Graph Neural Networks (GNNs) often suffer from degraded performance when encountering out-of-distribution (OOD) samples, particularly in multi-domain graph scenarios. Existing graph OOD detection methods typically require extensive modifications to data or model architectures, resulting in high computational costs and limited generalization. Moreover, prior approaches largely overlook local structural semantics and community-level patterns, leading to biased representations and suboptimal detection performance. To overcome these limitations, we propose community enhanced graph out-of-distribution detection (CEGOOD), a novel framework that incorporates community structure into GNN-based OOD detection. Specifically, we propose two community-aware view generation strategies: intra-community attribute aggregation (ICAA) to distill fine-grained feature coherence and inter-community edge dropping (ICED) to fortify structural robustness by pruning non-critical cross-community edges. Furthermore, We also design three community-level loss functions (compactness, separability, and balance) to optimize community hierarchical structures and improve community representation. Experimental results on various datasets show that CEGOOD outperforms state-of-the-art baselines by an average of 1.8% AUC, with notable gains of 2.4% on AIDS+DHFR and 2.8% on BBBP+BACE, demonstrating superior adaptability and effectiveness in graph OOD detection tasks.

Routing and task-scheduling in space–air–ground integrated networks (SAGINs) are usually time-dependent due to heterogeneous mobility, intermittent connectivity, and continuously-varying link rates. Existing studies mainly rely on the time-expanded graph (TEG) framework to accommodate mobile dynamics by discretizing continuous link variations into uniform time slots (segments). Consequently, overly coarse slots lead to information quantization loss, while overly fine slots result in a granularity mismatch with the minimum transmission unit and the scalability problem. To overcome these limitations, we propose a novel continuous-time graph (CTG) framework that directly characterizes link-rate functions in continuous time and thus eliminates the stringent dependence on slot granularity. Building upon this new framework, we develop a new CTG event-driven routing (CTG-EDR) algorithm that can perform multi-source, multi-task scheduling through event-driven verification of link and buffer calendars. Monte Carlo simulations demonstrate that our proposed new CTG-EDR scheme can achieve a significantly lower latency and a higher task-completion ratio than the representative baselines. Our simulation results justify that the proposed new CTG-EDR scheme is very promising for robust and scalable routing and task-scheduling in highly dynamic SAGIN environments.

Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.

In this article, we propose a deep reinforcement learning (DRL)-based multipoint localization scheme (MLS) to efficiently localize Internet of Things (IoT) devices using a single autonomous aerial vehicle (AAV) equipped with a large-scale multiantenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multiantenna array on the AAV captures both the Angle of Arrival (AoA) and received signal strength indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the AAV. This approach eliminates the need for multiple hovering points required by a single-antenna AAV (SA-AAV) or the deployment of multiple SA-AAVs. To enhance localization accuracy, key hovering points for the multiantenna AAV (MA-AAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-AAV to optimize the tradeoff between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the AAV.

The advent of extremely large antenna arrays (ELAAs) is crucial for meeting the performance demands of future sixth-generation (6G) wireless networks. However, ELAA introduces significant near-field communication (NFC) effects, characterized by spherical wavefront propagation, in contrast to the conventional planar waves observed in far-field models (FFMs). As NFC facilitates precise beamfocusing and spatial multiplexing, it inherently increases the risk of eavesdropping, making physical-layer security (PLS) a critical challenge for safeguarding confidential communication. This article investigates a multiple-input-multiple-output (MIMO) system in the near-field regime, utilizing NFC properties to enhance secrecy performance. Unlike FFMs that rely on the angular domain, our approach leverages both angular and distance domains to achieve robust PLS, even when an eavesdropper shares the same angular direction as a legitimate user. We propose a deep reinforcement learning (DRL)-based solution to optimize beamforming, power allocation, and antenna selection. By minimizing antenna use while maximizing secrecy rates, the approach avoids resource wastage and ensures superior security. Numerical simulations demonstrate significant secrecy rate improvements.

