5G serves as a catalyst for transformative digital innovation by enabling convergence with various services in our daily lives. The success of this paradigm shift undeniably hinges on robust security measures, with primary authentication— securing access to the 5G network—being paramount. Two protocols, 5G Authentication and Key Agreement (5G-AKA) and the Extensible Authentication Protocol for Authentication and Key Agreement Prime (EAP-AKA’), have been standardized for this purpose, with the former designed for 3rd Generation Partnership Project (3GPP) devices and the latter for non-3GPP devices. However, recent studies have exposed vulnerabilities in the 5G-AKA protocol, rendering it susceptible to security breaches, including linkability attacks. Furthermore, the advent of quantum computing poses significant quantum threats, underscoring the urgent need for the adoption of quantum-resistant cryptographic mechanisms. Although post-quantum cryptography (PQC) is being standardized, the lack of real-world deployment limits its proven robustness. In contrast, conventional cryptographic schemes have demonstrated reliability over decades of practical application. To address this gap, the Internet Engineering Task Force (IETF) has initiated the standardization of hybrid PQC algorithms (HPQC), combining classical and quantum-resistant techniques. Consequently, ensuring forward secrecy and resilience to quantum threats in the 5G-AKA protocol is critical. To address these security challenges, we propose the 5G-AKA-HPQC protocol. Our protocol is designed to maintain compatibility with existing standards while enhancing security by combining keys negotiated via the Elliptic Curve Integrated Encryption Scheme (ECIES) with those derived from a PQC-Key Encapsulation Mechanism (KEM). To rigorously and comprehensively validate the security of 5G-AKA-HPQC, we employ formal verification tools such as SVO Logic and ProVerif. The results confirm the protocol’s security and correctness. Furthermore, performance evaluations highlight the computational and communication overheads inherent to 5G-AKA-HPQC. With only average of 56.5 millisecond(+112.32%) on a total authentication time, proposed protocol provides its advantages. Also, on a environment of multiple UE registration, compared to single UE registration, the difference of increased rate of average authentication time is only 0.01%. The negligible difference 0.01% indicates that the addition of PQC does not lead to increased overhead in multi-UE registration environments, demonstrating its scalability and practical feasibility. In conclusion, our research provides significant insights into the design of secure, quantum-safe authentication protocols and lays the groundwork for the future standardization of secure authentication and key agreement protocols for mobile telecommunications.

Theadoption of Implantable Internet of Things Medical Devices (IoTMD) has revolutionized chronic disease management by enabling continuous monitoring and real-time data transmission, allowing patients to optimize treatment strategies. However, these advancements come with significant security risks, as IoTMD systems remain vulnerable to cyber threats that could compromise patient data and device functionality. Addressing this challenge, this study evaluates the fine-tuning performance of various lightweight Large Language Models (LLMs) for anomaly detection in IoTMD-enabled diabetes management control systems (DMCS). Among the evaluated models, LLaMA 3.2 1B-Instruct, fine-tuned with Low-Rank Adaptation (LoRA), achieves the highest performance, with 99.91% accuracy, perfect precision (100.00%), and a false positive rate of 0%. Comparative analysis against other lightweight LLMs-GPT-2, Phi-1 (1.3B), and Gemma 2B-Instruct-as well as traditional deep learning models such as IL-MLP, IL-CNN, FL-MLP, and FL-CNN, highlights the superior adaptability and robustness of transformer-based architectures in anomaly detection. These findings demonstrate the effectiveness of LLMs in securing IoTMD systems, providing a powerful solution for mitigating cyber threats while ensuring system reliability. The results underscore the potential of LLM-based anomaly detection in strengthening IoTMD cybersecurity, paving the way for safer and more reliable implantable medical devices in modern healthcare settings.

Cloud Manufacturing (CMfg) has revolutionized traditional manufacturing by enabling resource sharing across factory boundaries. As industry increasingly adopts cloud-edge-device collaborative ecosystems, effective authentication and key agreement (AKA) mechanisms play a critical role in safeguarding security across these complex multi-factory environments. Existing schemes, however, focus primarily on isolated binary security relationships (e.g., device-edge, device-cloud, or edge-cloud pairs individually), neglecting the integrated cloud-edge-device collaborative security demands inherent in multi-factory environments, while often relying on trusted authorities and high-overhead cryptographic mechanisms. This leads to redundant authentication processes, increased latency, and security vulnerabilities as devices must separately establish connections with each entity. To bridge these gaps, this paper introduces OMEGA: a comprehensive Cloud-Edge-Device Authentication and Key Agreement scheme for collaborative multi-factory manufacturing that pioneers an integrated security architecture. OMEGA’s distinctive advantage lies in its ability to establish all necessary secure connections (device-edge, device-cloud, and edge-cloud) through a single authentication request from the smart manufacturing device (SMD), significantly reducing authentication overhead. By leveraging lightweight hash operation, OMEGA creates a cohesive security fabric that enables SMDs to concurrently access specialized capabilities from multiple clouds while leveraging edge computing for time-sensitive operations. Security analysis using both Real-Or-Random (ROR) model and ProVerif formal verification tool demonstrates OMEGA achieves robust security while performance evaluation confirms its superior efficiency in industrial environments.

