As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

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.

Millimeter-wave (mmWave) small base stations (SBSs) enhance indoor 5G heterogeneous network by delivering high-speed wireless access and offloading macro base station (MBS) traffic. Due to the limited range of mmWave signals, dense yet often noncontinuous SBS deployments are required, leading to energy inefficiencies—especially during low-traffic periods—because of high-power consumption. To address this, traffic-aware sleep strategies dynamically switch SBSs between active and sleep modes. However, most analytical models assume continuous coverage, limiting their real-world applicability. This article proposes an analytical model tailored to noncontinuous mmWave SBS deployments, evaluating three metrics: 1) the proportion of SBSs in sleep mode; 2) the traffic offloading ratio; and 3) the frequency of discovery broadcasts during sleep. Simulation results validate the model under varying deployment and traffic scenarios, demonstrating its value in supporting energy-efficient design for indoor 5G networks.

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology for network performance enhancement by intelligently manipulating the propagation environment. In contrast to conventional reflecting-only RIS, the simultaneous transmitting and reflecting RIS (STAR-RIS) extends coverage to 360 degrees through transmission and reflection, offering more flexibility in channel reconfiguration. The application of STAR-RIS in wireless networks holds tremendous potential for significantly improving network connectivity by fostering cascaded paths. However, it remains open on the deployment strategy for STAR-RIS to guarantee network connectivity cost-effectively, especially when considering random blockages. In this paper, we focus on investigating the impact of STAR-RIS on network connectivity in blockage-prone scenarios. Leveraging percolation theory, we derive upper and lower bounds for the critical density of nodes in both no-STAR-RIS and STAR-RIS-aided networks. Our results quantify the impact of STAR-RIS on network connectivity, demonstrating its significant enhancement on network connectivity and providing valuable insights into cost-effective deployment schemes for STAR-RIS.

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.

Real-time population mobility pattern at a specific time is an essential indication for sudden events. With the high penetration ratio of mobile phones, the mobile network could serve as a sensor network to monitor the population distribution, and user positioning without extra cost because the distribution of mobile phones is approximately the same as the population distribution. The abnormal spatial-temporal transition of the population is one of the critical indicators for sudden events. In this article, using the log data obtained from the mobile networks, we propose an AI-based framework, anomaly detection for population distribution (ADPD), to detect abnormal population distribution in geo-space. Different from the previous works, to detect anomaly of a specific grid area, the ADPD uses only the population information in the grid, which makes the ADPD more practical in the actual situation. We investigate the performance of the ADPD by running experiments based on the log data of the mobile network obtained during an actual sudden event, the 2018 Hualien Earthquake in Taiwan. Our study shows that the ADPD can identify the abnormal population transition grids nearby the grids with sudden events.

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.

Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at system error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of system errors are rare events in the BSS (i.e., the dataset of system status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict system error for the BSS. To address this challenge, we propose a machine learning-based framework for the system error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.

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.

Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as mobile smart agents that communicating, cooperating, and competing for resources and information. The task between vehicles is to learn and make decisions depending on the policy to improve the effectiveness of the multi-agent system (MAS) that deals with the continually changing environment. The multi-agent reinforcement learning (MARL) is considered as one of the learning frameworks for finding reliable solutions in a highly dynamic vehicular MAS. In this paper, we provide a survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility. Furthermore, we show the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios.

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.

The NarrowBand Internet of Things (NB-IoT) is one of the most promising technologies that fits the requirements of the low-power wide area networks (LPWAN). NB-IoT targets on supporting communications for small amount of data during a relatively long period of time (i.e., delay tolerant), which is one of the key features of IoT applications. Several studies have been conducted by some standard working groups to define the standards for NB-IoT. For example, the working group 3GPP aims to provide communication protocols with low energy consumption, good coverage penetration, and so on. Compared with the existing wireless communication protocols, less spectrum is allocated for NB-IoT. Thus, how to more efficiently use the resource/spectrum (i.e., resource allocation and scheduling) is one of the key issues. In this paper, we first describe the design of the physical layer for the downlink communication, especially the scheduling process. Secondly, we discuss the issues for resource allocation in NB-IoT while the delay requirement in the physical layer is satisfied. Thirdly, we discuss the open questions and possible solutions.

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.

With the assistance of wireless and Internet technologies, the Machine-to-Machine (M2M) devices are connected to the application servers to perform collaborative tasks and provide various applications with little or no human intervention. Based on a general network architecture summarized from the proposals of the 3rd Generation Partnership Project (3GPP), European Telecommunications Standards Institute (ETSI), and Telecommunications Industry Association (TIA), this paper discusses the network domain functionalities to accommodate trillions of M2M devices. Then we elaborate on issues such as significant signaling overhead that should be addressed so that the existing mobile networks can accommodate large-scale M2M communications. We analyze the overhead resulted from the M2M applications such as Intelligent Transportation System (ITS) and e-healthcare M2M applications. Our analysis is compared with those from the Human-to-Machine (H2M) and the Human-to-Human (H2H) applications.

IoTtalk is a platform for Internet of Things (IoT) device interaction, which nicely integrates a reconfigurable multi-sensor device called MorSensor with the proposed IoT management platform in the network domain. The sensors can be dynamically plugged in/out of a MorSensor device without being turned off, and IoTtalk automatically generates/reuses the application software for these sensors. We propose a dynamic ranging concept that automatically specifies the value range of a sensor so that it can send “meaningful” data to any connected output IoT device. Therefore, a MorSensor device can “talk” to other IoT devices, such as a light bulb or an electric fan. We also show that IoTtalk is a simple yet almost free solution for automatic sensor calibration. Finally, we illustrate that IoTtalk is a powerful tool for developing interactive science experiments.

