Bitcoin mixing operations are essential for enhancing transaction privacy by obfuscating the link between transaction inputs and outputs. However, the transparency of blockchain transactions presents significant privacy risks, as transaction details are publicly recorded. To address these challenges, Bitcoin mixing operations are crucial for obfuscating the link between transaction inputs and outputs, thereby enhancing transaction privacy. Whereas, current mixing approaches are limited by several issues, including reliance on trusted third-party services, vulnerability to forgery by malicious participants, and suboptimal efficiency. In response to these challenges, we propose a decentralized Bitcoin mixing scheme named CoinMixMultiSig (CMMS). This scheme designs a two-round collaborative public address generation process, allowing participants to jointly create a public address and send their Bitcoin efficiently to this address before signing. This approach mitigates fraud risks, such as participants dropping out during the mixing process or maliciously altering transaction data, which could lead to asset loss. To enhance security and efficiency, CMMS incorporates a multi-signature mechanism that reduces the overall signature size. The multi-signature mechanism reduces computational and communication overhead, enhancing efficiency and practicality for real-world use. A rigorous security analysis demonstrates that CMMS ensures unforgeability and effectively resists rogue key attacks, providing a reliable and secure option for Bitcoin transactions.

Combining cloud radio access network (C-RAN) and mobile edge computing (MEC) can effectively reduce network service latency and improve network reliability. The locations of edge servers (ESs) in the architecture and the services deployed affect the network quality and the operator’s revenue. However, few studies have focused on ES or service deployment in this architecture, and almost no research has addressed both joint deployments. Furthermore, previous studies rarely consider the collaborative deployment across multiple communities (regions) and the cooperation among ESs simultaneously. To fill this gap, this study first constructs a C-RAN network system model with multilayer MEC involving multicommunities and establishes a multiobjective mixed-integer programming (MOMIP) model aimed at maximizing the profit of service providers and the comprehensive average resource utilization of the ESs. Then, for the established model, this study proposes a three-step heuristic algorithm to solve. The algorithm has been used to determine the number of ESs to be deployed in each community, the deployment locations of ESs at each layer, and the services to be deployed on each ES. Finally, this study conducts a series of simulation experiments. Experimental results show that the proposed algorithm outperforms the baseline algorithms, and the obtained deployment scheme is more reasonable.

The combination of Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) has been proven to effectively enhance network transmission rates and service capabilities. However, since the ground communication facilities in the architecture are fixed, have limited coverage and are easily damaged by natural disasters, some user requests may not be processed in a timely manner. This poses a significant challenge to the flexibility and post-disaster recovery capabilities of the architecture. To this end, this paper studies how to deploy Unmanned Aerial Vehicles (UAVs) to assist the recovery of post-disaster communication network when some ground facilities in the architecture are destroyed, and how to perform reasonable task scheduling based on user task priorities to achieve rapid and effective rescue. Moreover, this paper mathematically models the problem with the goal of maximizing the success rate of user task execution. Since the established model belongs to the Mixed-integer Nonlinear Programming (MINLP) model, which is non-convex and NP-Hard, this paper designs a priority-based algorithm for joint UAV deployment and task scheduling to obtain high-quality suboptimal solutions. The algorithm employs a two-layer optimization architecture with the characteristics of low memory usage and low time complexity. The experimental results indicate that the proposed algorithm outperforms the baseline algorithms and is more effective in handling rescue tasks under different user scales.

The advancement of intelligent connected vehicles and aerial computing has garnered extensive attention from scholars worldwide. In particular, high-altitude platforms (HAPs) and autonomous aerial vehicles (AAVs) have emerged as effective tools to extend vehicular network coverage and enhance real-time monitoring capabilities. This study, grounded in the data from the Yizhuang Intelligent Connected Autonomous Driving Demonstration Zone, introduces an air-ground integrated vehicular network model for abnormal driving behavior detection using D-S evidence theory. The model integrates aerial computing and vehicular networks in a cohesive manner, scrutinizes the data associated with routine driving behaviors, and integrates the outcomes of various analyses through evidence theory at the decision-making level, culminating in the estimation of the probability of abnormal driving behaviors. Simulation experiments results demonstrate that this algorithm not only enhances the precision of abnormal behavior identification to a remarkable 97.1% but also significantly accelerates the detection process and improves the robustness.

With the proliferation of sensors and the emergence of novel applications, IoT data has grown exponentially in recent years. Given this trend, efficient data management is crucial for a system to easily access vast amounts of information. For decades, B + -tree-based indexing schemes have been widely adopted for providing effective search in IoT systems. However, in systems with pre-distributed sensors, B + -tree-based indexes fail to optimally utilize the known IoT data distribution, leading to significant write overhead and energy consumption. Furthermore, as non-volatile memory (NVM) technology emerges as the alternative storage medium, the inherent write asymmetry of NVM leads to instability issues in IoT systems, especially for write-intensive applications. In this research, by considering the write overheads of tree-based indexing schemes and key-range distribution assumption, we rethink the design of the tree-based indexing schemes and propose an adaptive pre-built tree (APB-tree) indexing scheme to reduce the write overhead in serving insertion and deletion of keys in the NVM-based IoT system. The APB-tree profiles the hot region of the key distribution from the known key range to pre-allocate the index structure that alleviates online index management costs and runtime index overhead. Meanwhile, the APB-tree maintains the scalability of a tree-based index structure to accommodate the large amount of new data brought by the additional nodes to the IoT system. Extensive experiments demonstrate that our solution achieves significant performance improvements in write operations while maintaining effective energy consumption in the NVM-based IoT system. We compare the energy and time required for basic key operations such as Put(), Get(), and Delete() in APB-trees and B + -tree-based indexing schemes. Under workloads with varying ratios of these operations, the proposed design effectively reduces execution time by 47% to 72% and energy consumption by 11% to 72% compared to B + -tree-based indexing schemes.

Computer-supported collaborative learning (CSCL) has undergone significant advancements in the past two decades, revolutionizing the way learners engage with educational content and peers. This abstract explores key trends in CSCL, including the evolution of online communication and collaboration platforms, the impact of mobile technologies, the integration of data analytics, and the emergence of virtual reality (VR) and augmented reality (AR) environments. Additionally, the ongoing pandemic has accelerated the adoption of online and blended learning models, further shaping CSCL. These trends have transformed collaborative learning, allowing learners to exchange ideas, co-create knowledge, and engage in meaningful discussions. The utilization of data analytics enables personalized instruction and targeted support, enhancing learners' engagement and motivation. Immersive VR/AR environments promote active participation and deeper learning. Looking ahead, hybrid models combining face-to-face and online collaboration are likely to shape the future of CSCL. As instructional practices adapt and technology advances, creating engaging and effective learning environments remains a crucial focus in CSCL.

The Internet of Behavior (IoB) is a research theme that aims to analyze human behavior data on the Internet from the perspective of behavioral psychology, obtain insights about human behavior, and better understand the intention behind the behavior. In this way, the IoB can predict human behavioral trends in the future and even change human behavior, which can provide more convenience for human life. With the increasing prosperity of the Internet of Things (IoT), more and more behavior-related data is collected on the Internet by connected devices such as sensors. People and behavior are connected through the extension of the IoT—the IoB. Presently, the IoB has gradually been applied to our lives, but it is still in its early stages, and many opportunities and challenges are emerging. This article provides an in-depth overview of the fundamental aspects of the IoB: 1) we introduce the development process and research status of the IoB from the perspective of the IoT; 2) we propose the characteristics of the IoB and define its development direction in terms of three aspects: a) real time; b) autonomy; and c) reliability; 3) we provide a comprehensive summary of the current applications of the IoB, including specific discussions in five scenarios that give an overview of the application status of the IoB; and 4) we discuss the challenges of the IoB’s development and its future directions, which hopefully will bring some progress to the IoB. To the best of our knowledge, this is the first survey paper on the IoB. We hope that this in-depth review can provide some useful directions for more productive research in related fields.

The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost, and balance.

In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

Traditional education has been updated with the development of information technology in human history. Within big data and cyber-physical systems, the Metaverse has generated strong interest in various applications (e.g., entertainment, business, and cultural travel) over the last decade. As a novel social work idea, the Metaverse consists of many kinds of technologies, e.g., big data, interaction, artificial intelligence, game design, Internet computing, Internet of Things, and blockchain. It is foreseeable that the usage of Metaverse will contribute to educational development. However, the architectures of the Metaverse in education are not yet mature enough. There are many questions we should address for the Metaverse in education. To this end, this paper aims to provide a systematic literature review of Metaverse in education. This paper is a comprehensive survey of the Metaverse in education, with a focus on current technologies, challenges, opportunities, and future directions. First, we present a brief overview of the Metaverse in education, as well as the motivation behind its integration. Then, we survey some important characteristics for the Metaverse in education, including the personal teaching environment and the personal learning environment. Next, we envisage what variations of this combination will bring to education in the future and discuss their strengths and weaknesses. We also review the state-of-the-art case studies (including technical companies and educational institutions) for Metaverse in education. Finally, we point out several challenges and issues in this promising area.

To mine potential and helpful patterns, the majority of studies on pattern discovery from databases have been conducted in the last few decades. They have several obvious drawbacks: 1) Each thing stands out on its own and varies in significance based on factors including utility, risk, interest, and weight. 2) In specific application settings, an object has a favorable or unfavorable effect (e.g., products are often cross-sold and have positive or negative unit profits, which affect benefits). 3) The user could not have all the necessary information because frequent-based patterns typically only include a small percentage of the relevant patterns (for example, occupancy). To address this issue, we apply economic utility theory to the database and data mining fields. We provide a one-phase approach called pnHUO for discovering High Utility Occupancy patterns with positive and negative utility values that beyond frequency and usefulness. According to user interests, frequency, and utility occupancy, there are various utility occupancy patterns with positive and negative utility values. To hold the necessary data, a new frequency-utility tree and an indexed data structure called a positive-and-negative utility-occupancy list are created during the mining process. A number of pruning strategies are further developed using the determined upper bound of utility occupancy to reduce the search space. To evaluate the usefulness and efficiency of the suggested algorithm, five real datasets were tested in experiments, and the results were positive.

