Personalized Federated Learning (PFL) enhances traditional Federated Learning (FL) by addressing the challenges associated with non-independent and identically distributed (non-IID) data and retains FL’s privacy-preserving feature. However, this decentralized architecture still presents security challenges. This study focuses on data poisoning attacks, particularly label flipping attacks in PFL, where some malicious clients upload poisoned model parameters to the server, which could then compromise the personalized models of honest clients during the server’s aggregation process. Moreover, we discovered that even with a small number of attackers, targeted data poisoning attacks are feasible and can significantly affect the model’s performance on specific labels. Finally, we propose four defensive strategies that have proven effective in identifying malicious clients. Additionally, we provide a framework that assists users in selecting the most suitable defense strategy based on their specific circumstances, thereby enhancing the robustness of the PFL architecture.

Low Earth Orbit (LEO) satellite networks, known for their low signal latency, are ideal for 6 G real-time communications and Internet access. However, they are confronted with considerable challenges, including instability and restricted power resources. It is importance to guarantee reliable and energy-efficient operations in order to maintain uninterrupted and high-quality service. We propose an Energy-Efficient Reliable Distributed Routing Algorithm (EERRA) that dynamically updates satellite information, prevents data loops, and optimizes transmission paths to avoid eclipse regions. This approach extends satellite lifetime and improves network performance. The simulation results show that EERRA outperforms existing algorithms, including Global, DRA, and DSRA, across multiple metrics. EERRA demonstrates exceptional performance in terms of average end-to-end delay, average hop count, signaling overhead, and satellite lifespan. Compared to DRA and DSRA, EERRA achieves lower average end-to-end delay and hop count, and significantly reduces signaling overhead compared to the Global algorithm. Furthermore, EERRA excels in maintaining the highest average residual energy and the lowest life cycle consumption. These attributes make EERRA a highly efficient solution for managing the complex dynamics of LEO satellite networks, ensuring sustainable and reliable network operations over extended periods.

Detecting abnormal driving behavior is crucial for preventing traffic accidents, as they are responsible for a sig-nificant majority of incidents. However, existing methods for detection often come with high costs or execution restrictions. In this paper, we introduce EADD, an Edge-based Anomaly Detection platform for Driving behavior. EADD overcomes these limitations by detecting abnormal driving behavior without the need for additional sensors or restrictions. Additionally, EADD boasts low computational requirements and enables real-time detection on mobile devices like the Raspberry Pi 3 Model B.

The Long Short-Term Memory (LSTM) model has significantly improved time series prediction accuracy, but also brought forth concerns regarding reliability and security with its widespread adoption, particularly in the context of poisoning attacks. While there is substantial research on attacks and defenses for LSTM models, there’s limited focus on LSTM time series prediction models. In this paper, we propose an arithmetic-based poisoning attack methodology for a demonstrative LSTM time series speed prediction model. Furthermore, we employ the “red team/blue team exercises” commonly used in network security to develop defense strategies using support vector machine and linear regression analysis methods. Through the system-level simulation experiments, we verify the effectiveness of our proposed methodology. Our experiment results indicate that, regarding attacks, our methodology can identify the optimal attacks for the representative road segments. As for defenses, we demonstrate that the defended model’s performance is close to the real model’s performance.

Federated Learning (FL) is a distributed machine learning scheme that trains a global model across multiple end devices while protecting user privacy by keeping data locally, which has been shown to be useful for smart healthcare. By integrating virtualized container technology into edge platforms, flexible computing resources can be made available to Internet of Medical Things (IoMT) devices, enabling their participation in FL. Some studies have utilized the advantages of Kubernetes (K8s) for fast container deployment, offering general FL platforms for developers to rapidly deploy models. However, there are currently no FL development platforms suitable for smart healthcare, which exhibits unique challenges of personalized medical data and large-sized models for resource-limited IoMT devices. In this paper, we propose a Kubernetes-powered personalized FL (PFL) platform for resource-constrained IoMT. This platform, which incorporates personalized strategies to capture individual features and model compression mechanisms to reduce model size, allows model developers to formulate PFL training tasks for healthcare users to acquire personalized and compressed models, deployable on their devices. Via establishing a real testbed and deploying a demonstrating training task, our experiments verify the platform’s ability to support the development of personalized and compressed models.