Pricing and task allocation are vital to improving the efficiency in mobile crowdsensing, an emerging human-in-the-loop application paradigm. Previous studies focused on incentive mechanism design for specific sensing applications where one party (either task initiators or platform) can dominate the pricing and task allocation process. These results, however, are not applicable to a free crowdsensing market where multiple task initiators and task participants (mobile users), as peers, are engaged to maximize their own interests. New incentive mechanisms are pressingly needed to produce a solution, so that the interests of all participating parties can be considered. In this paper, appealing to exchange economy theory, we employ the notion of “Walrasian Equilibrium” as a comprehensive metric, at which there exists a price vector for mobile users and an allocation for task initiators such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). We consider a standard model where the utility function for sensing quality is monotonically increasing, differentiable, and concave, and the payoff function for a mobile user is linear. To address the problem, we first characterize the supply-demand pattern for a given price vector, which is the subset of mobile users selected by each task initiator to perform the task. We then devise methods for validating the existence of a Walrasian Equilibrium within each supply-demand pattern. One key step is to divide the space of prices into a collection of appropriate cells, based on the hyperplane arrangement, so that each cell has a unique supply-demand pattern. We devise an algorithm that can find a Walrasian Equilibrium in polynomial time, for a case of practical interest where the classes of mobile devices are bounded. Based on the insight, we further consider the general case and design an efficient pattern search (EPS) algorithm to reduce the search space, thus accelerating the search process accordingly. This is realized by choosing the supply-demand pattern which is closer to the “Walrasian Equilibrium” than the pattern in previous iteration in the search process. Our results show that EPS can find an $\epsilon $ -approximation Walrasian Equilibrium in polynomial time for the general case, given a constant $\epsilon $ .

Consisting of both small cells and a macro cell, the heterogeneous radio access network (Het-RAN) provides transmission service with higher data rate, offloads data traffic from the macro cell, and expands the service area of the macro cell. However, due to the small and non-continuous service areas of the small cells, frequent handover executions (i.e., significant handover cost) occur in the Het-RAN. To resolve the issue, we have proposed a delay timer algorithm (DTA) in our previous works. In this paper, we propose an analytical model to study the effects of the application traffic (based on the ETSI traffic model) and the user equipment (UE) mobility model on the performance of the DTA. We assume general distributions in the UE mobility model, so the analytical model is flexible enough for performance evaluation of the real Het-RAN system. We also construct a simulation model. The analytical model is validated against the simulation model. This analysis can help network operators run the Het-RAN more efficiently and effectively.

The performance of Smart Data Pricing (SDP) highly depends on the accuracy and reliability of measuring network bandwidth usage. The existing Internet protocol uses the distributed control, and the transport network protocols run together inside the routers and switches. However, it is hard to manage and retrieve online and precise measurements from the networks due to the large number of traffic flows, and the complexity of deploying measurement infrastructures. In this paper, we propose the Location-based Time-dependent SDP (LTDP) mechanism where based on SDN, we apply different data fusion models for fusing the measured Internet traffic status from different locations. Emulation experiments are conducted to investigate the performance of the proposed LTDP.

The small cell technology is proposed to provide wireless transmission services in the indoor environment and offload the traffic from a macro cell. Because of the small coverage of a small cell, there are usually a large number of small cells deployed in the mobile network, and it is likely that there are no User Equipments (UEs) in a small cell. A small cell may be idle most of the time and waste energy. In this paper, we propose a Connection-Driven (CD) mechanism for energy saving of small cells, where a small cell in the sleep mode is woken up when there are UEs (that have ongoing dedicated bearers) within its service area. We propose an analytical model and simulation experiments to investigate the performance of the CD mechanism.

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.

Machine-to-Machine (M2M) communication is the emerging technology for automation applications without human intervention. The 3GPP organization defines Machine Type Communications (MTC) to enhance support of M2M devices. Due to a potentially massive number of coexisting MTC devices, achieving efficient device identifier (ID) management and signaling is challenging. In this paper, a scheduled device grouping scheme is proposed to minimize the number of required device IDs and signaling overhead. The grouped devices share one ID while sequentially attaching to the network to execute application specific tasks. Applications with various goals are fulfilled considering expiration time and task dependency. Beyond formulating an integer programming problem, the issue is mapped into a variation of bin packing problem. The solution reaches 30% to 50% ID reduction by adopting a classic fitting algorithm.

This poster proposes an IoT-enabled mobile e-learning platform to enhance the performance of a learning process (i.e., assembling of fragmented learning time for a trainee) and the efficiency of learning material management. The informatics framework and implementation methodology for the proposed platform are described in the poster. The proposed platform has been used in the emergency department of National Taiwan University Hospital (NTUH) for training junior ultrasound physicians.

This paper considers a general multicast system in which multiple cells cooperate to serve multiple multicast groups in the same radio (i.e., time-frequency) resource. To improve the quality of service (QoS) and reduce the interference among the multicast groups, joint beamforming and power allocation among the cells are employed. By exploiting the concept of downlink-uplink duality and principal angle algorithm, an iterative approach is taken to jointly design the maximum signal-to-interference-plus-noise ratio (SINR) beamforming and power allocation under the per-cell power constraints. Simulation results demonstrate that the proposed method achieves a higher minimum SINR (hence the rate) for the multicast groups compared to the conventional single frequency network (SFN) based scheme and a semidefinite programming (SDP) based scheme in the literature.