The Level-up workshop will challenge exclusive and exclusionary models for undergraduate research experiences, and it will give participants tools to expand undergraduate research to serve all students. The model and associated tools are adaptable, and they have been implemented in 44 colleges and universities of varying sizes, settings and missions in 12 countries.

Abstract Recently, the 5G as the next‐generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra‐dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast‐growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first‐in‐first‐service (FIFS). The result shows the method used can be scheduled for the task efficiently.

This article proposes a new blockchain-envisioned key management protocol for artificial intelligence (AI)-enabled industrial cyber–physical systems (ICPSs). The designed key management protocol enables key establishment among the Internet of Things (IoT)-enabled smart devices and their respective gateway nodes. The blocks partially constructed with secure data from smart devices by fog servers are provided to cloud servers that are responsible for completing blocks, and then mining those blocks for verification and addition in the blockchain. The most important application of the private blockchain construction is to apply AI algorithms for accurate predictions in Big data analytics. A detailed security analysis along with formal security verification show that the proposed scheme resists various potential attacks in an ICPS environment. Moreover, practical testbed experiments have been conducted using the multiprecision integer and rational arithmetic cryptographic library (MIRACL). Furthermore, a detailed comparative analysis shows superiority of the proposed scheme over recent relevant schemes. In addition, the practical implementation using the blockchain for the proposed scheme demonstrates the total computational costs when the number of transactions per block and also the number of blocks mined in the blockchain are varied.

Multimedia Internet-of-Things (IoT) systems have been widely utilized in various computer vision tasks and significantly integrated computer vision and networking capabilities. In these systems, convolutional neural networks (CNNs) perform a preliminary analysis of the collected video or image information in the edge devices. However, the high computational cost and huge storage consumption of the complex CNNs prevent their deployment on mobile-edge devices that have limited computational resource and memory. In this article, we aim to simultaneously accelerate and compress CNNs via a multilevel filter pruning (MFP) algorithm, to alleviate the dependence on the hardware of IoT edge nodes. First, a global pruning sensitivity order is defined, which could guide us to perform preliminary pruning from the perspective of convolutional layers' sensitivity. Then, the functional index of each filter is judged by the image entropy of its output feature map, which contributes to further pruning from the perspective of filter function importance. Finally, the moderate fine tuning is adopted to recover the network capability. The experimental results show that the proposed MFP algorithm could reduce 54.5% floating-point operations and 31.9% graphics memory for VGG-16 on CIFAR-10, and achieve 5.45 × floating-point acceleration and 19.70 × storage reduction for VGG-16 on ImageNet. In the reconstruction phase, the algorithm could recover the network capability much faster than the existing pruning algorithms.

High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge discovery in databases. It consists of discovering subsequences that have a high utility (importance) in sequences, which can be referred to as HUSPs. HUSPs can be applied to many real-life applications, such as market basket analysis, e-commerce recommendations, click-stream analysis, and route planning. Several algorithms have been proposed to efficiently mine utility-based useful sequential patterns. However, due to the combinatorial explosion of the search space for low utility threshold and large-scale data, the performances of these algorithms are unsatisfactory in terms of runtime and memory usage. Hence, this article proposes an efficient algorithm for the task of HUSP mining, called HUSP mining with UL-list (HUSP-ULL). It utilizes a lexicographic q -sequence (LQS)-tree and a utility-linked (UL)-list structure to quickly discover HUSPs. Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper bounds on the utility of the candidate sequences and reduce the search space by pruning unpromising candidates early. Substantial experiments on both real-life and synthetic datasets showed that HUSP-ULL can effectively and efficiently discover the complete set of HUSPs and that it outperforms the state-of-the-art algorithms.

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability and interpretability. To obtain a human-explainable data intelligence model for decision making, in this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and, thus, expedite the mining process. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, and profit), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this article, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract Multi-Dimensional Utility-oriented Sequential useful patterns. To the best of our knowledge, this is the first study that incorporates the time-dependent sequence-order, quantitative information, utility factor, and auxiliary dimension. Two algorithms respectively named MDUS EM and MDUS SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on six real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum utility threshold ( minutil ) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the “ rare item problem ” and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator, which can be deployed between base stations and fog nodes to coordi- nate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that DistADMM-PVS minimizes the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that DistADMM-PVS converges much faster than some other existing algorithms. In addition, the joint network slicing with both bandwidth and computational resources offers around 15% overall latency reduction compared to network slicing with only a single resource.

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.

Data center benefits cloud applications in providing high scalability and ensuring service availability. However, virtual machine (VM) placement in data center poses new challenges for service provisioning. For many cloud services such as storage and video streaming, present placement approaches are unable to support network-demanding services due to overwhelming communication traffic and time. Therefore VM placement concerning link capacity is vital to cloud data centers. In this paper, we define the network-aware VM placement optimization (NAVMPO) problem based on integer linear programming. The objective function of NAVMPO problem aims to minimize communication time for VMs of the same service type. Then we propose the service-oriented physical machine (PM) selection (SOPMS) algorithm and link-aware VM placement (LAVMP) algorithm. The SOPMS algorithm selects the most appropriate PM based on service-oriented architecture, and then the LAVMP algorithm deploys the most suitable VM to target PM regarding to the link capacity between them. Simulation results show that the proposed placement approach significantly decreases communication time compared to existing non-service-oriented and service-oriented VM placement algorithms, and also improves the average utility rate of PMs with lower power consumption.

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

Sequence data, e.g., complex event sequence, is more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this paper, we propose an efficient utility mining approach namely UMEpi: Utility Mining of high-utility Episodes from complex event sequence. We propose the concept of remaining utility of episode, and achieve a tighter upper bound, namely episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategy can achieve better improvements in mining efficiency. Finally, experiments on two real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequence, while state-of-the-art algorithms fail to return the correct results. Besides, the improved variants of UMEpi outperforms the baseline.

Emerging wireless applications starve for the realization of the ultra-reliable and low-latency communication (URLLC). The reliability and latency requirements of URLLC for a specific single link have been explored extensively, but a comprehensive evaluation of the URLLC in a large-scale wireless network is still lacking. In this paper, by using the point process theory we evaluate the probability that the delay and reliability requirement of a typical URLLC user can be satisfied in the large-scale wireless network. This probability is also the ratio of users with satisfactory delay and reliability in the wireless network. In order to improve the performance of URLLC, we propose two retransmission policies, corresponding to time diversity and frequency diversity, in which the retransmissions are silenced randomly to reduce the interference correlation in different frames so to reduce the effect of correlations between different retransmissions. Simulation and numerical results reveal that both of the proposed two retransmission policies improve the performance of URLLC, and the retransmission policy of frequency diversity is better than that of the time diversity.

The conventional outage in wireless communication systems is caused by the deterioration of the wireless communication link, i.e., the received signal power is less than the minimum received signal power. Is there a possibility that the outage occurs in wireless communication systems with a good channel state? Based on both communication and heat transfer theories, a power-consumption outage in the wireless communication between millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) base stations (BSs) and smartphones has been modeled and analyzed. Moreover, the total transmission time model with respect to the number of power-consumption outages is derived for mmWave massive MIMO communication systems. Simulation results indicate that the total transmission time is extended by the power-consumption outage, which deteriorates the average transmission rate of mmWave massive MIMO BSs.

In recent decade, utility mining has attracted a great attention, but most of the existing studies are developed to deal with itemset-based data. Different from the itemset-based data, the time-ordered sequence data is more commonly seen in real-world situations. Current utility mining algorithms have the limitation when dealing with sequence data since they are time-consuming and require large amount of memory usage. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store necessary information of sequence-order and utility. By utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption. Besides, we propose a new upper bound named sequence extension utility. Several pruning strategies are further applied to improve the efficiency of ProUM. Experimental results show that the proposed ProUM algorithm significantly outperforms the state-of-the-art algorithms.

Cloud RAN (C-RAN) where Base Band Units (BBUs) are collocated in a computing/processing center remotely away from their correspondent Remote Radio Heads (RRHs) for the efficient resource sharing is the prevailing RAN (Radio Access Network) design for next generation mobile networks. However, in C- RAN, the possible high latency from a RRH to the centralized Cloud BBU pool is not desirable for some latency critical applications. Thus, several local and smaller BBU pools are necessary to be deployed close to the RRHs to constrain the latency. This RAN architecture is so-call Fog RAN (F-RAN). A Mobile Edge Computing (MEC) center can be deployed beside or nearby a F-RAN BBU pool for timely processing. In this paper, we tackle the BBU resource allocation problem (a modified bin packing problem) between the set of RRHs and the set of BBU pools in F-RAN so that only a minimal number of BBU pools, i.e., the bins in a bin packing problem, will be turned on to serve all RRHs and so to save the energy consumption. Also, for the stability and fault tolerance, in addition to saving energy, the proposed algorithms also perform load balancing amid serving BBU pools. Our extensive simulation results show that the proposed scheme for energy efficient BBU resource allocation is able to achieve the goal of saving energy consumption and load balancing.

Small cell aims at improving the cell coverage and capacity of macrocell. Network operators investigate cell planning for improving system performance and for satisfying user requirements with minimal construction cost and least unserved users. Most of the existing literature merely considered a single objective function or investigated cell planning for small‐scale and homogeneous networks. This study aims to optimise multiple objective functions of a large‐scale and heterogeneous wireless network with multiple macrocells and multiple small cells. The authors formulate the multi‐objective optimisation problem of cell planning for heterogeneous cellular networks. The three objects include construction cost, number of unserved users and network capacity. Then they propose the large‐scale cell planning genetic algorithm (LSCPGA) to find a planning result with the lowest fitness value. Simulation results show that LSCPGA economises 9–20% construction cost, eliminates the number of unserved users from 4 to 10% and enhances 2–15% in network capacity compared to other planning algorithms.