The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the truth, automated assistance is urgently needed. Current state-of-the-art approaches often involve fine-tuning existing models with contributions from domain knowledge bases. The black-box nature and interpretability issues of deep neural networks have increased interest in hybrid approaches, giving rise to deep neural fuzzy systems (DNFSs). This article presents the Hybrid Fact-Checking Framework leveraging a DNFS tailored to address the uncertainty inherent in fact verification tasks and enhance the reliability of model responses. The DNFS integrates a large language model with an adaptive neuro fuzzy inference system for automated fact verification. The framework utilizes relevant evidence from open-world and closed-world sources, leveraging deep language models and employing few-shot prompting without additional training to generate and justify verdicts. Including fuzzy rules and considering the trustworthiness and relevance of retrieved evidence enhances response reliability, thereby improving overall effectiveness and outcome interpretability. Experimental validations have been conducted on three publicly available datasets ranging in different domains of expertise: Climate-FEVER, SciFact, and FEVER. The results demonstrate that the proposed framework ensures better outcomes, transparency, and mindful decision-making.

The burgeoning domain of the metaverse has sparked significant interest from a diverse array of industries, including healthcare services. However, the metaverse and its associated applications present various challenges to existing networks. First, to meet the increasing demands of the metaverse, there is a need for enhanced bandwidth, reduced latency, and improved packet loss control. Furthermore, the transmission mechanism should exhibit flexibility to automatically adapt to the diverse hybrid needs of different healthcare services. In this article, a multipath transmission-based paradigm tailored for the metaverse-based healthcare services is developed. Significantly, we devise an orchestration framework to reconcile edge-side subflow management with diverse healthcare applications. Using machine learning techniques, the framework can produce near-optimal subflow adjustment strategies for client nodes and miscellaneous services. Comprehensive experiments are performed on applications with diverse requirements to validate the adaptability of the framework to the application needs. The experimental results demonstrate that the proposed method enables the network to autonomously adapt to changing network conditions and service requirements. This includes applications' preferences for high throughput, low delay, and high stability. Moreover, the test results show that the proposed approach can notably decrease the occurrences of network quality falling below the minimum requirement. Given its adaptability and impact on network quality, this work paves the way for future metaverse-based healthcare services.

Summary Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

Android-based mobile devices have attracted a large number of users because they are easy to use and possess a wide range of capabilities. Because of its popularity, Android has become one of the most important platforms for attackers to launch their nefarious schemes. Due to the rising sophistication of Android malware obfuscation and detection avoidance tactics, many traditional malware detection approaches have become impractical due to their limited representation capabilities. Inspired by the success of deep learning in representation learning, this article presents an effective improved deep neural network to safeguard Android devices from malicious apps called AMDI-Droid. The presented approach contains three enhancements: 1) from the ensemble classifier perspective, we propose a new architecture based on a deep neural network, where the predictive outputs obtained from all hidden layers are blended to produce a final prediction; 2) the first hidden layer learns an effective feature representation from the original data through multiple subnetworks; and 3) a loss function is formulated by combining the predictive loss of each base classifier connected to the corresponding hidden layer. The superior performance of the proposed model is verified via intensive evaluations against state-of-the-art techniques in terms of the accuracy, precision, recall,$F1$-score, and Matthews correlation coefficient (MCC) metrics.

Emerging mission-critical industrial applications pose serious challenges to the reliability of the industrial networks currently. Heterogeneous wireless terminals access the network through various industrial gateways, but the vulnerability of edge links hinders the safe operation on the site. Existing service protection methods of wireless links satisfy reliability requirements through naive redundancy mechanisms. However, these methods are difficult to ensure the long-term safety of industrial applications and consume excessive energy. Therefore, this article proposed an on-demand service protection (OSP) mechanism, which can retransmit data on time to meet reliability demands intelligently. First, a long-term reliability model and an energy consumption optimization problem are formulated. Second, we introduced a flexible module inside the industrial gateway to identify various requirements and detect crucial data loss events. Then, an intelligent agent is designed to avoid unexpected data loss, which can generate a retransmission policy according to application requirements. Finally, multiple validations on the OSP were conducted. Both numerical results and prototype experiments illustrate that the proposed solution outperforms existing candidates in terms of reliability and energy consumption.

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

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%.

Owing to the exponential proliferation of internet services and the sophistication of intrusions, traditional intrusion detection algorithms are unable to handle complex invasions due to their limited representation capabilities and the unbalanced nature of Internet of Things (IoT)-related data in terms of both telemetry and network traffic. Drawing inspiration from deep learning achievements in feature extraction and representation learning, in this study, we propose an accurate hybrid ensemble deep learning framework (HEDLF) to protect against obfuscated cyber-attacks on IoT networks. To address complex features and alleviate the imbalance problem, the proposed HEDLF includes three key components: 1) a hierarchical feature representation technique based on deep learning, which aims to extract specific information by supervising the loss of gradient information; 2) a balanced rotated feature extractor that simultaneously encourages the individual accuracy and diversity of the ensemble classifier; and 3) a meta-classifier acting as an aggregation method, which leverages a semisparse group regularizer to analyze the base classifiers’ outputs. Additionally, these improvements take class imbalance into account. The experimental results show that when compared against state-of-the-art techniques in terms of accuracy, precision, recall, and F1-score, the proposed HEDLF can achieve promising results on both telemetry and network traffic data.