This paper explores the usage of low-voltage Power-Line Communication (PLC) links for Enhanced Common Public Radio Interface (eCPRI)-based front-hauling in 5G Internet of Things (IoT) indoor mobile coverage environments, using a split Centralized Radio Access Network (C-RAN) architecture. This research aims to analyze how parameters such as wireless IoT device count, bandwidth, and transmission technology affect the delay performance of the proposed system. To achieve this goal, we develop detailed mathematical models that draw insights from queuing theory, stochastic geometry, and Markov models. Extensive system-level simulations verify these models’ accuracy, and the analytical results cover radio and access delay performance. We validate the system’s efficiency in supporting IoT indoor cellular applications and assess the feasibility of the proposed PLC-based front-hauling system, considering the strict delay requirements of the eCPRI standard.

Compatibility among acupoints is a fundamental principle in acupuncture treatment within traditional Chinese medicine, playing a vital role in enhancing the effectiveness and scope of therapeutic interventions. With the increasing availability of acupuncture-related data, link prediction offers a data-driven approach that facilitates the evidence-based exploration and validation of acupoint compatibilities. However, existing link prediction methods often focus on mapping acupoints and their compatibility relationships into lower-dimensional spaces. These approaches can overlook essential acupoint features and make the predictions susceptible to noise interference. To address these challenges, we propose a novel acupoint compatibility prediction model based on a Feature-Aware Residual Graph Attention Network and Matrix Factorization (FRGATMF). Our model introduces a feature-aware connectivity fusion strategy that integrates acupoint attributes with structural information to enrich acupoint representations. Following this, a deep non-negative matrix factorization approach is employed to construct a denoised feature matrix. This matrix is processed through a residual graph attention network to derive comprehensive and effective node embeddings, which are crucial for accurate link prediction. Experimental results on the acupuncture dataset, along with three public datasets, demonstrate that FRGATMF significantly outperforms seven existing comparison models across various evaluation metrics. Additionally, link prediction can identify previously unconsidered or undocumented acupoint combinations that may offer better therapeutic results, thus expanding the range of treatment options and highlighting its potential in improving the prediction of acupoint compatibility relationships.

Quantum key distribution (QKD)-enabled optical networks utilize quantum mechanics to secure communications by establishing secure quantum channels. A critical criterion for QKD-enabled optical networks is ensuring network availability, which requires each quantum key service to adhere to a maximum unavailability constraint to maintain the network availability. This paper addresses the challenge of offering dedicated path protection for quantum key services in QKD-enabled optical networks. It ensures that the unavailability gap between the working and protection paths remains within the allowable limit for each quantum key service, adhering to specific unavailability constraints. Considering the constraints of the limited timeslot resources and network availability, we proposed quantum key service approach named a maximum availability (MA) algorithm. Simulation results indicate that the MA algorithm surpasses both the traditional dedicated-path protection (TDP) and fixed dedicated-path routing (FDR) algorithms in reducing total timeslot consumption and enhancing average availability.

Space-air-ground integrated networks (SAGINs) hold immense potential for improved network coverage and dynamic service delivery. Yet, current methods often depend on separate, uncoordinated unmanned aerial vehicles (UAVs), leading to scalability issues and limited energy efficiency – challenges that persist even when applying intelligent machine learning (ML) methods. This paper discusses a four-tier aerial computing (AC) system, leveraging the collective capabilities of low-altitude UAVs (LAUs), high-altitude UAVs (HAUs), and satellites to fully realize the potential of SAGINs within AC. Incorporating advancements in tiny machine learning (TinyML), this system boosts onboard intelligence for immediate data processing and adaptive decision-making. Specifically, by utilizing the robust computational resources of higher-layer SAGIN entities, we introduce a tiny federated deep reinforcement learning (TinyFDRL) algorithm across multiple tiers to achieve energy-efficient trajectories for multiple LAUs. This proposed TinyFDRL algorithm independently plans multi-LAU trajectories in unpredictable environments by combining the strengths of federated learning (FL) and deep reinforcement learning (DRL). Extensive simulations validate the algorithm, confirming its efficiency in creating energy-saving paths for LAUs in the integrated AC network.

This article exploits the spatial–temporal features of user screen-touch trajectory (STT) to develop a user authentication framework for Industrial Internet of Things (IIoT) systems. We first model the STT as a trajectory image and apply the speeded-up robust features (SURF) algorithm for STT spatial feature characterization. We then model the STT as a time series and employ the hidden Markov model (HMM) for the STT temporal feature extraction. We further design a classifier based on HMM for the above temporal feature and also a classifier based on eXtreme Gradient Boosting for the spatial feature. By combining the two classifiers and assigning each classifier an appropriate weight, we develop a passive user authentication framework. The new framework has the potential to significantly impact the IIoT security practices by offering a flexible and efficient authentication method for IIoT systems, and it also can serve as a complementary solution or an enhancement for the traditional authentication mechanism of such systems.