Effective driver identification is one of critical aspects of Internet of Vehicles (IoV) applications, playing a pivotal role in various contexts, such as vehicle anti-theft, fleet management, personalized insurance, vehicle settings automation, digital forensics, and so on. In this article, we propose CPBW, a novel mechanism that combines change point detection and Bag-of-Words (BoW). The CPBW utilizes the smartphone triaxial accelerometer data to accurately identify drivers. The key innovation of CPBW lies in its exceptional efficiency within short time windows, significantly enhancing the real-time performance. The study adopts naturalistic driving studies, collecting the unrestricted real-world data to increase applicability. However, challenges arise from dynamic urban environments influencing driving behavior and the need to balance hardware costs, privacy concerns, and data reliability. In comparison to the previous methodologies, CPBW demonstrates a reduced time requirement for driver identification. Particularly, our proposed CPBW mechanism showcases impressive performance, achieving accuracy, precision, recall, and F1-score up to 98.1%, 98.1%, 98.1%, and 98.0%, respectively. As a result, CPBW markedly enhances the practicality of driver identification in real-world scenarios.

As technology advancements are leading to the creation of 5G and next-generation base stations (BS) that offer improved performance and application integration, current solutions are mostly reliant on established technical standards. By incorporating intelligent wireless resource management technology, the current small cell system can be optimized and its transmission performance enhanced. The implementation of deep reinforcement learning was then added. By using indication reports as the state, the smart agent is able to dynamically select the optimal GF parameters to achieve high-efficiency transmission. In the context of ultra-reliable low latency communication (URLLC) applications, we have utilized 5G ns-3 simulation to simulate an IIoT factory scenario that diverges from traditional uplink methods. By implementing grant-free (GF) techniques, we can reduce delays while maintaining a suitable level of reliability. To dynamically select the most appropriate transmission mode under varying conditions, we have developed reinforcement learning (RL) methods. Our numerical results demonstrate a promising trend in the overall satisfaction rate.

In recent years, self-driving vehicles have gradually been appearing on the road, but society has also begun to worry about the possibility of accidents caused by the anomaly self-driving system. Many researchers have begun to study the anomaly detection of self-driving vehicles, and each has proposed different detection algorithms. However, since self-driving vehicles are not yet popular, how to collect data, simulate attacks, and verify and compare multiple algorithms is a major obstacle to research. In this regard, we built an Internet of Vehicles platform, VADtalk, that facilitate anomaly detection modeling and deployment for self-driving vehicles. VADtalk contains programs such as anomaly detection model training and vehicle connection. When developers complete model uploading and setting through the GUI, the platform will automatically collect self-driving data, train the model, and even verify the operation of the model using a self-driving simulator, and then provide the results to the developer. After the developer determines the model, VADtalk can connect the trained model with the self-driving vehicle to actually perform real-time anomaly detection on it.

The 5G heterogeneous network (Het-Net) comprises macro cells and small cells. The small cells with the ultra-dense deployment can offload mobile data traffic from macro cells and extend service area while consuming less energy. However, frequent handoffs between the two types of cells result in high signaling costs and interference. Thus, determining when to switch small cells between active and inactive modes is crucial to reducing operation cost. This paper proposes a Reinforcement Learning-based network management mechanism for 5G HetNet, and simulation experiments were conducted to evaluate its performance, in contrast to previous works that utilized 3GPP standardized Self-Organizing Network (SON) for network management mechanisms.

The 3rd Generation Partnership Project (3GPP) is standardizing the Low Earth Orbit satellite network (LEO-SN), positioning it as a next-generation network (NGN) technology. Unlike conventional terrestrial networks, such as 4G and 5G, LEO satellites’ high mobility prompts recurrent handoffs with user terminals (UTs). This leads to challenges like elevated signaling traffic, diminished bandwidth efficiency, and prolonged handoff times, potentially compromising Quality of Service (QoS). This research introduces and assesses the User Terminal-Controlled Handoff (UCHO) and User Terminal-Assisted Handoff (UAHO) strategies through a 3GPP-based simulation model to evaluate their performances, offering valuable perspectives for LEO-SN standardization.

