With mobility and flexibility in non-terrestrial environments, unmanned aerial vehicles (UAVs) have shown the potential for emerging applications, such as remote surveillance and mobile base stations. This work raises the emerging need to chain UAV relays in support ofon-demand connectivityto faraway users. However, the ideal isotropic antennas are too simplified to ensure connectivity among UAVs. Since antennas used in realistic communication systems including UAVs are non-isotropic, and the angle of radiations (AoR) between a pair of transmitter and receiver significantly influences received signal strengths (RSSs). Therefore, this work takes the AoR into account to deploy UAV relays in a 3D environment. Not only positions but also the headings of UAVs are modeled in the AoR-based deployment problem to minimize the number of UAVs in the relay chain, while the end-to-end RSSs are guaranteed. We prove the NP-hardness of the AoR-based deployment problem. Then, theAngle-of- Radiation-based Deployment (AIM) algorithmis proposed to solve the problem. The extensive simulation results show that the AIM algorithm reduces the number of UAVs used in the relay chain by 59.6% compared to the baselines when the heading of each UAV is well-adjusted. Also, the proposed AIM algorithm outperforms the baselines by 61.2% in terms of the average number of UAVs and demonstrates adaptability to various terrains.

The rise of 6G networks brings ultra-fast communication and broad connectivity, enabling intelligent applications such as IoT, smart cities, and autonomous vehicles. However, training AI models in these environments often faces privacy concerns and limited labeled data. Federated Learning (FL) provides a privacy-preserving solution by allowing clients to collaboratively train models without sharing raw data. Yet, FL still struggles with the scarcity of labeled data due to privacy constraints. Semi-Supervised Learning (SSL) can alleviate this issue by leveraging both labeled and unlabeled data. While combining SSL and FL (FSSL) offers promise, it also introduces new challenges, such as confirmation bias and degraded performance under non-IID data distributions. Most existing FSSL methods assume uniform labeling capabilities across clients, which is rarely the case in practice. This leads to a new challenge only occur in FSSL called annotation heterogeneity, where some clients have many labeled samples while others have few or none. When combined with data heterogeneity, this dual-data heterogeneity (data and annotation heterogeneity) severely affects global model performance. Therefore, FSSL must learn under label scarcity while preserving privacy, remain stable when non-IID data is compounded by annotation heterogeneity, and stay communication-efficient for mobile deployment. In this work, we propose Federated Fisher Focus Filtering (FedF3), an enhanced framework built upon our previous SynFMPL method, to address the combination effect of dual-data heterogeneity. FedF3 introduces a two-stage strategy, Adaptive Loss Filtering stabilizes early training by suppressing unreliable contributions under dual heterogeneity, and Fisher Focus Selection preserves accuracy with Fisher-guided sparsity to meet communication budgets. Our method demonstrates robust performance improvements across diverse and heterogeneous FL settings, mitigating the negative effects of dual-data heterogeneity while preserving personalization and privacy.

Time-Sensitive Networking (TSN) is essential for Industrial Internet of Things (IIoT) applications demanding deterministic communication with zero jitter. Existing approaches are limited: Time-Aware Shaper (TAS) exhibits high computational complexity, while Cyclic Queuing and Forwarding (CQF) cannot guarantee zero jitter. Current integrated solutions fail to address scalability challenges for isochronous flows. We propose Zero-Jitter Cyclic Queuing and Forwarding (ZCQF). This novel architecture uses CQF for initial transmission and implements a reordering mechanism with TAS at the final hop to achieve zero-jitter transmission. Experimental results demonstrate that ZCQF outperforms existing methods across multiple network topologies, achieving higher schedulability and shorter runtime, validating its effectiveness for time-critical IIoT environments.

The development of smart grids has drawn increased interest from power utilities to support the stable operation of power grids. A key component, the wide-area measurement system (WAMS), integrates phasor data concentrators (PDCs) and phasor measurement units (PMUs) to collect the necessary electrical information for power grid monitoring, control, and protection. However, transmission delays and losses challenge the availability of measurement data in WAMS, especially as data travels through legacy communication networks. To address these issues, we investigate the time alignment mechanism PDCs use to synchronize data from multiple PMUs, identifying latency caused by the waiting time for delayed or failed PMUs. We propose an adaptive configuration mechanism for measurement data transmission that dynamically adjusts PDC wait time and PDC-PMU pairing to mitigate unnecessary latency without compromising data availability. The experiment results show that the proposed method outperforms the benchmarks regarding the latency of measurement data by up to 76 % improvement.

Recently, unmanned aerial vehicles (UAVs) have shown potential for a variety of emerging applications with their high mobility and versatility. To conduct the tasks and receive control and command signals, the seamless connectivity for the UAVs is crucial. While the aerial connection can be provided by the existing ground cellular network, the down-tilted antennas of the ground base stations (GBSs) and the beam lobes generated by the antenna pattern lead to inevitable null areas in the airspace, making it challenging to ensure connectivity and safety in the sky. In this paper, we leverage an unmanned aerial relay (UAR) to extend the signal coverage of existing GBSs, enabling connectivity for aerial user equipments (AUEs) in aerial null areas. We design an efficient algorithm, Connectivity-Aware UAR (CARE-U), optimizing the UAR's trajectory to serve AUEs with pre-scheduled tasks. By simulations in both synthetic and realworld settings, the connected time ratio of the AUEs improves up to 66.3 % compared to that without the UAR. Besides, CARE-U outperforms the approach that modifies trajectories of AUEs, showing the great compatibility of CARE-U.

Federated Learning (FL) is a promising framework for enabling edge intelligence while preserving privacy and enhancing communication efficiency. In real-world scenarios, limited and heterogeneous hardware resources among users threaten the overall training efficiency in FL. To address this issue, several model heterogeneity frameworks have emerged in recent years. However, existing studies primarily focus on mitigating idle times on powerful devices, overlooking the fact that strong devices may not always have a positive impact and can potentially become a burden instead. Furthermore, as the resource gap among clients widens or the number of weak devices increases, the model may be hindered by the influence of strong devices. Directly applying existing model heterogeneity frameworks leads to model performance degradation due to the neglect of contributions from weak devices. To make a performance breakthrough, we proposed a novel FL framework called Progressive Width Mixing (PWM), which extracts diverse features and balances the contribution of strong and weak devices and mitigates straggler effects under scenarios with various device distributions. To our best knowledge, we are the first model heterogeneity work that considered straggler effects from the weak devices' point of view. The experiments show that PWM maintains stability over different datasets and outperforms all baselines across various device distributions and non-IID scenarios. PWM makes a leap forward and surpasses all baselines by 7%, and mitigates the negative impact from the strong devices.

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts:glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements.

Various modern industrial applications necessitate deterministic communication, assuring that each packet of a data flow arrives at a specific time. Most existing Time-Sensitive Networking (TSN) scheduling mechanisms enforce temporary isolation (any two flows cannot simultaneously be in the same queue in a network device) to ensure such deterministic property. This approach has no significant side effects in a wired Ethernet environment. However, recent calls have been made for incorporating TSN and wireless technology, such as 5thgeneration (5G) network, due to the device’s mobility and desire for flexibility of many emerging industrial applications. In wireless environments, the fickle nature of wireless communication will cause the existing approach to require more network resources to maintain the deterministic property, resulting in decreased schedulability. Thus, this paper aims to propose a more efficient scheduling mechanism for deterministic communication under integrated 5G and TSN environments. First, we parameterize the residence time of the bridges and analyze the requirements of deterministic communication. Then, based on the analysis, we extend and develop new scheduling rules to improve schedulability while maintaining determinism. Finally, Our evaluation via performing a set of experiments in realistic settings showcases the improved schedulability of our method with the preservation of the deterministic property.

Recently, Google proposed a privacy-preserving framework, Federated Learning (FL), to realize the era of edge intelligence. However, the limited labeled data that can only be labeled by the data owner with domain know-how threatens the feasibility of FL. In fact, Semi-supervised Learning (SSL) is a key to leveraging the unlabeled data on the client side while the confirmation bias issue triggered by incorrect predictions in the old-fashion Teacher-Student structure drops the model accuracy significantly. In this paper, we introduce the state-of-the-art SSL technique called Meta Pseudo Labels (MPL), which advocates a feedback strategy in the Teacher-Student architecture to mitigate the performance degradation. Unfortunately, in FL the heterogeneous data without sharing among clients are non-Independent and Identically Distributed (non-IID). The knowledge that shuttles between the Teacher and Student model may be partial and undermine the effectiveness of MPL. To make a performance breakthrough, we proposed a novel FL framework called Federated Meta Learning with Synergetic Strategy (SynFMPL), which combines the advantages of both Teacher and Student models. To our best knowledge, we are the first work that integrates MPL technique in FL. Moreover, we specifically focus on the client side, which fully leverages the unlabeled data for model generalization. The experiment shows that SynFPML outperforms all baselines in various non-IID scenarios. The Student model in SynFMPL even exceeds the upper bound in all baselines by 4% and maintains a virtuous cycle in the learning process.

