Wildfire incidents have surged in frequency and severity in recent years highlighting the need for advanced technologies to predict wildfire behavior early and mitigate its impact. Recent strides in machine learning research, the increased availability of wildfire data, and computational resources have fueled the rise of data-driven approaches in wildfire management. This study aims to advance data-driven methods for predicting wildfire behavior and aid in timely decision-making and resource allocation efforts by adopting a Graph Neural Network (GNN)-based framework for predicting the burned area resulting from a wildfire ignition. GNNs have shown success in handling irregular-sized inputs and capturing the long-range dependencies inherent in geospatial data, such as wildfires, making them a viable alternative to CNNs which impose limitations on geospatial data due to their reliance on fixed-size inputs and local receptive fields. A framework is developed to represent spatial wildfire data and its influencing factors as graphs followed by the development of three distinct GNN models based on different message-passing mechanisms to process the graph-structured data. GNN models outperform CNN-based segmentation models in wildfire prediction, achieving higher AUPRC (0.4787), precision (0.4536), and AUROC (0.9377), and illustrating the efficacy of GNNs in modeling wildfire behavior by effectively capturing spatial dependencies.

In response to the growing incidence and severity of wildfires, this paper presents Vi-Net, a novel hybrid deep learning framework for next-day wildfire spread prediction. By integrating U-Net’s fine-grained spatial segmentation with the global contextual modeling of Vision Transformers (ViT), Vi-Net formulates wildfire spread prediction as a semantic segmentation task. The model is trained on a decade-long (2012–2020) multimodal wildfire dataset that integrates meteorological, topographical, and vegetation features. To address the severe class imbalance inherent in wildfire data, Vi-Net employs a Focal Tversky loss function. Experimental results show that Vi-Net achieves an F1-score of ∼97% and an Intersection over Union (IoU) of ∼94% on test data, significantly outperforming standalone U-Net and ViT models. These findings underscore Vi-Net’s potential to improve wildfire mitigation planning, resource allocation, and emergency response.

Traditional medical insurance post-claims processing relies heavily on manual efforts, increasing complexity and inefficiency. Similarly, verifying medical bills poses significant challenges for insurance companies in terms of data extraction and validation. To address these issues, we propose an advanced system that integrates Optical Character Recognition (OCR) for precise text extraction and Retrieval-Augmented Generation (RAG) with vector databases for efficient data retrieval. Furthermore, techniques for anomaly identification and fraud detection powered by AI improve accuracy while lowering the need for human interaction. According to experimental results, processing speed and accuracy have significantly increased, making it possible to detect inconsistencies in claims procedures. This method lowers expenses, improves post-claims processing efficiency, and decreases human mistakes by automating administrative activities and utilizing AI-driven solutions.

Large Language Models (LLMs) are increasingly prevalent in software engineering (SE) practice, yet their integration into education remains improvised and lacks theoretical grounding. This paper presents a quality-oriented framework for LLM integration in programming curricula, drawing on Cognitive Load Theory and Constructivism. We demonstrate its utility by using an LLM (Claude Sonnet 3.7) to redesign an Operating Systems assignment, followed by expert evaluation. Results show that LLMs can serve as tools for curriculum design and aids for student learning. For instructors, LLMs streamline the redesign of assignments while maintaining quality. For learners, they provide structured guidance and promote metacognitive development. We position LLMs as reusable educational components that can improve learning outcomes for students and streamline material design for instructors, while preparing both for professional environments where Artificial Intelligence (AI) collaboration is increasingly expected.

Forest fires or wildfires are becoming more prevalent across Canada. They are both beneficial and harmful. They promote forest health and aid ecological processes. However, they play a devastating role in impacting the economy of a nation and also impact the health of humans. Hence, it is important to consider all data sources relevant to forest fires or wildfires. The Canadian Wildland Fire Information System (CWFIS) calculates the danger of forest fires. The Canadian Forest Fire Weather Index (FWI) System is a critical part of CWFIS, which does not consider land vegetation in its calculations. Considering it is the vegetation that burns in a forest fire, it is important to have an insight into what types of vegetation are more prone to fires. Earth observation data for vegetation over land is now available across North America. This research primarily provides an analytical data visualization of the vegetation land cover impacted by and impacting forest fires. We look into open-source vegetation land cover data and provide insights into forest fires or wildfires. A look into the change of vegetation is also provided.

Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear program (MILP) that combines satellite CPU power, packet delivery rate (PDR), and end-to-end latency into a single objective, allowing a lightweight software-defined network (SDN) controller to steer traffic from the source node. Emulation of Telesat’s Lightspeed constellation shows that, compared with different routing schemes, the proposed design reduces the average CPU usage by 73%, maintains a PDR above 91% during traffic bursts, and decreases urgent flow delay by 18 ms between Ottawa and Vancouver. The results confirm Flex-Algo’s value as a slice-based traffic engineering (TE) tool for resource-constrained satellite networks.