This paper presents our analysis on the possibility of replay attacks with Radio Resource Control (RRC) packet on 5G Non-Standalone (NSA) network based on the 3GPP standard document. We analyzed the RRC Connection Setup process of User Equipment (UE), a malicious UE before and after sending an RRC connection request message, and processing procedures of the false base station and the commercial 5G base station. Through sending a strong signal, the false base station can cause a victim UE to establish the RRC connection to the false base station itself. Moreover, it may be able to launch a Denial-of-Service (DoS) attack, resulting in continuous denial of network access to the victim UE, by exploiting an RRC control message without integrity protection, and victim's temporary identity. In order to protect 5G communication services securely, we suggested practical measures to deter a replay attack based on the 3GPP standard document.

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network.

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.

With the evolution of wireless technologies, 5G and Beyond (5GB) communication is paving a way for efficient, ultrareliable, low-latent, and high converging services for the Internet of Things (IoT). Along with efficient communication, the security of messages is one of the concerns that must be maintained throughout the operations. Backhaul forms an essential part of 5GB with an ability to enhance the coverage and quality of service for IoT. However, conventional wired backhaul connection would cost operators thousands of dollars in the construction of 5GB infrastructure considering the ultradense nature of IoT. As a result, wireless backhaul is quickly becoming a feasible alternative to address 5GB’s direction toward network densification without affecting its other provisions. Wireless backhaul is expected to increase the landscape, covering from islands to mountains, which were difficult to access in the existing network generation. Moreover, it can effectively respond to the situation where the data traffic tremendously increased. Despite such provisioning, the wireless backhaul poses relatively various security threats and vulnerabilities due to the characteristics of wireless technologies. Several studies have been conducted to address the security problems; however, existing protocols do not support dynamic security policy and key management in a decentralized structure as well as secure handover in a specific scenario where Terminals (TMs) are moving. Motivated by this, we proposed the Mobile Terminal Handover (MoTH) security protocol to provide secure handover of mobile terminals between hubs. To solve the problem of existing protocols, a new entity called BMF is introduced to support distributed and dynamic security policy and key management in each serving network of the 5GB backhaul environment. The proposed protocol satisfies security requirements, including authentication and key management, confidentiality, integrity, and perfect forward secrecy. Additionally, it supports policy and key update services, and optimized handover. The security and correctness of the proposed protocol are thoroughly verified using the two formal security analysis tools: 1) BAN logic and 2) Scyther. Additionally, the performance evaluation shows that the proposed protocol is efficient.

Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an attention-based network that incorporates three key components for inter-sequence attention: global pointwise features, preceding attentive features, and contextual features while updating the rest of the components. Our model follows evaluation on two benchmark datasets cover tasks of textual entailment and question-answer matching. The proposed efficient Self-attention-driven Network for Text Matching outperforms the state of the art on the Stanford Natural Language Inference and WikiQA datasets with much fewer parameters.

Embedded systems are common in the Internet of Things domain: their integration in vehicles and mobile devices is being fostered in the Internet of vehicles (IoV). IoV has direct applications on intelligent transportation systems and smart cities. Besides basic requirements, such as ease of installation, cost-effectiveness, scalability, and flexibility, IOV applications need to guarantee energy-efficient and good-quality communication. In fact, IoV implementations are commonly based on wireless nodes, which rely on a limited energy source; therefore, an efficient communication among the nodes is desirable to prolong the lifetime of the devices. In particular, the alternation of active and sleep states and the regulation of the transmission power represent two common approaches to save energy. Based on this strategy, an effective fuzzy control system is presented in the paper to manage power consumption and quality of services of IoV applications. Two fuzzy controllers increase the battery life while keeping a good throughput to workload ratio. This technique has been simulated with two leading technologies in IoV: IEEE 802.11b/g/n and IEEE 802.11p. Experimental results show a network lifetime improvement ranging from 30% to 40%, according to the adopted medium access control protocol.

As a new computing paradigm, edge computing emerges in various fields. Many tasks previously relied on cloud computing are distributed to various edge devices that cooperate to complete the tasks. However, circumstantial factors in the edge network (e.g., functionality, transmission efficiency, and resource limitation) become more complex than those in cloud computing. Consequently, there is instability that cannot be ignored in the cooperation between the edge devices. In this article, we propose a novel framework to optimize edge cooperative network (ECN), called ECN-Opt, to improve the performance of edge computing tasks. Specifically, we first define the evaluation metrics for cooperation. Next, the cooperation of an ECN is optimized to improve the performance of specific tasks. Extensive experiments using real datasets from wearable sensors on the players in soccer teams demonstrate that our ECN-Opt framework performs well, and it also validate the effectiveness of the proposed optimization algorithm.