Addressing edge computing's economic issues is critical as we need to motivate edge devices as resource providers to devote their computing resources to the service. There are multiple users and resource providers in most edge computing service scenarios. The communication delays between users and providers are not the same since their physical distances are different. However, most existing works on edge computing regarding network economics adopt single market models that do not consider the influence of communication delay. Moreover, due to the high cost of deploying 5G ultra-dense small cells, barely a network operator can reach full network coverage. Users cannot rely on a single network, but existing multi-market models do not allow resource trading between different groups. Thus, in this paper, we propose a novel multi-market trading (MMT) framework to address these shortcomings. The framework combines the double auction at each group and a market selection game to enable the resource providers to participle in multiple auctions and analyze their behavior of choosing markets. Through extensive simulations using real-world datasets of vehicular networks, we show that the proposed framework can improve the social welfare by 14.45% and 36.74%, respectively, compared with classic multi-market and single market models.

Fog computing provides new ideas for solving big data problems in the Internet of Things (IoT) era. With fog, we can save much time on long-distance transmission, thereby increasing the efficiency of network service. However, to transform fog computing from a technology to a service, we first need to properly handle the trading relationship between users and service provider in fog. In this paper, we design an efficiency-aware dynamic service pricing strategy to optimize the payoffs of both users and provider in fog computing. In the modeling, we design a Stackelberg competition-based model while regarding provider as the market leader, and each individual user as a follower. Users in this model care about how to obtain the most cost-effective services. And provider pays attention to taking full advantage of the geo-distributed characteristic in setting prices with multiple customers. In the performance evaluation part, we carry out experiments using real-world datasets to simulate this continuous negotiation process between supply and demand in fog pricing. The results show that our strategy can solve the dual-objective optimization problem while establishing a stable trading relationship between the two sides.

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly$\mathcal {NP}$-hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time$\left({1 - \frac {1}{e}}\right)$-approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless$\mathcal {P} = \mathcal {NP}$, and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.

Recently, a novel network architecture called Time-Sensitive Networking (TSN), which supports high bandwidth and deterministic communication, emerges to satisfy the requirements of safety-critical real-time applications for industrial automation. TSN can operate in centralized and distributed models, and the distributed model is more suitable for the safety-critical applications that require high network availability and reliability. However, the existing approach for the TSN's fully distributed model makes a pessimistic latency bound estimation due to the problems of lower priority streams and dependency. The pessimistic estimation will result in a decrease in the schedulability of TSN streams. This paper identifies and evaluates the impact of the two problems via a case study and then proposes a TDMA-based stream reservation approach to alleviate the two problems. The simulation results validate the proposed approach in terms of the number of accepted streams and computation time.

With the upcoming edge AI, federated learning (FL) is a privacy-preserving framework to meet the General Data Protection Regulation (GDPR). Unfortunately, FL is vulnerable to an up-to-date security threat, model poisoning attacks. By successfully replacing the global model with the targeted poisoned model, malicious end devices can trigger backdoor attacks and manipulate the whole learning process. The traditional researches under a homogeneous environment can ideally exclude the outliers with scarce side-effects on model performance. However, in privacy-preserving FL, each end device possibly owns a few data classes and different amounts of data, forming into a substantial heterogeneous environment where outliers could be malicious or benign. To achieve the system performance and robustness of FL's framework, we should not assertively remove any local model from the global model updating procedure. Therefore, in this paper, we propose a defending strategy called FedEqual to mitigate model poisoning attacks while preserving the learning task's performance without excluding any benign models. The results show that FedEqual outperforms other state-of-the-art baselines under different heterogeneous environments based on reproduced up-to-date model poisoning attacks.

With the popularity of AIoT (Artificial Intelligence of Things) services, we can foresee that smart end devices will generate tremendous user data at the edge. In particular, it is critical to address how to properly distill knowledge from the edge network in a communication-efficient and privacy-preserving manner. Federated learning (FL), one of the promising machine learning frameworks, ensures data privacy by allowing end devices to collaboratively train a shared model without exposing raw data to an aggregation server. However, due to its distributed nature, the framework is vulnerable to two major threats: the Model Inversion Attacks and the Model Poisoning Attacks. An abnormal aggregator or malicious end devices may probably launch these attacks in the training phase. The former leaks sensitive information by reversing the model weights to users' raw data. Still, the latter can break the model security and mislead the global model to wrong inference results. Unfortunately, the existing research has not tackled such two-sided model attacks that occurred concurrently in FL. Therefore, in this paper, we propose a dual-masking federated learning (DMFL) framework that advocates partial weights uploading in the aggregation process and applies two kinds of masks on both the end device and the aggregator sides. Based on the benchmark data for image classification, our experimental results show that the proposed DMFL framework outperforms other baselines, confirming that it can successfully preserve weights privacy and protect model security for AIoT.

The emerging Internet of Vehicles (IoV) is expected to transform the everyday vehicular experience in the near future. However, IoV will also bring unprecedented challenges unseen in traditional wireless communications systems. Fortunately, the emergence of Open Radio Access Network (O-RAN) technology provides an intelligent base station (BS) solution for IoV networks. In this paper, we propose an edge intelligent framework in compliance with O-RAN architecture. A vehicle traffic flow prediction mechanism is performed in the developed prototype to verify the applicability and feasibility. Through the experiments with a real-world dataset, some insights and guidelines are provided in terms of operational efficiency and applicability. To the best of our knowledge, this work is the first attempt to study the edge intelligence for O-RAN based IoV networks.

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively.

In the past decades, the evolution of Industrial Inter-net of Things (IIoT) and Industry 4.0 faces several challenges, and one of them is the proprietary network which causes a rising cost due to the interoperability, compatibility, and strict requirements. To deal with the challenge, industry advocates an open Ethernet-based standard under the IEEE 802.1 family, named Time-Sensitive Networking (TSN), to support real-time and critical applications, where the routing with TSN is still an open and ongoing research problem. Although there are some existing routing approaches, they are offline algorithms and thus not suitable for future industrial automation that needs to support dynamic reconfiguration. Also, they consider either only TSN traffic or only AVB traffic to determine the routing paths, which limits the overall solution space and fails to make the best use of TSN features. To address the issues above, we aim to explore an online routing problem considering both TSN and AVB traffic in a TSN network. It is very first to investigate the online routing problem considering both TSN and AVB traffic. We propose an Ant Colony Optimization (ACO) based approach with a low time complexity to solve the problem. Experimental results show that the proposed algorithm outperforms a well-known solution in terms of the schedulability, performance, number of rerouted streams, and computational efficiency.

Mobile/multi-access edge computing (MEC) is therefore developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading and migration decision need to be jointly handled to maximize the utility of networks within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision problems in the MEC system. M-DRL is composed of two parts: DRL specialized in supporting multiple users joint training, and glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of future. Through integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the service provision problem in MEC with acceptable computation complexity and near-optimal performance.

Thanks to the advances in wireless communication and machine learning technologies, we can envision a novel AIoT (AI + IoT) service platform that collects video data from the individuals' edge devices. Then, it transforms the video data into useful information, providing services to IoT or smart city applications. However, collecting raw video data directly to the cloud server is merely possible due to network bandwidth limitations and data privacy concerns. One possible solution is to adopt federated learning, which enables edge devices to collaboratively train a shared model without sending the raw data to the cloud. Unfortunately, this scheme cannot directly be applied to the targeted scenario since it assumes labeled data for training, and only at the cloud, we have the human power and time to label the video data. Thus, to tackle those issues, we propose an edge learning system based on semisupervised learning and federated learning technologies. The system trains AI models at edge devices using an improved semisupervised learning scheme and periodically uploads the training results to the cloud server to form a single model by adapting the federated learning technology. Then, we observe that in the real world, the data on the end devices are nonindependent and identically distributed (non-IID) such that it may cause weight divergence during training and result in a considerable decrease in the model performance. Therefore, we propose a new operation called federated swapping (FedSwap) to replace partial federated learning operations based on a few shared data during federated training to alleviate the adverse impact of weight divergence. We evaluate our system on both image classification using the state-of-the-art benchmark data and object detection using real-world video data. The experimental results show that the proposed system can have up to 5.9% higher accuracy of object detection for the video analysis applications by fully utilizing unlabeled data, compared with the situation that only labeled data are used. Moreover, the proposed FedSwap can improve the accuracy of image classification by 3.8% and the object detection task by 1.1%.

Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.

