing algorithms have been proposed in the literature that exploit the mobility and storage space of the nodes to deliver messages even if a route never exists. To have a high probability of eventual delivery, flooding‐based or multi‐copy‐based forwarding strategy is adopted by majority of these schemes. Furthermore, in the human‐related environments containing correlated movements, performance is quantified in terms of the probability of the message delivery between any pair of nodes. However, in the multi‐copy case, the conventional individual delivery probability alone fails to address delivery opportunity from other nodes that also carry the same message copies. With this in mind, this paper presents several routing‐related key parameters and accordingly introduces a fuzzy logic‐based delivery framework (FLDF) to select and store messages that have higher delivery preference in the future. In the proposed mechanism, FL, which has the fundamental ability to deal with imprecise or uncertain information caused by intermittent connectivity as well as mobility randomness, is employed to handle the message evaluation and selection. Simulation results demonstrate the enhanced delivery performance offered by the proposed FLDF compared with the existing schemes. Copyright © 2008 John Wiley & Sons, Ltd.

Routing protocols that can facilitate application- specific service guarantee in wireless sensor networks (WSN) constitute one of the key design objectives of current WSN research. Since energy-efficient routing protocols in literature do not offer a complete framework for service differentiation, a newer approach is warranted. Formulating such routing approach requires the adoption of different cost metrics that can parameterize the application-specific requirements. To this effect, this paper proposes an application-aware routing protocol (AARP) that considers battery power, data transaction reliability and end-to-end delay for service differentiation. Two mathematical models namely analytical hierarchical process (AHP) and grey relational analysis (GRA) are incorporated for intermediate node selection (ranking and subsequent selection based on local weight calculation) for data transaction purposes. As demonstrated by the simulation results, the proposed routing approach offers service configurability across a range of applications.

In wireless mesh networks (WMNs), the achievable throughput of a mesh client to a gateway (GW) is limited by the minimum link capacity of the intermediate nodes. In instances when the intermediate nodes experience congestion due to large relay traffic, link failure etc., this throughput may drop below an acceptable level. Since the mesh topology implies multi-path routing capability, it is possible to satisfy the client service requirements by facilitating data traversal over multiple gateways (forming a coalition). Therefore, in this paper a game theoretic coalition formation algorithm is proposed that guarantees the minimum service requirements of clients. The algorithm is modeled as a cooperative game theory and incorporates Bilateral Shapley Value (BSV) to find the best coalition, whereupon a bargaining game theory is utilized to evaluate the throughput contribution of the member gateways. Such a game theoretic approach enables the proposed algorithm to offer a Pareto efficient solution that satisfies the minimum service requirements of the clients over dynamic network conditions. Performance at the proposed algorithm is evaluated using OMNET discrete event simulator.

Over the last few years, multimedia content sharing through different service providers have gained popularity among the Internet users. The development of wireless mobile devices with enhanced features have further fueled this phenomenon. While such content sharing in Internet is accommodated through an overlay architecture, for infrastructure-less environment such as wireless mobile ad hoc networks (MANETs) experiencing intermittent connectivity, the distribution mechanism is not that straightforward. Arising from inherent MANET limitations such as short radio transmission range, sporadic node densities and constrained power, intermittent connectivity limits the ability of the network to provide successful content delivery. Although some solutions have been proposed in literature, they fail to consider the mobility characteristics of nodes for making decisions regarding the caching of contents for future delivery. Moreover, user-centric service requirements are also not addressed where different subscribers prefer different content profiles. To address this issue, a novel content sharing framework with optimized caching strategy is proposed in this paper. Based on the cached subscription profiles, the framework exploits two decision-making technologies namely analytic hierarchy process (AHP) and grey relational analysis (GRA) to evaluate all possible content requests between two meeting nodes and subsequently request the preferred contents for future encounters. Simulation results validate the improved performance of the proposed distribution framework in comparison to the existing schemes.

In an all-IP environment of internetworked heterogeneous mobile data networks, ongoing data sessions from roaming users are subjected to frequent vertical handoffs. Under such circumstances, careful consideration must be given for the selection of appropriate vertical handoff mechanisms to ensure seamless service continuity and desired quality of service (QoS) levels. Therefore, efficient methods for evaluating and comparing such techniques are essential. One such evaluation technique is signaling cost analysis. This paper presents an analytical model for evaluating signaling cost of vertical handoffs in a heterogeneous mobile networking environment at the core network level for a roaming user. The numerical analysis and evaluation is based on a framework designed for interworking between Universal Mobile Telecommunications System (UMTS), CDMA2000 technology, and mobile WiMAX (worldwide interoperability for microwave access) networks. Results and analysis illustrate the behavior of the signaling cost metric against session arrival rate, network mobility rate, and the call-to-mobility rate.

Development of data aggregation techniques is thought as an effective way to save energy in order to prolong the lifetime of wireless sensor networks (WSNs). Particular characteristics of data gathered from spatial-temporal domain may represent certain level of correlation among data values. Based on this observation, we start with analyzing the optimal sampling rate in temporal correlation model to find out the best sleep time of sensor nodes. Then we propose an aggregation technique which exploits the spatial-temporal correlation using a discrete cosine transform (DCT). It transfers the spatial-temporal data into uncorrelated frequency domain coefficients. The WSN is split into several clusters. Original data are aggregated at an aggregation point which acts as a cluster head. The 3D-Zigzag sorting algorithm makes sure that the aggregation point transmits the frequency coefficients from lower frequencies which contain the main energy of the original data to higher frequencies. Simulation results show only a few coefficients are enough to recover original data under high correlation model within the user tolerable distortion rate.

This paper addresses potential security threats that arise from the interconnection of different access technologies in next generation mobile networks (NGMN). A generic hierarchical security architecture is proposed that is capable of detecting and isolating the dominant security threats namely denial- of-service (DoS), distributed DoS (DDoS) and worm attacks. The architecture utilizes an anomaly-based security detection mechanism, which resolves the attacks through a cooperative approach between the node under attack and its corresponding higher tier nodes. The results from the ns-2 based simulation indicate that the proposed security architecture is capable of mitigating these threats in an effective manner.