Recently, several Internet of Things (IoT) service platforms have been proposed to facilitate IoT application deployment. These platforms typically utilize the lightweight MQTT or CoAP application protocol, optimized for massive IoT applications. Unfortunately, these protocols are not suitable for the emerging, more sophisticated Artificial Intelligence of Things (AIoT). Session initiation protocol (SIP), in contrast, is a signaling and controlling protocol for real-time multimedia sessions, and has been viewed as a better candidate to provide a full range support of different broadband, critical, and industrial AIoT applications. However, there exists no generic SIP-based AIoT service platform that supports creations and operations of heterogeneous AIoT applications with various quality of service. This article presents the first SIP-based AIoT service platform, AIoTtalk, that enables rapid development of scalar and multimedia AIoT applications. Moreover, we deploy an experimental testbed and two real SIP-based AIoT applications to demonstrate the applicability and the performance of our AIoTtalk under both the cloud and edge scenarios. The experimental results show that, together with accurate model predictions and edge-virtualization auto scaling, AIoTtalk guarantees low latency and high quality of experience for messaging and streaming-based AIoT applications.

In recent years, more and more developers have been investigating robotic vehicles. Generally, the operations of robotic vehicles rely on navigation assistance systems, which make recommendations to guide robotic vehicles step-by-step using low-cost cameras until reaching the destinations. Some developers have gradually transferred relevant computing tasks of robotic vehicles without powerful computing power and training models to the edge computing platforms. However, none of such existing edge computing based navigation assistance systems can immediately scale a sufficient number of instances to adapt to the changing requested loads of robotic vehicles. In this paper, we propose a Kubernetes edge-powered vision-based navigation assistance system with a novel auto-scaling algorithm, allowing robotic vehicles to request navigation-related services. Once the requested load does not match the current load, the number of instances can be auto-scaled on demand. In order to evaluate the performance of our auto-scaling algorithm, we compare it with two selected auto-scaling algorithms. The experiment results demonstrate that our algorithm can immediately scale up to the most appropriate number of instances to reduce the latency of requests.

To guarantee low-latency and reliable end-to-end connectivity and provide suitable resources for vehicle-to-everything (V2X) services, the network function virtualization (NFV)-based multi-access edge computing (MEC) technology has been recognized as an effective solution. In the literature, few studies have proposed corresponding NFV-based MEC frameworks to provide V2X services. However, these works only discuss the concept of applying the NFV to the MEC to offer V2X services without implementing it in a real testbed. On the other hand, within the NFV-based MEC framework, several studies have investigated the NFV auto scaling mechanism to provide suitable resources for different services. However, these works are not suitable for V2X services, which exhibit unique traffic characteristics. In this paper, we (1) implement an NFV-based edge platform to provide vehicles with low-latency V2X services, (2) implement a vehicle mobility-driven NFV auto scaling mechanism that forecasts the resource requirements for V2X services and triggers the scaling operations if needed, and (3) use the open source software to build an NFV testbed and deploy our platform as virtual network functions to the NFV testbed. Finally, we implement a simple V2X service to validate our proposed edge platform, justifying that it, together with our NFV auto scaling mechanism, requires affordable processing delay and supports low V2X service latency.

Internet of things (IoT) technologies have been applied to realize various applications/services, ranging from massive, broadband, critical to industrial automation IoT applications. Recently, several IoT service platforms have been proposed to facilitate IoT application deployment, such as OpenMTC and IoTtalk. These platforms typically utilize the lightweight MQTT or CoAP application protocol to communicate with their served devices, both optimized for massive IoT applications within constrained networks. Unfortunately, these protocols are not applicable to those more advanced IoT applications, which demand high data rate and low latency. To provide a full range of supports for different IoT application scenarios, Session Initiation Protocol (SIP) can be a better candidate, which, besides instant messaging, can also handle the long session semantic and the publish-subscribe semantic. In the literature, different SIP semantics have been utilized to implement different IoT applications/systems. However, to the best of our knowledge, there exists no generic SIP-based IoT service platform that integrates relevant capabilities for developers to flexibly develop/deploy heterogeneous IoT applications, demanding different quality of service. This paper implements a SIP-based IoT service platform, iSIPtalk, which enables rapid development of more advanced IoT applications. To justify the applicability of iSIPtalk, we deploy a real testbed and implement a vehicular service within our proposed iSIPtalk. Finally, via delay measurements using the real testbed and the vehicular service, we demonstrate the performance of our iSIPtalk.