The evolution of cyber-physical system (CPS) benefits from substantial supports of many cutting-edge technologies. However, as a significant medium to bridge virtual and reality parts, the dependability of various network components is facing unprecedented challenges and threats. In this article, we propose a smart collaborative balancing (SCB) scheme to dynamically adjust the orchestration of network functions and efficiently optimize the workflow patterns. First, mathematical models of bandwidth allocation for multiuser with appropriate probability distribution are established. Matrix operations are utilized to solve the relevant issues based on individual congestion windows. Invasion defense mechanisms are also provided and discussed. Second, specific procedures of collaboration among different network components are presented. The capabilities of CPS, in terms of bandwidth allocation and invasion defense, are guaranteed via novel queueing policies and access control mechanisms. Third, we build a comprehensive prototype including multiple domains and users for validations. Experimental results in two scenarios illustrate that SCB not only supports service reliability of end hosts with different priorities, but also resists malicious attacks which are targeting the corresponding terminals inside domains. Compared to the benchmarks in software defined networks and traditional Internet, our scheme performs better in both available resource management and abnormal flow recognition aspects.

Advances of implantable medical devices (IMD) are transforming the traditional method of providing medical treatment, especially those patients under the most challenging condition. Accordingly, the IMD-enabled artificial pancreas system (APS) has now reached global market. It helped many patients suffering from chronic disease, called diabetes mellitus, in monitoring and maintaining blood glucose level conveniently. However, this advancement is accompanied by various security threats that place the life of patients at risk. Hence, protective measures, especially against yet unknown threats, are of paramount importance. This paper proposes a specification-based misbehavior detection system (SMDS) as an alternative solution to effectively mitigate security threats. Moreover, an outlier detection algorithm is also introduced to validate integrity of unprotected data transmitted by the different components. The monitor agent applies a smoothened-trust-based scheme to assess the trustworthiness of the APS. To demonstrate effectiveness of the proposed method, we first extend the UVA/Padova simulator for glucose-insulin data collection and subsequently simulate scenario with well-behave and malicious APS in MATLAB. The results show that there exists an optimal trust threshold that can achieve high specificity and sensitivity rate. Moreover, the proposed technique was compared to contemporary machine learning classifier including decision tree, support vector machine, k-nearest neighbor, and the SMDS called SMDAps. It is shown that our approach can dominate detection performance, especially to malicious behavior that manifests habitually (hidden mode).

Objective: To realize the full coverage, full spectrum, and full application, of 6G networks, the channel measurement, channel characteristics, and channel research of the 6G-oriented full-spectrum full-scene wireless network are explored. Methods: At present, constructing an information network covering three dimensions of sea, land and air is one of the research directions of 6G technology development. Meanwhile, the full coverage of three dimensions of communication requires multiple channels including multiple frequency bands and multiple scenarios, mainly including terahertz, light band, satellite, unmanned aerial vehicle, ocean, high-speed rail, and vehicle-to-vehicle. For different channels, different parameters are analyzed and targeted discussed according to channel characteristics. Also, there are many other problems of wireless communication channel that need to be solved. Here, only the above problems are analyzed in detail. Because of the current development of the Internet of Vehicles, the interconnection of connected vehicles is analyzed, and this scenario is also the most widely common scenario in daily life. Results: For the above-mentioned communication channels, the relevant measurement and modeling results of the 6G channel are shown; besides, the characteristics, time, and space correlation functions are obtained for each channel. Conclusion: The existing research results of 6G-oriented wireless channel measurement and modeling are analyzed; the research methods are summarized; new ideas are proposed to provide researchers with important references.

Ubiquitous power Internet of Things (IoT) is suffering unprecedented constraints and reliable tracing is a typical example. Motivated by software-defined and function virtualization capabilities of edge-cloud interplay, we propose a smart collaborative tracking scheme by investigating advanced parameter prediction skills and improved particle filter approaches. First, the range-based positioning issues are transformed into the vector nonlinear suboptimal estimation problem based on information fusion. Second, the importance of density function is provisioned to calculate locations and trajectories of the mobile node by obtaining cubature points, updating state estimation, and revising vector estimation. The Gauss-Newton iterative method has been utilized to achieve higher accuracy. Third, we implement our scheme into the simulation platform and prototype system. The practical deployment has been validated from multiple perspectives. Comparing with existing candidates, experimental results illustrate that the proposed algorithm is able to enhance the performance and demonstrate acceptable reliability. Potential usages are being expected in dynamic surveillance, equipment maintenance, and other emerging IoT scenarios.

Artificial intelligence is being utilized in multipath industrial networks to enhance service supporting ability. However, existing obstacles in controlling receive buffer restrict throughput even when higher bandwidth is available. Therefore, in this article, we propose a smart collaborative automation (SCA) scheme to improve resource usage and overcome buffer limitations. First, a mathematical model is established to describe primary system operations with considerations of chunk loss. The inf-supremum methodology and probability theory are adopted to track congestion window variations. Second, differences in disordered chunk expectations are analyzed to locate the critical condition of round numbers. Specific algorithm details are provided via simplifying comparison to achieve comprehensive policy selections. Third, evaluation topologies and environments are created with reasonable parameter settings. Validation results demonstrate that model-driven SCA can reduce unexpected occupations at the receiver-side. Comparing to intuition-driven schemes, overall performances, in terms of the sender's transmission capacity and receiver's buffer utilization, are improved under different experimental configurations.