This paper presents a novel framework called Failure-Alarm Correlation Tree based Failure Localization (FACT-FL), designed to localize failed optical layer device boards in an Optical Transport Network (OTN). Specifically, FACT-FL aims to construct a set of FACTs by correlating the failed boards and alarms, where each FACT takes one failed board and its correlated alarms as the root and leaves, respectively. Furthermore, a FACT consists of a suite of kth order Failure-Alarm Correlation Chains (k-FACCs) with different order values of k. Each k-FACC indicates the chain-like correlation established by k alarms due to one common failed board. To identify all previously undetected k-FACCs, a set of binary classifiers is trained that characterizes each k-FACC from various dimensions, including time, network topology, traffic distribution, and board/alarm attributes. Eventually, an integer linear programming (ILP) problem is formulated to extract the most likely FACT(s) from those k-FACCs. Extensive case studies demonstrate the superior results of FACT-FL in terms of metrics evaluating the identified failed boards and root alarms. We also analyze its performance under different maximum order values of k and environmental changes, including failure scenarios, network topologies, traffic distributions, and noise alarms.

Supervising illicit activities on blockchain networks, such as money laundering, fraud, extortion, Ponzi schemes, and funding for terrorist organizations, presents significant challenges. Emerging machine learning methods for detecting abnormal transactions face hurdles due to high labeling costs, limited labeled data, and data imbalance. To address this, this paper proposes aMinimalSubstitution-basedLabelPropagation(MSLP) model to provide more labeled data to balance the graph data and complement the sample for anomalous transaction detection service in the blockchain networks. As far as we know, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more abnormal transactions with under-labeling budget constraints. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improved the F1 score of illegal transaction node detection by 2.6% to 8.2%.

Passive user authentication is critical for the secure operation of Industrial Internet of Things (IIoT) systems. By jointly utilizing both the time-varying characteristics of the user sequential operation actions and spatial variation characteristics of channel state information (CSI) caused by these actions, this paper proposes a novel two-dimensional passive authentication framework for IIoT systems. In particular, we construct the time-varying operation action sequences from the routine work process of a user and apply the Hidden Markov Model to characterize behavioral biometric characteristics of the user, and also employ the eXtreme Gradient Boosting model to depict the spatial variation characteristics of CSI related to the user. By designing two classifiers corresponding these two characteristics and assigning each classifier an appropriate weight, we propose a two-dimensional user authentication framework for continuous and non-intrusive user authentication in IIoT scenarios. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate. We further investigate the related authentication efficiency issues like the sensitivity to the weights for classifiers, the sensitivity to authentication time and the capability of resisting against impersonation attacks.

Numerous new applications have been proliferated with the mature of 5G, which generates a large number of latency-sensitive and computationally intensive mobile data requests. The real-time requirement of these mobile data has been accommodated well by fog computing in the past few years, mainly through offloading tasks to fog nodes in the vicinity. On the other hand, the user-privacy hidden in the Internet-of-Things (IoT) data has not been sufficiently considered in the presence of insecure fog nodes. It is risky to offload an entire mission-critical task to just one fog node or several fog nodes owned by the same service provider (SP), especially when the SP is marked with low-security credit and tends to collect data information of users for malicious use. To address this issue, we classify IoT user tasks based on their security requirements, divide them into different numbers of smaller fragments, and, finally, offload the segments of a task to multiple fog nodes owned by the same or various SPs according to their security requirements. The selected fog nodes will collaboratively serve the divided fragments to avoid the possible damage caused by the leak of sensitive data due to compromised fog nodes of malicious SPs. For this, we propose an integer linear programming (ILP) model and a dynamic programming algorithm to maximize the number of successfully served IoT data tasks with satisfactory security requirements while minimizing the end-to-end transmission delay. The numerical results show that the proposed ILP model and algorithm can significantly increase the successful provisioning ratio for tasks with high-security requirements.