Moving computing components from headsets to cloud servers is a promising approach to increasing headsets' lifespan and comfort for Extended Reality (XR) users. However, latency is an unavoidable challenge for XR services due to longdistance transmission, especially when haptic feedback (usually requires 1 ms latency) is involved. To address this challenge, we leverage the Latent Diffusion Model (LDM) to propose a novel transmission architecture, which can accurately generate the future potential haptic feedback from current or previous video frames. Moreover, we also introduce an acceleration architecture to accelerate the haptic feedback generation process. The simulations indicate that the lifespan of headsets can be tripled by moving computing resources to the cloud. In addition, our proposed architecture can accurately generate future potential haptic feedback at least 170 ms before contact, which can satisfy the 1 ms latency requirement of haptic feedback.

Service Function Chaining (SFC) has the potential to revolutionize cloud computing and containerization paradigm. SFC jobs, predominantly deployed as containers within cloud-native infrastructures, require substantial computing, memory, and storage resources to operate Virtual Network Functions (VNFs) efficiently. However, scheduling SFC workloads in cloud-native infrastructures, such as Kubernetes clusters spanning geographically distributed data centers, presents significant chal-lenges. These challenges arise not only from the diverse scheduling strategies employed by each data center but also from the vast number of jobs processed on a daily basis. Furthermore, SFC tasks have stringent requirements for computing resources and network bandwidth, which complicates the scheduling process. This paper introduces a machine learning-based (ML-based) scheduling approach to address these challenges by employing the Long Short Term Memory (LSTM) model. This paper makes two main contributions. First, we formulate the dynamic SFC mapping problem by modeling SFC jobs in terms of computing and memory resources. Then, we use a decentralized multiagent LSTM framework to predict the hardware resource usages of nodes for each incoming SFC job, in order to select the most suitable node to optimize system resource utilization and minimize the job wait time. Our results demonstrate a 6%-20% improvement in scheduling efficiency and accuracy compared to non-machine learning-based schedulers.

Indoor deployment with low cost and high capacity has shown to be a cost-effective solution in 5G wireless networks. In indoor 5G networks, Radio Dot (RD) units handle the wireless interfacing between UE devices and the core network. Strategic placement of indoor 5G RD units to ensure higher coverage of the space with optimal performance is challenging, since various factors could affect signal penetration, including floor plan, building materials, wall construction, frequency band, interference, dynamic factors like user density, etc. Most static parameters are well considered during the deployment stage, with deployment tools and network planning strategy. However, the dynamic impact of user density and distribution on channel quality and performance is still an open research area. With the user equipment (UE) positioning and channel quality indicator (CQI), the areas with poor channel quality data could be detected and further analyzed to dynamically determine RD locations and adjust accordingly for better network performance, without extra radio hardware costs introduced. This paper adopted the K-means clustering algorithm to evaluate the scenario where all the RD unit locations can be adjusted. Further, a Node Adjustment algorithm for RD units was proposed to improve indoor 5G network performance for a cost-efficient solution. The number of UEs and their distribution were simulated, and a comparative evaluation was conducted for different algorithms and various scenarios. The experimental results showed that considering dynamic information to adjust RD unit placements in a building could provide a cost-efficient solution to optimize indoor 5G network performance.

With the ever-expanding network-based services, environmental impact has become a concern, as the surge in network traffic between devices has only intensified in increased energy consumption. This paper aims to exploit Segment Routing over IPv6 (SRv6) for energy efficiency purposes for data forwarding. SRv6 is a traffic engineering mechanism that enables data packet steering using segments in IPv6 headers. The main idea of the paper is to use SRv6 with automatic rerouting of network traffic based on the resource usage of network devices for higher energy efficiency compared to the traditional IP forwarding based on the shortest path first (SPF) algorithm. The method and system outlined in this paper dynamically created network topologies within Mininet and performed SRv6 using the ROSE platform to route packets through the most energy-efficient paths, all while actively collecting device usages, calculating dynamic weights, computing energy-efficient paths, and rerouting the traffic using SRv6. This paper successfully achieved the goal of energy-aware traffic rerouting. The results showed that the resource usage for SRv6 could be more than 70% lower than that of the SPF-based forwarding, depending on the network topology.

Prevention of forest fires increasingly impacted by climate change is essential to maintain ecological balance, preserve natural resources, prevent economic loss, and protect human and animal life. Data for forest fires is available from multiple sources and is huge. Federated learning can be implemented to distribute the computing across multiple edge devices by saving transmission costs, protecting data privacy, and maintaining security with no single point of failure as local models exist across multiple resources in different geographic regions. The proposed framework extends the Spatio-Temporal Agnostic Subsampling (STAS) technique by distributing the data into multiple computation nodes to leverage federated learning. It was found that the models trained using federated learning on weather data gained on average 0.3 in F1 for classifying the occurrence of fire. This study also demonstrates how to optimally choose the sources of data for either predicting the occurrence of fire or the severity of fire.

Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning’s role in addressing forest fire challenges.

In this study, the Transportation Safety Board’s (TSB) Findings data was analyzed to assist Transport Canada Civil Aviation (TCCA) in better informing safety policy decision-making. As the TSB Findings data was unstructured, various methods to categorize and analyze unstructured data were explored in the existing literature. It was found that Knowledge Graphs (KGs), in combination with Deep Learning and Natural Language Processing (NLP) models, such as Neuralcoref and REBEL, were versatile and adaptable to different data needs, which could provide insights into the analysis of the TSB data. This paper first emulated and validated the KG pipeline using the BBC News dataset and then applied the KG pipeline technique to the unstructured TSB Findings Reports data consisting of 4,121 rows, each containing text for an incident or accident. The results showed that the model detected an average of 1.03 entities per row of the data and a total of 5,484 relationships or 1.33 relationships per row. Further, the top-four relationships in the graph database structure obtained from Neo4j accounted for 50% of all relations, though not all relations were found to be valuable. However, a few less-frequent relations were also found to be valuable due to their ability to capture critical components of aviation safety. The results of this data pipeline can be used for further analysis and categorization of TSB’s Findings data to improve aviation safety.

Fake news has become a major concern due to its spread on social media. To combat this, various machine learning (ML) techniques have been proposed. However, there is a lack of research on the performance of transformer models using datasets from a wide range of domains. This paper investigates the performance of ML algorithms on three fake news datasets: LIAR, FNC-1 and Balanced Dataset for Fake News Analysis. Pretrained transformer language models such as BERT, RoBERTa, ALBERT and DistilBERT were chosen for this paper. The performance of the models was consistent across all datasets. RoBERTa obtained an accuracy of 69% when trained on the LIAR dataset, an 11% improvement over the existing traditional and deep learning ML model implementations, and an accuracy of 97% when trained on the FNC-1 dataset, proving to be the best-performing model across all the fake news detection datasets utilized in the experiments. DistilBERT trains at a significantly faster rate than the other three variants. The experimental results from the paper can help the research community to continue investigating and gain insights into fake news detection.

This research provides a spatio-temporal agnostic framework based on subsampling to generate generic deep learning models using publicly available weather data and to predict the probability of forest fire and severity. The aim is to show that this framework can be used to subsample and generate a balanced dataset for generic deep learning models to improve predictions for forest fires. The framework works for binary classification and regression deep learning models. It also works with limited variations between fire and non-fire data. Using this framework, 45 of the binary classification models built produced an F1Score greater than 0.95 while 35 of 54 regression models produced an R2Score greater than 0.91.

The proliferation of technologies and unstructured data on the internet poses a persistent challenge in extracting valuable information from diverse formats. To address this, research leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques. This study contributes to information extraction from unstructured text using a state-of-the-art pipeline, incorporating modules for coreference resolution (Neuralcoref), named entity linking (Wikifier API), and Relationship Extraction (RE) (OpenNRE and REBEL models). The resulting Knowledge Graph (KG) in Neo4j captures entity relationships. Experiments on a BBC news dataset analyzed the pipeline’s performance, focusing on RE. Accuracies of 61.4% (OpenNRE) and 87% (REBEL) were achieved. The research demonstrates the efficacy of the proposed pipeline in extracting structured knowledge from unstructured data, facilitating the preservation and utilization of valuable information.

Handover management is crucial for wireless networks and is more challenging for Fifth Generation (5G) networks due to strict requirements in quality of service (QoS), such as ultra-reliable low latency communications (URLLC) services. This paper extends the pre-connect handover (PHO) mechanism for user equipment (UE) using Deep Q-Networks (DQN) to support challenging handover management requirements in 5G networks. The proposed DQN-assisted PHO management facilitates the sequential decision-making problem of the target cell selection based on the Reference Signal Received Quality (RSRQ) values and RSRQ change rates of all the candidate cells. The performance of the DQN-assisted PHO management solutions has been evaluated extensively with various configurations using Network Simulator 3 (NS-3) and NS3-Gym. The experimental results demonstrated the DQN-assisted PHO technique can productively accomplish the optimal target cell selection to maximize the success rate of PHO.