The Internet of Vehicle (IoV) network refers to networking technologies that could support highly economical and effective communication channels for interactions between vehicles. The success of IoV relies on efficient vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) communications. Among all existing communication standards, the IEEE 802.15.4 standard, which comprises a simple physical (PHY) layer and an energy-efficient medium access control, is a promising candidate for IoV networks. In this paper, we propose analytical models for IEEE 802.15.4 nonbeacon-enabled mode for IoV by considering two major features, i.e., non-saturated traffic pattern and large-scale network, in IoV applications. We investigate the performance of IEEE 802.15.4 nonbeacon-enabled mode for IoV. Our study can provide guidelines for vehicles to dynamically adjust the broadcasting rate and achieve a higher probability of success in such communications.

In-home Smart Grid (SG), the integration of Renewable Power Systems (RPSs) with Conventional Power Systems (CPSs), calls for cost-effective management for the electricity usages of end users' household appliances. In this paper, by taking the charging process of RPSs and multiple types of household appliances in to consideration, we have developed analytical models to characterize the electricity cost in the in-home smart grid. Based on these models, we formulate the electricity cost minimization problem as a finite-horizon continuous-time Markov decision process (CTMDP), from which we obtain a threshold policy to minimize the cost. Numerical results show that the threshold policy can manage the electricity usage very effectively.

Third Generation Partnership Project (3GPP) proposed the Machine Type Communication (MTC) communications technology for machines or devices to communicate with each other without human intervention. The characteristics of MTC communications are different from that of conventional Human-to-Human (H2H) communications. The execution of an MTC device is task-based and triggered by the MTC application server. In most of scenarios, the time period for an MTC to perform a task is short. The number of MTC devices is expected to be several magnitudes higher than the number of H2H communication device. In MTC communications networks, an MTC device is addressed by its Mobile Subscriber ISDNs (MSISDNs) that is also used to address the H2H devices. Due to that huge number of MTC devices may coexist in the network, it is a challenge that the number of MSISDNs may insufficient to support both MTC communications and H2H communications. In this paper, we propose a grouping ID based device trigger mechanism for 3GPP MTC communications to resolve the issue “insufficient number of MSISDN.” Simulation experiments are conducted to investigate the performances of the mechanism.

This paper considers a joint beamforming design problem in the multicell multicast system with multiple groups that provide different multicast services. Inspired by previous works in the literature, we apply semidefinite relaxation (SDR) to perform joint beamforming design among the cooperating base stations. Per-cell power constraints are considered which make solving the problem challenging. We propose two algorithms to accommodate the per-cell power constraints while performing joint beamforming. Simulation results show that the proposed algorithms provide better performance compared to the existing multicast methods in the multicell environment.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices, uncertain spectrum supply from PUs and complex competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, secondary service provider (SSP), to facilitate the accessing of SUs without CR capability and harvest uncertain spectrum supply. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints. In view of the NP-hardness of the problem, we develop heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

DTN is an intermittently connected wireless ad hoc network with long and variable delay caused by the traditional store-carry-forward message dissemination over disconnected clusters. To reduce the end-to-end delay and increase reliability, this work proposes a Social-Based Broadcasting message dissemination protocol which incorporates long range communication with broadcasting. To increase reliability, we propose a rebroadcast node selection mechanism to increase the number of received nodes. To evaluate the performance of our proposed method, we conduct extensive simulation to investigate message dissemination efficiency, including penetration rate and reachability, of the proposed method and the greedy epidemic routing method in a large mobile network. The extensive simulation results demonstrate that our proposed method is practical, scalable and cost-effective for message dissemination in DTNs.

Femtocell technology has been proposed to offload user data traffic from the macrocell to the femtocell and extend the limited coverage of the macrocell in mobile communications networks. In existing commercial femtocell/macrocell networks, a widely accepted solution to configure the location areas (LAs) is to partition the femtocells overlapped with a macrocell into small groups and to assign each group with a unique LA ID different from that of the macrocell. Such configuration can reduce the paging cost in the mobility management, but increases registration signaling overhead due to discontinuous coverage of femtocells. To reduce signaling overhead in the femtocell/macrocell network, we propose a delay registration (DR) algorithm that postpones the registration until the delay timer expires when the mobile station (MS) moves into the overlapped femtocell. Analytical models and simulation experiments are proposed to investigate the performance of the DR algorithm. Our analytical models are generally enough to accommodate various MS mobility behaviors. Our study can provide guidelines for the operators to set up a delay timer to reduce signaling overhead while sustaining the traffic offloading capability of the femtocell.

Mobile sensor networks (MSNs) have been widely deployed to provide a ubiquitous solution to real-time monitoring applications such as traffic data collection in vehicular ad-hoc networks (VANETs), ocean data collection in underwater sensor networks (UWSNs), and biodata collection in wireless body area networks (WBANs). One major issue for designing MSNs is the energy-validity trade-off, that is, the trade-off between the energy efficiency for mobile sensors (MSs) and the validity of sensing reports. In this article, we propose a novel mechanism, Energy-Efficient Distributedly Controlled Reporting (E 2 DCR), to mitigate the energy consumption for MSs in real-time monitoring applications while keeping the sensing report valid. In this mechanism, we design dynamic sleeping adjustment (DSA) algorithms to adjust an MS's sleeping period using a heuristic method to reduce energy consumption. We provide analytical models to evaluate the performance of E 2 DCR in terms of the power savings and report validity. It has been shown that with E 2 DCR, MSs can report with less energy consumption while satisfying delay constraints for real-time monitoring applications.