With the flourishing growth of Internet-of-Things (IoT) applications, cellular-assisted IoT networks are promising to support the ever-increasing wireless traffic demands generated by various kinds of IoT devices. As one of the emerging technologies in the next-generation cellular systems, small cells, or so-called “femtocells” in indoor environments, are expected to be densely deployed for the ubiquitous coverage and capacity enhancement in a cost-effective and energy-efficient way. However, unplanned femtocell deployment and complex interior layouts of buildings may lead to severe coverage and capacity problems and even possible negative impacts on the dense deployment. In this paper, we aim at developing a data-driven indoor diagnostic framework for fault detection in cellular-assisted IoT networks. The framework utilizes machine learning techniques to analyze crowd-sourced measurements uploaded from diverse end devices. Then, some well-trained prediction models are used to construct a global view of a radio environment, namely, radio environment map (REM), for diagnosis and management purposes. Moreover, REMs enable operators to detect existing coverage and capacity problems at any interested location. To verify the feasibility of indoor diagnoses, we collect a real trace data from an indoor testbed and then conduct a series of experiments to evaluate the performance of the machine-learning algorithms in terms of the accuracy, the time complexity, and the sensitivity to data volumes. The experimental results provide an insightful guideline to the indoor deployment of small cells in cellular-based IoT networks.

Wireless body area networks (WBANs) have emerged recently to provide health monitoring for chronic patients. In a WBAN, the patient's smartphone is deemed an appropriate sink to help forward the sensing data to back-end servers. Through a real-world case study, we observe that temporary disconnection between sensors and the associated smartphone can happen frequently due to postural changes, causing a significant amount of data to be lost forever. In this paper, we propose a scheme to parasitize the data in surrounding Wi-Fi networks whenever temporary disconnection occurs. Specifically, we model data parasitizing as an optimization problem, with the objective of maximizing the system lifetime without any data loss. Then, we propose an optimal offline algorithm to solve the problem, as well as an online algorithm that allows practical implementations. We have also implemented a prototype system, where the online algorithm serves as the underlying technique, based on Arduino. To evaluate our scheme, we conduct a series of experiments with the prototype system in controlled and real-world environments. The results show that the lifetime is prolonged by 100 times, and it could be further doubled if the health monitoring application permits a few packet losses.

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components (i.e., function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship (i.e., call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.

Software-defined networking (SDN) brings new opportunities in the design of more reliable and flexible consistent network update mechanisms by making the update process independent of the underlying routing protocols. With the advance of wireless transmission technology, more and more enterprises have deployed wireless antennas into their data centers. However, to the best of our knowledge, none of the state-of-the-art solutions consider radio interference and resource competition into their model design of consistent network update. In this paper, we first propose a wireless dependency model to formulate radio interference. Based on traffic delivery and resource constraints, we formulate the resource competition problem as an update time minimization problem for ensuring the consistency properties, and then propose a greedy-heuristic scheme to minimize the total update time. Simulation results show that the proposed scheme is efficient and outperforms a well-known solution designed for network update in SDN. The simulation results also reveal the importance of resolving resource competition during network update.

Fog computing, evolves from the cloud and migrates the computing to the edge, is a promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Via the forward-looking perspective, we advocate a Fog Radio Access Network (F-RAN) model, which leverages the existing infrastructure such as small cells with limited computing power, to achieve the ultra-low latency by joint edge computing and near-range communications across multiple Fog groups. We formulate the low latency design as an NP-hard optimization problem, which demonstrates the tradeoff between communication and computing in the time domain. Due to each F-RAN node's potential as each user's master F-RAN node with 1) different self computing power; and 2) different cooperative power of assisted F-RAN nodes, we first tackle globally optimized master F-RAN node selection for each user and propose a latency-driven cooperative Fog algorithm with dynamic programming solution for simultaneous selection of the F-RAN nodes to serve proper heterogeneous Fog resource allocation for multi-Fog groups. Considering the limited heterogeneous Fog resources shared among all users, we propose the one-for-all strategy for every user putting him/herself into others' shoes and reaching a “win-win” outcome. The numerical results show that the low latency services can be accomplished by F-RAN via latency-driven Fog cooperation approach.

One viable and cost-effective method to fulfill the ever-increasing mobile broadband traffic and to achieve coverage and capacity improvement is the employment of mobile small cells in next generation cellular networks. Being agile and resilient, aerial small cells (ASCs), which are small cells mounted on unmanned aerial vehicles (UAVs), are deemed promising platforms for the provision of wireless services. Since the lifetime of an airborne network highly depends on the residual battery capacity available to each ASC, it is essential to account for the energy expenditure on various flying actions in a flight plan. Therefore, the focus of this paper is to study the 3D deployment problem for a swarm of ASCs, in which a trade-off among flight altitudes, energy expenses and available lifetimes is observed. The objective is to maximize the total throughput of all users. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can bring tremendous increase in the total throughput for all users by properly coping with the trade-off, compared to the two user-aware approaches with random and minimum altitude assignments.

Software-defined networking (SDN) paradigm and high-speed wireless technologies have been widely adopted by industries to improve the performance of data center networks. In SDN, a switch commonly uses ternary content addressable memory (TCAM) to store rules for fast flow table lookup. However, due to the limited TCAM entries, rule replacement will occur frequently when traffic load in data centers is heavy. This issue will be more serious when flow characteristics in data centers are diverse. Although wireless transmissions can benefit for reducing TCAM usage, we observe that routing and forwarding designs without considering the flow characteristics will result in rule replacement storm in switches and significantly harm the SDN scalability for wireless data centers. As a result, switches will issue massive flow setup requests which cannot be processed by an SDN controller in real time. This paper studies the SDN scalability problem with the objective of minimizing the number of flow setup requests under the hybrid architecture with wired and wireless links. We propose a routing and forwarding algorithm with the consideration of flow characteristics to tackle the problem. A series of simulation results agrees our observations and shows that our algorithm outperforms two well-known solutions for wired data centers and three approaches for wireless data centers. The results also provide useful insights and guidelines for designing SDN-based wireless data centers.

With the advance of wireless technologies, the rapid mobile data traffic growth will lead to severe network resource consumption and exceptionally long latency to access services, especially for cloud-based applications. To tackle these issues, Fog Radio Access Network (F-RAN) is recently emerged for next generation cellular networks. F-RAN is considered as an extension of the cloud computing paradigm to the edge of the network and a highly virtualized platform that provides computing, storage, and network services for mobile devices. However, how to appropriately place virtual machines (VMs) into fog nodes in F-RAN systems is very challenging, and will significantly affect the bandwidth consumption of backhaul links. Thus this paper studies the replication-based VM placement problem, and aims at minimizing the total backhaul traffic generated by VM migrations and data transmissions. We observe that the VM placement operation should not be frequently executed and practically considers the problem in a long term aspect. Then we propose a heuristic algorithm to solve the problem. The simulation results agree our observation and show that compared with a greedy approach and an optimal algorithm, the proposed algorithm demonstrates with favorable results for the overall backhaul network usage.

Fog computing is emerging as one promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Taking a forward-looking perspective, we propose a Fog-Radio Access Network (F-RAN) model, which utilizes the existing infrastructure, e.g., small cells and macro base stations, to achieve the ultra-low latency by joint computing across multiple F-RAN nodes and near-range communications at the edge. We treat the low latency design as an optimization problem, which characterizes the tradeoff between communication and computing across multiple F-RAN nodes. Since this problem is NP-hard, we propose a latency-driven cooperative task computing algorithm with one-for-all concept for simultaneous selection of the F-RAN nodes to serve with proper heterogeneous resource allocation for multi-user services. Considering the limited heterogeneous resources shared among all users, we advocate the one-for-all strategy for every user taking other's situation into consideration and seek for a "win-win" solution. The numerical results show that the low latency services can be achieved by F-RAN via latency-driven cooperative task computing.

The rapid growth in multimedia services and diverse mobile technologies have recently brought about sudden surges in traffic demands. To keep up with this trend, small cells are considered as a promising solution to supplement macrocell coverage and enhance network capacity in next generation cellular networks. However, the isotropic characteristics of conventional omni- directional antennas installed in small cells reveal a trade-off between coverage and power consumption. Boosted power levels allow for larger coverage areas while resulting in serious interference among cells. On the other hand, although weak power emissions can mitigate interference, coverage holes may appear instead. To overcome the physical limitations, a beamforming technique called switched multi- element antenna (SMEA) is introduced in this work, which is capable of shaping kinds of radiation patterns toward desired users. We propose an efficient radiation pattern selection scheme using SMEA, with the objective of maximizing the total number of served users within the coverage ranges of small cells. We then construct a series of simulations to evaluate the performance of our proposed scheme. Compared with a uniform power control scheme using omni-directional antenna and a distributed pattern selection scheme using SMEA, simulation results show that our proposed scheme can significantly enhance coverage and deal with the trade-off issue as well.