The mobile data traffic produced by live streaming is expected to see an exploding growth in recent years. To offload live streaming traffic from mobile networks for proximity-based social networking, device-to-device (D2D) communication is considered a promising technology. Unfortunately, the D2D discovery process requires constant signaling and is energy-consuming. One way to improve this drawback of D2D discovery is to exploit the ETSI multi-access edge computing (MEC) technology, where the knowledge of devices in proximity can be maintained in an MEC server and used to simplify the discovery procedure. In this paper, we aim to implement an MEC-assisted D2D live streaming service that offloads data traffic from mobile networks hierarchically with MEC and D2D communication. Specifically, the service includes an MEC App and a User App. The MEC APP can assist D2D discovery and cache live streams in the MEC server to reduce network latency. The User App can establish D2D connections between devices and share live streams over a D2D network using Wi-Fi Direct, offloading data traffic from mobile networks. We further design a rate adaptation heuristic that is capable of determining a suitable quality level in adaptive streaming for a multi-hop D2D network to improve the overall quality of experience (QoE). The experiment results justify that our proposed architecture and rate adaptation heuristic can provide improved network performance and user QoE for the mobile live streaming service.

Machine Type Communications (MTC) supports various Machine-to-Machine (M2M) applications and Internet of Things (IoT) services, where the energy efficiency is a key issue. In addition, because the number of IoT devices is rapidly growing, the improvement of the quality of service (QoS) becomes necessary. Many studies investigated the trade-off between the power consumption and the QoS, but did not improve the both at the same time. In this paper, we propose an scheduling algorithm assisted by a emerging technology, Multi-access Edge Computing (MEC), and our algorithm reduces the power consumption the latency as much as possible. In order to evaluate our algorithm, we use OpenAirInterface (OAI) as a testbed, because the simulation platform of LTE is fully developed and reliable. In this paper, the overview of OAI scheduler is introduced, and the implementation of our algorithm using OAI are detailed. Compared with Round-Robin (RR) and Proportional fair (PF) algorithm, our performance study shows that our algorithm improves the performance in terms of the energy consumption and the system performance. Moreover, through the simulation results, we analyze the relation between the computational capability of the MEC server and the system performance.

Cache placement is a critical issue in fog networks. It is essential to simultaneously consider the quality of network connection, the demand of contents, and the users' activities. This paper proposes an efficient cache placement by placing the files based on popularity categories within a fog node cluster. Requested files are categorized into three popularity levels and strategically cached in fog nodes of various activity levels This work aims to reduce energy consumption by reducing the number of cells to serve the users based on content popularity. Another contribution of this paper is the clustering method for the fog nodes to select the node to host for the cache contents. The effectiveness of the algorithm is tested using the simulation regarding energy efficiency.

Recently, more and more network operators have deployed cloud environment to implement network operations centers that monitor the status of their large-scale mobile or wireline networks. Typically, the cloud environment adopts container-based virtualization that uses Docker for container packaging with Kubernetes for multihost Docker container management. In such a container-based environment, it is important that the Kubernetes can dynamically monitor the resource requirements and/or usage of the running applications, and then adjust the resource provisioned to the managed containers accordingly. Currently, Kubernetes provides a naive dynamic resource-provisioning mechanism which only considers CPU utilization and thus is not effective. This paper aims at developing a generic platform to facilitate dynamic resource-provisioning based on Kubernetes. Our platform contains the following three features. First, our platform includes a comprehensive monitoring mechanism that integrates and provides the relatively complete system resource utilization and application QoS metrics to the resource-provisioning algorithm to make the better provisioning strategy. Second, our platform modularizes the operation of dynamic resource- provisioning operation so that the users can easily deploy a newly designed algorithm to replace an existing one in our platform. Third, the dynamic resource-provisioning operation in our platform is implemented as a control loop which can consequently be applied to all the running application following a user-defined time interval without other manual configuration.

Ever increasing numbers of users are surfing the net via an LTE access network. It has been predicted that three quarters (75%) of the world's mobile data traffic will be video-based by 2020. As the video space grows, media companies have been using adaptive bitrate technology for many years. The MPEG-DASH is the widely used HTTP-based adaptive streaming solution based on TCP. To address the bandwidth utilization issue, in this paper we investigate the ETSI Mobile Edge Computing (MEC) Intelligent Video Acceleration Service mechanism to improve video transmission over 4G LTE. Specifically, with the radio information provided by the MEC server, the video server can adaptively transmit just one copy of suitable bitrate data based on the throughput. We use OpenAirInterface (OAI), a 4G compliance emulator, as our testbed to implement and verify our proposed MEC-based adaptive video transmission mechanism. We design and implement a “filter” module in the Medium Access Control (MAC) layer over the OAI platform, which can reference Channel Quality Indicator (CQI) as a parameter to decide which bitrate type of data should be passed to the MAC Packet Scheduler for real resource allocation. By using constant-bitrate as our control groups, the testbed indicates that, consistent with our expectations, our mechanism can significantly benefit from lower latency with a reasonable throughput.