Today, Internet services are utilized as major service platforms to provide business services. Web-based Internet applications that follow the multi-tier architecture are a well-accepted computation model when providing Internet services. Such a model suffers from interruption caused by software failures, hardware errors or software maintenance. The interruption may incur significant financial loss to Internet service providers. Thus, Service Continuity is one of the main challenges for Web-based Internet applications. Although several service continuity technologies have been developed, many of them focus on a single tier of the applications and do not guarantee end-to-end service continuity. In this paper, we describe the design, implementation, and performance evaluation of our proposed service continuity technology. This technology resolves the long time interruption problem caused by database schema evolution, handles software components upgrade, and achieves end-to-end Service Continuity. We propose a mechanism that contains multiple modules running interactively between each module. This approach only requires minimal amount of code rewriting and a small storage space. Comparing with existing approaches, our approach is more efficient with the least overhead.

In Machine-to-Machine (M2M) communications, machines are wirelessly connected to accomplish collaborative tasks without human intervention, and provide ubiquitous solutions for real-time monitoring. The real-time monitoring application is one of the killer applications for M2M communications, where M2M nodes transmit sensed data to an M2M gateway, and then the M2M gateway can have real-time monitoring for each sensing region. In real-time monitoring application, the energy consumption for the M2M nodes to send sensed data to the M2M gateway is an important factor that significantly affects the performance of the system. In this paper, we first consider the energy consumption as well as the validity of sensed data to design either centralized or distributed energy-efficient reporting mechanisms. We then analyze the complexity of the reporting mechanisms. Simulation experiments are conducted to investigate the performance of the proposed mechanisms, and show that the distributed mechanism outperforms the centralized mechanism when the M2M nodes are mobile.

Spectrum trading creates more accessing opportunities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs' devices and complex conflict and competition relationship among different CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum bands and facilitate the accessing of SUs without CR capability. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Leveraging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP's revenue maximization problem under multiple cross-layer constraints in multi-hop CRNs. In view of the NP-hardness of the problem, we have also developed heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one.

Different from unicast data delivery through dedicated channels, next generation Multicast and Broadcast Service (MBS) delivers data with higher bandwidth efficiency through multicasting. When a group of online users receive the same MBS content, the average MBS network cost per user can be significantly reduced. However, traditional charging methods are unable to account for the variation of network resource. To address this shortfall, this paper develops an innovative concept in MBS charging to realize the goal that “the more users join an MBS session, the more everyone saves.” Specifically, we propose a multi-tariff charging method for MBS with a competitive advantage that attracts more users to subscribe to MBS services. Analysis and numerical results demonstrate that operators can alleviate customer concerns about user credit consumption with dynamic tariff while remaining profitable in MBS. These features in our multi-tariff charging method can create a “win-win” situation for both operators and subscribers.

In this paper, we study a wireless multi-hop network where multiple flows co-exist and share the network resource collectively. Each flow is associated with a user which has specific requirements on its tradeoff between cost and quality of service. To support heterogeneous transmissions efficiently, we propose a quality-aware dynamic pricing algorithm, namely, QADP, which provably maximizes the overall network revenue while maintaining the stability of the network. Our proposed scheme enjoys the merit of self-adaptability due to its online nature.

ABSTRACT The Evolved Packet System (EPS) is standardized to support a common IP‐based core network with heterogeneous radio accesses in the latest 3GPP release. In the EPS, the Policy and Charging Control (PCC) framework provides dynamic policy control combined with real‐time charging management. Long core network signaling for PCC degrades session continuity performance during handover. In this paper, we investigate the handover for an EPS session. Based on the results, we propose a proactive scheme along with a user mobility tracking model to improve session continuity. More specifically, our proposed scheme attempts to reduce PCC reauthorization signaling delay during the handover procedure. Then, we formulate a Markov process to model the user mobility pattern and to derive the session continuity probability. Numerical results show that our scheme can significantly keep the session continuity at a user acceptable level even when the mobility pattern is high. We also provide guidelines for operators to set up the network configuration parameters according to various traffic conditions. Copyright © 2011 John Wiley & Sons, Ltd.

Internet services are now the major service platforms to provide services for all sectors of business and industries. To assure the correctness for concurrent data access, transaction-based Internet service is a well-accepted computation model. Because of the proliferation of networked devices and globalization, internet services can barely undertake any service interruption during any time of the day. However, transaction-base service model does not guarantee service continuity and could lower the quality of service in many circumstances. In this paper, we describe the design and implementation for service continuity technology to provide seamless online service upgrade for transaction-based Internet services. In particular, we report the implementation details on enterprise testbed. The developed mechanism is verified against on-line telecommunication web-services. The experimental results show that the recovery overhead is negligible and the mechanism is practical for deployment.

The service providers introduce new telecom services, which are managed by different Operations Support Systems (OSSs), to retain existing and attract new customers. The working group, TeleManagement Forum (TM Forum), proposed a unified network infrastructure, Next Generation Operations Systems and Software (NGOSS), as the standard interface between different OSSs to reduce the overhead for integration of sophisticated operating environments, and to shorten the time for launching new telecom services. For most of service providers, existing legacy OSSs are generally not compatible to the NGOSS framework. Due to the fact that those legacy systems have been working for a long time, modification or replacement of all systems incurs extra overhead for implementation and management. This paper proposes a systematic approach Middleware Architecture (MA) for migrating the existing legacy systems to the NGOSS compliant systems. We use the OSS through Java Initiative (OSS/J) Service Provisioning as an example to show how MA exercises for provision of new telecom service. Through the proposed approach, service providers can perform the migration in an incremental manner.