Artificial intelligence is one of the important technologies for industrial applications, but it needs a lot of computing resources and sensing data to support. Therefore, big data transmission is a challenge for current network architectures. In order to have high-performance computing requirements, this paper proposes an emerging network architecture that combines edge computing and cloud computing to reduce the transmission of useless data and solve bottleneck problems. Moreover, we define the resource allocation problem about multiple remote radio heads and multiple baseband unit pools in the cloud radio access network for fifth generation. The long short-term memory is used to predict dynamic throughput and genetic algorithm based resource allocation algorithm is used to optimize resource allocation. The simulation results represented that the proposed mechanism can achieve high resource utilization and reduce power consumption.

Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and Internet of Things applications. Computationally intensive artificial intelligence (AI) tasks are well suited to be offloaded to the Cloudlet server, but there is a lack of energy-delay optimization models specifically designed for this edge AI scenario. In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights and computing capacities of virtual machines, at the same time guaranteeing the quality of the results (QoRs). We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks.

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

In the field of data mining, many utility-oriented mining approaches have been extensively studied. Previous studies have, however, the limitation that they rarely consider the inherent correlation of items among the discovered patterns. For example, from the purchase behavior, a high-utility group of products (w.r.t. multi-products) may contain the items with both high or low utility. This pattern is also considered as a valuable pattern even if they may not be highly correlated, or even happened together by the chance. In this paper, we propose an efficient utility mining approach namely non-redundant Correlated high-Utility Pattern Miner (CoUPM) by considering both strong positive correlation and profitable value of the products. The derived patterns with high utility and strong correlation can lead to more insightful availability than those patterns only have high utility values. The utility-list structure is maintained and applied to store necessary information of correlation and utility. Several pruning strategies are further developed to improve the efficiency for discovering the desired patterns. Experimental results show that the non-redundant correlated high-utility patterns have more effectiveness than some other kinds of patterns. Moreover, the proposed CoUPM algorithm significantly outperforms the state-of-the-art algorithm.

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices' energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Inspired by security applications in the industrial Internet of things, this paper focuses on the usage of impulsive neural network (NN) synchronization technique for intelligent image protection against illegal swiping and abuse. A class of nonlinear interconnected NNs with transmission delay and random impulse effect is first formulated and analyzed in this paper. In order to make network protocols more flexible, a randomized broadcast impulsive coupling scheme is integrated into the protocol design. Impulsive synchronization criteria are then derived for the chaotic NNs in the presence of nonlinear protocol and random broadcast impulse, with the impulse effect discussed. Illustrative examples are provided to verify the developed impulsive synchronization results and to show its potential application in image encryption and decryption.

In the upcoming 5G era, many new types of networks will greatly expand the connectivity of the world such as vehicular ad hoc networks (VANETs), Internet of Things (IoT), and device‐to‐device communications (D2D). Network coding is a promising technology that can significantly improve the throughput and robustness of these emerging 5G multihop networks. However, network coding is generally very fragile to malicious attacks such as message content corruption and node compromise attacks. To take advantage of network coding in performance gain while refraining malicious network attacks is an interesting and challenging research issue. In this paper, we propose a new error‐detection and error‐correction (EDEC) scheme that can jointly detect and remove the malicious attacks based on the underlying error‐control scheme for general multihop networks that can model the 5G multihop networks. The proposed scheme can increase the throughput for network with pollution attacks compared to existing error‐detection based schemes. Then we propose a low‐density parity check (LDPC) decoding based EDEC (LEDEC) scheme. Our theoretical analysis demonstrates that the LEDEC scheme can further increase the throughput for heavily polluted network environments. We also provide extensive performance evaluation and simulation results to validate the proposed schemes. This research ensures the expected performance gain for the application of network coding in the 5G network under malicious pollution attacks.

The constant rapid growth and development of modern medical technology has resulted in an ever-growing demand for higher quality health monitoring systems. This is especially true for the development of the Internet of Things: as the Internet of Things becomes more ubiquitous in dally life, so does the possibility of, and demand for, the ability to remotely monitor the health of patients at anytime, anywhere, using a wide variety of biometrie information. Thus e-Healthcare has become a significant trend in the medical field. In addition to monitoring a patient's health remotely, data can also be relayed to doctors or hospitals in real-time, in order to assist in correct medical decision-making. Hospitals have begun to implement this technology, and patients therefore have immediate access to required diagnoses and care. However, the use of a large number of remote medical sensors requires significant bandwidth, especially when relaying real-time information. Hospital networks thus become susceptible to problems arising from network congestion. This study proposes a load-balancing mechanism based on SDN-SFC for the optimization of hospital remote-monitoring network planning, which simultaneously eliminates the need for large amounts of hardware.

As an important component of the intelligent transportation system, internet‐of‐vehicles technology has attracted considerable attention. The dissemination of event‐driven emergency messages with high reliability and low delay is vital to ensure traffic safety and improve traffic efficiency in urban environment. In this study, the authors propose a reliable emergency message dissemination protocol taking into account the urban road characteristics and scalability requirements, which consists of a layout‐aware ready‐to‐broadcast‐emergency‐message and clear‐to‐broadcast‐emergency‐message handshake mechanism and a redundant relay node adaptation mechanism. Finally, the simulation results confirm the feasibility and effectiveness of the proposed scheme.

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable.

The latest High Efficiency Video Coding (HEVC) standard could achieve the highest coding efficiency compared with the existing video coding standards. To improve the coding efficiency of the intra frame, a quad-tree-based variable block size coding structure that is flexible to adapt to various texture characteristics of images and up to 35 intra-prediction modes for each prediction unit (PU) is adopted in HEVC. However, the computational complexity is increased dramatically because all the possible combinations of the mode candidates are calculated in order to find the optimal rate distortion cost using the Lagrange multiplier. To alleviate the encoder computational load, this paper proposes an adaptive mode decision algorithm based on texture complexity and direction for HEVC intra prediction. First, an adaptive coding unit selection algorithm according to each depth levels' texture complexity is presented to filter out unnecessary coding block. Then, the original redundant mode candidates for each PU are reduced according to its texture direction. The simulation results show that the proposed algorithm could reduce around 56% encoding time on average while maintaining the encoding performance efficiently with only a 1.0% increase in BD-rate compared with the test model HM16 of HEVC.

The wireless rechargeable sensor network is a technology to solve lifetime problem for wireless sensor network. Most researches discussed about the outdoor scenario but there are few studies focused on the indoor scenario. Actually, wireless sensor network is very important for the indoor scenario. The reason is that sensors help factory control production for better quality as well as both of disaster relief and prevention are also need to deploy sensors in the indoor scenario. These instances have the concept of sustainable development because sensors cannot be suddenly interrupted so that the expected benefits cannot be realized. As in the example just mentioned, quality of production is no longer to be guaranteed and disaster will not be prevented. That is why the wireless rechargeable sensor network becomes more popular. There are many low cost charger deployment algorithms have been proposed. However, these works almost have opportunity to fall into local optimum. This paper will present a layoff algorithm to combine simulated annealing-based algorithm to achieve optimization for wireless rechargeable sensor network. The simulation results show that the proposed method can reduce the number of chargers efficiently but just need same time with simulated annealing-based algorithm.

Following recent advances in sensing and wireless technologies, Internet-of-Things (IoT) applications are being exploited in various fields. The scale of IoT systems and the number of devices that they include has become huge, and the construction of IoT applications is, therefore, becoming increasingly challenging. This work proposes a script framework as a convenient development interface for service-oriented architecture (SOA) scheduling of web-based information of IoT applications, called ScriptIoT, which is composed of the IoT fundamental in case of all type of devices integration and a scriptable agent. Based on the IoT fundamental class, various IoT devices may be developed and the scriptable agent enables IoT applications to be configured using scripts. The proposed ScriptIoT framework, which offers both polling an event-driven mechanism for delegating IoT applications to the agent and reporting event of the specified device, contributes to large-scale applications. Experiments herein reveal that in the proposed ScriptIoT framework, the access time and CPU loading are slightly greater than those achieved using traditional C programming by 3% and 13%, respectively, but the proposed framework exhibits improved flexibility and scalability.

Abstract Unarguably, the physical memory acquisition of contemporary computing devices has been an extremely pivotal and indispensable errand for digital forensics specialists because of its volatility of the embedded data, which was deposited from related computing resources. Nowadays, hundreds of millions of global naive or sophisticated end users exploit LINE application program (AP) as the instant messaging toolkit to conduct real‐time communications including sending text messages or video clips. Unfortunately, this cutting‐edge innovation has been taken advantage by the cybercrime syndicates, terrorists, or the extremists to fulfill their lucrative purposes. Hence, this research provides generic research methodologies for digital forensics experts to conduct whenever digital artifacts become mandatory in order to disclose some cybercrimes or the noncompliance of computer usage concerning LINE AP especially for the witness experts to testify in a court of law. On the other hand, this research paper pinpoints the volatile raw memory contains valuable and imperceptive digital artifacts that could reflect the interactions when LINE AP was utilized under Mac OS X. Copyright © 2016 John Wiley & Sons, Ltd.

In harsh and heterogeneous wireless environments, the communication reliability and latency of industrial wireless sensor networks (IWSNs) seriously suffer from both intra- and interinterference. This paper presents an interference resistant approach for IWSNs by utilizing cognitive radio techniques. To improve the interference detection performance while uploading data as little as possible, we present a new computationally efficient and effective belief function (BF) theory-based reliability-probability decision fusion rule for cooperative sensing. A factor called reliability degree is introduced to characterize the imprecision of sensor observations, and the basic belief assignments are constructed by combining this reliability degree and local detection performance. Unlike the inefficient existing BF-based fusion schemes, the proposed rule has an explicit form and it is equivalent to the well-known Chair-Vashney (CV) rule in high signal-to-noise ratio conditions. We applied the proposed rule in interference resistant IWSNs to detect and avoid interference. Both numerical results and tests results demonstrate that the proposed rule has significant improvement in detection performance, diversity gains, and throughput compared with existing BF fusion schemes and CV rule.