Machine-to-machine (M2M) communications protocols, e.g., Machine Type Communications (MTC) in 3GPP LTE, support a variety of Machine to Machine (M2M) applications, which requires real-time data reporting. Many uplink scheduling algorithms are primarily designed for human-to-human applications rather than for M2M applications. These scheduling algorithms typically are based on the fact that the machines in MTC are battery-powered and aim to reduce the energy consumption. We notice that the current literature considers either the problem of sleep-time maximization or transmission power minimization, but not both. In this paper, we systematically consider all energy-consumption factors of M2M real-time reporting. Using MTC in LTE as an example for M2M communications, in this paper, we propose two energy-efficient scheduling algorithms to minimize the total energy consumption of machines for M2M communications. The two proposed algorithms utilize the distance between a machine and the serving eNB (i.e., base station in LTE). Our performance study shows that our algorithms have performance enhancement in terms of energy consumption compared to other algorithms in the previous works. Furthermore, our algorithms can maintain high scheduling success ratio and fairness.

We propose an application (APP) layer scheduling scheme for scalable video transmissions over multiple radio access technologies (multi-RATs). More specifically, the proposed scheme adaptively adjusts the transmission parameters based on estimated network characteristics so that the decoding quality of experience (QoE) at user end is maximized. These parameters include number of transmitted video layers (source coding rate), the APP layer forward error correction (FEC) redundancy for each video layer (channel coding rate), and the transmission data rates in both cellular network and wireless local area network (WLAN). The network conditions are estimated by real-time protocol (RTP) and real-time control protocol (RTCP). Since the proposed scheme is an APP layer design, it can be easily implemented without changing the configurations of lower layers (e.g., transport or MAC layers). Simulations are conducted in network simulator 3 (NS-3), and demonstrate the effectiveness of our proposed scheme.

The increasing Machine-Type Communications (MTC) devices will make the network congestion because enormous devices simultaneously perform the random access to obtain the uplink resources. The group paging mechanism may solve this issue via centralized control to disperse the devices. This paper develops an analytic model to study the operation of the LTE multi-group random access procedure.

Machine-to-Machine (M2M) or machine-type communication technology standardized by ETSI/3GPP has recently gained a great deal of attention, and has been utilized in a variety of M2M applications, which commonly require real-time data reporting. This paper investigates the energy minimized scheduling problem for real-time reporting of data-critical M2M applications. Although many uplink scheduling algorithms have been proposed for different wireless mobile networks, they are mainly designed for human-to-human communication paradigms. This paper proves this energy minimized scheduling problem is NP-hard, and proposes a heuristic energy-efficient algorithm to address it. Our algorithm effectively schedules the transmissions of an M2M node in the same time slots, so that the active time of the M2M node can be minimized. The experiment results show that under limited bandwidth resource, our algorithm can maintain fairness and low data dropping ratios while achieving energy efficiency for a reasonable number of M2M nodes.

Quality of user Experience (QoE) is one of the most important issues for scalable video over OFDMA networks. Given the OFDMA frame structure, scheduling tasks can be classified into 1) intraframe scheduling and 2) interframe scheduling. While many studies have focused on how to enhance the QoE through intraframe scheduling, few studies have examined the potential of interframe scheduling. The only previous work on video interframe scheduling has considered which OFDMA frame the video traffic should start from; it is the single parameter that is adjustable, which leads to some loss of flexibility in scheduling. To enhance the QoE, this paper considers the video-transmission time point to be fully adjustable as long as the video delay bound can be satisfied. Accordingly, we formulate the interframe scheduling problem as an optimization problem with strong NP-hardness, and we propose a multiple-knapsack-based algorithm (MKBA) to solve the problem efficiently.