Currently, mobile ad hoc network (MANET) has drawn great attention for being part of the ubiquitous network. Unlike the conventional network, MANETs have many unique features such as node resource constraint. That is why several efficient routing protocols have been proposed specifically for MANETs. Among these protocols, optimized link state routing (OLSR) is one of the four important routing protocol identified by IETF. The current OLSR protocol assumes that all nodes are trusted. However, in hostile environment, the OLSR is known to be vulnerable to various kinds of malicious attacks. In this paper, we propose a new security aware optimized link state routing (SA-OLSR) which is a secured version of current OLSR. Our approach is based on exchanging acknowledgement between 2-hop neighbors when the control traffic is successfully received. The main advantage of our approach is that it can protect against many sophisticated attacks such as link spoofing, colluding misrelay attack, and wormhole attack without requiring any location information as well as the knowledge of complete network topology. Our simulation results show that the proposed solution can achieve higher packet delivery ratio compared to the network using the standard OLSR in the presence of malicious nodes.

Due to limited radio transmission range, sporadic node densities and power limitations, mobile ad hoc networks (MANETs) may become intermittently connected thereby disrupting the data routing process. Existing routing schemes for MANETs are not equipped to address this routing problem. In an attempt to resolve this, various "store-carry-forward" routing mechanisms have been proposed in literature. Although they exploit the mobility of the nodes for message delivery, most of them only focus on one particular routing factor such as delivery probabilities in particular cases, and cannot work well under various mobility scenarios. Furthermore, application-specific and user-centric service requirements are also not considered in any of these routing approaches. As such, in this paper we present a novel routing framework, AHP-GRA based routing framework (AGRF), that utilizes two mathematical models namely analytic hierarchy process (AHP) and grey relational analysis (GRA) to offer routing capability in intermittently connected MANETs (ICMANs). In the proposed mechanism, when any two nodes meet each other in the network, each node selects the messages it prefers to store, carry and forward in the future by ranking all possible messages according to various preferred factors. Simulation results demonstrate the enhanced performance measure offered by the proposed framework over existing schemes.

This paper aims at proposing a network structure to minimize the energy consumption in cluster-based wireless sensor networks (WSNs), which is directly related to the lifetime of the network. In large scale wireless sensor networks, data aggregation is known as an effective technique to save the energy by a trade off between the complexity of local processing and the amount of data transmitted in networks. Normally, in cluster-based WSNs, the sensor node which is elected as the cluster head will become the aggregation point which has the responsibility to aggregate the data from cluster members. In this paper, we analyze the aggregation characteristic in a correlated data field, and then find out the optimal cluster radius for each sensor node in the case of that node has been elected as cluster head. Furthermore, we propose a novel network structure called Unbalanced Clustering (UBC) and compare it to a LEACH-like cluster based network and an equal-sized cluster based network to see how much energy UBC can save and the potentiality of making the energy dissemination among the sensor nodes more evenly.

In recent years, information leakage through the Internet has become a new social problem. Many information leakage incidents are caused by illegal applications such as peer-to-peer (P2P) file sharing software. To prevent information leakage, early detection and blocking of the traffic exchanged by illegal applications is strongly required. In this paper, we propose a method for application discrimination of monitored traffic based on the transition pattern of payload length during start up phase of the communication. The proposed method does not need port numbers, which can be spoofed easily. Through experiments using real network traffic, we show that the proposed method can quickly and accurately discriminate applications.

Abstract Despite well‐defined security models to prevent fraudulent intrusion, a number of security attacks have been reported in literature for legacy networks. While a section of these attacks are due to inherent design limitations, others stem from the pervasive introduction of Internet Protocol (IP)‐based applications and external attacks. The former can be addressed by the continual evolution of the system (as is the case with existing systems) whereas the latter requires careful designing. With the advent of all‐IP next generation mobile network (NGMN) inter‐connecting disparate access technologies, the design process assumes paramount importance in relation to detecting/preventing/eliminating migration of attacks through infected terminals. Taking into consideration the fact that standardization bodies are responsible for individual network evolution, a cooperative NGMN security architecture is proposed in this paper that identifies and isolates/eliminates security attacks. The cooperation is encased by the network elements within the NGMN hierarchy and utilizes an anomaly‐based attack detection mechanism. Several dominant security attacks are simulated to demonstrate the effectiveness of the proposed architecture. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Large and sudden variations in packet transmission delays are often unavoidable in wireless networks. Such large delays, refer to as delay spikes (DSs), are likely to exceed several times the typical network round‐trip‐time figures, which can cause TCP spurious timeouts. The spurious timeouts lead to unnecessary retransmissions and reduction of the TCP sender's transmission rate, and degradation of TCP throughput. In this paper we propose a new scheme called DS‐Agent. The spurious timeout is detected by a DS‐Agent and thus TCP sender can response to this spurious timeout accordingly. The simulation results show the better performance of DS‐Agent scheme compared with F‐RTO and TCP Reno in the presence of DSs which is caused by mobility. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless sensor networks (WSN) will witness a huge growth in the near future and both the academia and industry have commenced intensive research. Routing protocol design in WSN is one of the most important areas of active research due to the open issues and its importance. WSNs usually contain a large number of nodes typically with highly correlated collected data. These networks can be categorized according to the network structure and their protocol operations. Network structure based categorization of WSN can be flat or hierarchical. In flat networks, all the nodes in the networks take the same responsibility, while in hierarchical networks the cluster-heads perform several special functions such as maintaining the clusters and aggregation. To support large-scale wireless sensor network management and local data aggregation, hierarchical routing techniques can be regarded as superior to flat routing approaches. Low power consumption as well as smart way of distributing the load is crucial to the routing protocol design in order to attain elongated WSN lifetime. We have already proposed a distributed routing algorithm; optimized forwarding by fuzzy inference systems (OFFIS) for the flat networks, where the decision is based on the distance power and link uses. In addition, we have proposed a two-layer OFFIS (2L-OFFIS) for environmental data collection in cluster-based sensor networks. In this talk, major routing protocols for the WSNs will be presented and the results will be compared with those that are achievable by the OFFIS and 2L-OFFIS techniques. Simulation results show that the network lifetime can be significantly elongated by utilizing the new protocol in hierarchical sensor networks.

Network lifetime is the most important concern in designing a routing protocol for wireless sensor networks. To address the issue we have proposed optimized forwarding by fuzzy inference systems (OFFIS) for flat sensor networks [1]. The OFFIS protocol selects the best node from candidate nodes in the forwarding paths by favoring small hops, shortest path, maximum remaining battery power and link usage. The core strategy of OFFIS is to conserve as well as to distribute energy dissipation evenly in a decentralized manner. In this paper we compare OFFIS with the minimum transmit energy (MTE) technique and show that the lifetime can be improved notably.