The great diversity of mobile sensor networks (MSNs) has emerged in different networks, including vehicular ad-hoc networks (VANETs), underwater sensor networks (UWSNs), and wireless body area networks (WBANs), which provide ubiquitous solutions for real-time monitoring. To prolong the network lifetime of MSNs, energy conservation for mobile sensors needs to be taken into consideration while we design the scheduling for MSNs. In this paper, we define the energy minimization problem for energy- aware transmission scheduling for MSNs, and prove that the problem is NP-hard. To have an optimal solution for the problem, we first formulate the problem as an Integer Linear Programming (ILP) problem, and then propose a greedy algorithm to approximate the solution of the ILP problem. We show that the computational complexity of the proposed greedy algorithm is low. We also propose a reporting mechanism to accommodate our greedy algorithm in MSNs. Simulation experiments are conducted to investigate the performance of the proposed reporting mechanism. Our performance evaluation shows that our mechanism has the mobile sensors transmit real-time sensed data with less energy consumption.

In wireless Multicast Broadcast Service (MBS), the common channel is used to multicast the MBS content to the Mobile Stations (MSs) on the MBS calls within the coverage area of a Base Station (BS), which causes interference to the dedicated channels serving the traditional calls, and degrades the system capacity. The MBS zone technology is proposed in Mobile Communications Network (MCN) standards to improve system capacity and reduce the handoff delay for the wireless MBS calls. In the MBS zone technology, a group of BSs form an MBS zone, where the macro diversity is applied in the MS, the BSs synchronize to transmit the MBS content on the same common channel, interference caused by the common channel is reduced, and the MBS MSs need not perform handoff while moving between the BSs in the same MBS zone. However, when there is no MBS MS in a BS with the MBS zone technology, the transmission on the common channel wastes the bandwidth of the BS. It is an important issue to determine the condition for the MBS Controller (MBSC) to enable the MBS zone technology by considering the QoS for traditional calls and MBS calls. In this paper, we propose two Dynamic Channel Allocation schemes: DCA and EDCA by considering the condition for enabling the MBS zone technology. Analysis and simulation experiments are conducted to investigate the performance of DCA and EDCA.

Several contention-based Medium Access Control (MAC) protocols have been proposed for the vehicles to gain the radio channels to distribute active safety messages timely, e.g., Safety Critical Application (SCA) information, for inter-vehicle communications in Vehicular Ad Hoc Networks (VANETs). In these MAC protocols, a contention period is introduced before the vehicle for channel access so that we may not have the timely and reliably message dissemination in VANETs. To reduce the contention period, this paper proposes a Region-based Clustering Mechanism (RCM) to be applied in these MAC protocols. We propose analytical models to investigate the performance of the RCM, which are validated by the simulation experiments.

Internet services are now the major service platforms to provide services for all sectors of business and industries. To assure the correctness for concurrent data access, transaction-based Internet service is a well-accepted computation model. Because of the proliferation of networked devices and globalization, internet services can barely undertake any service interruption during any time of the day. Service interruption includes scheduled service upgrade, bug fixes, power outage, and service attacks. While considering the QoS (Quality of Service) for the transaction-based Internet service, service continuity is not negligible. However, transaction-base service model does not guarantee service continuity and could lessen the quality of service in many circum-stances. Although several service continuity technologies are developed, many of them focus on providing backup service when failure occurs and do not guarantee that the clients can continue their requested services step when the system is back to service. Existing technologies that support on-line service upgrade are usually too complicated to implement or have tremendous runtime overhead. In this paper, we describe the design, implementation and performance evaluation for service continuity technology to provide seamless online service upgrade for transaction-based Internet services. This approach requires minimal amount of code rewriting and has least overhead, comparing with existing approaches. We present the performance evaluation results and show that the overhead of our design is negligible.

The Multicast Broadcast Service (MBS) zone technology is proposed to provide MBS with high QoS on Mobile Communications Networks (MCNs). An MBS zone consists of a group of Base Stations (BSs) synchronized to transmit the same MBS content using the same multicasting channel, which potentially reduces the time delay for Mobile Stations (MSs) to handoff between different BSs in the same MBS zone. However, significant time delay still incurs while MSs handoff between different BSs belonging to different MBS zones (i.e., the inter-MBS zone handoff). To reduce the possibility for the inter-MBS zone handoff, we may increase the size of an MBS zone (i.e., more BSs contained in an MBS zone), which may result in poor multicasting channel utilization. This paper proposes the OverLapping Scheme (OLS) for more flexible MBS zone configuration to get better performance for MBS in terms of QoS and radio resource utilization. We conduct propose the simulation experiments to investigate the performance for the original MBS zone technology (namely the Basic scheme), and the OLS scheme.

The mobile sensor networks (MSNs) have been widely deployed to provide an ubiquitous solution for time-sensitive applications in a specific area with low deployment cost. The monitoring area of an MSN can be divided into several sensing regions (SRs). In an SR, the mobile sensor (MS) is responsible for reporting the sensed data to the sink node. For the time-sensitive applications on MSN, the time is divided into multiple monitoring periods (MPs). During every MP, a sensing report transmission in an SR is invalid if the sensing report is generated before the beginning of the MP. In this paper, we propose a region-based reporting mechanism, namely Energy-Efficient Distributed-Control Reporting (E2DCR) mechanism, for the MSNs. During every MP, the E2DCR mechanism attempts to have only one MS transmit the sensing report in an SR, and the other MSs in the same SR can stay in the sleep mode for power saving. Simulation experiments are conducted to investigate the performance of the proposed mechanism. Our study shows that E2DCR can meet the delay constraint of the time-sensitive applications with less power consumption.