Internet of Things (IoT) has recently emerged as an enabling technology for context-aware and interconnected “smart things.” Those smart things along with advanced power engineering and wireless communication technologies have realized the possibility of next generation electrical grid, smart grid, which allows users to deploy smart meters, monitoring their electric condition in real time. At the same time, increased environmental consciousness is driving electric companies to replace traditional generators with renewable energy sources which are already productive in user's homes. One of the most incentive ways is for electric companies to institute electricity buying-back schemes to encourage end users to generate more renewable energy. Different from the previous works, we consider renewable energy buying-back schemes with dynamic pricing to achieve the goal of energy efficiency for smart grids. We formulate the dynamic pricing problem as a convex optimization dual problem and propose a day-ahead time-dependent pricing scheme in a distributed manner which provides increased user privacy. The proposed framework seeks to achieve maximum benefits for both users and electric companies. To our best knowledge, this is one of the first attempts to tackle the time-dependent problem for smart grids with consideration of environmental benefits of renewable energy. Numerical results show that our proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.

The software-defined networking (SDN) architecture has been widely deployed in data centers to support diverse cloud services. In order to provide fast access for flow table lookup, SDN switches usually use ternary content addressable memory (TCAM) to store their routing rules given by a controller. However, the number of TCAM entries is relatively small due to its high cost and high energy consumption. As traffic in data centers significantly increases, rule replacement will occur very frequently in SDN switches. As a result, massive flow setup requests are generated and cannot be processed by the controller in real time. To address the scalability issue, we propose a novel rule-reduction-based policy to minimize TCAM usage, thus alleviating the burden on the SDN controller. Specifically, we integrate the 60 GHz wireless technology into data centers and formulate a routing rule minimization problem under the hybrid architecture with wired and wireless links. The objective is to minimize the number of installed rules in switches. We then propose an efficient algorithm, with the consideration of TCAM limitations and flow sizes, to solve the problem. Results of a series of simulations show that our algorithm outperforms a well-known solution for wired data centers and two approaches for wireless data centers. The results also provide useful insights and guidelines for designing scalable SDN-based data centers.

Joint transmission (JT), an attractive coordinated multipoint (CoMP) downlink transmission technique, is used to increase cell-edge user throughput, but has to distribute the same user data to multiple base stations termed a JT cluster, thus leading to a huge burden on backhaul bandwidth. Therefore, recent studies considered caching user data at the base stations to reduce the backhaul bandwidth consumption. However, we observe that even with the caches at the base stations, JT clustering decisions, without considering the data caching, cannot efficiently use the data in the caches and will result in unnecessary backhaul data traffic. In this paper, we study the joint transmission clustering problem with the consideration of data caches at the base stations. The objective is to minimize the backhaul data traffic while each user's data rate requirement can be satisfied under the limited radio resource blocks. Then, considering the cache effect, we formulate the target problem and propose an algorithm to solve this problem. The simulation results agree our observation and show that compared with a JT clustering algorithm, our proposed algorithm can significantly reduce more backhaul data traffic.

Due to limited resource, noise and unreliable link, data loss and sensor faults are common in medical body sensor networks (BSN). Most available works used data reconstruction to improve data quality in traditional wireless sensor networks (WSN). However, existing data reconstruction schemes using redundant information of WSN can not provide a satisfactory accuracy for BSN. In light of this, a Bayesian network based data reconstruction scheme is formalized in this paper, which rebuilds data using conditional probabilities of body sensor readings to recover missing data and sensor faults, rather than the redundant information collected from a large number of sensors. Experiments on extensive online data set show that the performance of our scheme outperforms all available data reconstruction schemes.

In the Internet of Things(IoT), the size constraint of those small and embedded devices limits the network lifetime because limited energy can be stored on these devices. In recent years, energy harvesting technology has attracted increasing attention, due to its ability to extend the network lifetime significantly. However, the performance of IoT devices powered by energy harvesting sources has not been fully analyzed and understood. In this paper, we model the energy harvesting process in IoT devices using slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA /CA) mechanism of IEEE 802.15.4 standard, and analyze the performance in terms of delay and throughput. Our new model successfully integrates the energy harvesting process and binary backoff process through a unified Markov chain model. Finally, the new model is validated by simulation and the throughput errors between simulation and analytical model are no more than 6%. We demonstrate the application of the model with different energy harvesting rate corresponding to different sources such as solar and vibration energy harvesters.

Wearable devices, which are small electronic devices worn on a human body, are equipped with low level of processing and storage capacities and offer some types of integrated functionalities. Recently, wearable device is becoming increasingly popular, various kinds of wearable device are launched in the market; however, wearable devices require a powerful local-hub, most are smartphone, to replenish processing and storage capacities for advanced functionalities. Sometime it may be inconvenient to carry the local-hub (smartphone); thus, many wearable devices are equipped with Wi-Fi interface, enabling them to exchange data with local-hub though the Internet when the local-hub is not nearby. However, this results in long response time and restricted functionalities. In this paper, we present a virtual local-hub solution, which utilizes network equipment nearby (e.g., Wi-Fi APs) as the local-hub. Since migrating all applications serving the wearable devices respectively takes too much networking and storage resources, the proposed solution deploys function modules to multiple network nodes and enables remote function module sharing among different users and applications. To reduce the impact of the solution on the network bandwidth, we propose a heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We conduct series of experiments, and the results show that the proposed solution can reduce the bandwidth consumption by up to half and still serve all requests given a large number of service requests.

With the increasing demand for ultra-low latency services in 5G cellular networks, fog with edge computing is one of promising solutions which migrate the computing from the cloud to the edge of the network. Rather than relying on the distant cloud or additional servers, we propose the Fog-Radio Access Network (F-RAN), which leverages the current infrastructures in the radio access network, such as small cells and macro base stations, to pursue the ultra-low latency by joint powerful computing of multiple F-RAN nodes and near-range communications at the edge. The optimization problem is firstly formulated to tackle the tradeoff between communication and computing resources into time domain within distributed computing scenario, and then we propose a cooperative task computing operation algorithm to simultaneously decide how many F-RAN nodes should be selected with proper communication resource allocation and computing task assignment. The numerical results show that the ultra low-latency services can be achieved by F-RAN via cooperative task computing.

With the explosive growth in mobile data traffic, femtocell technology is regarded as the most effective way to enhance the mobile service quality and system capacity of cellular networks. However, the major problem with femtocell deployment is finding an appropriate access control mode that mobile operators and users are willing to adopt. Among the various kinds of access control modes, the hybrid access mode is considered the most promising because it allows femtocells to give preferential access to femtocell owners, while other public users can only access femtocells with certain restrictions. Because all femtocell owners are selfish, how to provide sufficient incentives so that they will share their femtocell resources is a challenging issue. To address the problem, we propose an economic framework for mobile operator and femtocell users based on game theoretical analysis. We also exploit the concept of revenue sharing, which provides a positive cycle to sustain the femtocell service. In the framework, a femtocell game is formulated where the femtocell owners determine the proportion of femtocell resources they will share with public users, while the operator maximizes its benefit by setting the ratio of the revenue distributed to femtocell owners. We analyze the existence and uniqueness of the Nash Equilibrium of the game. The results of extensive simulations show that the proposed framework maximizes the operator's benefit and satisfies the users' service requirements.

Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is NP-hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.

With the rapid growth of mobile broadband traffic, adopting small cell is a promising trend for operators to improve network capacity with low cost. However, static small cells cannot be flexibly placed to fulfill time/space-varying traffic. The static small cells might stay in idle or under-utilized mode during some time periods, which wastes resources. Therefore, this paper utilizes the mobile small cell concept and studies the deployment problem for mobile small cells. The objective is to maximize the service time provided by mobile small cells for all users. If a finite number of mobile small cells can serve more users for more time, the mobile small cell deployment will have more gains. Specifically, we show an interesting trade-off in the service time maximization. Then, we prove our target problem is NP-hard and propose an efficient mobile small cell deployment algorithm to deal with the trade-off to maximize the total service time. We construct a series of simulations with realistic parameter settings to evaluate the performance of our proposed algorithm. Compared with a static small cell deployment algorithm and a random mobile small cell deployment algorithm, the simulation results show that our proposed scheme can significantly increase the total service time provided for all users.

With the rapid growth of mobile data traffic, operators are expected to densely deploy base stations to meet user demands. Recent researches have indicated that the densely deployed base stations lead to the significant increase of the operational expenses of operators due to the electricity bills to maintain their operation, and thus the profit of operators is greatly decreased. Different from the past works in dynamically switching on/off base stations for energy saving, we propose to consider the benefits of users in service fee discounts as a joint optimization process in cutting down the energy consumption of base stations to maximize the total profit of operators. The optimization problem is formulated and shown being NP-hard. We then propose a profit-aware algorithm to switch off base stations, as needed, with the adjustment of the data rates provided to the users who are willing to receive discounts. The simulation results show that the proposed algorithm can significantly increase the total profit of operators and introduce a win-win situation to both users and operators.