This paper presents a novel approach towards realizing a unified mobility and session management platform for next generation mobile networks at the core network level. In particular, this framework enables a common platform for interworking between universal mobile telecommunications system (UMTS), CDMA2000 technology, and wireless local area networks (WLANs). Within the proposed interworking architecture, the IP multimedia subsystem (IMS) is used as a universal coupling mediator for real-time session negotiation and management over a mobile IP (MIP) based IP mobility management framework. Despite numerous architectural challenges on how IMS and MIP may contribute for session management and IP mobility, the proposed framework achieves this with minimal changes to the existing standards. The paper concludes by presenting simulation results obtained for validating this model using an OPNET based model.

The concept of clustering can provide an efficient means of establishing a hierarchical structure in large-scale mobile ad hoc networks. In this paper we introduce a new stability-driven clustering algorithm for pseudo-linear highly mobile ad hoc networks such as that of communication between aircraft, ships, trains and cars on highways. The new algorithm aims at establishing stable clusters, where clusterhead re-election is reduced, and cluster membership periods are increased in the targeted system. The scheme is suitable even in systems where global positioning systems (GPS) is not available. The algorithm involves dynamic clusterhead election. Simulations show that the proposed clustering algorithm provides highly stable clusters with several advantages over previous 1-hop clustering schemes.

This paper presents a promising architecture for converging third-generation (3G) cellular data networks and wireless local area networks (WLANs) by using the IP multimedia subsystem (IMS), proposed by the 3G Partnership Project (3GPP), as an arbitrator. The IMS provides real-time session management, and unified session control. Within the scope of the presented framework, terminal and session handoffs are investigated for two different roaming scenarios. The first scenario investigates handoff from UMTS to WLAN and the second scenario investigates handoff from WLAN to UMTS. The paper concludes by presenting some simulation results obtained for these handoff scenarios using an OPNET based simulation model.

Wireless sensor networks (WSNs) usually have a regular lattice structure and can be considered as multi-hop multi-route networks. In multi-route transmissions, even if the link quality of several routes decreases, the whole performance of the communication would be compensated by the help of the other routes, which is referred to "the route diversity effect". As generally known, forward error correction (FEC) techniques can enhance the route diversity gain. In this paper, route diversity effect in WSNs using turbo codes is investigated. We introduce a WSN having a triangle lattice structure. BER performances of an additive white Gaussian noise channel and a slow Rayleigh fading channel are evaluated in order to show the route diversity effect.

Flooding-based approaches are incorporated in reactive routing protocols as the fundamental strategy for route discovery. They overtly affect traffic as the frequency of route discovery increases along with the mobility of users in a mobile ad hoc network (MANET). This paper presents a scheme for reducing overall traffic and end-to-end delay in highly MANET networks. Firstly a new routing algorithm is proposed to reduce the frequency of flood requests by elongating the link duration of the selected paths. In order to increase the path duration, non-disjoint paths are also considered. This concept is a novel approach in route discovery as previous reactive routing protocols seek only disjoint paths. Secondly another novel approach is presented to estimate the link expiration time without the need for global positioning system (GPS) devices. To prevent broadcast storms that may be intrigued during the path discovery operation, another scheme is also introduced. The basic concept behind the proposed scheme is to broadcast only specific and well-defined packets, referred to as "best packets" in the paper. The new protocol is simulated with regard to traffic overhead. Although our main aim in this paper is to reduce the net control traffic in a MANET network, there are other benefits arising from the proposed schemes, namely the increase in link duration, reduction in the end-to-end communication delay, less disruption in data flow, and fewer path setups.

Today, with the rapid advance in broadband technology, digital contents delivery applications have been used widely and the streaming technology has made the contents delivery more popular. Nowadays, there is high expectation on Digital Rights Management (DRM). Traitor Tracing is one of the DRM technologies and enables us to observe user's contents streaming and detect illegal contents streaming. However, malicious users can interrupt tracing with illegal processes at user-side computers. To prevent all illegal processes at the user- side, routers should analyze information embedded into packets, which is unrealistic. In this article, we propose a system to detect illegal contents streaming by using only traffic patterns which are constructed from the amount of traffic traversing routers. We also investigate a method to cope with random errors and burst errors which occur frequently in wireless environment and show the satisfactory result which we have obtained in a practical testing environment.

Rapid advances in wireless networking technologies have made it possible to construct a mobile ad hoc network (MANET) which can be applied in infrastructureless situations. However, due to their inherent characteristics, MANETs are vulnerable to various kinds of attacks which aim at disrupting their routing operations. To develop a strong security scheme to protect against these attacks it is necessary to understand the possible form of attacks that may be launched. Recently, researchers have proposed and investigated several possible attacks against MANET. However, there are still unanticipated or sophisticated attacks that have not been well studied. In this paper, we present a collusion attack model against optimized link state routing (OLSR) protocol which is one of the four standard routing protocols for MANETs. After analyzed the attack in detail and demonstrated the feasibility of the attack through simulations, we present a technique to detect the attack by utilizing information of two hops neighbors.

In this paper, we propose a fair call admission control (CAC) algorithm that prioritizes vertical handoffs in a multi-traffic and hierarchical Beyond Third Generation (B3G) network. Fairness is governed by the combined call blocking probability (includes both new and handoff calls) for both fixed capacity (e.g. 2G) and soft-capacity (e.g. 3G, WLAN etc.) networks. Average traffic profiles are considered to formulate an approximate admission solution. In fixed capacity networks handoff is prioritized by optimizing the number of reserved channels in accordance to the varying traffic profile while in soft- capacity networks prioritization is achieved by optimizing the priority ratio of new and handoff calls. Because of its simplicity, the proposed algorithm can easily coexist with traditional CAC algorithms supporting horizontal handoffs, thus preserving the individual networks within the B3G framework.

In light of the fast propagation of recent Internet worms, human intervention in securing the Internet during worm outbreaks is of little significance. In order to reduce the damage worms may cause, existing intrusion detection systems (IDSs) need to be adaptive to the security-related requirements of their monitoring networks. This paper presents a Multilevel security based Autonomic Parameter Selector (MAPS) that can be implemented over any existing IDSs. The deployment architecture consists of a number of hierarchically placed local security managers, metropolitan security managers, and a global security manager. These security managers report events to a worm advisory system (WAS). WAS accordingly sets the threat level of the network. Based on this level, MAPS selects the most optimum parameters for the entire IDS to combat against the propagating worm. The MAPS architecture maintains the system performance by constantly evaluating three metrics, namely False Negative Avoidance, False Positive Avoidance, and performance overhead. Extensive experiments, using real network traffic and a recently proposed worm detection system, demonstrate that MAPS is capable of advising an IDS with optimum parameter values to effectively and promptly hinder further propagation of worms.