Cognitive Radio technology releases the spectrum from shackles of authorized licenses and facilitates the trading of spectrum bands. In the spectrum market, primary users (PUs) set prices for their vacant bands and sell them for monetary gains, and secondary users (SUs) buy the bands and opportunistically use them to satisfy their service demands when the PUs are not active. However, when there are multiple bands available for the SUs to access, the SU confronts the challenges of how to choose bands and how to split his traffic over them considering both the contention from peer SUs as well as the unpredictable activities of the PUs. In this paper, we propose a return and risk model to represent these concerns of the SU, and help the SU to make appropriate decisions of traffic distribution over available spectrum bands, either the bands belonging to SU itself or the bands shared with PUs. The simulation and analysis show that our spectrum sharing scheme is efficient in terms of maximum return for given risk or minimum risk for given return, and is also effective in improving the spectrum utilization and SUs' satisfactory degrees.

The indoor femtocell technology (known as femto BSs) with low power and low cost is proposed to provide comprehensive indoor wireless converge of macro BSs, which is known as the femtocell network. The Location Area ID for a femtocell is different from that for the macrocell that covers the femtocell, which results in frequent location update procedure (i.e., the location update cost increases) when the Mobile Station has a significant level of mobility between femtocells and macrocells. We propose the Cell Priority Transition (CPT) mechanism to reduce the location update cost for the femtocell network. The simulation experiments and analysis are conducted to investigate the performance of the CPT mechanism. Our study shows that the proposed CPT mechanism significantly reduces the location update cost for femtocell networks.

Abstract The 3GPP working group proposes the Long‐Term Evolution (LTE) system as the 4G mobile communications technology. To reduce the transmission latency in the radio access network, Evolved Universal Terrestrial Radio Access Network (E‐UTRAN), of LTE, the base station and radio network controller are merged into a single network node, namely eNB. In E‐UTRAN, the Stop‐And‐Wait Hybrid ARQ (SAW‐HARQ) protocol in the Media Access Control (MAC) layer deals with transmission errors without the need to engage the ARQ protocol in the Radio Link Control (RLC) layer. The HARQ and ARQ protocols interact only in case of dealing HARQ residual errors. In this paper, we propose an analytical model to model the behavior of the HARQ–ARQ Interaction. Our analytical model is validated against the simulation experiments that provide more performance metrics (which cannot be obtained through our analytical model). Finally, we investigate the impacts of the HARQ–ARQ Interaction on the Protocol Data Unit (PDU) transmission delay based on our simulation experiments. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The Multicast Broadcast Service (MBS) zone technology is proposed to provide MBS with high QoS on Mobile Communications Networks (MCNs). An MBS zone consists of a group of Base Stations (BSs) synchronized to transmit the same MBS content using the same multicasting channel, which potentially reduces the time delay for Mobile Stations (MSs) to handoff between different BSs in the same MBS zone. However, significant time delay still incurs while MSs handoff between different BSs belonging to different MBS zones (i.e., the inter‐MBS zone handoff). To reduce the possibility for the inter‐MBS zone handoff, we may increase the size of an MBS zone (i.e., more BSs contained in an MBS zone), which may result in poor multicasting channel utilization. This paper proposes the OverLapping Scheme (OLS) and the Enhanced OverLapping Scheme (EOLS) for more flexible MBS zone configuration to get better performance for MBS in terms of QoS and radio resource utilization. We propose the analytical models for the original MBS zone technology (namely the Basic scheme), and the OLS scheme, which are validated against the simulation experiments. Based on the simulation results, we investigate the performance for the Basic scheme, the OLS scheme, and the EOLS scheme. Copyright © 2010 John Wiley & Sons, Ltd.

The Authentication, Authorization, Accounting (AAA) infrastructure in Mobile IP network is designed to distribute keys to network entities for signaling message protection. In Mobile IP network, Regional Registration is employed to migrate the high signaling delay when a mobile user moves between network agents within the same visited domain. How to distribute keys in Mobile IP Regional Registration is still an open issue and adopting AAA infrastructure may be a suitable solution. However, in the literature, no work has a sound analytical study on Regional Registration in Mobile IP network with AAA infrastructure. In this paper, we develop a complete analytical model to investigate handover delay of Mobile IP network with and without Regional Registration. The accuracy of this model is validated by the developed simulations. From the proposed model, this paper precisely justifies performance improvement of Regional Registration in Mobile IP network.

Wireless mesh networks (WMNs) have emerged as one of the major technologies for 4G high-speed mobile networks. In a WMN, a mesh backhaul connects the WMN with the Internet, and mesh access points (MAPs) provide wireless network access service to mobile stations (MSs). The MAPs are stationary and connected through the wireless mesh links. Due to MS mobility in WMNs, mobility management (MM) is required to efficiently and correctly route the packets to MSs. We propose an MM mechanism named Wireless mesh Mobility Management (WMM). The WMM adopts the location cache approach, where mesh backhaul and MAPs (referred to as mesh nodes (MNs)) cache the MS's location information while routing the data for the MS. The MM is exercised when MNs route the packets. We implement the WMM and conduct an analytical model and simulation experiments to investigate the performance of WMM. We compare the signaling and routing cost between WMM and other existing MM protocols. Our study shows that WMM has light signaling overhead and low implementation cost.