We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics.

Internet of Things (IoT) is an emerging technology in the modern era of big data. It concerns a variety of applications ranging from smart homes, connected vehicles to smart factories, and more. IoT infrastructure typically comprises millions of connected objects and devices that store and exchange sensitive and confidential information. Theft and fraud scenarios, such as hacking and identity forgery, are serious threats to such IoT devices. Embedded hardware security techniques could be a potential solution to preserve the highest level of security within this infrastructure. Physically unclonable functions (PUFs) are among the potential solution to data security and counterfeiting problems. Many more intrinsic hardware security techniques are underway for a highly secure IoT infrastructure as strongly demanded by the IoT community. The focus of this Special Issue is to provide readers with the latest advances in securing IoT infrastructure from the physical layer point-of-view.

Mobile edge computing (MEC) reduces the computational distance between the source and the servers by fortifying near-user site evaluations of data for expedited communications, using caching. Caching provides ephemeral storage of data on designated servers for low-latency transmissions. However, with the network following a hierarchical layout, even the near-user site evaluations can be impacted by the overheads associated with maintaining a perpetual connection and other factors (e.g., those relating to the reliability of the underpinning network). Prior solutions study reliability as a factor of throughput, delays, jitters, or delivery ratio. However, with modern networks supporting high data rates, a current research trend is in ultrareliability. The latter is defined in terms of availability, connectivity, and survivability. Thus, in this paper, we focus on the ultrareliable communication in MEC. Specifically, in our setting, we use drones as on-demand nodes for efficient caching. While some existing solutions use cache-enabled drones, they generally focus only on the positioning problem rather than factors relating to ultrareliable communications. We present a novel neural-blockchain-based drone-caching approach, designed to ensure ultrareliability and provide a flat architecture (via blockchain). This neural-model fortifies an efficient transport mechanism, since blockchain maintains high reliability amongst the peers involved in the communications. The findings from the evaluation demonstrate that the proposed approach scores well in the following metrics: the probability of connectivity reaches 0.99; energy consumption is decreased by 60.34%; the maximum failure rate is affected by 13.0%; survivability is greater than 0.90; reliability reaches 1.0 even for a large set of users.

We propose and analyze a 3-tier cloud-cloudlet-device hierarchical trust-based service management protocol called IoT-HiTrust for large-scale mobile cloud Internet of Things (IoT) systems. Our mobile cloud hierarchical service management protocol allows an IoT customer to report its service experiences and query its subjective service trust score toward an IoT service provider following a scalable report-and-query design. We conduct a formal scalability analysis along with an ns-3 simulation performance analysis demonstrating that IoT-HiTrust not only achieves scalability without compromising accuracy, convergence, and resiliency properties against malicious attacks but also outperforms contemporary distributed and centralized IoT trust management protocols. We test the feasibility by applying IoT-HiTrust to two case studies: 1) a smart city travel service composition and binding application and 2) an air pollution detection and response application. The results demonstrate that IoT-HiTrust outperforms contemporary distributed and centralized trust-based IoT service management protocols in selecting trustworthy nodes to maximize application performance, while achieving scalability.

The rapid development of Internet-of-ings (IoT) techniques in G/ G deployments is witnessing the generation of massive amounts of data which are collected, stored, processed, and presented in an ea ...

Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under networked UAV environments. In particular, we classify the existing IDS mechanisms according to information gathering sources, deployment strategies, detection methods, detection states, IDS acknowledgment, and intrusion types. We conclude this paper with research challenges, insights, and future research directions to propose a networked UAVIDS system which meets required standards of effectiveness and efficiency in terms of the goals of both security and performance.

This article discusses a lightweight behavior rule specification-based monitoring solution for identifying misbehavior of an embedded IoT device. These unusual activities are exhibited because of attacks exploiting the vulnerability exposed through automatic model checking and formal verification. It is conclusive in the presented research that rule specification-based misbehavior detection technique outperforms contemporary anomaly-based misbehavior detection techniques for an unmanned aerial vehicle (UAV) cyber-physical system.

Summary Multipath TCP (MPTCP) is a promising transport protocol that allows a multihomed device to simultaneously use multiple network interfaces to send application data over multiple paths. However, although applying MPTCP to data delivery introduces many and attractive benefits, the MPTCP is vulnerable to network attacks. When a path within the MPTCP connection suffers from some types of attacks (eg, a denial‐of‐service attack) and becomes underperforming, it will undoubtedly cause transmission interruption in the stable paths and thus degrade the application‐level performance. Unfortunately, the MPTCP path management mechanism is very simple and cannot timely prevent the usage of underperforming paths in multipath transmission. In this paper, we introduce a new “potentially underperforming” (PU) concept to MPTCP and propose a novel PU‐aware path usage management mechanism ((PU)2M2) for MPTCP aiming to (1) detect and declare an underperforming path and prevent the usage of underperforming paths in multipath transmission, (2) provide a finite‐state‐machine model to change per‐path's state accordingly and effectively manage multiple paths for data transmission, and (3) alleviate the packet reordering problem and make MPTCP avoid throughput performance degradation during network underperforming. We demonstrate the benefits of applying (PU)2M2 to MPTCP.