Abstract In this paper, the planning problem of multiple evolved Node Bs (eNBs) and two types of relay nodes is defined base on linear programming. Type 1 relay nodes are placed in the center of eNB, and Type 1a relay nodes are deployed at cell edge. Three algorithms are proposed to investigate the Type 1a relay node placement and communication interference. All algorithms are designed on the basis of graph theory and analyzed in planning case and simulation results. The ultimate goal is to maximize the average throughput of all served users with minimum communication interference. Results showed that the proposed interference coordination algorithm not only provides the lowest construction cost with slightly fewer numbers of served users but also eliminates the communication interference with the highest average throughput. Most importantly, it achieves the best communication quality for next generation mobile networks. Copyright © 2015 John Wiley & Sons, Ltd.

Unlike a traditional network architecture, a software-defined networking architecture is divided into the control plane and the data plane. Network administrators use the centralized control plane to manage network authority and determine where network traffic is to be sent in the data plane. However, a centralized control structure causes a bottleneck with an overloading flow or under a DDoS attack. Under such conditions, the probability of network misconfiguration may increase rapidly and network performance may decline rapidly. This work paper proposes a hash-based mechanism that operates in the control plane to increase the reliability and scalability of the network. The hash function is utilized to assign incoming packets to queues in the control plane. The controller schedules the queues using a round-robin method to reduce the probability of failure in response to malicious attacks and to reduce transmission delay when network congestion occurs. The experimental results reveal that the proposed mechanism effectively distributes the workload and increases the reliability of the network under high-density data transmission.

The limited energy of sensor can be regarded as an optimization problem aimed at finding some useful deployment strategies for a set of chargers, but it typically might spend a lot of computation costs. In order to provide a good solution for reducing a large number of chargers caused by all the sensor nodes need to be covered, add the motor to some particular chargers is an alternative solution. That is why some recent studies attempted to develop the movable charger based algorithms to enhance transmission range. However, most movable charger-based algorithms are the greedy or rule-based algorithms, they will easy fall into local optimum at early iteration and consequently the end results will far away to global optimum. That is why it still has rooms for improvement. This paper will present a simulated annealing-based algorithm to improve the deployment result of wireless sensor network. The simulation results show that the proposed method can reduce the number of chargers significantly for full coverage.

In this paper, we propose a cooperative game theoretic approach for data-rate tuning among sensors in a Wireless Body Area Network (WBAN). In a WBAN, the body sensor nodes implanted on a human body typically communicate through a capacity-constrained single channel. This is a serious concern because most applications in WBANs involve real-time data streaming and providing useful notifications and efficient feedback to the patients or other users according to their health conditions. To increase the Quality of Service (QoS), we need an efficient data-rate tuning mechanism, which tunes the data-rate of a sensor based on the criticality of health parameter measured through it. Our approach considers the unique features typical of WBAN applications, and provides a generalized solution for the problem. We propose a cooperative game theoretic approach, based on the Nash Bargaining Solution (NBS), which does not only provide priority-based tuning, but also maintains the fairness axioms of game theory. The proposed approach yields 10% average increase in data-rates for the sensor nodes that have critical physiological data to transmit. We also validate the approach through real system implementation with the help of real sensor devices such as heart rate sensor, and pulse oximeter.

Abstract A utility‐based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game‐theoretic heuristic load‐balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy‐efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load‐balanced trees, resulting in over 30%longer network lifetime compared with the cumulative algorithm proposed in the literature. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, the coverage problem of network planning in mobile multi-hop relay networks is defined on the basis of integer linear programming. In order to provide desired utilities and also meet deployment limitations for network planning, we propose a supergraph tree algorithm to place base stations and relay stations at the lowest cost position. Furthermore, another algorithm for avoiding the interference between base stations, which is called interference aware tree algorithm is also proposed. Both the proposed algorithms are formulated on the basis of a graph theoretic technique and analyzed in the simulation results. The results show that the supergraph tree algorithm provides the lowest construction cost with different network scenarios, and the interference aware tree algorithm provides the highest communication quality for mobile multi-hop relay infrastructure-based communication network planning. Copyright © 2013 John Wiley & Sons, Ltd.

Internet technology is advancing with each passing day, the user's demand is also increasing. Of course the users will more concern to quality of service. The vendors must find out a win win method of resource allocation to meet users and itself. Therefore, the resource allocation of cloud computing has become one of hottest topics. In literatures, some researchers have proposed resource allocation methods which include allocation of virtual machines and service classification, etc. However, these methods are based on subjective observations that lead to the overall cloud architecture becomes imbalance. In order to prevent such situation happened, we use the Data Envelopment Analysis (DEA) to solve the imbalance problem. In this paper, our analysis is start from the user's requests, and use the DEA to evaluate the whole cloud parameters. Then we can find out the resource allocation policy which is the most suitable between the users and vendors.

Abstract Key establishment is the most fundamental cryptographic primitive in all kinds of applications where security is a concern. However, the nature of limited resources on sensor nodes restricts the use of conventional key management techniques in wireless sensor networks. Many researchers have conducted different techniques to propose different types of key distribution schemes. In this paper, we survey the existing solutions and describe the state‐of‐the‐art techniques. We not only classify these proposals into location‐independent key distribution schemes and location‐dependent key distribution schemes but also provide a comparison table. We hope to attract more research attentions on this direction. Copyright © 2011 John Wiley & Sons, Ltd.

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

No abstract available.

We proposed two multiparty quantum secret sharing schemes based on n-particle Greenberger–Horne–Zeilinger-states (GHZ states), which are transformed from Einstein–Podolsky–Rosen pairs by entanglement swapping. In our schemes, the dealer imposes messages by performing local unitary operations (I, σ x, iσ y, σ z) on the n-particle GHZ state she holds, and the agents collaborate to deduce the dealer's messages by performing local unitary operations on their own qubit. The amount of dealer's secret message is positively related with the number of agents. The need of qubits is one-third less than the former schemes and, also, they can be reused for the next new round. The scheme (II) does not have to pre-share the code table, which increases the security without the risk of being stolen.

ABSTRACT The vehicular ad hoc network (VANET) is a novel wireless network and suffers security threats as other networks. Information exchanged in the network might be stolen by malicious users, and some secure mechanisms are needed to provide the privacy. In VANET, the vehicles can be separated to different groups. When vehicles in the same group want to communicate with others, they can use the group key to protect the messages, and the privacy in the group communication is preserved. In this paper, we propose a group key management method with centralized key management scheme for VANET. In each group, there is a group leader, and the group leader will manage the members and the keys. By managing the group keys properly, the communication in the group can be secure. In the performance evaluation and analysis, our proposal provides better rekey mechanisms to related works and also achieves the forward secrecy and backward secrecy. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud multimedia services provide an efficient, flexible, and scalable data processing method and offer a solution for the user demands of high quality and diversified multimedia. As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering the limited bandwidth available for mobile streaming and different device requirements, this study presented a network and device-aware Quality of Service (QoS) approach that provides multimedia data suitable for a terminal unit environment via interactive mobile streaming services, further considering the overall network environment and adjusting the interactive transmission frequency and the dynamic multimedia transcoding, to avoid the waste of bandwidth and terminal power. Finally, this study realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environments.

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using "tags” or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. In this paper, we introduce an object-centered social network based on tagging practices across different sources, and then we show how this network can be built and emerged over time.

In recent years new research studies have appeared that concern the issue of network planning in LTE-Advanced. In this paper, the coverage problem in LTE-Advanced relay networks is formulated based on integer linear programming (ILP). We propose the Enhanced tree (E-Tree) algorithm to place the evolved Node B (eNB) and relay station (RS) at the location which has the lowest construction cost. The goal is not only satisfy the minimum requirement but also meet the two hop relaying limitation in LTE-Advanced networks. The E-Tree algorithm is proposed based on graph theoretic technique, and analyzed with the simulation results. The simulation results show that the proposed algorithm provides a rapid planning method and the lowest construction cost with various network environment.

When the mobile station (MS) is far away from the base station (BS) or terrain and buildings interfere with transmission, the data rate decreases with the increased distance from the BS, and the coverage range of the BS will be shortened to only a few kilometers. The IEEE Working Group has proposed the 802.16j relay technology. This technology can be used to forward data for a BS while overcoming the signal strength attenuation. Since the cost of constructing the relay station is low, the MS will have to scan more than one signal when entering the Mobile Multi-Hop Relay (MMR) network. In this paper, we introduce a novel path selection mechanism that exploits the remaining slot and signal strength indicator (SSI). Since the signal strength will affect the Modulation and Coding Scheme (MCS) combination, we propose to use SSI integrated with the MCS, taking the hop count into account. We demonstrate that our proposed scheme is capable of producing an optimal path selection.

In the recent years, Peer-to-Peer (P2P) sharing network has become very popular in the Internet. However, most P2P protocols are designed for traditional wired networks. When deployed in wireless network environment, many challenges are encountered. For instance, the nodes in an unstable wireless network tend to leave or rejoin the P2P network easily. In this case, the routing information in every node must become overdue, which may lead to lookup failures when the nodes retrieve these overdue routing information. In this paper, we propose a modified Chord protocol called MobileRobust-Chord (MR-Chord). MR-Chord is designed with the aim of keeping the Finger Table fresh. To achieve this goal, we have modified the Distributed Hash Table (DHT)-based protocol a Chord Protocol in such a way that the Finger Table is kept updated to provide the necessary lookup services in the P2P network. Simulations studies show that our proposed MR-Chord protocol outperforms the original Chord protocol in the following aspects: (1) increase in the lookup success rate and overlay consistency, (2) reduction of the lookup delay time.

Securing the routing of message in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes an enhanced trust-based multipath Dynamic Source Routing (DSR) protocol (so-called ETB-MDSR) to securely transmit messages in MANETs. Our method consists in a combination of soft-encryption, novel trust management strategy, and multipath DSR routing. Simulation results are presented to validate our proposal, showing that our ETB-MDSR scheme outperforms a recently proposed Trust-Based Multipath DSR message scheme (TB-MDSR), in terms of route selection time.