IEEE 802.16m resource scheduling remains a challenging issue for video multicast. Given the OFDMA frame structure, the IEEE 802.16m scheduling task is comprised of: (1) intraframe scheduling; and (2) interframe scheduling. In the literature, while many studies concentrated on the development of intraframe scheduling mechanisms, few studies looked at the potential of interframe scheduling mechanisms. This paper is the first attempt to investigate the energy efficiency potential of interframe scheduling algorithms to support scalable-video multicast services over IEEE 802.16m networks. Under the premise that the bandwidth requirements of scalable-video subscribers must be satisfied, we first prove that the interframe scheduling problem of minimizing video-subscriber energy consumption is NP-hard. To tackle the NP-hard problem, we propose a multiple bin-packing algorithm, MBPA, for energy-efficient scheduling. The proposed MBPA has full compatibility with the existing intraframe scheduling mechanisms and with the IEEE 802.16m sleep-mode operations. By applying the divide-and-conquer strategy, MBPA effectively eliminates unnecessary wake-up periods and unnecessary state transitions (between wake-up and sleep states), and thus achieves high energy-efficiency. Through theoretical analysis, we show that MBPA is a p-approximation algorithm, where p is a finite value no less than one. Finally, the simulation results show the effectiveness of the proposed MBPA in energy efficiency, user satisfaction, and computational complexity.

Machine-to-machine (M2M) communication technologies have been rapidly developed in recent years to support different kinds of applications, such as automatic monitoring from user equipments (UEs) to home devices. Unfortunately, such applications introduce heavy traffic in both the radio access networks and core networks (CNs). 3rd generation partnership project (3GPP) has defined its local IP access (LIPA) solution, which provides UEs with the ability to access home networks directly without traversing CNs. However, 3GPP LIPA can not support the universal LIPA service. Specifically, a UE's home LIPA service can not be accessed when the UE moves into the coverage area of a visited LIPA network or a macrocell. On the other hand, the recently developed traffic offload enhancements for 3GPP networks did not consider the authentication issue. This paper proposes a telecom-based universal LIPA solution, uLIPA, for 3GPP networks. To completely offload both the UE's traffic and control signaling messages from the CN, uLIPA first enhances the existing 3GPP LIPA architecture and routing path establishment procedure by incorporating the concept of local SGSN and local GGSN. Then, an effective one-pass signaling procedure is devised for mutual authentication among the UE, the visited LIPA network, and the home LIPA network. Since this authentication procedure is based on the 3GPP authentication framework, it can readily be adopted by the 3GPP and compatible mobile networks.

This paper proposes the first resource allocation scheme in the literature to support scalable-video multicast for WiMAX relay networks. We prove that when the available bandwidth is limited, the bandwidth allocation problems of 1) maximizing network throughput and 2) maximizing the number of satisfied users are NP-hard. To find the near-optimal solutions to this type of maximization problem in polynomial time, this study first proposes a greedy weighted algorithm, GWA, for bandwidth allocation. By incorporating table-consulting mechanisms, the proposed GWA can intelligently avoid redundant bandwidth allocation and thus accomplish high network performance (such as high network throughput or large number of satisfied users). To maintain the high performance gained by GWA and simultaneously improve its worst case performance, this study extends GWA to a bounded version, BGWA, which guarantees that its performance gains are lower bounded. This study shows that the computational complexity of BGWA is also in polynomial time and proves that BGWA can provide at least 1/ρ times the performance of the optimal solution, where \rho is a finite value no less than one. Finally, simulation results show that the proposed BGWA bandwidth allocation scheme can effectively achieve different performance objectives with different parameter settings.

Infrastructure-to-vehicle (I2V) group message delivery is a common operation required by a wide variety of vehicular ad hoc network (VANET) applications. However, due to the highly dynamic mobility in VANETs, it remains a difficult problem to deliver messages to a group of moving vehicles scattering over the whole network. The contribution of this paper is to propose the first trajectory-based I2V group message delivery protocol, TGMD, that exploits vehicle trajectories to improve the group-message delivery performance. TGMD contains two phases. The first phase determines a few rendezvous points from which the member vehicles can receive the message. Then the second phase transmits the group message over multiple hops to these selected rendezvous points using a multicast-like forwarding scheme to avoid redundant packet transmissions over the overlapped road segments. TGMD aims to minimize the required number of network-layer packet transmissions under a relatively loose delay constraint. Our extensive simulation results indicate that in comparison with the trajectory-based single-destination I2V message delivery scheme (through N-unicasting), TGMD significantly reduces the required number of packet transmissions (and thus bandwidth consumption) while achieving a high delivery ratio around 90%.