With the rise of the Industrial Internet of Things (IIoT), more and more industrial devices can be connected via the network. Data collection, processing, analysis, task execution, and other devices that can product network traffic volume are gradually being deployed to IIoT. However, under the limited spectrum resources and low-cost and low-energy production requirements of enterprises, how to ensure the interconnection and intercommunication of industrial networks while realizing the effective use of network communication resources is currently a hot topic. Among them, network traffic prediction is considered to be a very important task. The time variability and interpretability, especially the time-varying features of traffic sequences, greatly challenge this task. To address those, this article proposes a method calledFlow2graphto predict network traffic in IIoT. Specifically, some key segments, i.e., shapelets are extracted from the network traffic sequence according to time-varying traffic; then uses the relationship between the traffic sequence and shapelets to convert the flow into a shapelets conversion graph; Subsequently, the graph isomorphism network are used to learn the specificity of the flow sequence from different devices, thereby to predict its traffic value for a period of time in the future; finally, we conduct extensive experiments on real data to verify the effectiveness of the proposed method.

The secure exchange of messages between different communication devices is a major issue of Internet-of-Things (IoT) applications in future smart cities. Current security mechanisms focus on multiple antennas technology, such as spatial modulation (SM), but in space shift keying (SSK), there is still a space to explore. In this article, we propose a secrecy-enhancing SSK scheme for IoT applications by applying security technologies in the physical layer wherein the number of transmit antennas is arbitrary rather than the value of power of two. In this scheme, the security performance of the communication system is improved by using two technologies, namely, artificial noise (AN) and antenna selection. We assume that the application scenario of the SSK system is under the classic eavesdropping model. First, we design ANs according to the channel state information (CSI) to interrupt the eavesdropper and benefit the legitimate receiver via the appropriate cancellation technology. Second, the antenna selection method is designed based on the signal to leakage noise ratio (SLNR) to further boost the secrecy performance by expanding the mutual information difference between the main channel and the eavesdropping channel. Results from our simulations indicate that by the use of the proposed scheme, significant secrecy enhancing can be achieved in terms of bit error ratio (BER) and secrecy rate (SR) when compared with existing schemes. This achieved secrecy enhancing can benefit the suitable IoT communication applications in the smart city environment to avoid the leakage of data transmission.

The paper introduces a novel hybrid network coding scheme in a three-node two-way relaying network in a wireless fading environment. The proposed hybrid scheme is featured by an opportunistic mechanism which employs one of the two network coding schemes, namely physical-layer network coding (PNC) and digital network coding (DNC), according to the instant rate requirement and dynamic channel condition. We first develop a close-form expression for the power consumption of the proposed scheme, which is further used to formulate a series of optimization problems under both symmetric and asymmetric traffic scenarios. Solving the optimization problems yields important system parameters that determine the optimal power allocations, time split between uplink and downlink transmissions as well as between PNC and DNC, respectively. We will discuss the possible overhead in the real implementation of the proposed scheme. Extensive numerical experiments are conducted to compare the performance of the proposed hybrid scheme with the conventional PNC and DNC scheme, respectively.

As the occurrence of cloud computing, the exponential growth of various application services results in the urgent demand for green computing and resource sustainability on the premise of guaranteeing service performance. Especially, Cloud Radio Access Network (CRAN) has been recognized as a promising approach to provide smart computing and sustainable resource usage for fulfilling the increasing traffic demand. Moreover, the secure network environment is also a vital element for achieving reliable application services. In this paper, we propose a cluster-based secure Cloud Radio Access Network (CSC-RAN), which optimizes the trade-off between performance and resource utilization to satisfy the requirements of the sustainable green network. Based on the powerful ability of cloud computing, the abundant network resource can be dynamically allocated according to the varying traffic. A traffic-aware RRHs cluster formation (TRCF) algorithm is proposed for realizing efficient resource utilization while improving the Quality of Service(QoS). Furthermore, a spectrum allocation genetic algorithm (SAGA) is introduced to solve the optimal spectrum allocation problem for the formatted cluster, which is proved to be a mixed-integer programming problem. Finally, the effectiveness of the TRCF and SAGA algorithms are verified through a series of numerical simulations.