SUMMARY In two‐tier high‐performance networks (HPNs), some facilities are constructed to form a powerful supercomputing environment, and to alleviate server load. Then, the applications are provided by them in co‐operated, parallel and distributed manners. A proper way to select facilities is crucial to the performance of two‐tier HPNs The problem of selecting facilities can be regarded as a kind of the facility location problem, which is to determine an optimal subset of facilities that will be open to serve users. The traditional facility location problem aims to minimize the incurred costs between the users/servers and their assigned facilities. In two‐tier HPNs, the incurred costs can be regarded as the transmission costs, e.g. transmission latency, bandwidth overhead. We observe that most of the packets are transmitted among the facilities for application servicing and framework maintaining. In this paper, we address the problem of selecting facilities in two‐tier HPNs by minimizing the transmission costs from servers to users by passing through the selected facilities. Our problem is different from the traditional facility location problem, which only considers the transmission costs between the users/servers and their assigned facilities. In our problem, the transmission costs between the selected facilities are further considered. The problem is formulated as a 0/1 integer non‐linear programming (0/1 INLP) and 0/1 integer linear programming (0/1 ILP). Further, a simple heuristic algorithm is proposed for obtaining a feasible solution when the network sizes increase, since solving INLPs and ILPs for large‐scale problems takes long time. Copyright © 2010 John Wiley & Sons, Ltd.

Based on the multiparty quantum secret sharing (QSS) protocol called Zhang–Man protocol, the authors proposed two schemes to enhance the transmission efficiency of whole protocol. In the proposed improved protocol, the dealer can transmit double amount of secret messages at a time securely instead of doing the original Zhang–Man protocol twice. Besides, the authors save a lot of cost of whole QSS system. When the amount of secret messages is large and the number of agents is more, the performance of our protocol is much better than the Zhang–Man protocol. The authors also proposed a reuse scheme, after the secret message had been deduced, the left qubits can be reused for a new round. All of the scheme the authors mentioned above could be extended to multiparty cases in practice.

Wireless Sensor Network (WSN) is still a hottest research topic which can be applied to a lot of new research field such as robotic and smart grid. Its not only used for detecting the new environment but also monitoring any situation which has dynamic variation. However, the sensor node is relied on the battery energy that limited the lifetime of entire network. In this paper, we proposed a novel scheme called Dynamic Duty cycle and Dynamic Schedule Assignment (DDDSA) which not only can reduce number of RTS/CTS (Request to Send / Clear to Send) packets but also dynamically update the Duty Cycle value. Furthermore, the proposed scheme does not calculate the traffic information repeatedly in overlapping areas. This scheme will reduce more idle nodes to achieve energy efficiency.

In recent years, multimedia network services have moved from a single service to rich services which integrate multiservice capabilities integration. If all service requests require the user to send the request by himself, this will result to a huge control function load and complex service collaboration. In order to address service interaction and reuse the service capability, 3GPP proposes a Service Capability Interaction Manager, which can provide service capabilities invocation and service interaction management between Application Servers (ASs) and Serving-Call Session Control Function (S-CSCF). However, its architecture may cause joint and cooperation problems between the different service providers. In this paper, we propose a scalable service capability interaction framework called ServiceChord that can process multiple service capabilities with different ASs and reduce the call set-up delay while communicating with the S-CSCF. The Chord DHT technique is used to improve the framework, leading to a reduction of message redundancy on the S-CSCF while achieving an efficient service capability interaction, and providing scalability for IMS services and ASs.

Mobile Ad Hoc Networks (MANETs) offer a dynamic environment in which data exchange can occur without the need of a centralized server or human authority, providing that nodes cooperate among each other for routing. In such an environment, the protection of data en route to its destination is still a challenging issue in the presence of malevolent nodes. This paper proposes a message security approach in MANETs that uses a trust-based multipath AOMDV routing combined with soft-encryption, yielding our so-called T-AOMDV scheme. Simulation results using ns2 demonstrate that our scheme is much more secured than traditional multipath routing algorithms and a recently proposed message security scheme for MANETs (our so-called Trust-based Multipath Routing scheme (T-DSR)), chosen as benchmark. The performance criteria used are route selection time and trust compromise.

Securing the transfer of data in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes a method for providing message security in MANETs when nodes cooperate in routing. Our approach combines a trust-based multipath routing scheme and a real-time recurrent neural network-based (RRNN) cipher (yielding our so-called TR-RRNN scheme) to deal with the issues underlying message confidentiality, integrity, and access control. Simulation experiments using QualNet were conducted, showing that the proposed scheme is much more secured compared to the traditional multi-path routing algorithms and a recently proposed message security scheme for MANETs (our so- called Original Trust-based Multi-path Routing scheme (OTMR)). The route selection time and trust compromise are used as the performance criteria.

Cognitive Radio (CR) is an innovative wireless sensing technology which can improve efficiency of spectrum usage. Based on CR, Cognitive network (CN) further focus on end-to-end communications for the global optimization and policy enforcement. However, there is a lack of global control channel or middleware for cognitive optimization in CN environment. In this paper, we proposed a Cognitive IP Multimedia Subsystem framework called Cog IMS, which consists of benefits from CN and IP multimedia subsystem (IMS). In this framework, the cognitive optimization is modeled as a service over IMS architecture. By adopting the proposed service-oriented framework, the global optimization is simple to achieve under standardized architecture and the computation requirement is reduced from user to core network. Finally, we simulate wireless users who using services to compare Cog IMS with CN and IMS. The simulation results show that the proposed Cog IMS can achieve the optimization of wireless resource allocation with different network situations.

This paper proposes a quantum search based algorithm to improve the dynamic maximum network flow problem. First, we convert the time-dependent process into time-independent process. Second, we use the draining algorithm to find out the maximum flow and compare with classical algorithms. Third, the draining algorithm is speed up by utilizing quantum search. Finally, we also provide the analysis of time complexity.

Due to Internet Protocol (IP) is the most important protocol in Next Generation Networks. 3GPP utilizes Session Initial Protocol (SIP) based on IP as the base protocol for negotiating sessions in IP Multimedia Subsystem (IMS). Different from traditional circuit-switched network, the media traffic and signaling are delivered through IP transport layer in IMS. The media traffic may affect the signaling efficiency in core network because the traffic collisions and delivery packets in best effort. This paper proposes a novel path-migration mechanism for enhancing the traffic efficiency in IMS. The simulation results show that by adopting proposed path-migration mechanism, the interference and traffic collision can be reduced and the signaling efficiency in core network can be improved.

To solve the secure localization problems, a number of secure localization schemes have been developed at present. However, most of these techniques cannot survive collusion attacks where a majority of malicious nodes launch colluding attacks. In this paper, we introduce a new collusion attack model called Collaborative Collusion Attack Model (CCAM) and propose a novel scheme called Two-Step Format Detection (TSFD) that is well suited to WSN which is a resource constrained environment. The TSFD has reasonable and acceptable communication cost and algorithm complexity. Through simulations, we compare the performance of TSFD with other secure localization schemes and show that TSFD has more efficient and resilient performance.

In this paper, an investigation toward mobile P2P architecture brings a new insight to realize future mobile cloud computing systems. Although mobile devices capacity in storing data and running computational process is very limited, the main problem for implementing mobile cloud computing environment is still motivated by the classic issue of information properties query process. Heterogeneous wireless networks (HWNs) architecture then exploited to solve this fundamental problem. The HWNs information properties dispersion model based on coverage area of mobile node aid to simplify the querying process. By HWN and mobile P2P integration a reliable mobile cloud computing service can perform services and resources exchange among peer nodes. In addition this integrated architecture gives the extension of mobile P2P into Network as a Service (NaaS) approach to resource sharing among mobile users.

The packet-switched NGN (Next Generation Network) architecture allows users to access multimedia services via the Internet anytime and anywhere. Through the core IP Multimedia Subsystem (IMS), NGN can provide multimedia services, such as voice, video, data and messages over heterogeneous networks. However, for high-quality services in practical environment, the performance and QoS (Quality of Service) of NGN must be guaranteed. Thus, based on the Testing and Test Control Notation version 3 (TTCN-3) specifications, this paper develops a test system utilizing Open IMS Core for conformance test of IMS session initiation for fixed and mobile networks. We also verify the QoS and analyze the performance of SUT (System Under Test) from the experimental results.

Among various network topologies, tree-like networks, also known as hierarchical networks are proposed to decrease the overhead of the routing table especially for the situation involving many network nodes. Usually, the routing table size and the routing complexity are the two crucial concerns in designing a large network. Although there have been various algorithms to optimise the routing strategies for the hierarchical networks, hardly exists any work in studying and evaluating the routing table size and the routing complexity rigorously in the statistical sense. In this study, the authors generalise a new mathematical framework by applying the point process in random geometry. The new framework proposed by the authors leads to the explicit statistical measures of the routing table size and the routing complexity, which can be specified as the functions of the hierarchical network parameters including the number of the hierarchical levels and the cluster population for each hierarchical level. After the relationship between the network topology and these two network performance measures (routing complexity and routing table size) is established, a cluster-population optimisation method for hierarchical networks is presented. The simulation results are also provided to demonstrate the advantage of a hierarchical network over the associated conventional network without hierarchy.

As Internet Protocol (IP) multicast allows the efficient use of network bandwidth for multipoint communication, it is expected to be an essential communication type for delivering multimedia services to mobile nodes (MNs). In this study, the authors address an issue of consumer multicast traffic support in a Proxy Mobile IPv6 (PMIPv6) environment wherein network-based mobility management is deployed for MNs. The recently standardised PMIPv6 multicast listener support provides options for deploying multicast listener functions in a PMIPv6 domain, whereas it does not address specific optimisations and efficiency improvements of multicast routing. The authors first review the PMIPv6 multicast listener support and point out the limitations of the current approach. Then, propose an improved multicast handover procedure that optimises multicastgroup management by utilising the context of consumer's MN running multicast applications. The authors develop analytical models to evaluate the proposed multicast handover procedure compared with the base one. From the conducted analysis, it is demonstrated that the proposed multicast handover procedure minimises the service interruption time and prevents the multicast packet loss during handovers. Furthermore, in terms of signalling cost consumption, the proposed multicast handover procedure operates on an equal basis with the base one.