Many multi-channel Media Access Control (MAC) protocols have been studied in the past decade. With an eye to hardware cost and device capability, many researchers have proposed protocols using only one transceiver, even though the potential capacity of a multiple-transceiver network is higher than that of one with a single transceiver. In this paper, we introduce a novel, cooperative, multi-channel MAC protocol that incorporates the concept of cooperative communication into multi-channel MAC protocols, enabling a single transceiver to carry out the work of multiple transceivers. The proposed protocol, based on the well-known Slotted Seeded Channel Hopping (SSCH) multi-channel protocol, requires only one transceiver per device. For use in an IEEE 802.11 multi-channel multi-hop wireless network environment, our approach selects a relay node to become a virtual node with an intermediate node. We also design a channel assignment mechanism to let the virtual node transmit and receive data simultaneously on different channels, thus improving network performance. Simulation results show that our protocol significantly outperforms the original SSCH in terms of network capacity and average packet delay.

Mobile WiMAX is a promising technology to enable the wireless video multicast services, such as mobile IPTV and live athletic events. For supporting such bandwidth-eager and energy-hungry applications, the efficient bandwidth-allocation and energy-saving mechanisms should be simultaneously employed. In this paper, we first investigate how the WiMAX energy-saving mechanism significantly degrades the performance of the bandwidth-allocation mechanisms. We present a theoretical model for illustrating this interaction problem. To solve the problem, we propose a novel sleep-mode interleaving algorithm beyond the existing mechanisms. By appropriately adjusting one sleep-mode parameter, the proposed algorithm effectively guarantees the bandwidth efficiency of the video multicast mechanisms while the mobile stations can execute the standard sleep-mode operations. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of the user satisfaction, power consumption and computational complexity.

Abstract SIP‐NEMO has previously been proposed in the literature as a NEtwork MObility (NEMO) solution. However, the SIP‐NEMO handoff delay has been shown to be more than 320 ms, which is intolerable for real‐time services. In this paper, we focus on reducing the SIP‐NEMO handoff delay. We design a soft handoff mechanism for SIP‐NEMO and implement a test‐bed for it with soft handoff support. The experimental results indicate that our SIP‐NEMO with soft handoff support can effectively reduce the handoff disruption time from those of SIP‐NEMO without soft handoff and MIPv4‐NEMO with fast handover. Thus, our proposed soft handoff mechanism for SIP‐NEMO can improve the handoff performance so it can meet the delay requirements of real‐time multimedia applications. Copyright © 2009 John Wiley & Sons, Ltd.

This paper targets to investigate the interaction between IEEE 802.16e scheduling algorithms and sleep-mode operations. To achieve high energy efficiency, an energy-efficient scheduling architecture is first proposed. Based on this architecture, a state transition mechanism is designed for IEEE 802.16e mobile subscriber stations (MSSs) to switch their real-time connections between awake mode and sleep mode. The proposed energy-efficient scheduling algorithm is different from the existing scheduling algorithms in terms of the scheduling priority order and the state transition mechanism for MSSs. The simulation results highlight that with proper scheduling priority and state transition mechanism, scheduling algorithms can effectively improve QoS for IEEE 802.16e networks while achieving high MSS energy efficiency.

The Universal Mobile Telecommunications System (UMTS) all-IP network supports IP multimedia services through the IP multimedia subsystem (IMS). This paper proposes a mobile quality-of-service (QoS) framework for heterogeneous IMS interworking. To reduce the handoff disruption time, this framework supports the IMS mobility based on the concept of session initiation protocol (SIP) multicast. In our approach, the mobility of a user equipment (UE) is modeled as a transition in the multicast group membership. With the concept of dynamic shifting of the multicast group's members, the flow of actual data packets can be switched to the new route as quickly as possible. To overcome mobility impact on service guarantees, UEs need to make QoS resource reservations in advance at neighboring IMS networks, where they may visit during the lifetime of the ongoing sessions. These locations become the leaves of the multicast tree in our approach. To obtain more efficient use of the scarce wireless bandwidth, our approach allows UEs to temporarily exploit the inactive bandwidths reserved by other UEs in the current IMS/access network. Analytic and simulation models are developed to investigate our resource reservation scheme. The results indicate that our scheme yields comparable performance to that of the previously proposed channel assignment schemes.