Deploying femtocells has been taken as an effective solution for removing coverage holes and improving wireless service performance in 3G‐beyond wireless networks such as WiMAX and Long Term Evolution (LTE). This article investigates a novel framework of dynamic spectrum management for femtocell networks, called cognitive‐empowered femtocells (CEF), aiming at mitigating both cross‐tier and intratier interferences with minimum modifications required on the corresponding macrocell network. With the proposed framework, each CEF base station (BS) and the femtocell users can utilize spatiotemporally available radio resources for the access traffic. We conclude that the proposed CEF framework can effectively complement the existing femtocell design and serve as a value‐added feature to the state‐of‐the‐art femtocell technologies, while achieving high scalability and interoperability by minimizing the required modifications on the macrocell protocol design.

Cloud services are widely used to virtualize the management and actuation of the real-world the Internet of Things (IoT). Due to the increasing privacy concerns regarding querying untrusted cloud servers, query anonymity has become a critical issue to all the stakeholders which are related to assessment of the dependability and security of the IoT system. The paper presents our study on the problem of query receiver-anonymity in the cloud-based IoT system, where the trade-off between the offered query-anonymity and the incurred communication is considered. The paper will investigate whether the accepted worst-case communication cost is sufficient to achieve a specific query anonymity or not. By way of extensive theoretical analysis, it shows that the bounds of worst-case communication cost is quadratically increased as the offered level of anonymity is increased, and they are quadratic in the network diameter for the opposite range. Extensive simulation is conducted to verify the analytical assertions.

Multimedia transmissions for IoT (Internet-of-Things) sensing has a high demand of route capacity and tight requirements of end-to-end delay. In this paper, we address the problems on how to guarantee delay-related QoS requirements and to balance the energy consumption, while using multipath routing to offer high transmission capability for IoT multimedia sensing. This motivates us to design a Multipath Planning for Single-Source based transmissions routing scheme, namely MPSS, which establishes desirable multiple route paths following B-spline trajectories based on geographical information of source and sink node, sending and receiving angles, and inter-path distance. We further utilize a factor of hop distance to reduce the cumulated error of each hop due to the density of nodes, and to guarantee the delay-related QoS requirements. A Multipath Planning for Multi-Source routing scheme is also designed, namely MPMS, to assign the angle scope according to the source node's priority and traffic. Experimental results show that MPSS can effectively generate well-patterned multiple spline-based routes, and the end-to-end delay is under control according to the delay QoS requirement, while the total energy consumption is minimized.

Abstract With the widespread deployment of cloud services, data center networks are developing toward large‐scale, multi‐path networks. Conventional switching‐oriented data center network meets difficulties in terms of scalability and flexibility to support increasing bandwidth requirements for cloud services. To solve this problem, a simple and scalable architecture, MatrixDCN, is proposed in this paper. MatrixDCN is an approximate non‐blocking network, in which switches and servers are arranged in rows and columns that compose a matrix structure. A MatrixDCN network can accommodate up to hundreds of thousands of servers without bandwidth bottlenecks. Furthermore, the physical topology of a MatrixDCN network can be designed consistently with its logic topology, which helps to reduce the complexity of the management and maintenance of a data center. An efficient routing algorithm, named fault‐avoidance routing (FAR), is well designed for MatrixDCN to fully leverage the regularity in the topology. FAR builds two routing tables for a router. A BRT is built based on local topology, and a novel negative routing table (NRT) is increasingly built based on learned partial network failures, which really avoids the problem of network convergence and further shortens the calculating time of routing tables. FAR also greatly reduces the size of routing tables by introducing NRTs at routers. Theoretical analysis and simulations show that MatrixDCN has advantages on the scalability of topology, network throughput, and the performance of FAR. Copyright © 2015 John Wiley & Sons, Ltd.