In order to support seamless mobility in next-generation overlapping wireless networks, we develop a novel network selection scheme, which decides an optimum network through discovering a tradeoff among users' preferences, operators' benefits, network conditions and application requirements. A merit function is defined to select the best possible network for a mobile user. A negotiation process between the user and the selected network operator is developed to guarantee the operator can obtain benefits from accepting the user's handoff request. The network environment is formulated as a semi-Markov decision process (SMDP) in the negotiation process. An optimal policy that maximizes the network revenue without violating any quality-of-service (QoS) constraints is found by resolving the SMDP problem using Q-learning. The simulation results demonstrate that the proposed network selection scheme enhances the performance in terms of handoff call-dropping probability (HCDP) and network revenue.

Due to geographical and/or climatic constraints, the community of future satellite users will exhibit a significant variance in its density over the Globe. This density variance will yield a scenario where some satellite links are congested while others are underutilized. To ensure an intelligent engineering of traffic over satellite networks, this paper proposes a routing protocol that enables neighboring satellites to explicitly exchange information on their congestion status. A "soon-to-be-congested" satellite requests its neighboring satellites to decrease their data forwarding rates. In response, the neighboring satellites search for less congested paths that do not include the satellite in question and communicate a portion of data, primarily destined to the satellite, via the retrieved paths. By so doing, congestion, and the resulting packet drops, can be avoided. A better distribution of traffic among satellites can be guaranteed as well. The proposed scheme is dubbed "Explicit Load Balancing" (ELB) scheme. A set of simulations is conducted to evaluate the performance of the ELB scheme using the Network Simulator. In terms of Quality of Service, encouraging results are obtained: better traffic distribution, higher throughput, and lower packet drops.

This paper describes the architecture of a global sensor network based on a constellation of LEO satellites. The considered sensor network is heterogeneous: two types of sensor nodes are envisioned. One type does the sensing and relays the gathered data to the other type that performs data aggregation and communicates it directly to the satellites. The main challenging tasks in the design of the architecture are explored and adequate solutions are provided. A set of data dissemination techniques is then presented. Following this, a mathematical model is developed to evaluate the energy use of the sensors. Open research issues for the realization of such architecture are finally discussed.

In this paper, we propose the method for reducing out-of-order packets at handover event in LEO satellite networks. In LEO satellite networks, every communicating terminal handovers independently. Therefore, delay between terminals varies drastically within a short period. This drastic delay variation causes out-of-order packets and unnecessary fast retransmission of TCP. To avoid such delay variation, the proposed method makes a satellite to predict and control handover timing of connected user terminals. In the proposed method, two communicating terminals handover in a synchronized manner. By doing this, out-of-order packets at a handover can be reduced and this contributes to avoid occurrence of TCP's false retransmissions.

Sustaining long link durations in highly mobile ad hoc networks presents a great challenge, mostly untreated in recent literature. In this paper we introduce a new routing algorithm based on the relative velocity of mobile nodes, which also incorporates Quality of Service (QoS), termed QoS Multipath Doppler Routing (QaS-MUDOR). The primary aim of QoS-MUBOR is to maintain long link durations, whilst meeting QoS constraints. The routing protocol proposed is based on data retrieval from nodes, where nodes act as content providers. This simulates scenarios such as downloading a file, a web page, or any form of data from other nodes which can provide it. We will show how utilizing the relative velocity of nodes using the Doppler shift subjected to packets assists in selecting stable paths, whilst maintaining the QoS requirements in highly mobile pseudo-linear systems such as an aeronautical ad hoc network.

Internet Worms pose a serious threat to today's Internet. Signature matching is an important approach to detect worms. However, as most signature development processes are manual, they require significant time. They are thus not efficient in reducing the damage worms may cause. In this paper, an efficient signature-based method is proposed for automatic detection of worms over large-scale networks. In the proposed system, detection is performed in a hierarchical manner. Security managers of local networks collect worm-like or suspicious flows and handle these flows to high-hierarchy metropolitan managers. In response, the latter use this information to generate robust signature. The global manager which lies on top of the hierarchy, multicasts the signature to local managers via metropolitan managers. This enables local managers to detect worms that try to penetrate into their networks. The proposed system is evaluated using an off-line real network traffic that contains traces of worms. Experimental results indicate that the proposed system exhibits high detection rates with low false alarm rates.

Over the past few years, a number of private networks have emerged. In these private networks, a server provides its subscribed clients with Internet services, forming a one-to-many network topology. Given the fact that users are located at different distances from the server, usage of the Transmission Control Protocol (TCP) for communication results in drastically unfair bandwidth allocations among the users. In this regard, this paper addresses the fairness and efficiency issues of TCP in such one-to-many IP (Internet Protocol) networks. The efficiency of TCP is controlled by matching the aggregate traffic rate of all TCP connections to the sum of the link capacity and total buffer size. On the other hand, its unfairness issue is mitigated by allocating bandwidth among individual flows in relative proportion with their RTTs. Simulation results elucidate that the proposed method makes better utilization of the network resources, reduces the number of packet drops, and provides a fair service to users.

Recently, sensor networks have been drawing many attentions. The networks can be applicable to many communication systems such as emergency communications, intelligent transportation systems, and so on. They could have a multi-route multi-hop structure by the signal relaying via multiple sensor nodes. Since the wireless links have very severe condition for the communications, a method which can maintain the quality of the links is required. Forward error correction techniques and turbo codes are effective ways to prevent performance degradation by the wireless links. In the multi-hop multi-route structure, the route diversity can be enhanced by using the error correction codes. For the decoding process of the turbo codes, the receiver should estimate the channel information to obtain high decoding performance. Although many channel information estimation methods are not applicable to the multi-hop structure, the channel information estimation method utilizing parity bits can be applied. In this paper, we investigate the route diversity effect of the channel information estimation scheme. We also provide performance comparison between the proposed scheme and the pilot aided scheme. The results suggest that the proposed scheme is effective in obtaining the route diversity.