The need to develop smart unmanned aerial vehicles (UAVs) which are capable of deciding their trajectories is increasing at a rapid pace. Due to their usage in wide range of applications, such as-military, security, communications, survey mapping, disaster management, etc., the provisioning of end-to-end quality of service (QoS) is a challenging task in UAV environment. Moreover, with limited power, the efficiency of the UAVs can be enhanced if adaptive decisions with respect to their itineraries is considered dynamically. However, most of the solutions reported in the literature are not efficient with respect to QoS preservations for various applications. Motivated by this, several recently proposed meta-heuristic optimization schemes for reactive path planning of UAVs have been explored while designing a UAV path planning problem using multiverse optimizer (MVO). By carrying out the simulations over 1000 iterations, it has been demonstrated that MVO algorithm performs better in majority of the cases with average fitness function value of 0.152 and average execution time of 33.686 s.

Wireless body sensor networks (WBSNs) mostly consist of low‐cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle‐computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity‐driven green‐oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128‐bit RN, which is much more efficient when compared to the electrocardiography‐based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes.

Today, due to the popularity and convenience of wireless communications, people communicate with others by using mobile phones almost every day, as one of their important daily activities. Currently, LTE-4G is the mainstream of mobile communication. Recent studies on the upcoming LTE-5G wireless environments employ AES to protect delivered messages. However, using this cryptography standard to encrypt/decrypt messages will cause the performance bottlenecks. Also, AES will be insecure in the near future since it has been partially solved. In other words, it will not meet the security and performance needs of LTE-5G, meaning that a new generation of safer and higher performance encryption method is indeed required. Therefore, in this paper, we present a new generation of encryption methods, namely "3D Encryption with Shifting Mapping Substitution Mechanism" (3DESMS), to effectively enhance the safety for LTE-5G and improve its performance. The 3DESMS effectively integrates two encryption schemes, i.e., three-dimensional encryption method (3D encryption for short) and Shifting Mapping Substitution Mechanism (SMSM), where the 3D Encryption uses three fundamental operators, i.e., Exclusive-OR operator (⊕), Binary- Addition operator (+2) and Rotate-Equivalence operator (Θ), to encrypt/decrypt operands. The SMSM is a mapping substitution with constant shifting displacement for data encryption. Our theoretical analyses show that the 3DESMS has achieved the practical secure in a wireless communication environment, and according to the results of our implementation, the performance ranking of the 3DESMS, SeFEM and AES is 3DESMS > SeFEM > AES, indicating that the 3DESMS is more suitable for LTE-5G wireless environments than AES.

This paper introduces the 9th International Workshop on Managing Insider Security Threats (MIST 2017), which takes place in conjunction with the 24th ACM Conference on Computer and Communications Security (ACM CCS 2017). Its objective is to present recent challenges and advanced technologies in managing insider security threats by publishing high-quality work that will be a trigger for further research related to this subject.

In this paper, a brief introduction is presented on the 8th International Workshop on Managing Insider Security Threats (MIST 2016). MIST 2016 takes place in conjunction with the 23rd ACM Conference on Computer and Communications Security (ACM CCS 2016). Its main goal is to provide a forum for sharing the most recent challenges and advanced technologies in managing insider security threats.

Mobility management has become a core function in internet services and networks as mobile devices have been widely used and their capabilities have dramatically advanced. It is expected that proxy mobile IPv6 (PMIPv6), which is the most prominent solution, will play an important role in supporting these devices’ mobility. To protect PMIPv6 networks, several authentication schemes have been presented. However, due to their static approach, the existing schemes failed to keep a good balance between security and efficiency. Motivated by this, the authors study an adaptive authentication scheme for mobile devices in PMIPv6 networks. In particular, the proposed scheme considers mobile nodes’ context information to decide authentication strength, based on which adaptive authentication is performed. It is shown from the formal security verification and the example study that the proposed scheme is not only correct, but also achieves a good trade‐off between security and efficiency

Abstract People‐centric sensing (PCS), which collects information closely related to human activity and interactions in societies, is stepping into a flourishing time. Along with its great benefits, PCS poses new security challenges such as data integrity and participant privacy. Hierarchical identity‐based signature (HIBS) scheme can efficiently provide high‐integrity messaging, secure communication, and privacy protection to PCS. However, key escrow problem and low computation efficiency primarily hinder the adoption of HIBS scheme. In this paper, we propose an escrow‐free online/offline HIBS scheme for securing PCS. By utilizing user‐selected‐secret signing algorithm and splitting the signing phase into online and offline procedures, our scheme solves the key escrow problem and achieves high scheme performance. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we provide a brief summary of the 7th International Workshop on Managing Insider Security Threats (MIST 2015). MIST 2015 is held in conjunction with the 22nd ACM Conference on Computer and Communications Security (ACM CCS 2015). The workshop aims to showcase the most recent challenges and advanced technologies for managing insider security threats