3GPP 33.246 proposes key management mechanism (KMM) to distribute security keys for universal mobile telecommunications system (UMTS) multimedia broadcast and multicast service (MBMS). KMM introduces extra communication overhead to UMTS. The previous study, key-tree scheme (KTS), resolves this issue for the IP multicast network. However, this scheme may not be so efficient while applied in UMTS MBMS due to lots of storage space and heavy multicast traffic introduced, which may decrease the QoS of UMTS MBMS. In this paper, we propose a more efficient scheme, hash function scheme (HFS), to release both storage and communication overhead for KMM in UMTS MBMS. We first modify the KTS applied in the UMTS MBMS and then detail the execution of HFS, which is proven to be correct. We conduct an analytical model and simulation experiments to compare the performance between the UMTS KMM with KTS and with HFS. Our study shows that the proposed HFS can reduce both communication and storage overhead without damaging QoS of UMTS MBMS.

3GPP 23.246 proposed the multimedia broadcast multicast service (MBMS) to deliver multicasting content in the Universal mobile telecommunications system (UMTS). In MBMS, the common logical channel is enabled to serve multiple MBMS calls at the same time. Use of the common logical channel may cause interference to the dedicated logical channels serving the traditional calls. To more efficiently utilize the radio resource to serve both traditional and MBMS calls, this paper proposes two channel allocation algorithms: Reserved Resource for Multicasting (RRM) and Unreserved Resource for Multicasting (URM). We propose analytic models and conduct simulation experiments to investigate customer Satisfaction Indication (SI) for the two algorithms. Our study indicates that URM outperforms RRM in terms of customer SI.

When the basic functionalities of a wireless mobile network have been achieved, customers are then more interested in value-added mobile applications. In order to attract more customers to such mobile applications, a solid, secure and robust trading model is a must. This paper proposes such a secure trading model named mobile electronic payment (MEP) for wireless mobile networks, which applies the emerging ID-based cryptography for key agreement and authentication. Our MEP attempts to alleviate the computational cost, reduce the memory space requirement in mobile devices, and meet the requirements for secure trading: avoidance of overspending and double spending, fairness, user anonymity and privacy. Our design is transparent to the bearer networks and is of low deployment cost. We expect that our MEP provides a viable trading architecture model for the future mobile applications.

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.

3GPP 33.246 proposes key management mechanism (KMM) to distribute security keys for universal mobile telecommunications system (UMTS) multimedia broadcast and multicast service (MBMS). KMM introduces extra communication overhead to UMTS. The previous study, key-tree scheme (KTS), resolves this issue for the IP multicast network. However, this scheme may not be so efficient while applied in UMTS MBMS due to lots of storage space and heavy multicast traffic introduced, which may decrease the QoS of UMTS MBMS. In this paper, we propose a more efficient scheme, Hash function scheme (HFS), to release both storage and communication overhead for KMM in UMTS MBMS. In this paper, we first modify the KTS to be applied in UMTS MBMS. Then we detail HFS. We prove the correctness of HFS. Our study shows that the proposed HFS can reduce both communication and storage overhead without damaging QoS of UMTS MBMS.

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.

In recent years, many modern phased-array radars are built with commercial off-the-shelf components, and the functions of many hardware components are also reimplemented by software modules. In such systems, radar tasks could be modeled as distributed real-time tasks which require end-to-end deadline guarantees and have precedence constraints. Different from most previous work on either algorithms with restrictions in resource utilization or heuristics without analytical ways for schedulability guarantees, the objective of this paper is to propose a joint real-time scheduling algorithm for both transmitter/receiver and signal processor workloads with an analytical framework for offline probabilistic analysis and online admission control. The strength of our approach is verified by analysis results and a series of experiments based on a real phased-array radar for air defense frigates

Abstract Recently, mobile networks and internet technologies have been widely developed for the voice communication and information retrieval services all over the world. Compared with the wire‐line internet environment, mobile networks have lower bandwidth, longer transmission latency and unreliable connection, and the capability of mobile terminals is restricted by the limited memory size, lower CPU computation capability and inconvenient I/O interface. These limitations restrict the development of the wireless internet applications. In this paper, we design and implement a ‘wireless internet remote access platform’ (WIRAP). The WIRAP platform interconnects the wireless network and internet to provide mobile users a remote centralized storage and computation environment. A mobile user can store large volume of data and execute complex computations on WIRAP instead of on the mobile terminals. WIRAP supports different network interfaces (e.g. short message service (SMS), wireless markup language (WML) and hyper text markup language (HTML) and users may use terminals (with different network capabilities) to access WIRAP. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Recently, as the service areas of wireless local area network (WLAN) increase rapidly, the usage of WLAN gets popular. People expect more intelligent applications on WLAN. The location‐aware application is one of the killer applications. This paper proposes a SIP‐based mobility management platform named ‘WLB’ for WLAN location‐aware applications. The WLB accommodates the mobility management, broadcasting, and multicasting mechanisms for mobile user. Within the WLB, the coverage areas of WLANs are partitioned into several service areas on the application level. All signaling in WLB is based on Session Initialization Protocol (SIP), which extends the capability of WLB to be integrated with other mobile networks (e.g., UMTS and GPRS). In this paper, we detail the design and implementation of the WLB platform, and discuss a possible integration of the WLB with UMTS all‐IP network. Copyright © 2005 John Wiley & Sons, Ltd.