Multi-hop multi-route networks are promising candidate for achieving the high channel capacity. The networks have high applicability to various new communications. Especially sensor networks are one of the attractive applications of the multihop multi-route networks. When the signals are transmitted over multiple routes, a route diversity effect would be possible. The effect can help to prevent performance degradation caused by quality loss of every route. Forward error correction schemes and turbo coding can be expected to enhance the route diversity. As it would be well known, a turbo encoder generates one message stream and two parity streams and the message stream is more important than the parity streams for reliable communications. Thus by allocating the message stream to a reliable route, the performance degradation could be prevented. In this paper, we examine the effect of the route diversity on multi-hop networks using turbo codes. The diversity effect against the quality loss of the multiple routes is investigated. The results suggest that the multi-route transmissions have high diversity effect. They also suggest that large performance degradation can be prevented by appropriate route assignment of the streams even though the link quality of the multiple routes can not be guaranteed.

In this paper, we propose a method to reduce the interference and increase the throughput of orthogonal frequency division multiplexing (OFDM) systems in co-working wireless local area networks (WLANs) by using joint adaptive multiple antennas (AMA) and adaptive modulation (AM) with acknowledgement (ACK) eigen-steering. The calculation of AMA and AM are performed at the receiver. The AMA is used to suppress interference and to maximise the signal to noise plus interference ratio (SNIR) by adjusting its weights dynamically. The improved SNIR is then used by AM as an input to allocate OFDM sub-carriers, power, and modulation mode subject to the constraints of power, discrete modulation, and the bit error rate (BER). The transmit weights, the allocation of power, and the allocation of sub-carriers are obtained at the transmitter using ACK eigen-steering. The derivations of AMA, AM, and ACK eigen-steering are shown. The performance of joint AMA and AM for various AMA configurations are evaluated through the simulations of BER and spectral efficiency (SE) against SIR. The simulation results for joint AMA-AM also show that joint AM and receive beamforming produce the most effective solution in co-working OFDM-WLANs

Wireless sensor networks (WSNs) usually contain a large number of nodes typically with highly correlated collected data. To support large-scale wireless sensor network management and local data aggregation, hierarchical routing techniques can be regarded as superior to flat routing approaches. Low power consumption as well as smart way of distributing the load is crucial to the routing protocol design in order to attain elongated WSN lifetime. We have already proposed a distributed routing algorithm; optimized forwarding by fuzzy inference systems (OFFIS) for the flat networks, where the decision is based on the distance power and link uses. In this paper, we propose a two-layer OFFIS (2L-OFFIS) for environmental data collection in cluster-based sensor networks. Simulation results show that the network lifetime can be significantly elongated by utilizing the new protocol in hierarchical sensor networks

This paper presents a time-adaptive vertical handoff decision scheme for overlapping wireless networks. The proposed scheme discovers all the reachable networks and then selects the most suitable one as the handoff target through performance evaluation that relies on user preferences and service requirements. In order to guarantee the right decisions can be made timely with the minimum battery power consumption on interface activation, two algorithms of producing dynamic activating intervals are developed. The simulation results reveal that the proposed method can effectively balance the spent time and consumed power for making handoff decisions

In the near future, RTP/RTCP-based multimedia streaming will become the norm not only in wired networks but also in mobile environments. Presently when a handoff occurs between heterogeneous networks (with different available bandwidths), a RTP sender cannot stream media at a suitable rate over the new network. Furthermore, RTCP fails to precisely adapt to sudden changes in network resources due to handoff. In order to solve these issues, 1) senders should be aware when mobile nodes are about to perform a handoff; 2) senders should then efficiently probe the available bandwidth in the new network and accordingly adjust their streaming rates. In this paper, we propose a scheme that allows mobile nodes to explicitly notify their handoff timing by using newly-defined RTCP packets. In the proposed scheme, senders probe the available bandwidth in the new network using low-priority RTP dummy packets. The performance of the proposed scheme is evaluated and compared with conventional schemes through extensive simulations. The simulation results show that the proposed scheme achieves appropriate bandwidth utilization immediately after a handoff occurrence and lowers packet losses during the handoff. The proposed scheme exhibits also high TCP-friendliness

There has been an enormous growth in mobile ad hoc networks (MANETs) in land based small to medium size networks with relatively strict power and resources. In this paper the concept of ad hoc networking between aircraft is introduced, which can be considered as a novel approach in increasing the data rate and practicality of future in-flight broadband Internet access. This method also reduces the Internet traffic load on satellite nodes and also propagation delay for real-time traffic transmissions, by effectively bypassing the satellite link for nonreal time data. A dynamic routing algorithm is also proposed for efficient routing in this kind of system. A new cost metric for increasing path duration is introduced to assist routing in the proposed ad hoc network

Internetworking over vehicle ad-hoc networks (VANETs) is getting increasing attention from all major car manufacturers. The design of effective vehicular communications poses a series of technical challenges. Guaranteeing a stable and reliable routing mechanism over VANETs is an important step towards the realization of effective vehicular communications. In current ad-hoc routing protocols, the control messages in reactive protocols and route update timers in proactive protocols are not used to anticipate link breakage. They solely indicate presence or absence of a route to a given node. Consequently, the route maintenance process at both protocol types is initiated only after a link breakage event takes place. This paper argues the use of information on vehicle headings to predict a possible link breakage event prior to its occurrence. Vehicles are grouped according to their velocity vectors. When a vehicle shifts to a different group and a route, involving the vehicle, is to be broken, the proposed protocol searches for a more stable and "more durable" route that includes vehicles from the same group. The proposed scheme is dubbed velocity-heading based routing protocol (VHRP). Whilst the proposed scheme can be implemented on any existing routing protocol, the paper considers the case of VHRP over destination-sequenced distance vector (DSDV) routing protocol. The performance of the scheme is evaluated through computer simulations. Simulation results indicate that knowledge on the vehicles' heading adds major benefits to routing in terms of reducing the number of link breakage events and increasing the end-to-end throughput