With the explosive expansion of the information and communication technology (ICT) section, its energy saving has become an important issue and is receiving wide interest. In this study, we propose an adaptive clock frequency strategy for router cards to minimize the total energy consumption of an IP over WDM network. Rather than always running at full speed, the clock frequency of a router card is adaptively adjusted according to its actual-carried traffic demand. Given forecast traffic demand matrixes between different node pairs in different time slots, we develop a mixed integer linear programming (MILP) model to optimally choose the clock frequencies for each router card in different time slots such that the total energy consumption of the router cards is minimized. For lower computational complexity, the optimization model is also decomposed into two models, which correspond to the two subproblems of the optimization problem. The first subproblem minimizes the total number of router cards at each network node based on the peak-hour traffic, and the second subproblem optimally chooses the clock frequencies for each router card in different time slots. Due to the high-computational complexity of the MILP models, we also develop an efficient heuristic algorithm, in which two key steps that tackle the two subproblems are specifically developed. The joint MILP model provides a lower bound on the energy consumption, which shows to save more than 40% energy compared to the case without adaptive router card clock frequency. It is also found that the heuristic algorithm is efficient and performs close to the MILP models. In addition, the results also show that a router card supporting a small number of discrete clock frequencies can perform close to a card with continuously changed clock frequencies, and the benefit of adaptive clock frequency becomes weak with increasing router card power consumption overhead.

Sensing coverage is one of the most important performances of sensor networks, which characterizes how well a sensing area is monitored. Due to the limited energy supply, a minimized subset of sensor nodes should be selected to meet the requirements of coverage. Meanwhile to acquire accurate and rich information, hybrid sensor networks are designed to monitor multi-targets separately or cooperatively. In this paper, we consider the energy-efficient K-cover problem in hybrid sensor networks. First, the K-cover problem is investigated in the situation that each node is equipped with various types of sensors. Then, it is appropriately formulated as a coverage game and proved that the optimal solution is a pure Nash equilibrium. Finally, a new K-cover algorithm is designed based on game theory, where the different sensing ranges of sensors are fully considered. Simulating results validate that the proposed algorithm has high performance in coverage and can extend the network lifetime.

Facing the increasing demands and challenges in the area of chronic disease care, various studies on the healthcare system which can, whenever and wherever, extract and process patient data have been conducted. Chronic diseases are the long-term diseases and require the processes of the real-time monitoring, multidimensional quantitative analysis, and the classification of patients' diagnostic information. A healthcare system for chronic diseases is characterized as an at-hospital and at-home service according to a targeted environment. Both services basically aim to provide patients with accurate diagnoses of disease by monitoring a variety of physical states with a number of monitoring methods, but there are differences between home and hospital environments, and the different characteristics should be considered in order to provide more accurate diagnoses for patients, especially, patients having chronic diseases. In this paper, we propose a patient status classification method for effectively identifying and classifying chronic diseases and show the validity of the proposed method. Furthermore, we present a new healthcare system architecture that integrates the at-home and at-hospital environment and discuss the applicability of the architecture using practical target services.

Electrical switching, when used to interconnect tens to hundreds of pods (each having a thousand of servers) in the core of a data center, incurs a high cost and power consumption and is expected to be replaced with optical switching soon. In this paper, we consider hypercube-based interconnection using optical switches in the core and study novel routing and wavelength assignment schemes for a new paradigm called Labeled Optical Burst Switching with Home Circuits (LOBS-HC). In particular, we propose a simple scheme called complementary HC assignment (CHA) for a 2-dimensional cube and ring, and extend the study to a n-cube (n >; 2) and generalized hypercube (GHC) by applying the concept of Spanning Balanced Trees (SBTs). We determine the number of wavelengths (and transceivers) needed in each case and show that it can be significantly lower than that needed with conventional wavelength routing using optical circuit switching (OCS). We also show compared the proposed solution with other proposed electronic or hybrid switching based solutions.

To make an immune-inspired network intrusion detection system (IDS) effective, this paper proposes a new framework, which includes our avidity-model based clonal selection (AMCS) algorithm as core element. The AMCS algorithm uses an improved representation for antigens (corresponding to network access patterns) and detectors (corresponding to detection rules). In particular, a bio-inspired technique called gene expression programming (GEP) is integrated with artificial immune system (AIS) in detector representation. In addition, inspired by the avidity model of immunology, this paper also defines new avidity/affinity functions (corresponding to the metric for quantify the interactions between detector and antigens) that take the priorities of attribute into account. Accordingly, the proposed algorithm integrates both negative selection and positive selection with a balance factor k to assign appropriate weights to self and non-self avidity. The well known KDD CUP'99 DATA set is used for performance evaluation. The results show that the intrusion detection based on AMCS provides a higher detection rate of DoS attack, a lower false alarm rate, and a lower detectors generation cost. Our results indicate that breaking the bottleneck of immune-inspired network IDS through adjusting basic elements is feasible and effective.