3GPP 43.318 specifies the generic access network (GAN) for interworking between wireless local area network (WLAN) and Universal Mobile Telecommunications System (UMTS) core network. A dual-mode mobile station (MS) is equipped with two communication modules to support both WLAN and UMTS radio technologies, which shortens the battery lifetime of the MS. This paper proposes an analytical model and conducts simulation experiments to study the power consumption of dual-mode MSs in terms of the power consumption indicator and mean packet waiting time. Our study provides guidelines for designing WLAN-UMTS dual-mode MSs.

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.

The universal mobile telecommunications system (UMTS) utilizes the discontinuous reception (DRX) mechanism to reduce the power consumption of mobile stations (MSs). DRX permits an idle MS to power off the radio receiver for a predefined sleep period, and then wake up to receive the next paging message. The sleep/wake-up scheduling of each MS is determined by two DRX parameters: the inactivity timer threshold and the DRX cycle. In the literature, analytic and simulation models have been developed to study the DRX performance mainly for Poisson traffic. In this paper, we propose a novel semi-Markov process to model the UMTS DRX with bursty packet data traffic. The analytic results are validated against simulation experiments. We investigate the effects of the two DRX parameters on output measures including the power saving factor and the mean packet waiting time. Our study provides inactivity timer and DRX cycle value selection guidelines for various packet traffic patterns.

The IEEE 802.16 standard proposes the Media Access Control (MAC) protocol for the Wireless Metropolitan Area Network (WMAN). Two transmission modes are defined in the IEEE 802.16, including Point-to-Multipoint (PMP) mode and mesh mode. In the 802.16 mesh mode, allocation of minislots can be handled by the centralized and distributed scheduling mechanisms. This paper proposes the Combined Distributed and Centralized (CDC) scheme to combine the distributed scheduling and centralized scheduling mechanisms so that the minislot allocation can be more flexible, and the utilization is increased. Two scheduling algorithms, Round Robin (RR) and Greedy, are proposed as the baseline algorithms for the centralized scheduling mechanism. We conduct simulation experiments to investigate the performance of the CDC scheme with the RR and Greedy algorithms. Our study indicates that with CDC scheme, the minislot utilization can be significantly increased.

This paper investigates the discontinuous reception (DRX) mechanism of universal mobile telecommunications system (UMTS). DRX is exercised between the network and a mobile station (MS) to save the power of the MS. The DRX mechanism is controlled by two parameters: the inactivity timer threshold and the DRX cycle. Analytic and simulation models are proposed to study the effects of these two parameters on output measures including the expected queue length, the expected packet waiting time, and the power saving factor. Our study quantitatively shows how to select appropriate inactivity timer and DRX cycle values for various traffic patterns.

In general packet radio service (GPRS), a mobile station (MS) is tracked at the cell level during packet transmission, and is tracked at the routing-area (RA) level when no packet is delivered. A READY timer (RT) mechanism was proposed in 3GPP 23.060 to determine when to switch from cell tracking to RA tracking. In this mechanism, a threshold interval T is defined. If no packet is delivered within T, the MS is tracked at the RA level. When a packet arrives, the MS is tracked at the cell level again. However, the RT mechanism has a major fallacy in that the RTs in both the MS and the serving GPRS support node may lose synchronization. This paper considers another mechanism called READY counter (RC) to resolve this problem. In this approach, a threshold K is used. Like the RT approach, the MS is tracked at the cell level during packet transmission. If no packets are delivered after the MS has made K cell crossings, the MS is tracked at the RA level. We also devise an adaptive algorithm called dynamic RC (DRC). This algorithm dynamically adjusts the K value to reduce the location update and paging costs. We propose analytic and simulation models to investigate RC, RT, and DRC. Our study indicates that RC may outperform RT. We also show that DRC nicely captures the traffic-mobility patterns and always adjusts the K threshold close to the optimal values.

In a personal communications service (PCS) network, mobility databases called visitor location registers (VLRs) are utilized to temporarily store the subscription data and the location information for the roaming users. Because of user mobility, it is possible that the VLR is full when a mobile user arrives. Under such a circumstance, the incoming user has no VLR record and thus cannot receive PCS services. This issue is called VLR overflow. To resolve the VLR overflow problem, a VLR record can be selected for replacement when the VLR is full and then the reclaimed storage is used to hold the record of the requesting user. This paper considers the most idle replacement policy to provide services to mobile users without VLR records. In this policy, the record with the longest idle time is selected for replacement. We propose an analytic model to investigate the performance of this replacement policy. The analytic results are validated against simulation experiments. The results indicate that our approach effectively resolves the VLR overflow problem.