The Internet of Things (IoT) has already emerged as one of the most popular directions in today’s information and communication technology (ICT) domain. With its advancement over different application areas, such as smart home, smart healthcare, industry 4.0, etc., a huge amount of data has been generated by billions of IoT devices, which aggravates the shortcomings of the network layer (IP)-based networks, such as limited expressiveness of IP addressing, inefficient support for mobility, and in-network caching. Building IoT on top of information-centric networking (ICN) is believed to be a promising solution to tackle the above challenge, especially the in-network caching of ICN can significantly benefit IoT in terms of reducing data and saving IoT devices’ energy. However, caching IoT data is more challenging than caching traditional Internet content, e.g., video, because IoT data are usually valid within a certain period of time, and IoT devices are typically constrained with battery. Hence, in this survey, we first review the current implementation proposals of ICN-based IoT (ICN-IoT). Next, we present the conventional caching decision policies and replacement policies which could be adopted to mitigate the aforementioned challenges, e.g., reducing IoT traffic, saving energy, and reducing data retrieval latency. Further, since leveraging machine learning (ML) techniques have the potential to further improve the caching efficiency by dealing with uncertainties, e.g., predicting unknown information, adaptively interacting with the environment, we also demonstrate the recently proposed ML-based caching schemes for ICN-IoT. In addition, we outline the open research issues and point out the future opportunities of caching in ICN-IoT.

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep$Q$-networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).

The impact of the pandemic on the airline industry has been severe. Various factors such as lockdowns, travel bans, travel restrictions and passenger footfall led to changes in the airfare. This is not limited to a few years of the pandemic as there is a possibility of a similar situation recurring in the future. To address this situation and to assess future possibilities, this paper is an attempt to apply multi-agent simulation and modeling on airfare in pandemic conditions. The objective of this paper is to develop a multi-agent model for airfare during the pandemic. We also ran simulation on the developed model based on the pandemic information available from news articles. The proposed multi-agent model has long-term utility and can be used by the airline industry, the travelers, the governments, academia, and research organizations.

Energy efficiency for network devices becomes an important topic, as they consume a significantly amount of energy. Various techniques have been proposed to address energy-aware traffic engineering (TE), including Segment Routing (SR) and Software-defined Networking (SDN), which provide lower operational complexity and higher flexibility. However, existing approaches have not exploited some evolving SR and SDN features for efficient TE, e.g., path computation, sub-50 msec protection, and local/global segments. Consequently, those approaches result in higher complexity or extra overhead. This paper provides a holistic view of green TE using evolving SDN and SR-specific features without adding much additional computational tasks, and also considers SR segment processing overhead for energy efficiency. The proposed approach can simplify green TE by reusing SR features and improve energy efficiency and robustness.

The research community has been moving attention from Virtual Network Function (VNF) to Cloud-native Network Function (CNF) since cloudification has brought the Network Function Virtualization (NFV) to an advanced level. It has already been demonstrated that cloud-native technology brings high flexibility and efficiency to large-scale network service deployment compared to the traditional VNF with Virtual Machines (VMs). However, more work is needed to provide a flexible and reliable Service Function Chaining (SFC) development solution in a cloud-native environment. This paper proposes a web-based orchestrator system to deploy an SFC use case consisting of multiple CNFs in a multi-node Kubernetes cluster using Network Service Mesh (NSM). We demonstrate a cloud-native SFC framework that allows users to dynamically create container-based SFC rather than the traditional VMs with NFV/SDN controller approach. Further, additional work is presented with the support of an open-source monitoring system, Prometheus, to validate the SFC path.

Considered the preferred routing protocol for many Internet of Things (IoT) networks, the routing protocol for low-power and lossy networks (RPL) incorporates three security modes to protect the integrity and confidentiality of the routing process: 1) the unsecured mode (UM); 2) preinstalled secure mode (PSM); and 3) the authenticated secure mode (ASM). Both PSM and ASM were originally designed to protect against external routing attacks, in addition to some replay attacks (through an optional replay protection mechanism). However, recent research showed that RPL, even when it operates in PSM, is still vulnerable to many routing attacks, both internal and external. In this article, a novel secure mode for RPL, the chained secure mode (CSM), is proposed using the concept of intraflow network coding (NC). The CSM is designed to enhance RPL’s resiliency and mitigation capability against replay attacks. In addition, CSM allows the integration with external security measures such as intrusion detection systems (IDSs). An evaluation of the proposed CSM, from a security and performance point of view, was conducted and compared against RPL in UM and PSM (with and without the optional replay protection) under several routing attacks: the neighbor attack (NA), wormhole (WH), and CloneID attack (CA), using average packet delivery rate (PDR), end-to-end (E2E) latency, and power consumption as metrics. It showed that CSM has better performance and more enhanced security than both the UM and PSM with the replay protection while mitigating both the NA and WH attacks and significantly reducing the effect of the CA in the investigated scenarios.

Google’s QUIC (GQUIC) is an emerging transport protocol designed to reduce HTTP latency. Deployed across its platforms and positioned as an alternative to TCP+TLS, GQUIC is feature rich: offering reliable data transmission and secure communication. It addresses TCP+TLS’s (i) Head of Line Blocking (HoLB), (ii) excessive round-trip times on connection establishment, and (iii) entrenchment. Efforts by the IETF are in progress to standardize the next generation of HTTP’s (HTTP/3, or H3) delivery, with their own variant of QUIC. While perfor-mance benchmarks have been conducted between GQUIC and HTTP/2-over-TCP (H2), few analyses, to our knowledge, have taken place between H2 and H3. In addition, past studies rely on Page Load Time as their main, if not only, metric. The purpose of this article is to benchmark the latest draft specification of H3 and dig into a user’s Quality of Experience (QoE) by using Lighthouse: an open-source (and metric diverse) auditing tool. Our findings show that, for one of H3’s early implementations, H3 is mostly worse but achieves a higher average throughput.