Human mobility models strongly affect the performance in Mobile Ad Hoc Network (MANET). In order to get a more precise estimation of routing performance, it is crucial to construct a more realistic human mobility model. There are three properties investigated in literature which represent the patterns of human mobility. This paper analyzes real human traces and introduces a new property that the inter-hub movement of a human is not straight. We then construct a new random human mobility model called Irregular Orbit Levy-walk (IROL), and verify its ability to capture all of the four properties simultaneously. To investigate the feasibility of IROL, we measure routing performance of real traces, IROL, and other models in MANET on two data sets. In both data sets, IROL describes the real traces the best in terms of throughput and receive rate.

With the increasing sophistication and maturity of biomedical sensors and the significant advances on low-power circuits and wireless communications technologies, wireless body area networks (WBANs) have emerged recently to provide pervasive health monitoring for humans. In WBANs, smart phones can serve as data sinks to forward the sensing data to back-end servers. Due to the battery concern of smart phones and the postural changes of humans, temporary disconnection between sensors and their associated smart phones may frequently happen in WBANs. In this case, the sensing data would be lost when the limited memory space of sensors overflows. To prevent excessive data loss, this paper proposes a scheme to parasitize the data on existing public Wi-Fi networks, once the links from sensors to the smart phones become unavailable. Specifically, an optimization problem to maximize the time during which data loss can be avoided by exploiting the data parasitizing scheme is formulated, where a decision set of the packets' size and sending timing to public Wi-Fi networks needs to be determined. We develop an offline algorithm to obtain an optimal decision set and present an efficient online algorithm for practical implementations. The feasibility of the proposed scheme and the efficacy of the algorithms are demonstrated through prototype implementations on a WBAN testbed with biomedical sensor devices for real-world experiments.

With the explosive growth of mobile data traffic, the femtocell technology is one of the proper solutions to enhance mobile service quality and system capacity for cellular networks. However, one of the key problems for femtocell deployment is to find appropriate access control in which mobile operators and users are willing to be involved. Among all kinds of access control modes, the hybrid access mode is considered as the most promising one, which allows femtocells to provide preferential access to femtocell owners and subscribers while other public users can access femtocells with certain restriction. Since all femtocell owners are selfish, how to provide enough incentives to the owners for sharing their femtocell resources is challenging. In this paper, we construct an economic framework for mobile operator and femtocell users by a game theoretical analysis and introduce the concept of profit sharing to provide a positive cycle to sustain the femtocell service. In this framework, a femtocell game is formulated, where the femtocell owners determine the proportion of femtocell resources shared with public users while the operator can maximize its own benefit by determining the ratio of revenue distribution to femtocell owners. The existence of the Nash equilibrium of the game is analyzed. Extensive simulations are conducted to show that the profit of the operator can be maximized while the service requirements of users can be maintained by the proposed framework.

Recently, large-scale data centers are widely built to support various kinds of cloud services, which are mostly delivered by multicast. Even with multicast, cloud services may still generate a large amount of data traffic in some bottleneck links and, even worse, cause network congestion. Thus, how to reduce the redundancy of data transmissions to mitigate network congestion is essential. In addition to wired transmissions, modern data centers adopt wireless links to augment network capacity. Under the coexisting scenario of wired and wireless links, this paper studies multicast data delivery problem. Specifically, a multicast tree problem is defined, and the objective is to minimize the total multicast data traffic. We prove the problem is NP-hard and propose an efficient heuristic algorithm to solve the problem. A series of experiment results shows that our proposed algorithm is very effective, compared with an optimal solution designed for traditional wired data centers.

ABSTRACT In order to satisfy quality‐of‐service requirements for real‐time multimedia applications over wireless networks, IEEE 802.11e has been proposed to enhance wireless‐access functionalities. In IEEE 802.11e, collisions occur frequently as the system load becomes heavy, and then, the latency for successfully transmitting data is lengthened seriously because of contention, handshaking, and backoff overheads for collision avoidance. In this paper, we propose a fragment‐based retransmission (FBR) scheme with quality‐of‐service considerations for IEEE 802.11e‐based wireless local area networks. Our FBR can be used in all proposed fragmentation‐based schemes and greatly decrease redundant transmission overheads. By utilizing FBR, the retransmission delay will be significantly improved to conform strict time requirements for real‐time multimedia applications. We develop an analytical model and a simulation model to investigate the performance of FBR. The capability of the proposed scheme is evaluated by a series of simulations, for which we have encouraging results. Copyright © 2011 John Wiley & Sons, Ltd.

Wireless charging technology is considered as one of the promising solutions to solve the energy limitation problem for large-scale wireless sensor networks. Obviously, charger deployment is a critical issue since the number of chargers would be limited by the network construction budget, which makes the full-coverage deployment of chargers infeasible. In many of the applications targeted by large-scale wireless sensor networks, end-devices are usually equipped by the human and their movement follows some degree of regularity. Therefore in this paper, we utilize this property to deploy chargers with partial coverage, with an objective to maximize the survival rate of end-devices. We prove this problem is NP-hard, and propose an algorithm to tackle it. The simulation results show that our proposed algorithm can significantly increase the survival rate of end-devices. To our knowledge, this is one of very first works that consider charger deployment with partial coverage in wireless rechargeable sensor networks.

Given the explosive growth of mobile subscribers, network operators have to densely deploy base stations to serve the exponentially increasing access demands. Nevertheless, recent researches have pointed out that base station operation has been identified as a significant portion of total system energy consumption and 90% of the traffic is carried by only 40% of base stations even under peak traffic demand. Therefore, switching off underutilized base stations for saving power is an important issue with the increasing awareness of environmental responsibility and economical concerns of network operators. This paper targets the problem of dynamic base station operation, with an objective to minimize total power consumption of all base stations. We prove this problem is NP-hard and cannot be approximated in polynomial time with a ratio better than 3 over 2. Then, we propose a distributed algorithm to tackle it. The simulation results show that our proposed algorithm can significantly reduce the network power consumption.

ZigBee, a unique communication standard designed for low-rate personal area networks, has extremely low complexity, cost, and power consumption for wireless connectivity in inexpensive, portable, and mobile devices. Among the well-known ZigBee topologies, the cluster tree is especially suitable for wireless sensor networks because of its support for power saving operations and light-weight routing. However, the restricted routing paths caused by the cluster tree would form transmission bottlenecks such that it is difficult to guarantee quality of service (QoS) for critical packet deliveries in mission-critical networks. To address the problem, we propose a QoS-oriented framework with an analytical model to provide stochastic QoS guarantees for urgent information deliveries in ZigBee cluster-tree networks. Through analysis and simulations, we demonstrate the efficacy of the proposed framework.

Layer-based video coding, together with adaptive modulation and coding, is a promising technique for providing real-time video multicast services on heterogeneous mobile devices. With the rapid growth of data communications for emerging applications, reducing the energy consumption of mobile devices is a major challenge. This paper addresses the problem of resource allocation for video multicast in fourth-generation wireless systems, with the objective of minimizing the total energy consumption for data reception. First, we consider the problem when scalable video coding is applied. We prove that the problem is {\cal NP}-hard and propose a 2--approximation algorithm to solve it. Then, we investigate the problem under multiple description coding, and show that it is also {\cal NP}--hard and cannot be approximated in polynomial time with a ratio better than 2, unless {\cal P}={\cal NP}. To solve this case, we develop a pseudopolynomial time 2-approximation algorithm. The results of simulations conducted to compare the proposed algorithms with a brute-force optimal algorithm and a conventional approach are very encouraging.

ZigBee, a unique communication standard designed for low-rate wireless personal area networks, has extremely low complexity, cost, and power consumption for wireless connectivity in inexpensive, portable, and mobile devices. Among the well-known ZigBee topologies, ZigBee cluster-tree is especially suitable for low-power and low-cost wireless sensor networks because it supports power saving operations and light-weight routing. In a constructed wireless sensor network, the information about some area of interest may require further investigation such that more traffic will be generated. However, the restricted routing of a ZigBee cluster-tree network may not be able to provide sufficient bandwidth for the increased traffic load, so the additional information may not be delivered successfully. In this paper, we present an adoptive-parent-based framework for a ZigBee cluster-tree network to increase bandwidth utilization without generating any extra message exchange. To optimize the throughput in the framework, we model the process as a vertex-constraint maximum flow problem, and develop a distributed algorithm that is fully compatible with the ZigBee standard. The optimality and convergence property of the algorithm are proved theoretically. Finally, the results of simulation experiments demonstrate the significant performance improvement achieved by the proposed framework and algorithm over existing approaches.