In this paper, the concept of a new form of mobile network formed in the sky is introduced, where the mobile routers are simply the commercial aircraft. This implementation aims to eliminate two main problems arising from the current in-flight broadband implementation. The first problem is the resource management issue that may arise from the rapid increase of in-flight broadband Internet use in the near future. This could consequently limit current satellite resources, and bandwidth. The other issue is the inherent problem associated with Internet use over satellite, such as the degraded performance of delay sensitive applications due to the long propagation delay of a satellite link. A system model for data access, stable clustering of aircraft, and efficient routing schemes are introduced, which are suitable for the aeronautical mobility model. Link stability is predicted by a novel approach using Doppler shift subjected to control packets to dynamically form stable clustering and routing protocols. Another aim of this paper is to show that relative velocity between nodes is adequate as a stability metric, dominating relative distance, and this becomes evident in the simulations presented. An outline of how the new system could potentially interact with the traditional Internet using Mobile IP is also briefly discussed

Support of ubiquitous roaming in fourth generation or beyond third generation (4G/B3G) networks (M.R. Kibria et al., 2005) will require accurate vertical handoff prediction in order to reserve resources in neighboring cells and to keep the handoff call dropping probability within an acceptable level. However with random terminal mobility and different cell structures (e.g. microcells, macrocells), it becomes extremely difficult to accurately predict the time to initiate such a vertical handoff. In this paper we propose a mobile terminal (MT) controlled three-step handoff prediction algorithm that utilizes geographical location information to predict handoff. The location aware (step-I) MT initiates a handoff when a better access network becomes available at its current location (step-II). A suitable network selection process, adhering to user preferences, is therefore proposed to optimize system performance and resource usage. Following step-II, the terminal proceeds with predicting handoff to neighboring networks through a hysteresis based triggering mechanism as it approaches the handoff threshold near the current cell boundary (step-III). Simulation results demonstrate the accuracy of our algorithm in both microcells and macrocells

One technique to provide high data transmission for large number of users in mobile cellular networks is to arrange the network in a multi-hop structure. Multi-hop networks have large channel capacity and can achieve multi-route transmissions. When the information bit streams are transmitted over multiple routes, a route diversity effect would also be possible. In multiroute transmission, we usually require the routing protocols which can make every route condition be equal. The requirement, however, makes the routing algorithm be complex and causes large power consumption. Forward error correction schemes and turbo coding can be far from the strict requirement although they enhance the effect of route diversity. When applying the turbo codes, the receiver should know the channel condition for iterative decoding. Thus we have to consider how to obtain the channel information over the multi-route multi-hop network. Furthermore, the performance would improve when the destination knows the difference of the route reliability. In this paper, we propose the turbo coded multi-route multi-hop networks using parity check bits. By using parity bits, we can easily estimate the channel information in multi-route multi-hop networks. Using numerical examples we show that the proposed scheme improves the performance in additive white Gaussian noise and slow fading channel conditions

The development of fourth generation or beyond third generation (4G/B3G) networks is driven by the need to offer the subscribers with the convenience of using the same end terminal to obtain seamless services across heterogeneous networks. In this paper, we propose a consolidated 4G/B3G architecture that is open, hierarchical, layered and modular with cross-layer coordination and distributed network functionalities. Well-defined message interfaces enable the layers/levels within the architecture to support the cross-layer coordination under dynamic network conditions. Novel augmentations in mobility and resource management schemes are proposed that result in consolidating the corresponding salient areas of 4G/B3G architectures discussed in the literature. Seamless services in the network can be accessed through our reconfigurable multi-antenna end terminal. A common mechanism for control/signaling across heterogeneous networks is adopted to facilitate wireless system discovery and paging. A multicast protocol is proposed to ensure the reliable transfer of control/signaling messages over this incorporated signaling system.

The predominant objective of next-generation networks (i.e. 4G) is to support high bandwidth with high mobility. 3G technologies provide low bandwidth at a cellular level, and wireless local area network (WLAN) supports much higher bandwidth at a local level. The two types of technologies are seamlessly integrated in 4G networks. In this paper, we present an efficient network selection mechanism for next-generation networks to guarantee mobile users being always best connected (ABC). Two mathematical techniques are combined in the mechanism to decide the optimum network for mobile users through finding the tradeoff among user's preference, service application, and network condition.

Large and sudden variations in packet transmission delays are often unavoidable in wireless networks. Such large delays, refer to as delay spikes, are likely to exceed several times the typical network round-trip-time figures, which can cause TCP spurious timeouts. The spurious timeouts lead to unnecessary retransmissions and reduction of the TCP sender's transmission rate, and degradation of TCP throughput. In this paper we study the effect of delay spikes caused by handover on TCP performance by using three different mobility models. The results show that the throughput of TCP connection over a single bottleneck link is decreased significantly in the presence of delay spikes. Furthermore, it is shown that the fairness feature of TCP is also severely affected in the presence of delay spikes.

Mobile ad hoc networks (MANET) are usually formed without any major infrastructure. As a result, they are relatively vulnerable to malicious network attacks and therefore the security is a more significant issue than in infrastructure-type wireless networks. In these networks, it is difficult to identify malicious hosts, as the topology of the network changes dynamically. A malicious host can easily interrupt a route for which the malicious host is one of the forming nodes in the communication path. In the literature, there are several proposals to detect such malicious host inside the network. In those methods usually a baseline profile is defined in accordance to static training data and then they are used to verify the identity and the topology of the network, thus avoiding any malicious host to be joined in the network. Since the topology of a MANET is dynamically changing, use of a static profile is not efficient. In this paper, we propose a new intrusion detection scheme based on a learning process, so that the training data can be updated at particular time intervals. The simulation results show the effectiveness of the proposed technique compared to conventional schemes

The course of migration from third generation networks (3G) to fourth generation or beyond third generation (4G/B3G) networks is strewn with challenges to offer any type service - anytime, anywhere and anyhow. The objectives of this paper are two-fold: firstly to define our enhanced 4G/B3G network architecture and secondly to dimension it. In this regard, we give an overview of our enhanced 4G/B3G architecture topology and compare its salient features with the corresponding features of other key 4G/B3G architectures examined by us from the literature. A review is provided of some of the work that has been conducted for achieving an efficient network dimensioning of a 3G packet switched network that offers integrated multimedia services. The network dimensioning of our enhanced 4G/B3G architecture deals with the dimensioning of the radio access network as well as of the core network elements. For both dimensioning processes we have considered voice only traffic so as to ensure that the infrastructure has sufficient number of elements to handle at least voice service whenever there is an outage of other services. This consideration also enables us to apply the Erlang-B formula

The operation of a 4/sup th/ generation or beyond 3/sup rd/ generation (4G/B3G) network will depend largely on the close coordination between mobility, resource and quality of service (QoS) management schemes. The 4G/B3G network is expected to serve stationary as well as mobile subscribers under dynamic network conditions and offer anytype service - anytime, anywhere and anyhow. In this paper, we present and discuss a hierarchical, layered and modular 4G/B3G network architecture with cross-layer coordination and distributed network functionalities. A common mechanism for control/signaling is adopted to facilitate wireless system discovery and paging. Novel augmentations in mobility, resource and QoS management schemes with well-defined message interfaces are proposed to enhance cross-layer coordination. These augmentations will consolidate the salient areas of the 4G/B3G architectures that have been discussed in the literature. We have also proposed a reconfigurable multi-antenna, multi-service and QoS supported mobile terminal architecture to enable seamless mobility across different access technologies.