The current standard of Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM). While the PSM and ASM are intended to protect against external routing attacks and some replay attacks (through an optional replay protection mechanism), recent research showed that RPL in PSM is still vulnerable to many routing attacks, both internal and external. In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intra-flow Network Coding. The main goal of CSM is to enhance RPL's resilience against replay attacks, with the ability to mitigate some of them. The security and performance of a proof-of-concept prototype of CSM were evaluated and compared against RPL in UM and PSM (with and without the optional replay protection) in the presence of Neighbor attack as an example. It showed that CSM has better performance and more enhanced security compared to both the UM and PSM with the replay protection. On the other hand, it showed a need for a proper recovery mechanism for the case of losing a control message.

As the routing protocol for low power and lossy networks (RPL)s became the standard for routing in the Internet-of-Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in RPL's standard, mainly because there was no implementation for these features in any Internet of Things (IoT) operating systems yet. A partial implementation of RPL's security mechanisms was presented recently for the Contiki operating system (by Perazzo et al.), which provided us with an opportunity to examine RPL's security mechanisms. In this article, we investigate the effects and challenges of using RPL's security mechanisms under common routing attacks. First, a comparison of RPL's performance, with and without its security mechanisms, under four routing attacks [Blackhole, Selective-Forward (SF), Neighbor, and Wormhole (WH) attacks] is conducted using several metrics (e.g., average data packet delivery rate, average data packet latency, average power consumption, etc.). This comparison is performed using two commonly used radio duty-cycle protocols. Second, and based on the observations from this comparison, we propose two techniques that could reduce the effects of such attacks, without having added security mechanisms for RPL. An evaluation of these techniques shows improved performance of RPL under the investigated attacks, except for the WH.

The considerable increase of multimedia services, such as video-on-demand (VoD) services, is a significant contributor to the total Internet traffic. Software-defined networking (SDN) and information-centric networking (ICN) are two promising technologies that can be combined to facilitate video delivery and to reduce network delays. In this paper, we first formulate the caching decision problem as a 0-1 integer linear programming (ILP) problem. Second, in contrast to existing approaches that solve the formulated ILP problem by assuming all future video requests are known, we consider the impact of the time scale, which transforms the static 0-1 ILP problem into a dynamic problem. By solving the dynamic 0-1 ILP problem, we find more accurate optimal solutions compared to existing approaches. Third, since the formulated 0-1 dynamic ILP problem is NP-hard, we leverage the in-network caching of ICN and the global view of the SDN controller to propose a novel SDN-based caching decision policy. Finally, extensive evaluations are performed, and the results demonstrate that the proposed SDN-based caching decision policy provides solutions that are close to the optimum in substantially less computation time. The SDN-based caching decision policy also outperforms existing practical ICN caching decision policies in terms of the cache hit ratio and the average number of hops, which are directly related to the video delivery latency. Moreover, the SDN-based caching decision policy can substantially reduce the number of generated and broadcasted interest packets, which is a shortcoming of the current ICN.

As the Routing Protocol for Low Power and Lossy Networks (RPL) became the standard for routing in the Internet of Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in the protocol's standard, due to the fact that there was no implementation for these features in any IoT operating system yet. A partial implementation of RPL's security mechanisms was presented recently for Contiki operating system (by Perazzo et al.), which provided us with the opportunity to examine RPL's security mechanisms. In this paper, we investigate the effects and challenges of using RPL's security mechanisms under common routing attacks. First, a comparison of RPL's performance, with and without its security mechanisms, under three routing attacks (Blackhole, Selective-Forward, and Neighbor attacks) is conducted using several metrics (e.g., average data packet delivery rate, average data packet delay, average power consumption... etc.) Based on the observations from this comparison, we come up with few suggestions that could reduce the effects of such attacks, without having added security mechanisms for RPL.

Since multimedia services will become fundamental in the upcoming 5G networks, how to improve the user quality of experience (QoE) is becoming a major challenge. In this paper, we integrate the concept of Information-Centric Networking (ICN) to the infrastructure of 5G networks. Due to the in-network caching feature of ICN, proactive caching can be beneficial in 5G networks. More precisely, this paper introduces a novel proactive caching approach (called smart caching) which leverages the non-negative matrix factorization (NMF) technique to predict the future ratings of user preferences on all videos for 5G-ICN networks. To solve the shortcoming of the NMF technique that generates inaccurate predictions for high rated but unpopular videos, we also take video historical popularity into consideration. Thus, the user future demands can be predicted based on the user preferences (i.e. the predicted ratings) and the historical popularity of videos. Simulation results show that the proposed smart caching outperforms existing approaches in terms of hit ratio, average video retrieval delay, and user satisfaction.