Zigbee, formalized by the IEEE 802.15.4 standard, is a specification for wireless personal area networks with low power, low cost, and a low data rate. In Zigbee, tree topology is commonly practiced to form wireless sensor networks and perform data delivery applications. In Zigbee wireless applications, data de livery failures occur constantly due to the node movements and topology changes of networks. To tackle the topology changes, conventional route reconstruction often involves huge resource consumption. In this paper, we utilize the regularity of mobility patterns to reduce the frequency of route reconstructions and achieve higher efficiency in sending data to mobile nodes. To increase the data delivery ratio, we introduce the metric of mobility-robustness in a tree topology, and propose tree construction with an objective to maximize the mobility-robustness of the constructed tree. We develop an efficient algorithm for effective tree construction. The effectiveness of network topologies constructed using this mobility-robustness metric is demonstrated by NS2 simulations against a real-world scenario.

As the Web 2.0 technology is becoming popular, people are eager to access the Internet at anytime and anyplace. In this paper, we present an Internet connectivity sharing system, called iShare, based on the concept of ad-hoc networking. In iShare, the efficiency and load-balancing issues are considered. Also, the functionalities of gateway discovery/selection and routing inter-operability as well as address assignment, IP tunneling, and firewall/NAT are designed and implemented. The experimental results for our implementation prototype indicate that the performance of our iShare system is acceptable for Internet connectivity to access HTTP services.

Scalable video coding with adaptive modulation and coding is a promising technique to provide real-time multicast services for heterogeneous mobile devices. Nevertheless, as the rapid growth of data communication for emerging applications, energy consumption is an critical challenge of mobile devices. This paper targets the problem of resource allocation for scalable video multicast with adaptive modulation-coding schemes in next generation cellular wireless networks, with an objective to minimize the total energy consumption of all mobile devices for reception. We show the NP-hardness of the target problem and propose a 2-approximation algorithm. Extensive simulations are conducted to compare the proposed algorithm with a brute-force optimal algorithm and a conventional approach, which provides some useful insights into power-aware scalable video multicast in 4G wireless systems.

The emerging wireless relay networks (WRNs) are expected to provide significant improvement on throughput and extension of coverage area for next-generation wireless systems. We study an optimization problem for multihop link scheduling with bandwidth and delay guarantees over WRNs. Our optimization problem is investigated under a more general interference model with a generic objective. The objective can be based on various kinds of performance indexes (e.g., throughput, fairness, and capacity), which can be determined by service providers. Through our theoretical analysis, the intractability and inapproximability of the optimization problem are shown. Due to the intractable computational complexity, we present efficient algorithms to provide a reasonable small approximation factor against any optimal solution even for a worst-case input. Furthermore, some experimental results indicate that our algorithms yield near-optimal performance in the average case.

In order to decrease the overhead of the dynamic routing mechanisms in large networks, the hierarchical routing protocols have been proposed in the early 80's. The routing complexity and the routing table size are the two most important functional blocks in a dynamic route guidance system. Although various algorithms exist for finding the best routing policy on a hierarchical network, hardly exists any work in studying and evaluating the aforementioned measures of routing complexity and routing table size for a hierarchical network. In this paper, by applying the random geometry theory, we can generalize the mathematical framework from the previous work which discussed the worst-case deterministic models. Our proposed new framework can carry out the averages of the routing complexity and the routing table size, which can be specified as the functions of the hierarchical network parameters such as the number of the hierarchical levels and the subscriber densities (cluster-population) for each hierarchical level. After establishing the relationship between the structure of a hierarchical network and these two crucial network performance measures (routing complexity and routing table size), we present a novel cluster-population optimization method for hierarchical networks and the associated statistical analysis.

IEEE 802.15.4 standard is designed to achieve low-power transmissions in low-rate and short-distance wireless personal area networks (WPANs). For the sake of reducing the control overheads, the modified CSMA/CA protocol used by 802.15.4 does not have the hidden device protection mechanism, such as RTS/CTS mechanism. Previous studies indicate that the probability of any two devices in a infrastructure network unheard of each other is around 41%. Therefore, the hidden device problem (HDP) results in inefficient data transmission and serious power consumption issues in WPAN. In this paper, we propose a cross-layer detection and allocation (CL-DNA) scheme to solve the HDP in IEEE 802.15.4 without the cost of extra control overhead in data transmissions. The proposed scheme detects hidden device relationships based on the overlapped signals and then allocates the hidden devices into distinct sub-periods for transmissions. Simulation results show that the proposed scheme significantly improves the goodput, and with reduction in the MAC delay and the power consumption.

The emerging wireless relay networks (WRNs) are expected to provide significant improvement on throughput and extension of coverage area for next-generation wireless systems. We study an optimization problem for multi-hop link scheduling with bandwidth and delay guarantees over WRNs. Our optimization problem is investigated under a general interference model with a generic objective. The objective can be based on various kinds of performance indexes (e.g., throughput, fairness and capacity) determined by service providers. We present efficient algorithms for maximizing the objective function. The experimental results indicate that our presented algorithms yield near-optimal performance.

In the IEEE 802.11 Distributed Coordination Function (DCF) protocol, there are two access modes: the basic access mode and request-to-send/clear-to-send (RTS/CTS) access mode. However, the effect of wireless channel, e.g., the channel signal-to-noise ratio (SNR), and the associated appropriate choice between these two access modes are hardly addressed in the existing literature. In this paper, we design a linear programming algorithm to reduce the complexity of the throughput optimization with respect to the minimum contention window size, which arises from a complicated and difficult nonlinear integer programming problem. Since the network performance of the DCF protocol has been shown to tremendously depend on the channel SNR and the number of competing stations, we propose a new algorithm which selects the access mode and the size of minimum contention window jointly by utilizing the estimated parameters from the feedback network information. We numerically evaluate our proposed new DCF protocol and the results show that our new MAC scheme significantly outperforms the original IEEE 802.11 protocol. Copyright © 2009 ACM.

The maximum-length shift-register sequences (m-sequences) are the often-used pseudo-random sequences for multi-access communications. It is well known that the generation of the m-sequences relies on the initial seed and the cyclic shift. Although we can identify a particular m-sequence using a unique initial seed and the step of the cyclic shift, the categorization or the classification of the m-sequence structures has never been studied to the best of our knowledge. In this paper, we study the m-sequence structures by means of the attributes (common subsequences or patterns). Such patterns are a set of binary sequences with finite length occurring in the full-length m-sequences and they can be used to denote the special attributes (common features) for transceivers. In addition, we design a parallel method to compute all the possible positions of bits "0" and "1" for each underlying pattern using the Berlekamp's algorithm and then we employ the solution to a generalized traveling salesman problem for constructing the shortest binary sequences, each of which contains all underlying patterns. From these shortest binary sequences, we can thus evaluate the number of m-sequences that include the underlying patterns. We also define the attributability, and discriminability for the population analysis of the jointly- or exclusively-attributed m-sequences. Copyright © 2009 ACM.

In modern communication systems and protocols, the sophisticated packet design, which involves multiple-layer requirements, is in demand. Hence any need for adding more features, system control functions, and reserved prefix headers will cause the dramatic change in the packet format and the corresponding communication standard revision. To combat this problem, we introduce a new concept of cursor patterns and propose to employ these cursor pattern sequences, together with a new pattern exclusive coding scheme, to explore the possibility for removing the restriction of the current packet boundaries. In this paper, we derive the crucial mathematical properties for the pattern exclusive codes with respect to an arbitrary set of cursor patterns, such as the coding efficiency of the PECs and the peak-to-average-power ratio measure of transmitting the PECs with respect to a pattern set. Our proposed method to use the pattern sets with the associated PECs can have potentials towards a number of communication applications, such as data payload limiters, transmitter identification, and even short training sequence for estimation and synchronization, etc.

IEEE 802.11 wireless networks utilize a hard handoff scheme when a mobile station is travelling from one area of coverage to another within a transmission duration. When the station is running a real-time multimedia application receiving data from the wireless network, in each pair of neighboring MAPs, contention scheme is used to transmit data. The latency for successfully transmitting data is seriously lengthened so that the deadlines of data frames are missed with high probabilities. In this paper, we propose a fuzzy-based cross-layer transmission (FCLT) scheme with QoS considerations for IEEE 802.11 mesh wireless networks, which utilizes the grey model and the fuzzy theorem. In the handoff procedure, some ratio of real-time data frames will be relayed to the next MAP based on received signal strength indication of PHY layer and contention window of MAC layer, which can meet the minimal QoS requirements. By utilizing FCLT, the ratios of data frames missing deadlines will be significantly improved to meet timing requirements for real-time multimedia applications. We develop a simulation model to investigate the performance of FCLT. The capability of the proposed scheme is evaluated by a series of experiments, for which we have encouraging results.