Over the last decade, mobile agent (MA) systems for surveillance applications in wireless sensor networks (WSNs) has gained much attention. However, a conventional MA-based WSN may have the issues of energy efficiency and task duration as the scale of the network is increased. In order to overcome the drawbacks of using a single MA, dispatching two or more MAs for data collection simultaneously is a promising alternative in a WSN. The authors first discuss the itinerary planning issues for multiple MAs: deciding the number of MAs to be dispatched, grouping of source nodes for each MA, routing of each MA for its assigned source nodes. The authors then survey the existing algorithms for these issues, and evaluate their performance by OPNET.

Traditionally, the authors could establish secure computation protocols using variants of public key cryptology whose security is based on hard mathematics problems. However, classical protocols will become insecure owing to the emergence of quantum algorithms like Shors. In this paper, the authors demonstrate that nanoscale phenomenon can be applied not only in device level but also in high layer applications, such as secure computation. The authors study the possibility of performing secure computation by building non-local machines based on quantum entanglement and non-locality, which are phenomena available only at the nanometre scale. Comparing with classical secure computation algorithms, the security of this protocol is based on physical laws, instead of any unproven mathematic conjecture.

High quality multimedia forensics service is increasingly critical for delay-sensitive applications over heterogeneous networks. Up to now, it is a challenging problem, where the demand for less forensics overhead, higher authentication level and smaller transmission delay needs to be reconciled with the limited and often dynamic network resources. Traditional multimedia forensics mechanisms, however, either overlook the available network resource or neglect the interaction between multimedia forensics and network scheduling. This work presents a novel framework for delay-sensitive multimedia applications over resource-limited heterogeneous networks by jointly considering multimedia forensics, network adaptation, and deadline-driven scheduling. In particular, we develop a joint forensics-scheduling scheme, which allocates the available network resources based on the affordable forensics overhead and expected quality of service, adaptively adjusts the scalable media-aware forensics, and schedules the transmissions to meet the application's delay constraints. Through analysis and simulation, we demonstrate that the proposed scheme not only can provide a satisfying multimedia forensics service with nearly full utilization of the network resource, but also can achieve substantial performance improvements compared to other reference approaches.

Abstract Windows Live Messenger (WLM) is one of the most prevalent ubiquitous Instant Messaging (IM) application programs being used by voluminous people worldwide to establish their cyber social lives. It dramatically changes the way of communication for human beings in the past decade in all aspects. However, WLM is like a sharp double‐edge sword, especially the exploited vulnerabilities concerning information security. Consequently, there is an urgent necessity for Digital Forensics (DF) to be strongly enforced within the organizations in order to specifically identify the real causes of the information breaches when utilize WLM. Few researches have formally incorporated the DF of WLM into generic guidelines for the associate personnel to follow. In this paper, we provide the system architecture of the experiment accompanied with our proposed Check Point (CP) methodology trying to disclose the possible digital evidences that could be explicitly collected and scientifically presented as probative evidences with respect to the persistently mushrooming information security incidents in the next generation wireless communication networks. Copyright © 2010 John Wiley & Sons, Ltd.

The Internet Protocol (IP) multimedia subsystem (IMS) plays an important role when migrating to next-generation networks. Based on the all-IP network architecture, at least an IP address is assigned to user equipment for communication. It thus brings not only value-added services, but also security issues such as attacks and threats from traditional IP networks. This paper proposes a novel scheme to detect the anomaly attempts or attacks in an IMS core network. Our proposed scheme adopts the support vector machines (SVMs) technique as our data-mining module. We also utilize the high-order Markov kernel to enhance the detection and prediction rate in sequence calls. Furthermore, the detection algorithm and feature selection for SVM kernel are designed by utilizing IMS transaction patterns. It is proved that the proposed algorithm can efficiently filter out the anomaly attempts in an IMS core network.

Abstract Portable electronic communication devices can be used for many purposes and they are capable of integrating with ubiquitous computing (UC) infrastructures to carry on mobile multimedia communications. As those devices become prevalent, they incur potential network security threatening to organizations. Nowadays, the Skype is the most popular P2P VoIP application program, which is being used by millions of global users to place IP phone calls, transfer files, or communicate via instant messaging (IM). This phenomenon already generates imminent network security issues that are indispensable to digital forensics researchers or the law enforcement agencies worldwide. Cellular phones, smart phones, and personal digital assistants (PDAs) are the representative ones of those devices and there are some open sources or commercial software toolkits that can be utilized to proceed the forensics investigation concerning the electronic crimes in next generation communications (NGCs). A case review was conducted to illustrate the hidden digital trails within the PDA from the Registry of the Windows Mobile and volatile data in the RAM to discover the possible network security leakage scenarios that resulted in the vandalism of intangible digital assets of the organization. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, wireless and mobile communication networks have become increasingly popular and the huge number of smart mobile devices and applications has brought the mobile operators with challenges from various aspects. This trend will continue to affect the way in which we live, work, and play with many smart and innovative services, and the intensive mobility control messages and data will give great impact on the traditional wireless and mobile communication networks. For this reason, the mobility management support is one of very important issues for the future generation of wireless and mobile networks and services, and the design of efficient and robust mobility management is one of the most challenging research tasks. Observing the great need for an in-depth research of the mobility management in both academia and industry, we have put together this special issue through an open call for papers. More than 10 high-quality papers from all around the world were submitted. Although many of them were of high quality, we had room for only three papers that best fit the theme. To guarantee higher quality of this special issue, we also have selectively collected four expanded papers from the proceedings of MobiWorld 2008 workshop while inviting two comprehensive surveys. Accordingly, this special issue brings together nine papers. The first two papers are devoted to review and survey recent developments and methods of mobility management, and the others propose and analyze some of the most interesting and innovative solutions dealing with the mobility management in wireless mobile networks. The paper “Next generation mobility management: an introduction” by F. Richard Yu, Vincent W. S. Wong, Joo-Han Song, Victor C. M. Leung, and Henry C. B. Chan reviews recent research trend and developments in location management, and surveys methods for inter-system handover management between heterogeneous systems. It classifies the inter-system handover management schemes according to the protocol layer, so that the readers can gain in-depth insight into the topic. It also provides open problems and research direction for the next negation mobility management. Recently, communication devices are being installed in more and more vehicles and roadside infrastructure. In the near future, traveling vehicles will be able to communicate while forming rapidly changing ad hoc networks. The paper “Mobility and Handoff Management in Vehicular Networks: A Survey” by Kun Zhu, Dusit Niyato, Ping Wang, Ekram Hossain, and Dong In Kim presents a good comprehensive survey on existing mobility management works for both vehicle-to-vehicle and vehicle-to-infrastructure communications in vehicular networks, and also provides several open research issues of the topic. The following four papers refer to the proxy mobile IPv6 (PMIPv6) which is a famous mobility management protocol recently standardized by IETF. PMIPv6 is a representative network-based localized mobility management protocol and has been of great interest to both academia and industry. Unlike host-based IP mobility management protocols, PMIPv6 does not require the participation of mobile nodes in mobility signaling. The paper “Implementation and analysis of proxy MIPv6” by Jianfeng Guan, Huachun Zhou, Zhiwei Yan, Yajuan Qin, and Hongke Zhang analyzes the singling cost of PMIPv6, provides their test-bed implement to evaluate its performance, and shows that its performance is better than the other mobility management protocols. The paper “Smart Buffering for seamless handover in Proxy Mobile IPv6” by Hyon-Young Choi, Kwang-Ryoul Kim, Hyo-Beom Lee, Sung-Gi Min, and Youn-Hee Han presents a PMIPv6 buffering scheme using only network-side information to prevent packet loss by proactively buffering packets that will be lost during handover, and also provides redundant packet elimination and packet reordering methods to minimize duplicate packet delivery and disruption of connection-oriented data flows. The paper “The applicability of virtual interface for inter-technology handovers in Proxy Mobile IPv6” by Ryuji Wakikawa, Sawako Kiriyama, and Sri Gundavelli analyzes the required software function on the mobile node for performing inter-technology handovers and investigates the applicability of virtual interface support available in Linux for implementing the PMIPv6-based inter-technology handover. The paper “QoS aware dynamic route optimization for Proxy Mobile IPv6 networks” by A. Dev Pragad, Vasilis Friderikos, Paul Pangalos, and A. Hamid Aghvami provides a QoS aware dynamic route optimization scheme where the network identifies the lower QoS sessions, and establishes a binding update with the correspondent node (CN) rather than with the LMA. The paper “A Cross-Layer Partner-Assisted Handoff Scheme for Hierarchical Mobile IPv6 in IEEE 802.16e Systems” by Yuh-Shyan Chen and Kun-Lin Wu presents a new partner-assisted handoff mechanism based on cross-layer approach by the combination of layer 2 and layer 3 in hierarchical mobile IPv6 which is a protocol standardized by IETF. The paper “Soft handoff support for SIP-NEMO: design, implementation, and performance evaluation” by Shun-Ren Yang, Ya-Jun Huang, and Chun-Wei Chiu provides a soft handoff mechanism to effectively reduce the handoff disruption time which may be long in thesession initiation protocol and network mobility (SIP-NEMO). The paper “An end-to-end framework of transport layer mobility management” by Yi Wu, Yanqun Le, and Dongmei Zhang extends the TCP migrate work by resolving the simultaneous mobility issue that the existing transport layer mobility schemes do not support and proposes an improved end-to-end framework of TCP migrate scheme through handover redirection and transmission resuming. We would like to thank all the authors for their great work and for considering this special issue for submitting their papers. We would like to extend our gratitude to the anonymous reviewers who spent much of their precious time reviewing all the papers and providing substantive comments on paper improvements. We also would like to thank the devoted staff of Wiley for their high level of professionalism, and particularly express our sincere thanks to the Editor-in-Chief of WCMC, Professor Mohsen Guizani, for inviting us to edit this special issue and for their continuing keen interest. It has been a pleasure to put together this special issue on this very timely topic and we hope you enjoy it.

Binary exponential backoff (BEB) algorithm, the defacto standard contention resolution algorithm providing collision avoidance over ad hoc wireless networks, has several technical limitations. For instance, high collision rate problem, unfair channel access, and throughput degradation are several widely known issues. In this paper, a self-adaptive backoff algorithm, namely slow start backoff (SSB) algorithm, is proposed and analyzed for ad hoc networks. Numerical results show that the proposed SSB algorithm outperforms the legacy BEB algorithm. In addition to the theoretical analysis, simulations were conducted to verify the analysis, showing that the proposed scheme works very well in ad hoc wireless networks.