The motivation behind Software-Defined Networking (SDN) is to allow services and network capabilities to be managed through a central control point. Moving Target Defense (MTD) introduces a constantly changing environment in order to delay or prevent attacks on a system. For the effective use of MTD, SDN can be used to help confuse the attacker from gathering legitimate information about the network. This paper investigates how SDN can be used for some network based MTD techniques and evaluate the benefits of integrating techniques in SDN and MTD. In the experiment, network assets are kept hidden from inside and outside attackers. Furthermore, the SDN controller is programed to perform IP mutation to keep changing real IP addresses of the underlying hosts by assigning each host a virtual IP address at a configured mutation rate to prevent attackers from stealing the real IP addresses or using fake IP addresses. The paper demonstrates experimental evaluation of the MTD technique using the Ryu controller and mininet. The results show that the MTD technique can be easily integrated into the SDN environment to use virtual IP addresses for hosts to reduce the chance of poisoning attacks.

Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

The growing interest in the smart device/home/city has resulted in increasing popularity of Internet of Things (IoT) deployment. However, due to the open and heterogeneous nature of IoT networks, there are various challenges to deploy an IoT network, among which security and scalability are the top two to be addressed. To improve the security and scalability for IoT networks, we propose a Software-Defined Virtual Private Network (SD-VPN) solution, in which each IoT application is allocated with its own overlay VPN. The VPN tunnels used in this paper are VxLAN based tunnels and we propose to use the SDN controller to push the flow table of each VPN to the related OpenvSwitch via the OpenFlow protocol. The SD-VPN solution can improve the security of an IoT network by separating the VPN traffic and utilizing service chaining. Meanwhile, it also improves the scalability by its overlay VPN nature and the VxLAN technology.

Network traffic anomaly may indicate a possible intrusion in the network and therefore anomaly detection is important to detect and prevent the security attacks. The early research work in this area and commercially available Intrusion Detection Systems (IDS) are mostly signature-based. The problem of signature based method is that the database signature needs to be updated as new attack signatures become available and therefore it is not suitable for the real-time network anomaly detection. The recent trend in anomaly detection is based on machine learning classification techniques. We apply seven different machine learning techniques with information entropy calculation to Kyoto 2006+ data set and evaluate the performance of these techniques. Our findings show that, for this particular data set, most machine learning techniques provide higher than 90% precision, recall and accuracy. However, using area under the Receiver Operating Curve (ROC) metric, we find that Radial Basis Function (RBF) performs the best among the seven algorithms studied in this work.

The use of cloud computing is on the rise because of the cost effectiveness in providing the same resources to different tenants. Highly customizable Software-as-a-Service (SaaS) provide high scalability and lower cost as a result of multitenant nature of its cloud applications. However, there are several challenges that make its adoption difficult. Some of the weaknesses of current cloud offerings are complex configuration, development, deployment and management. This paper, investigates the different techniques and methods such as adaptation and variation management used to address these challenges. In addition, the details about how techniques from Software Product Line Engineering and Service Oriented Architecture are applied are addressed. Therefore, the paper extends an existing framework for constraint-based configuration management for cloud applications by integrating existing feature modeling tools with a XML filtering tool –Yfilter. The objective of the integration is to automate the process to identify matched tenant-specific requirements with the SaaS cloud application feature model. The automated process results in lower manual efforts and possible errors, and as a result less complex deployment and management of the SaaS application.

This paper proposes a novel ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and an application of adaptive selection of the most suitable model for each data instance. Ensemble methods, an important type of machine learning technique, have drawn a lot of attention in both academic research and practical applications, and they use multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. The proposed approach in this paper based on a hybrid model has been validated on Repeat Buyers Prediction dataset, and the experiment results show up to 18.5% improvement on F1 score, compared to the best individual model. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score.

This paper investigates the impact of the database layer on the scaling actions of the business layer of a 3-tier web service system in cloud resource provisioning. The research question is "What is the impact of the database layer on the business layer auto-scaling decisions?" In this work two hypotheses are tested: 1) "Database tier capacity has no effect on the business tier scaling decisions" and 2) "Scaling up of a database tier increases Service Level Agreement (SLA) violations." To test the hypotheses, an auto-scaling simulation package based on Queuing Network Models (QNM) and Layered Queuing Network Models (LQNM) is developed. The auto-scaling simulation package is used to investigate the database impact on the business tier scaling decisions in the cloud environments with three different workload patterns (growing, periodic, and unpredictable patterns). This paper also provides an analytical investigation that empirically validate the hypotheses. The results suggest that the database tier has no effect on the business tier scaling decisions. However, decreasing the capacity of the database layer increases the rate of the SLA violations.