The enormous growth and demand for in-flight Internet access has driven the need for producing effective simulation tools in order to identify effectively the best resources, such as satellites and gateways, for routing in these large-scale systems. We introduce a simple simulation tool called Aerouter, which can assist in identifying the best satellites and gateways for routing in aeronautical applications. The proposed simulation tool includes new routing techniques and also takes Doppler and atmospheric attenuations into account in its automatic calculations. Preliminary results illustrate the effectiveness and usefulness of Aerouter for future in-flight Internet access networks.

The paper summarizes the development of a genetic-based rate control algorithm. The aspect of rate control design focused on is specifically for implementation on the link/network layers. In this respect, the underlying concept is to adjust the transmission rate dynamically, subject to variations in network conditions such that distinct QoS requirements from individual users are complied with. The novelty of this work is mainly from two points of view: the consideration of near-far effects in the assignment of transmission power; the dynamic allocation of bandwidth in accordance with the variations in network conditions which include, but are not limited to, traffic characteristics and network loading. Simulation results show that the proposed rate control algorithm not only reduces the undesirable outage probability, but also increase the overall system throughput due to a reduced number of necessary retransmissions.

Middleware for service creation in wireless sensor networks requires new architectures because of the tiny size of wireless sensors. This article preconizes a new service creation architecture named Archises (architecture of intelligent services for sensor networks). Archises supports mechanisms for self-organization, networking and energy optimization to build higher-level service structures in wireless sensor networks.

Abstract Provision of quality of service (QoS) support with mobility has received impetus from the evolution that has occurred in mobile communication technologies. This is especially the main driver behind 4G/B3G networks that will enable a user to access any type of services anytime, anywhere, and anyhow. In this paper, we first review some of the state‐of‐the‐art techniques in the area of QoS and resource management based mobility. We then propose an augmented 4G/B3G architecture that addresses key limitations in the salient 4G architectures examined in the literature. Our proposed novel augmentations in mobility, QoS, and resource management schemes consolidate operation of the 4G architecture. The other defining attributes of our proposed 4G architecture are that it is open, hierarchical, layered, and modular with cross‐layer coordination and distributed network functionalities. Well‐defined message interfaces enable the layers/levels within the architecture to support cross‐layer coordination under dynamic network conditions. A signaling network that operates independent of the heterogeneous access networks in the architecture is incorporated to carry out wireless system discovery and paging. This facilitates to conserve mobile terminal's battery power. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract The success of broadband service has attracted more people to surf internet with the demand of high mobility and high bandwidth. The ongoing wireless local area network (WLAN) standardization and development activities allow WLAN to provide high data rate in unlicensed spectrum. It motivates cellular network operators to adopt WLAN as a complement to cellular 3G systems in hot spot areas. Consequently, it is imperative to develop a network selection technique to assure quality‐of‐service (QoS) for the integrated cellular/WLAN system. In this paper, an optimization scheme for mobile users to select a network in an integrated WLAN and universal mobile telecommunication system (UMTS) system is proposed. In order to provide users a prospect of being always best connected, analytic hierarchy process (AHP) and grey relational analysis (GRA) techniques are integrated to make decision. The former defines selection criteria and the latter evaluates network alternatives. The network selection module is built at the link layer, and collects the time‐varying QoS information through cross‐layer message signaling. Simulations conducted in a heterogeneous system with UMTS and WLAN show that the proposed network selection technique can successfully satisfy QoS requirements for a variety of applications through a fair decision on the optimum network for mobile users at any time. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper, we propose a new adaptive scheme in which an optimal distance-based update threshold is selected not only as a function of the call-to-mobility ratio, but also as a function of mobile's traveling patterns, modeled by a transitional directivity index. We demonstrated that by means of the optimal threshold selection, the impact variations in the inter-ring transitional probability on the operational cost can be determined. Simulation results show that the additional information made available about roaming mobile's transitional directivity is critical to ensure the realization of an optimal update threshold. Other advances include the simplification of the existing Markovian movement model presentation.

A new flow control scheme improving performance of the future broadband satellite networks is proposed and analyzed in this paper. The proposed scheme is particularly suitable for the Internet-based services in broadband satellite networks. The complexity of the proposed scheme is comparable with the existing flow control techniques, as it does not require the additional information to be provided by either the satellite or the Earth stations. The throughput and the satellite queue size performances of the proposed scheme are mathematically analyzed and simulated. The results show the significant improvement in the proposed scheme comparing with the conventional window-based and rate-based flow control techniques. Such a scheme can be used in the future satellite networks, which are an important component in the global information infrastructure (GII).

The increasing variety and complexity of traffic in today's mobile wireless networks means that there are more restrictions placed on a network in order to guarantee the individual requirements of the different traffic types and users. Call admission control (CAC) plays a vital role in achieving this. We propose a precision predictive CAC (PPCAC) scheme in which existing methods to determine the call occupancy distribution in each cell are modified in order to predict the number of calls of each individual service. Information about the channel usage of each service is then used to calculate the capacity of the cell to ensure that each service's bit error rate (BER) requirements are met. Priorities assigned to each service are used to allocate the network capacity. An expression for the handoff dropping probability is derived and the acceptance rate for each service is calculated in order to meet the guaranteed quality of service (QoS). Each call is then accepted with equal probability throughout the duration of a control period. The new call acceptance rate is adjusted to ensure that the dropping probability remains fixed. The complexity of this algorithm is offset by the fact that most of the computations can be completed offline and it only requires readily available network measurements. Simulations conducted in a CDMA environment with voice, short message service (SMS) and World Wide Web (WWW) sources show that the proposed CAC can successfully meet the hard restraint on the dropping probability and guarantee the required BER for multiple services.