Geographic Forwarding is part of geographic routing that each node only needs to know the location of its neighbor and the destination. This method can reduce the cost that table-driven routing needs to maintain the whole path even if the path are not in use, and save time when searching the path compared with the reactive routing. When we using geographic forwarding, it usually encountered the local maximum that cannot forward the packet directly to the destination; this fundamental problem is also called "Dead End". We use the algorithm called "A star" that usually used in role playing game or strategic game to detour the terrain that cannot pass through directly. Nodes must vote their agent in a specific area to be a decision-maker to find a reference route for source node. When the route is decided, then we will use geographic forwarding according to this reference path to the destination to avoid the dead end.

Wireless sensor networks (WSNs) have been widely used in many applications. The highly correct location information of sensor nodes is crucial for these applications. Nowadays there are mainly two types of localization algorithms: Range-based localization algorithms and Range-free localization algorithms. The drawback of former type is strict requirements on the hardware configuration. The latter type is cost-effective alternative approach. However, this approach can achieve high accuracy only in some ideal scenarios, and some methods even require a lot of complex calculations. In this paper, we put forward a novel PAV-based Calibration localization method (PAVC) locating the unknown nodes. The Pool Adjacent Violators (PAV) algorithm can be used to calibrate the average distance per hop of hop-based positioning algorithm. PAVC improves the localization accuracy compared with previous hop-based algorithms, which is demonstrated by the simulation and testbed results.

Radio frequency identification (RFID) is a kind of electronic identification technology that is becoming widely deployed. Compared to traditional RFID system, tags in the proposed smart RFID system would store not only the fixed ID information but also some information which is “active” and encoded in the form of mobile codes indicating the up-to-date situation and associated services' directives. In the proposed system, the service that the RFID tag bearer needs can be explained in a context-aware decision making system to provide a situation-aware system response and offer a good quality of service (QoS).

Trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing works of TARS suffers from the problem that they need extra user efforts to label the trust statements. The authors propose a novel model named iTARS to improve the existing TARS by using the implicit trust networks: instead of using the effort-consuming explicit trust, the easy available user similarity information is used to generate the implicit trusts for TARS. Further analysis shows that the implicit trust network has the small-world topology, which is independent of its dynamics. The rating prediction mechanism of iTARS is based on the small worldness of the implicit trust network: the authors set the maximum trust propagation distance of iTARS approximately equals the average path length of the trust network's corresponding random network. Experimental results show that with the same computational complexity, iTARS is able to improve the existing TARS works with higher rating prediction accuracy and slightly worse rating prediction coverage.

In a multi-agent system, there are many intelligent agents distributed in the network. To connect with these agents easily and arbitrarily, wireless ad hoc network is a candidate. Wireless ad hoc network is a distinct network environment with characteristics in self-organisation, dynamical network topologies and easy construction. To provide such networks with secure communication, many researchers tried different ways to create the session key(s). However, there are some drawbacks in session key generation procedure, and the robust session key could not be created. The authors propose a cluster-based secure communication mechanism in wireless ad hoc networks. The proposal applies the Diffie–Hellman key exchange protocol for the session key creation. It helps the clusterhead to reduce the communication overload, to avoid the time synchronisation problem in nodes authentication and to preserve the complete secrecy requirements.

The rapid development in electronic commerce and information technology drives the traditional physical product trading evolved to digital product trading. With the effect of the multi-agents system in the Internet environment and the promotions of Government, digital product industry grows fast. The authors proposed a digital product transaction mechanism for electronic auction in the multi-agents system environment. The research introduced a convenient platform to protect the privacies of both buyers and sellers, and track digital product further in an electronic auction environment. In addition, by using simple cryptography techniques supplemented with encryption, the authors ensure the security of information transactions, thereby providing a mechanism of safe and fair digital product electronic auction.

Outliers in wireless sensor networks (WSNs) are sensor nodes that issue attacks by abnormal behaviours and fake message dissemination. However, existing cryptographic techniques are hard to detect these inside attacks, which cause outlier recognition a critical and challenging issue for reliable and secure data dissemination in WSNs. To efficiently identify and isolate outliers, this study presents a novel outlier detection and countermeasure scheme (ODCS), which consists of three mechanisms: (i) abnormal event observation mechanism for network surveillance; (ii) exceptional message supervision mechanism for distinguishing fake messages by exploiting spatiotemporal correlation and consistency and (iii) abnormal behaviour supervision mechanism for the evaluation of node behaviour. The ODCS provides a heuristic methodology and does not need the knowledge about normal or malicious sensors in advance. This property makes the ODCS not only to distinguish and deal with various dynamic attacks automatically without advance learning, but also to reduce the requirement of capability for constrained nodes. In the ODCS, the communication is limited in a local range, such as one-hop or a cluster, which can reduce the communication frequency and circumscribe the session range further. Moreover, the ODCS provides countermeasures for different types of attacks, such as the rerouting scheme and the rekey security scheme, which can separate outliers from normal sensors and enhance the robustness of network, even when some nodes are compromised by adversary. Simulation results indicate that our approach can effectively detect and defend the outlier attack.

Abstract CBR (constant bit rate) traffic is expected to be an important traffic source in wireless networks. Such sources usually have stringent jitter or delay requirements and in many cases they should be delivered exactly as they were generated. In this paper, we propose a strictly guaranteed QoS (quality‐of‐service) provisioning CAC (call admission control) scheme with a polling‐based scheduling policy for CBR traffic in IEEE 802.11e wireless LANs. Under such a scheme, the proposed transmit‐permission policy for HCCA (HCF controlled channel access) method can forecast the maximum suffered delay for each packet and derive sufficient conditions so that all the CBR sources satisfy their time constraints to provide deterministic QoS guarantees. A simple analytical model is carried out to estimate the average queueing delay of the proposed scheme. In addition to theoretical analysis, simulations are conducted to validate its promising performance. Our simulation results show that the proposed scheme maintains a high throughput with respect to the whole range of system load. Copyright © 2009 John Wiley & Sons, Ltd.

In a pervasive wireless network, malicious nodes can initiate attacks to well-behaved nodes. This paper argues that our recently proposed probabilistic trust management scheme for pervasive computing can be used in pervasive wireless networks to provide protection against few typical attacks usually targeted at such systems. Simulation experiments are provided to assess the achievement of the stated goal. The performance metric used is the average packet loss ratio, representing the ratio of packets lost to the total packets generated in a certain time period.

There are various emergency services based on wireless sensor network being proposed recently. However, the ability of these services/networks is inherently limited by geographical restrictions and need to be deployed in advance. This paper proposes an application level approach to enhance the service coverage and availability of emergency services. Specifically, we augment these services with All-IP network infrastructure based on IP Multimedia Subsystem (IMS). Furthermore, we integrate the IMS Emergency Services architecture with Cooperative Network technology to provide ubiquitous emergency services. We also investigate the prime problems of cooperation between heterogeneous networks and IMS. Finally, we present and discuss the experimental results of performance in our Cooperative Emergency IMS Testbed.

In a traditional intercom system that only allows place-to-place communication in a house, it is necessary to dial the extension number of a specific family member, or dial each extension number individually via the intercom broadcasting, in order to reach the desired person. Additional master stations controlled the intercom system are required, and it is load to maintain the stations for general users. In order to solve these problems, we propose the digital living network alliance (DLNA) compatible family intercom system (DFIS): the architecture to support user mobility. This architecture makes it possible to quickly reach a family member without the knowledge of the extension number that caters to a fixed location. In other words, it will be not necessary to dial the extension number of the location of the desired person. We introduce call control and phone handling under the subject of DFIS architecture and demonstrate that the proposed DLNA family intercom device and DLNA family intercom adaptor can work well.

In wireless sensor networks (WSNs), the operation of sensor nodes has to rely on a limited supply of energy (such as batteries). To support long lifetime operation of WSNs, an energy-efficient way of sensor deployment and operation of the WSNs is necessary. A new controlled layer deployment (CLD) protocol to guarantee coverage and energy efficiency for a sensor network is proposed. CLD outperforms previous similar protocols in that it can achieve the same performances and guarantee full area coverage and connection using a smaller number of sensors. It can also ameliorate the ‘cascading problem’ that reduces the whole network lifetime. Finally, analysis and simulation results show that CLD can use fewer sensor nodes for coverage and also increases the lifetime of the sensor network when compared with the probing environment and adapting sleeping (PEAS) protocol.

This study presents a healthcare monitoring architecture coupled with wearable sensor systems and an environmental sensor network for monitoring elderly or chronic patients in their residence. The wearable sensor system, built into a fabric belt, consists of various medical sensors that collect a timely set of physiological health indicators transmitted via low energy wireless communication to mobile computing devices. Three application scenarios are implemented using the proposed network architecture. The group-based data collection and data transmission using the ad hoc mode promote outpatient healthcare services for only one medical staff member assigned to a set of patients. Adaptive security issues for data transmission are performed based on different wireless capabilities. This study also presents a monitoring application prototype for capturing sensor data from wireless sensor nodes. The implemented schemes were verified as performing efficiently and rapidly in the proposed network architecture.

To more accurately extract the characteristics of application flows, this paper proposes a set of flow attributes to characterize the possible negotiation behaviors of each flow in application layer perspective. The discriminators are available in the early stage, so they are suitable to support real-time based traffic classification and engineering. The ability of flow attributes was tested with several machine learning algorithms. On the other hand, we also compare the accuracy of our method with other related works that addressed real-time traffic classification problem based on the same sample traffic. The result shows that our method outperforms other previous works in protocol level identification with more than 8%~21% accuracy improvement based on fixed-ratio sample flow sets. Furthermore, the proposed method is also suitable to identify encrypted protocols.