The growing popularity of mobile devices and Internet of Things (IoT) has inspired the advent of the Cloudlet concept-a "small data center" close to users at the edge. It is believed that the Quality of Experience (QoE) of end users would greatly improve if they can access required resources within a one-hop distance from Cloudlet servers. Over the years, many researchers have proposed using virtual machines (VMs) as such service-provisioning servers. However, seeing the potentiality of containers-a lightweight virtualization tool, this paper adopts LXC containers as Cloudlet platforms. To facilitate container migration between Cloudlets, CRIU (Checkpoint/Restore in Userspace) has been chosen as the migration tool. Since the migration process goes through the Wide Area Network (WAN), which may experience congestion or network failures, this paper adopts the MPTCP (Multipath TCP) protocol to address the challenge. The multiple subflows established within a MPTCP connection can improve the resilience of the migration process and reduce migration time. We have conducted a number of experiments to validate the proposed approach. The experimental results show that LXC containers are suitable candidates for the problem and MPTCP protocol is effective in enhancing the migration process.

Information centric networking (ICN) is one of the emerging Internet paradigms proposed to overcome the shortcoming of the current host-centric Internet. With ubiquitous in-network caching, ICN can facilitate content delivery and reduce network delay. In this paper, we propose a novel collaborative caching scheme based on routers' position to cache popular videos on the edge routers which are closer to users. A priori knowledge of videos' popularity is not required as the proposed scheme adapts itself to the user requests. The benefits of our proposed approach are: light-weight, short content delivery time, and reduced network usages and publisher load. We use a simple topology to show how the proposed scheme works. Then, we use a realistic topology with real data traces to evaluate the performance of the proposed scheme. Simulation results show that our scheme outperforms existing schemes in terms of average number of hops and reduced publisher load ratio for both scenarios.

Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time responses. Also, it is necessary to decide what type of data has to be uploaded to the cloud, without burdening the core network and the cloud. For this purpose, the cloud on the edge of the network, known as Fog or Micro Datacenter (MDC), plays an important role. Fog resides between the underlying Internet of Things (IoTs) and the mega datacenter cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. To achieve this, Fog requires an effective and efficient resource management framework, which we propose in this paper. Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can stop using resources at any moment. In our proposed methodology for resource estimation and management through Fog computing, we take into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. With the intent of showing practical implications of our method, we implemented it on Crawdad real trace and Amazon EC2 pricing. Based on various services, differentiated through Amazon's price plans and historical record of Cloud Service Customers (CSCs), the model determines the amount of resources to be allocated. More loyal CSCs get better services, while for the contrary case, the provider reserves resources cautiously.

Software-defined networking (SDN) has received tremendous attention from both industry and academia. The centralized control plane in SDN has a global view of the network and can be used to provide more effective solutions for complex problems, such as traffic engineering. This study is motivated by recent advancement in SDN and increasing popularity of multicasting applications. We propose a technique to increase the resiliency of multicasting in SDN based on the subtree protection mechanism. Multicasting is a group communication technology, which uses the network infrastructure efficiently by sending the data only once from one or multiple sources to a group of receivers that share a common path. Multicasting applications, e.g., live video streaming and video conferencing, become popular, but they are delay-sensitive applications. Failures in an ongoing multicast session can cause packet losses and delay, which can significantly affect quality of service (QoS). In this study, we adapt a subtree-based technique to protect a multicast tree constructed for OpenFlow switches in SDN. The proposed algorithm can detect link or node failures from a multicast tree and then determines which part of the multicast tree requires changes in the flow table to recover from the failure. With a centralized controller in SDN, the backup paths can be created much more effectively in comparison to the signaling approach used in traditional multiprotocol label switching (MPLS) networks for backup paths, which makes the subtree-based protection mechanism feasible. We also implement a prototype of the algorithm in the POX controller and measure its performance by emulating failures in different tree topologies in Mininet.

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.

Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive auto-scaling systems are divided into Predictor and Decision-maker components, and experiments have been conducted to measure the influence of the Predictor results on the Decision-maker output. We have used Support Vector Machine (SVM) and Neural Networks (NN) as the Predictor component and the threshold-based technique as the Decision-maker component. In addition, the influence of different workload patterns on the prediction accuracy of SVM and NN has been investigated in this paper. The experiment results show that although either of the prediction algorithms (i.e., SVM and NN) is suitable for a specific workload pattern, SVM and NN predictions lead to similar scaling actions in 97.9% of time, which suggests that focus should be on the decision-maker techniques.