Summary Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet‐based delay‐sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling‐based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling‐based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

Intelligent technologies and applications for big data analytics Data are being generated at a phenomenal and ever growing rate.It comes from numerous sources including distributed sensors, social media, images and text documents.This 'tsunami of data' needs to be stored and analysed speedily and efficiently.Traditional approaches to managing data are simply not powerful and capable enough to handle the large data volumes.The characteristics and features of such big data, such as data from social media or generated by ambient sensors, are entirely different in nature from traditional enterprise data or information.New solutions and computational paradigms which allow us to analyse, understand, store, retrieve and visualize big data are badly needed.They are fostering new research directions in computer science and technology, and in the cognitive sciences.In addition to collection, storage, processing and retrieval of large quantities of data, some research seeks to understand the mechanisms by which humans or intelligent systems can absorb such information and apply that information to solve complex problems or provide different services.Obviously, technologies which support storing and analysis of large quantities of data must also meet important requirements for secure operation to guarantee the confidentiality and the integrity of the collected data.Such features can be ensured thanks to new paradigms of distributed computing and the development of specialized infrastructure attached to the service sector that collects and processes shared data in big repositories or in the cloud.Secure collection and intelligent management of data are now clearly a part of many domains and aspects of our life and an important part of scientific research associated with ambient technologies and ubiquitous computing.In view of the many interesting research topics in the rapidly developing field of Big Data, we decided to produce this Special Issue on 'Intelligent Technologies and Applications for Big Data Analytics'.The general aim of this special issue is to document the state of the art in intelligent and advanced approaches to the entire management cycle for big data.From the several dozen papers submitted to this Special Issue, eight articles of particular interest were selected for publication.In our view, they present really interesting solutions to some problems in the areas covered by this issue.• 'BGG: A Graph Grammar Approach for Modelling the Software Architecture of Big Data-Oriented Software Systems', by Chen Li et al.The authors propose a Breeze Graph Grammar (BGG) to model the software architecture in both its static and dynamic aspects.A BGG model is used for reliability modeling and evaluation of the software.The idea is developed in three directions.First, a BGG definition is used to specify the software architecture and map the system dynamic evolution to BGG transformation rules.Second, a BGG reliability model is proposed in which error attributes are added to the BGG graph to capture the system error information, and system error state transitions are modeled with BGG graph rewriting rules.Finally, the authors present rules to map the BGG reliability model to a Generalized Stochastic Petri Net (GSPN) model, which can be used for reliability evaluation.• 'Making sense from Big RDF Data: OUSAF for measuring Ontology Usage' by Jamshaid Ashraf et al. The authors propose the Ontology USage Analysis Framework (OUSAF). OUSAF provides a methodological approach to performing the various phases such as identifying, analysing, representing and utilizing the Ontology usage results from Big RDF Data.The paper describes

The notion of database outsourcing enables the data owner to delegate the database management to a cloud service provider (CSP) that provides various database services to different users. Recently, plenty of research work has been done on the primitive of outsourced database. However, it seems that no existing solutions can perfectly support the properties of both correctness and completeness for the query results, especially in the case when the dishonest CSP intentionally returns an empty set for the query request of the user. In this paper, we propose a new verifiable auditing scheme for outsourced database, which can simultaneously achieve the correctness and completeness of search results even if the dishonest CSP purposely returns an empty set. Furthermore, we can prove that our construction can achieve the desired security properties even in the encrypted outsourced database. Besides, the proposed scheme can be extended to support the dynamic database setting by incorporating the notion of verifiable database with updates.

Today the quick development of computers and networks not only enriches our everyday life, but also provides us with a convenient living environment. However, hackers are everywhere in the world. Hence, our sensitive data need to be securely protected. Currently, AES and DES as the most widely used block ciphering mechanisms are popularly employed to encrypt important data. But DES was publicly cracked several years ago. On the other hand, due to the fast computer processing speed and processing power, currently no one dare to say that AES is still secure. It may be cracked someday. Therefore, in this paper, we propose a secure encryption method, called the Random Wrapping Encryption Method (RWEM for short), which is developed by employing information of current time as a part of the parameters to wrap up ciphertext in its encryption process. If a file is encrypted at different time points, the content and the length of the wrapped ciphertext file will not be the same. Thus, it is hard for hackers to analyze and crack the ciphertext. We also analyze the security of the DES, AES and RWEM, and explain why the RWEM can effectively protect some specific attacks, and why it is more secure than the DES and AES.