With the popularity of VoIP, Push-To-Talk (PTT) services over the IP networks have gained their momentum. This research aims at essential design issues for the quality of service of PTT services. We explore the relationship between the speech quality and system design issues. MOS (Mean Opinion Score), which is raised by the ITU-T (International Telecommunication Union) as one of the most common benchmarks, is studied under factors in system designs and implementations. In particular, we focus on the analysis of packet delay, jitter, and packet loss for PTT services. Experiments are designed and executed with the considerations of PTT characteristics. Our goal is to provide more understanding of MOS versus PTT and deliver insights in future PTT system designs.

IEEE-1394 is widely adopted in various commercial products for computing, communication, and entertainment. Although many services with Quality-of-Service (QoS) supports are now available in systems over IEEE-1394, little work is done for QoS-based resource allocation. In this article, we aim at the design of a bandwidth reservation mechanism and its policy for isochronous requests, such as those from cameras. We then address the QoS support issue for asynchronous requests, such as those from disks, and an analytic framework for probability-based QoS guarantees. This work is concluded by the proposing of a topology configuration algorithm for IEEE-1394 devices. The capability of the proposed methodology and the analytic framework are evaluated by a series of experiments over a Linux-based system prototype.

Security has become one of the major issues for data communication over wired and wireless networks. Different from the past work on the designs of cryptography algorithms and system infrastructures, we aim at the proposing of a dynamic routing algorithm that could randomize delivery paths for data transmission. The algorithm is easy to implement and compatible with popular routing protocols, such as routing information protocol in wired networks and destination-sequenced distance vector protocol in wireless networks, without introducing extra control messages. An analytic study on the proposed algorithm is presented, and a series of simulation experiments are conducted to verify the analytic results and to show the capability of the proposed algorithm.

In emerging wireless relay networks (WRNs) such as IEEE 802.16j, efficient resource allocation is becoming a substantial issue for throughput optimization. In this paper, we propose an algorithm for joint routing and link scheduling in WRNs. The developed theoretical analysis indicates that the performance of the proposed algorithm is within a factor of three of that of any optimal algorithm in the worst case. Through simulation experiments, the numerical results show that our algorithm outperforms the previously proposed routing and link-scheduling algorithms. Furthermore, the proposed algorithm can effectively achieve near-optimal performance, and provide much better throughput than the theoretical worst-case bound in the average case.

ZigBee, a unique communication standard designed for low-rate wireless personal area networks, has extremely low complexity, cost, and power consumption for wireless connectivity of inexpensive, portable, and moving devices. ZigBee uses the IEEE 802.15.4 standard as its communication protocol for medium access control (MAC) layer and physical (PHY) layer. The IEEE 802.15.4 MAC layer achieves duty-cycle operations by setting two system parameters, macBeaconOrder (BO) and macSuperFrameOrder (SO), to achieve low power consumption for ZigBee devices. This study comprehensively analyzes IEEE 802.15.4 duty-cycle operation. Specifically, a novel analytical model that accommodates a general traffic distribution is developed. An NS-2 based simulation model, which is validated by the developed analytical model is also proposed. Through the experiments conducted by the analytical and simulation models, some important performance-evaluation insights are gained that can be used as guidelines for future low-power ZigBee network deployment.

Wireless sensor networks have been extensively adopted in numerous application scenarios. However, increasing environmental constraints and performance requirements has spurred the development of tools to expedite deployment and minimize human intervention. This study extends previous work by the authors in network development and aims at the performance evaluation of applications operating in to-be-deployed networks. We prove that the determination of a ZigBee cluster tree over an underlying network is NP - hard and propose an algorithm for generating tree topology. Based on the proposed algorithm/technologies, an integrated development tool is developed. The capability of the tool is demonstrated by a case study of the system deployed in an office building.

In this paper, we propose a Medium Access Control (MAC) extension, called Medium Access for Contention Assistance (MACAT), to enhance Quality-of-Service (QoS) for IEEE 802.11 and IEEE 802.11e co-existing networks. In MACAT, IEEE 802.11e stations act as assistants for channel access of legacy stations with real-time traffic. With the assistance of 802.11e stations, the legacy stations have a higher probability to deliver their real-time data. The performance of our MACAT protocol is investigated through the NS-2 simulator. The experimental results indicate that in terms of bitrate, delay, and packet loss rate, the performance of the legacy stations with real-time traffic is upgraded by our MACAT while the QoS of IEEE 802.11e stations can be maintained.

This paper presents a power-aware secure routing protocol for wireless embedded networks (WENs). We propose an efficient secure relay scheme called ESRS for ad-hoc-based embedded-network communications. ESRS is a two-phase scheme which includes end-to-end secure channel establishment and relay-behavior monitoring. With end-to-end secure channel establishment, the transmitted data will be protected under an end-to-end secure channel. A power-aware secure route will be also provided in this stage. Then attacks of routing are monitored and controlled in the monitoring phase. As the experimental results are shown, ESRS provides an efficient way to relay data securely for ad-hoc-based embedded network communications.

IEEE 802.15.4 is a new standard uniquely designed for low-rate wireless personal area networks. It targets ultralow complexity, cost, and power for low-rate wireless connectivity among inexpensive, portable, and moving devices. IEEE 802.15.4 provides a guaranteed time slot (GTS) mechanism to allocate a specific duration within a superframe for time-critical transmissions. This paper proposes an adaptive GTS allocation (AGA) scheme for IEEE 802.15.4, which considers low latency and fairness. The scheme is designed based on the existing IEEE 802.15.4 medium access control protocol, and IEEE 802.15.4 devices can receive this AGA service without any modification. A simulation model and an analytical model are developed to investigate the performance of our AGA scheme. The numerical results show that the proposed scheme significantly outperforms the existing IEEE 802.15.4 implementation.

Abstract This paper proposes iPTT, a peer‐to‐peer (P2P) Push‐to‐Talk (PTT) service for Voice over IP (VoIP). In iPTT, a distributed and mobile‐operator independent network architecture is presented to accelerate the deployment of the PTT service. Based on the serverless architecture, we develop two mechanisms, that is, flooding‐based floor control mechanism (FFC) and tree‐based floor control mechanism (TFC), for real‐time talk‐burst determination. The determination algorithms and the corresponding message flows for these two mechanisms are designed to show the feasibility of FFC and TFC. The performance of FFC and TFC is investigated through our analytical and simulation models in terms of the determination latency and the number of floor‐control message exchanges. Copyright © 2008 John Wiley & Sons, Ltd.

In order to satisfy QoS requirements for real-time multimedia applications over wireless networks, IEEE 802.11e has been proposed to enhance wireless-access functionalities. However, as system load is heavy, collisions occur most often and then the time needed for successfully transmitting data is lengthened seriously due to contention, handshaking, and long backoff processes for collision avoidance. It is important to keep QoS requirements satisfied. Therefore, we propose a fragment-based retransmission (FBR) scheme with QoS considerations for IEEE 802.11 wireless local area networks (WLAN). By utilizing IEEE 802.11e with FBR, the retransmission delay will be improved to conform strict time requirements for real-time multimedia applications. The capability of the proposed scheme is evaluated by a series of experiments, for which we have encouraging results.

The multimedia broadcast/multicast service (MBMS) originally proposed by UMTS supports the delivery of multimedia streams to a group of mobile subscribers. With the heterogeneity of device capability and the variety of subscriber preference, an MBMS source has to provide multiple levels of service quality to its group members. To achieve this, it is essential to adopt the layer-encoding (i.e., scalable-coding) technique to encode multiple layers of multimedia data. This paper investigates the distribution of layer-encoded multimedia data for UMTS MBMS. Specifically, we propose a new channel allocation scheme for layer-encoded MBMS. The performance of our scheme is evaluated through our simulation model.

ZigBee is uniquely designed for low-rate wireless personal area networks. It targets ultra-low complexity, cost, and power for wireless connectivity among inexpensive, portable, and moving devices. In ZigBee, the IEEE 802.15.4 standard is utilized as the communication protocols of medium access control (MAC) layer and physical layer. The IEEE 802.15.4 MAC layer provides a power-saving mechanism by setting the two system parameters, macBeaconOrder (BO) and macSuperframeOrder (SO), to achieve the low duty-cycle operation for the ZigBee network. In this paper, we analyze the power-saving mechanism in IEEE 802.15.4, and investigate the effects of the low-power attributes on the system performance. The performance evaluation is conducted through NS-2 simulation experiments under various BO and SO values and traffic loads. Based on the numerical results, some guidelines for the IEEE 802.15.4 low-power operation can be provided.