In recent years, many modern phased array radars are built with commercial-off-the-shelf components, and the functions of many hardware components are also re-implemented by software modules. In such systems, radar tasks could be modelled as distributed real-time tasks, which require end-to-end deadline guarantees and have precedence constraints. Different from most previous work on either algorithms with restrictions in resource utilization or heuristics without analytical ways for schedulability guarantees, the objective of This work is to propose a joint real-time scheduling algorithm for both transmitter/receiver and signal processor workloads with an analytical framework for off-line probabilistic analysis and online admission control. The strength of our approach is verified by analysis results and a series of experiments based on a real phased array radar for air defense frigates [A. G. Huizing et al. (1996)].

The Universal Mobile Telecommunication System (UMTS) adopts WCDMA as the air interface that provides variable transmission rate services. Four traffic classes are identified in UMTS, given as controversial, streaming, interactive, and background classes. To efficiently utilize the radio bandwidth, the shared channel technology is proposed to deliver interactive and background traffics. This paper proposes a "shared-channel assignment and scheduling" (SCAS) algorithm to allocate shared channels to interactive and background connections. We formally prove that the SCAS algorithm can satisfy the transmission rate requirement of each interactive connection and can efficiently utilize the radio bandwidth.

This paper proposes overflow control schemes to support high-speed downlink packet access (HSDPA) mechanism in the universal mobile telecommunication system (UMTS). To access the UMTS services, a user equipment (UE) communicates with all cells (base stations) in an active set. However, multiple links between the UE and the cells in the active set may reduce the transmission speed due to interference. Third-Generation Partnership Project specification TR 25.950 proposes HSDPA. In this mechanism, the UE only selects one cell (referred to as the serving cell) in the active set for high-speed downlink transmission. In HSDPA, the radio network controller sends the packet frames to the cells in the active set. For the serving cell, the packet frames are forwarded to the UE. On the other hand, every nonserving cell in the active set queues the packet frames in a buffer. If the link quality between the serving cell and the UE degrades below some threshold, the UE selects the best cell in the active set as the new serving cell. Since the nonserving cells do not send packet frames to the UE, their buffers may overflow. In this paper, we propose schemes to address the buffer overflow issue. Our schemes guarantee that when the buffer of a nonserving cell is full, the previously received packet frames in the buffer can be safely dropped, and after the UE has switched wireless link to the new serving cell, no packet frames are lost.

The Third Generation Partnership Project (3GPP) specification TR 25.924 proposed the opportunity driven multiple access (ODMA) mechanism for the universal mobile telecommunication system (UMTS). According to the maximum transmission bit rate that can be achieved between a UMTS Node-B and user equipment (UE), the coverage area of a Node-B is divided into two regions: high-bit-rate region (where a high bit rate can be used to deliver the packets for the UEs) and low-bit-rate region (where, due to hostile losses, the packets for the UEs can only be transmitted with low bit rate). The ODMA mechanism increases the transmission bit rate for the UEs in Region L by relaying the transmissions between a Node-B and UEs in Region L over a number of hops. However, more radio resource is consumed to serve a transmission with ODMA than that without ODMA, which may affect the quality of service (QoS) for other packet transmissions. In this paper, we propose an analytical model and simulation experiments to investigate the performance of the ODMA and non-ODMA networks in terms of the average transmission rate, packet blocking probability, and code channel utilization. Our study indicates that when the network traffic is not heavy, the ODMA mechanism significantly improves the average transmission rate and code channel utilization for the user packets by slightly reducing the QoS for other packet transmissions (i.e., packet blocking probability).

This paper proposes overflow control scheme to support high-speed downlink packet access (HSDPA) mechanism in the Universal Mobile Telecommunication System (UMTS). To access the UMTS services, a user equipment (UE) communicates with all cells (base stations) in an active set. However, multiple links between the UE and the cells in the active set may reduce the transmission speed due to interference. 3GPP specification TR 25.950 proposes HSDPA. In HSDPA, the UE only selects one cell (referred to as the serving cell) in the active set for high-speed downlink transmission. In this mechanism, the radio network controller sends the packet frames to the cells in the active set. For the serving cell, the packet frames are forwarded to the UE. On the other hand, every non-serving cell in the active set queues the packet frames in a buffer. If the link quality between the serving cell and the UE degrades below some threshold, the UE selects the best cell in the active set as the new serving cell. Since the non-serving cells do not send packet frames to the UE, their buffers may overflow. In this paper, we propose the scheme to address the buffer overflow issue. Our scheme guarantees that when the buffer of a non-serving cell is full, the previously received packet frames in the buffer can be safely dropped, and after the UE has switched wireless link to the new serving cell, no packet frames are lost.

As wireless technology advances, wireless products are integrated with enterprise networking to offer cordless terminal mobility. Most corporations have deployed wireless PBXs at the departmental level. However, the mobility management mechanism that integrates these facilities at the corporation level may not be available. This paper describes a mobility management mechanism for an enterprise wireless telephone network. We show how, to modify the call model of the private branch exchange (PBX) to accommodate mobility management for an enterprise network. Our design was implemented on a commercial PBX product called Jupiter. An analytical model is proposed to evaluate the performance of the implemented system. Our study shows that with a large number of WPBXs and long Internet message delays, the misrouting probability can be limited to within 1%. This performance result is considered satisfactory.

Global system for mobile communications (GSM) call reestablishment service allows a mobile station to resume a call in which the radio link has been temporarily interrupted due to interference or bad signal (which is referred to as an interrupted call). This service increases end user satisfaction and network quality perception. We propose analytic models to study the performance for call reestablishment service. Our study indicates that call reestablishment can significantly reduces dropping for interrupted calls.