As a promising mobility protocol, Proxy Mobile IPv6 (PMIPv6) can provide mobility support without the involvement of the mobile nodes (MNs). It depends on the Local Mobility Anchor (LMA) and Mobility Access Gateway (MAG) to emulate the home network for the attached MNs. Based on this design principle, PMIPv6 works well only for small scale networks. However, with the rapid increase of the MNs as well as the huge traffic loads, PMIPv6 as a centralized mobility management protocol in which both the location management and dada forwarding are performed by the LMA, will induce lots of limitations, such as non-optimal routing, single point of failure, scalability and so on. Therefore, a trend to distributed mobility management (DMM) is popular, and several distributed solutions for PMIPv6 have been proposed until now. In this paper, we survey the different schemes of DMM and focus on the related solutions for PMIPv6 by qualitative analysis, and conclude the remaining challenges and open issues for future development.

Recent work has shown that Proxy Mobile IPv6 (PMIPv6) is a promising mobility management protocol for its salient features, such as supporting unmodified Mobile Node (MN). However, PMIPv6 does not consider the multicast routing support. Moreover, current research mainly concerns on the multicast receiver mobility, but it is a critical and challenging issue to ensure service connectivity for mobile multicast senders, which has not been addressed well. In this paper, we propose an efficient multicast sender mobility scheme for PMIPv6 (PMIP-BT), in which the multicast data can be transmitted through the PMIPv6 tunnel and the multicast sender mobility is transparently enabled in the PMIPv6 networks. Numerical results show that the proposed scheme outperforms the current scheme in terms of signaling cost.

Collaborative Filtering is one of the most important techniques in recommender systems. Current researches on Collaborative Filtering focus on how to improve the accuracy. However, it is of the same importance to recommend more potential interests to users because many of them have more expectations for recommendation list besides the accuracy. Current recommender systems did not address this problem. This paper focuses on how to help users find more interests in the recommendation list. We propose an sampling-based algorithm Probabilistic Top-N Selection to recommend potential interests for users, and propose two metrics, average predicted rating and category coverage, to assess the quality of the recommendation list. Then we conduct a series of experiments on Movie Lens dataset, experimental results demonstrate that our algorithm can significantly improve user experience through providing them with more potential interests.

In the last decade, computer science researchers have beenworking hard to prevent attacks against the security ofinformation systems. Different adversary models haveincarnated the malicious entities against which researchershave defined security properties, identified securityvulnerabilities, and engineered security defenses. Theseadversaries were usually intruders, that is, outsiders tryingto break into a system’s defenses.

This paper surveys remarkable incidents that were related to the Homeland Security and Defense such as terrors, disasters and cyber-attacks and overviews the existing projects given by the department of Homeland Security and Defense of the US government. Through the overview, technological foundations in the projects are extracted and discussed. Additionally, this paper introduces a common framework, as an example, supporting the delivery service for RFID Tracking, Sensor Network, Video Surveillance and Image Screening, which are the major technological foundations in the Homeland Security and Defense. As providing an outline of the technological aspects of the Homeland Security and Defense, this paper is expected a reference for initiators of the related projects.

In web service environment, long transactions always take long time to finish, which demands the resources - often the database - to be locked for a long time. This would bring down the performance of the transaction processing system. The normal solution is compensating transaction, which creates a relative transaction with the opposite effect. When the transaction is being executed, the sub-transactions are allowed to commit independently without waiting for other sub-transactions. If the transaction fails, the respective compensating transaction will be executed to eliminate the effect brought by the original transaction. However, there are still some problems in compensating transaction model. This paper proposes a novel pipeline based processing model of long transaction (PPMLT), which parallelizes the transaction processing mainly aimed at the serially executed long transaction. This model could improve the performance the transaction processing evidently without the compensating transaction. Our experiments show that the pipeline based processing model of serial long transaction has notable improvement when executing the long transaction.

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.

Context data is important information for the behaviors of the applications in a pervasive space. To effectively restore huge amount of data, tree-liked layered storage architecture are proposed, where the leaf nodes collect the data from the sensing devices located in its domain. However, the sensing devices may be moving among different domains. In order to integrate the data from the same device, related leaf nodes should upload and store the data to a certain up-layer node, called host node. This paper presents a deep study of the data storage problem and proposes an online algorithm DHS to dynamically select the host node, which reduces the communication cost significantly. We prove the correctness of the algorithm theoretically. The experiment results also show that DHS is correct and effective.

In this paper, we propose a network mobility supporting scheme (N-NEMO) in Proxy Mobile IPv6 (PMIPv6) network, which is an issue still up in the air for the PMIPv6. In the N-NEMO, a tunnel splitting scheme is used to differentiate the inter-Mobility Access Gateway (MAG) and intra-MAG mobility. The performance analysis and comparison between other related schemes show that N-NEMO reduces the signaling cost significantly. Besides, it enhances the efficiency and scalability to provide the comprehensive network mobility in the PMIPv6 context.

In this research, we improved pre-study which was necessary independent compiler and hardware, separating with game service, for prevention of game service counterfeiting, and developed technologies, which executing file itself can execute reverse-analysis by itself and prevent counterfeiting. In detail, suggested method through this research was to install two loaders which can encode and doble-code by separating data section and code section within execution file. For anti debugging, game service is automatically terminated when the motion of external debugger is sensed, and, the header of execution file which is referred by disassembling program is changed and made this impossible for anti disassembling. Lastly, the data that should be protected are copied in temporary shared memory for anti memory dump as solution.