In this paper, we propose a new paging technique, sectional paging, that reduces the paging cost while complying with the delay constraint for mobiles roaming with traceable patterns. Without having to install much additional intelligence, the developed scheme predicts the likelihood of residence and assigns the optimal paging boundaries. Thus, while complying with the required delay constraints, QoS measures will not need to be sacrificed as a result of increasing the update threshold. Under the same network conditions and mobile characteristics, simulation results reveal that sectional paging outperforms the ring-structured paging scheme as long as the mobile has a granularly traceable movement pattern. It is concluded that the usage of the newly proposed scheme is most suitable when the roaming pattern is either traceable or can be predicted with reasonable precision. As an additional advantage, it is noted that the designed technique could be used for all basic update schemes.

Due to common usage of the TCP/IP protocol stack in Internet applications that require reliable data transfer, it is important to keep the protocol stack (and also the network element structure) as unchanged as possible even when mobility features required in wireless Internet are added into the network. However, TCP performs poorly in wireless networks running at high bit error rates. In this paper, a new enhancement to TCP based on a previously proposed scheme is introduced. The method is based on the Snoop protocol and is similarly used for fixed host to mobile host direction. The proposed method has been compared with the Snoop protocol and other enhancement protocols proposed for the wireless TCP. It has been shown that the new explicit loss notification with acknowledgement has superior performance to Snoop and other protocols. The proposed method improves the end-to-end reliable transport performance in mobile environments.

The increasing quality of service (QoS) requirements for global mobile IP generates the usage of ATM based low Earth orbit (LEO) mobile satellite (SATATM) networks as the backbone network. Such networks can take advantages from both ATM and mobile satellite networks. In this paper, in order to reduce the system P/sub HBP/ (probability of handoff blocking) and delay jitter caused by handoff events, an effective routing scheme, named traffic-oriented path preserved (TOPP) scheme, is proposed. According to the TOPP scheme, firstly, real-time channel traffic detection is processed prior to the path selection, which is supposed to alleviate the system traffic congestion situation. Secondly, predictable knowledge of a LEO satellite network is used to preserve some potential path candidates for the coming handoff events, which is supposed to reduce the system P/sub HBP/. Finally, a sliding-width parameter is used to alleviate the fluctuation caused by the changing of path length during the handoffs to gracefully reduce the variance of end-to-end delay and thus to decrease the system delay jitter.

In this paper, aiming at improving the utilization of network resources and providing the network with better QoS guarantees such as new call blocking probability (CBP) and handoff call blocking probability (HBP), we present an adaptive channel management scheme (ACMS) for routing and handoff in WATM mobile satellite networks. In this scheme, according to both the system mobility feature and the traffic situation in the inter/intra satellite links (ISLs), part of the channels will be dynamically reserved particularly for the handoff calls in order to reduce the HBP while guaranteeing a certain CBP requirements. Besides this, the channel traffic will be real-time and fully investigated during the path selecting so as to make more effective utilization of the network resources and thus improve the network performance from QoS HBP and CBP points of view.

This paper aims to compare different location management techniques and their suitability for adaptation in the next generation wireless IP networks. It is found that in a macro-mobility environment, the pointer forwarding scheme would be the best alternative for managing mobility. Its performance is particularly promising in a scenario where a more frequent mobility pattern is to be expected. By contrast, for micro-mobility scenarios, the local anchoring scheme seems to give the most satisfactory overall performances. Although it does attract more database accessing loads, the extra signaling can be easily justified by the significant reductions achieved in the updating loads.

Next-generation broadband satellite networks are being developed to carry bursty Internet and multimedia traffic in addition to the traditional circuit-switched traffic (mainly voice) on a global basis. These satellites provide direct network access for personal applications as well as interconnectivity to the terrestrial remote network segments. The main requirement in success of these networks is that they should be able to transmit high data rate traffic with prescribed quality of service (QoS). Thus, the broadband satellite network has no choice other than the rise of ATM technology and to be optimized for Internet-based traffic. ATM is the promising technology for supporting high-speed data transfer potentially suitable for all varieties of private and public telecommunications networks. IP, on the other hand is the fast-growing Internet layer protocol that is applicable over any data link layer Internet-based applications are the emerging source of traffic in the future wireless networks and broadband satellite networks should consider Internet as the primary service. In this paper, we discuss the traditional ATM and wireless ATM networks and explain the characteristics of the wireless IP networks. The paper then uses those concepts in defining the criteria for the broadband satellite networks such as the QoS and traffic management. Application of the broadband satellite networks is also proposed.

The effect of multiple-access interference on the throughput performance of a direct-sequence spread-spectrum low-Earth-orbiting satellite communications network is discussed. To recognize the effect of interference when their sources are either inside or outside the service area of a satellite, we develop a stochastic model for the location of users. We show that the effect of interference on the performance degradation from users with large propagation distance to their connecting satellites is the dominant factor. Hence, to improve the performance of the system, we propose a method in which the transmissions of packets are controlled according to their distances to connecting satellites as well as the traffic distribution.

A transmit permission control method for improving the throughput characteristics of a low Earth orbit (LEO) satellite communication system employing spread-slotted ALOHA multiple-access scheme is proposed. Both nonfading and fading satellite links are considered. The basic idea of the proposed scheme is to decrease the level of interference at each satellite and, hence, to increase the probability of packet success, by prohibiting the packet transmission from the users with relatively high propagation loss to their connecting satellites. It is shown that the method has the ability to improve the throughput performance in heavy traffic loads and the peak value of the throughput, significantly. It is also shown that the average delay performance of the system employing the proposed scheme is superior to that of the conventional system at heavy traffic loads.

In some recent studies, the use of low Earth-orbit satellites in various applications is considered. In all of these studies, uniform distribution of traffic load is assumed. In this paper, the performance of a low Earth-orbit satellite communication system which is designed to service to two kinds of users; i.e., voice users and data users is estimated. The distribution of population of these users is assumed to be nonuniform. According to the simulation results, it is shown that the nonuniformity in traffic affects the performance of the system by decreasing the signal quality at the satellite which has to service to populated areas and increasing it superfluously at the satellite with not so populated service area, in a given period of time. By modeling the satellites during their movements, the change in signal quality while experiencing a peak of traffic load in their route is also determined. A modified power control method based on the amount of traffic load of each satellite is also examined and is shown that this method can make some performance improvements in signal quality, which is limited by special features of low Earth-orbit satellite systems.>