With the advance of wireless technologies, wireless mesh backhaul networks (WMBNs) are emerging an alternative to conventional wired backbones for metropolitan areas. As real-time applications (e.g., voice over IP and video streaming) rapidly grow, provisioning quality-of-service (QoS) guarantees is one of the most important issues for WMBNs. In this paper the QoS problem for routing and scheduling in TDMA-based WMBNs was considered. An integrated routing and scheduling mechanism was proposed to provides QoS guarantees to real-time services with unrestricted topologies for TDMA-based WMBNs. A linear programming optimization is devised to solve the amount of non-collision bandwidth for a path according to the available resource and the interference. A simulation model is developed to investigate the performance of the proposed mechanism. Through the developed simulation, a series of experiments are conducted to show the capabilities of the proposed mechanism.

The wireless mesh network (WMN) has been an emerging technology in recent years. Because the transmission medium used in networking mesh APs is radio, WMN is not only easy and cost effective in deployment, but also has good scalability in coverage area and capacity. The IEEE 802.11 MAC protocol has been adopted as the de-facto medium access control. We assess the fitness of the IEEE 802.11 over WMN, and contend that it is really necessary to be changed so that it can be adapted to WMN. In this paper, we propose the MASH (medium access with scheduling) protocol, which schedules the packet transmission between mesh APs in a fully-distributed manner. Since out-of-band signaling is used and scheduling is executed to solve the hidden terminal problem and exposed terminal problem, the simulations show that both the network throughput and end-to-end delay can be significantly improved in this wireless multi-hop network.

IEEE 802.15.4 is a new standard uniquely designed for low-rate wireless personal area networks. It targets ultra-low complexity, cost, and power for low-rate wireless connectivity among inexpensive, portable, and moving devices. IEEE 802.15.4 provides a Guaranteed Time Slots (GTS) mechanism to allocate a specific duration within a superframe for time-critical transmissions. In this paper, we propose an adaptive GTS allocation (AGA) scheme for IEEE 802.15.4 with the considerations of low-latency and fairness. The scheme is designed based on the existing IEEE 802.15.4 medium access control protocol without any modification. A simulation model validated by the developed mathematical analysis is presented to investigate the performance of our AGA scheme. The capability of the proposed AGA scheme is evaluated by a series of experiments. It is shown that the proposed scheme significantly outperforms the existing IEEE 802.15.4 implementations.

This paper presents a system prototype to help elder persons being aware of changes in terrain ahead of them. Ultrasonic signal is used for the detection of terrain changes because of its stability in air transmission speed and its simplicity and cost in hardware designs. The prototype is designed and built using commercial-off-the-shelf (COTS) components with an 8051 microprocessor. It can detect obstacles of heights no more than 10 cm three steps away.

Strong connectivity has been an important feature explored in many network applications, such as sensor networks. This research focuses on a dual power assignment problem, where each sensor node has two transmission power levels. The objective is to minimize the number of wireless sensor nodes assigned to transmit messages at the high transmission power level, while the resulting sensor network is strongly connected. We propose an efficient 1.75-approximation algorithm for this challenging problem. We not only show that the approximation ratio of the proposed algorithm is tight but also demonstrate the capability of the proposed algorithm in terms of simulation experiments.

Based on multimedia broadcast/multicast service architecture(MBMS), this paper proposes an efficient QoS-based multicast approach for the universal mobile telecommunications system (UMTS). Our approach adopts the existing scalable-coding technique to provide multiple levels of service quality to diverse mobile devices in the UMTS system. In this approach, two kinds of transmission modes are developed to utilize fully the network resources for wireline and wireless links. An analytic model is presented to investigate the performance of our approach and the 3GPP 23.246 approach. The numerical results indicate that in terms of transmission costs of core/radio networks, our approach outperforms the 3GPP 23.246 approach.

The session timer mechanism for the session initiation protocol (SIP) has been proposed to track the states of the communicating sessions for proxy servers. Upon the occurrence of the session failure (e.g., radio link disconnection), the proxy server can quickly release the resources allocated for the failed session by using the SIP session timer. Based on the SIP session timer, we propose a dynamic session refreshing approach to adjust the session timer depending on the radio link conditions for wireless VoIP subscribers. The study indicates that with our dynamic session refreshing approach, the session failure can be efficiently detected without a considerable increase of signaling traffic.

Both IPv6 and session initiation protocol (SIP) are default protocols for Universal Mobile Telecommunications System (UMTS) all-Internet protocol (IP) network. In the existing mobile telecommunications environments, an IPv6-based UMTS all-IP network needs to interwork with other Internet protocol version 4 (IPv4)-based SIP networks. Therefore, mobile SIP applications are typically offered through an overlay structure over the IPv4-Internet protocol version 6 (IPv6) interworking environments. Based on 3GPP 23.228, we propose an IPv4-IPv6 translation mechanism (i.e., SIPv6 translator) that integrates different IP infrastructures (i.e., IPv4 and IPv6) to provide an overlay network for transparent SIP application deployment. In this paper, we present the architecture and the call flows of the SIPv6 translator. An analytic model is proposed to investigate the fault tolerance issue of our approach. Our study provides guidelines to select appropriate number of processors for fault tolerance.

This paper describes the mobility management mechanisms for mobile telecommunications networks. There are two major types of mobility: radio network mobility and core network mobility. Radio network mobility supports radio link switching of a mobile user during conversation, and core network mobility provides roaming and tunnel-related management for packet re-routing due to user movement. Impact of mobility on both the radio and the core networks is addressed in this paper. Also, potential research issues on these topics are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

Based on IEEE 802.15.4 low-rate wireless personal area networks (LR-WPANs), this paper proposes a memorized backoff scheme (MBS) with the exponential weighted moving average (EWMA) approach to dynamically adjust the size of the contention window. The proposed scheme can be implemented in the standard IEEE 802.15.4 medium access control (MAC) protocol without adding any new message type and without modifying the communicating procedure. An analytic model and a simulation model are developed to evaluate the performance of IEEE 802.15.4, MBS and MBS+EWMA. The numerical results indicate that in terms of goodput, completion rate, average MAC delay, average queuing delay and average number of collisions for each data frame, our proposed scheme significantly outperforms the standard IEEE 802.15.4 backoff scheme.

To support real-time multimedia services in UMTS all-IP network, 3GPP TR 25.936 proposed two approaches to support real-time serving radio network controller (SRNC) switching, which require packet duplication during SRNC relocation. These approaches significantly consume extra system resources. This paper proposes the fast SRNC relocation (FSR) approach that does not duplicate packets. In FSR, a packet buffering mechanism is implemented to avoid packet loss at the target RNC. We propose an analytic model to investigate the performance of FSR. The numerical results show that packet loss at the source RNC can be ignored. Furthermore, the expected number of packets buffered at the target RNC is small, which does not prolong packet delay.

As the universal serial bus (USB) 2.0 becomes a major driving force in the peripherals market now, the variety of USB devices and their request types also impose challenging issues on resource allocation of bus bandwidth. This research aims at the proposing of a USB-compliant system architecture and real-time scheduling algorithms for the resource allocation of USB 2.0 and 1.1 device requests jointly in a quality-of-service (QoS) fashion. Periodic requests, such as isochronous and interrupt transfers, are guaranteed with preservation of bus bandwidth and schedulability tests. Sporadic requests, such as control and bulk transfer, are serviced with probabilistic performance guarantees. The capability of this work is demonstrated with performance evaluations over a Linux system prototype, for which we have encouraging results.

To support real-time multimedia services in UMTS all-IP network, Third-Generation Partnership Project TR 25.936 proposed two approaches to support real-time serving radio network controller (SRNC) switching, which require packet duplication during SRNC relocation. These approaches significantly consume extra system resources. This paper proposes the fast SRNC relocation (FSR) approach that does not duplicate packets. In FSR, a packet buffering mechanism is implemented to avoid packet loss at the target RNC. We propose an analytic model to investigate the performance of FSR. The numerical results show that packet loss at the source RNC can be ignored. Furthermore, the expected number of packets buffered at the target RNC is small, which does not prolong packet delay.

In mobile telecommunications operation, radio channels are scarce resources and should be carefully assigned. One possibility is to deploy the hierarchical cellular network (HCN). We study a HCN channel assignment scheme called repacking on demand (RoD). RoD was originally proposed for wireless local loop networks. We expend this work to accommodate mobile HCN. A simulation model is proposed to study the performance of HCN with RoD and some previously proposed schemes. Our study quantitatively indicates that RoD may significantly outperform the previous proposed schemes.

Abstract Based on media gateway control protocol (MGCP), this paper describes GSM‐IP, a voice over IP service for GSM mobile network. With the signaling protocol translation mechanism provided by the MGCP signaling gateway, we demonstrate how to interwork the MGCP elements with the home location register, visitor location register and mobile switching center in the GSM network. Then we present the message flows for GSM‐IP registration, call origination, call delivery, call release and inter‐system handoff procedures. We show the feasibility of integrating GSM with the MGCP‐based VoIP network without modifying the GSM network. Furthermore, our approach does not introduce new protocol primitives to MGCP. Copyright © 2001 John Wiley & Sons, Ltd.