Multi-constrained quality-of-service (QoS) routing is used to find routes in a network to satisfy multiple independent QoS constraints. This problem is considered to be NP-complete, and most existing QoS routing algorithms are based on maintaining a global network state at every node. A multi-constrained, flooding-limited, QoS routing method to deal with limited available resources and minimum computation in a dynamic environment is proposed. The solution is based on decomposition of a routing area and a restriction in the exchange of routing information. It reduces the size of the control messages, restricts the amount of routing information, minimises the overhead from the flooding of control traffic and decreases the complexity of path selection. It is also proved that the flooding-limited-path heuristic can achieve very high performance by maintaining entries in each node, which indicates that the performance of the limited-path heuristic is not sensitive to the number of constraints. Simulation results show that this protocol provides better performance than other protocols, especially with regards to end-to-end delay, throughput and packet loss.

Group communication is an important technique for many network computing applications. In group communication, a member in a group sends a message to others normally by multicast. Group signature guarantees the integrity of the exchanged data and provides source authentication. In a virtual machine (VMs) based computing system, a virtual machine monitor (VMM) allows applications to run in different VMs strongly isolated from each other. A trusted VMM (TVMM) based platform can provide stronger security protection for group signature systems than traditional computing platforms can. The authors first introduce a TVMM-based group signature architecture and a TVMM security protection mechanism for group signature components. Then, the authors propose a group signature scheme using the function of message checking based on the discrete logarithm problem. Finally, the authors prove the correctness of the group signature scheme and analyse its security in virtual computing environments.

Abstract Wireless equipment has become ubiquitous. However, in facing various software attacks, wireless linked networks are more vulnerable than wire linked networks. The general problem with Mobile IPv6 (MIPv6) (Table I) is the long handover latency period. To reduce the security latency, we propose early security key exchange for encryption in MIPv6 handoff. In our approach, two issues are addressed in dealing with the latency within the encryption technology during the handover. First, we extend the Early Binding Update (EBU) method to deal with the long security exchange negotiation time for the MIPv6 handoff. Second, we adopt the Security Access Gateway (SAG) to solve the limited computing and memory in the Mobile Node (MN). Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Multimedia transmissions are delay‐sensitive Internet applications. Because mobile stations are in continual motion, handoff processes are necessary and unavoidable in wireless network environments. Because handoff processes tend to break the communication link, the research has been conducted on reducing the break time and arrival delay during Internet multimedia applications. In this paper, we propose an approach based on Fuzzy Logic to evaluate the average variation in signal strength received by a mobile station and produce a FitAP factor. This factor indicates the possible handoff access point that is suitable for the mobile station. According to the FitAP factor, a mobile station needs only to execute an active scan process once. Therefore, smaller handoff latency is expected. We compare the results from the current cell search scheme with that of the Fuzzy Logic approach. The statistical results show that the proposed method outperforms the current cell search scheme with an improved handoff latency performance. Copyright © 2007 John Wiley & Sons, Ltd.

PIM multicast network is very convenient and can be fully applied in applications that provide one-to-many services, such as video conference, online games and real time voice stream, etc. The key point is the share tree environment formed by Rendezvous Point (RP) and Core node. When RP node fails, the share tree network will solve the crash. In this paper, an efficient bootstrap router (BSR) based RP mechanism was proposed to solve the RP recovery problem in share tree network of PIM while the RP fails.

In many scenarios, sensor nodes have to rely on a limited supply of energy (using batteries). To support long lifetime of wireless sensor networks (WSN), an energy-efficient way of operation of the WSN is necessary. In this paper, we propose a new controlled layer deployment (CLD) routing protocol to guarantee coverage and energy efficiency on a sensor network. CLD outperforms PEAS (probing environment and adaptive sleeping) and the TTDD (two-tier data dissemination) protocols in that it can guarantee full area coverage and connection. It can also solve the "cascading problem" which reduces the whole network lifetime. Finally, we show the results of the simulation to prove that the new protocol can use fewer sensor nodes for coverage and increase the lifetime as compared to the PEAS protocol.

Abstract The effective coverage distance of wireless LAN (WLAN) being small, users may leave the coverage area of the specific access point (AP) from time to time while roaming. However, the wireless network is a shared medium. The air is open for everyone. In general there is collision if a few users attempt to transmit with the same channel that is more rigorous during handoff period because of active scan mode. The active scan will perform requests for searching available AP. Unfortunately, this function consumes too much resource in wireless communication, and also affect total performance. We will propose an advanced active scan to improve it. In our proposal, we convert RF signal distribution to a simple classification problem, like as XOR classifier with artificial neural network (ANN). We combine ANN with active scan to achieve our goal. And the weight, which trained by ANN presents the connection character of geography. Moreover, the weight could be stored in AP for reusing and is called geographical fingerprint. The average enhancement of reducing the active scan area is about 62%. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Mobile IPv6 is a novel technology that supports mobile Internet communications. When a mobile node approaches another scalable domain, such as another ISP or mobile network company, serious problems occur such as registration delay and transmission latency owing to the binding update that the mobile node registers back to its original home agent. This work attempts to construct a scalable mobile IPv6 global network anycast by dynamically determining the nearest home agent to the current home agent for registration and transmission. This scheme is called Global Dynamic Home Agent Discovery (GDHAD). This investigation also performed and evaluated an anycast simulation to find how to obtain the best performance. Copyright © 2006 John Wiley & Sons, Ltd.

Weighted fair queueing (WFQ) is a popular scheme because of its guaranteed bandwidth and bounded delay. To make WFQ more flexible, we propose sliding weighted fair queueing (SWFQ) by combining priority-driven and share-driven scheduling for a real-time network. In our proposal, SWFQ can balance the share and priority-driven characteristics and can allow WFQ application to various network environments. This queueing algorithm can also be applied to either IPv4 or IPv6 networks as long as it can provide an adequate quality of service (QoS) support.

Multimedia transmissions are delay sensitive Internet applications. Because mobile stations move all of the time, handoff processes are necessary and unavoidable in wireless network environments. Since handoff processes tend to break the communication link, some researches have been done to reduce the break time and arrival delay during Internet multimedia applications. In this paper, we propose an approach based on fuzzy logic to evaluate the average and variation in signal strength received by a mobile station. According to our investigation, a mobile station needs to execute an active scan process only once to obtain the complete handoff parameters. Therefore, smaller handoff latency is expected

Data integrity assurance and data origin authentication are essential security services in financial transactions, electronic commerce, electronic mail, software distribution, data storage and so on. Nowadays, consumer electronics has been shifted toward Internet or intelligent appliances (IA) with network capability to exchange information through Internet. Therefore, a hardware based security mechanism is essential to be combined into the IA so that security and performance can be both preserved. In the Internet protocol security (IPSec) mechanism, the authentication header (AH) is an important portion. The two authentication algorithms specified for AH are MD5 and SHA-1 which have been implemented and evaluated in FPGA. With the proposed enhanced (register usage and concurrent statement) operation core design, a 6% improvement for slice utilization plus 24% more throughput for MD5 are obtained comparing to the previous one.

The use of IP (Internet protocol) technology in the information and communications industry constitutes a major global trend. A highly efficient service architecture, enabling technologies and advanced applications are essential to rapid multimedia services in an all-IPv6 network environment. This work presents an all-IPv6 service platform based on open service architecture (OSA) to support a set of standard interfaces and applications. The all-IPv6 network environment was integrated using a network-processor-based IPv4/IPv6 translator and a mobile router (MR) supported IPv6 network mobility. The feasibility of the open service platform for all-IPv6 network environments and of the designed application programming interfaces was examined using three applications: e-commerce, video-on-demand, and on-line gaming. The performance analysis indicates that the system throughput increased from 10.5 to 60.5 Mb/s as the number of users increased from 1 to 80; the mean response time increased from approximately 1 to 10.5 ms, and the delay time increased from 0.1 to 1 ms.

Abstract As the cover range for the radio cells becomes gradually smaller than before, the procedures for mobile terminals call setup and control become complicated due to the high handoff frequency. The broadcast nature of WDM star couplers makes the handoff scheme easier and reduces the number of reconnections. In this study, we proposed a channel allocation algorithm, called pre‐empted assigned offset (PAO) scheme, for the WDM‐ and SCM/WDM‐based PCN architectures. The main idea is to keep the offset conflict probability lower. The simulation results show that the proposed scheme outperforms those previously introduced schemes. Especially in offset conflict probability under a moderate roaming rate for WDM and even under heavy roaming situations for SCM/WDM. Copyright © 2001 John Wiley & Sons, Ltd.

To provide cellular data services with differentiated QoS, a shared resource scheme based on the optimization theory and LaGrange /spl lambda/-calculus was developed. This scheme can generate a fair schedule for a diverse mix of traffic with diverse QoS requirements in a limited radio spectrum. We define acceptance indication (AI) as the QoS measurement of the shared resource scheme. The experimental results show that the average acceptance rate of the schedule derived from our approach provides 2.5% higher than those of other existing heuristics.

As the range of radio cells is gradually getting smaller, the procedures for mobile terminal call setup and control become complicated due to the high handoff frequency. The broadcast nature of WDM star couplers makes the handoff scheme easier, and reduces the number of continuous reconnections. In this paper, based on the WDM-based PCN architecture, a channel allocation algorithm is used to keep the offset conflict probability as low as possible. Simulation results show that the wavelength-offset assignment scheme performs better than the other schemes in offset conflict probability under moderate roaming rate.

The initial cost to provide personal communications services (PCS) based on the conventional networks is relative high. As the radio cells move toward smaller size, the traditional procedures for call setup and control are not suitable well due to the high handoff frequency. The cable TV (CATV) network is one of the most attractive backbones for PCS due to its prevalent and broadcast nature. This significantly reduces the implementation costs and the handoff overheads. This paper proposes two architectures for the CATV-based PCS system. In the first architecture, each base station is equipped with multiple fixed receivers to provide fast and seamless handoffs for mobile terminals. Nevertheless, it suffers from the expensive hardware cost. In the second architecture, each base station is only equipped with one tunable receiver. This simple and economic architecture suffers from the possibility of offset conflict when mobile terminals handoff between the cells. Three channel allocation algorithms are proposed to resolve the offset conflict problem. Simulation results indicate the one with the concept of clustering performs much better than the other two schemes in terms of offset conflict probability.