Techniques using a nearby virtual machine (VM) based cloudlet have been proposed for mobile cloud computing (MCC) to enhance the performance of real-time resource-intensive mobile applications. After a mobile device (MD) discovers a cloudlet in the vicinity, it takes time to set up a VM inside the cloudlet before data offloading from the MD to the VM starts. The time between discovering the cloudlet and actual offloading of data is considered as a service initiation time. When multiple cloudlets are presented in a nearby geographical location, initiating a service with each cloudlet may be frustrating for cloudlet users when changing location. In order to eliminate the delay caused by the service initiation time after moving away from the source cloudlet, this paper proposed a seamless live VM migration between neighbour cloudlets. A seamless live VM migration is achieved with the prior knowledge of the migrating VM IP address in the destination cloudlet and more importantly with multipath TCP (MPTCP). We have performed a number of experiments to validate the proposed approach using the Linux KVM hyper visor. The experimental results demonstrate the feasibility of the proposed approach and also performance improvement. Specifically, there is zero downtime at the destination cloudlet after the migration is completed.

A significant proportion of the operational cost for datacenters is attributed to their energy consumption. Using advanced virtualization techniques in datacenters is enabling the control of electricity use in servers. However, as servers are becoming more energy-proportional, datacenter networks are starting to consume a greater portion of overall power although networks devices often remain under-utilized. This paper proposes an energy aware management technique for reducing the consumption of energy by the network for a Spine-Leaf topology-based datacenter. The main idea of the system is to keep track of the dynamic workload and enable only switches that are necessary for handling the current network traffic. We have developed an energy aware management system for dynamically controlling the number of Spine switches in Spine-Leaf datacenter networks and performed simulation using CloudSim for a number of scenarios. The simulation results show that the system can work effectively to save energy by as much as 63% of the energy consumed by a datacenter comprising a fixed static set of Spine switches.

Fuzzy C-means (FCM) clustering has been used to distinguish communication network traffic outliers based on the uncommon statistical characteristics of network traffic data. The raditional FCM does not leverage spatial information in its analysis, which leads to inaccuracies in certain instances. To address this challenge, this paper proposes an adaptive fuzzy clustering technique based on existing possibilistic clustering algorithms. The proposed technique simultaneously considers distance, density, and the trend of density change of data instances in the membership degree calculation. Specifically the membership degree is quickly updated when the distance or density is beyond the pre-defined threshold, or density change does not match the data distribution. In contrast, the traditional FCM updates its membership degree only based on the distance between data points and the cluster centroid. The proposed approach enables the clustering to reflect the inherent diversity nature of communication network traffic. Further, an adaptive threshold is introduced to speed up the iterative clustering process. The proposed algorithm has been evaluated via experiments using traffic from a real network. The results indicate that the adaptive fuzzy clustering reduces false negatives while improves true positive results.

The major advancement in distributed and High Performance Computing (HPC) systems is the development and evolution of clouds, applications that operate these clouds, and services provided by them. Cloud computing applications are expected to facilitate running complex systems on data centers containing storage and computing units in the range of tens to hundreds of thousands of devices. Meeting the needs of cloud computing systems makes the software deployment process a challenging task. The challenge comes from difficulty in managing the tradeoffs over various dimensions, such as interaction, performance, and security while making deployment decisions. Making deployment decisions exceeds human capability in light of huge increase in computation/storage units in the clouds and software systems running on these clouds. Therefore, autonomic approaches to assist software designers in making the software deployment decisions are important. In this paper, we propose an approach based on clustering techniques for deploying software components on the cloud using requirements decomposition. The paper also demonstrates a validation study of the proposed approach with a case study.

Smart Home minimizes user's intervention in monitoring home settings and controlling home appliances. This paper presents an approach to the development of Smart Home applications by integrating Internet of Things (IoT) with Web services and Cloud computing. The approach focuses on: (1) embedding intelligence into sensors and actuators using Arduino platform, (2) networking smart things using Zigbee technology, (3) facilitating interactions with smart things using Cloud services, (4) improving data exchange efficiency using JSON data format. Moreover, we implement three use cases to demonstrate the approach's feasibility and efficiency, i.e., measuring home conditions, monitoring home appliances, and controlling home access.

While applying clustering techniques to software system decomposition, the software designer faces two practical issues: (1) determination of the number of clusters that will be mapped to software modules and (2) determination of a specific cluster or software module for some highly coupled components. This paper presents an approach for software architecture decomposition with an emphasis on finding solutions to those two issues. The approach uses fuzzy c-means clustering together with three hierarchical agglomerative clustering methods and the adaptive K-nearest neighbor algorithm. We applied the approach to real industrial software systems. The results show that our approach provides objective and insightful information to the software designer in dealing with those two issues.

The rising complexity of network anomalies necessitates increased attention to developing new techniques for detecting those anomalies. The majority of current network and security monitoring tools utilize a signature-based approach to detect anomalies. This approach must be complemented with other methods to widen the coverage and speed of anomaly detection. In recent years, a great deal of effort has been spent on studying network traffic anomaly detection techniques by security researchers. Those techniques include the statistical analysis technique referred to as PCA (Principal Component Analysis), clustering and Wavelet-based spectral analysis of network traffic. This paper makes three key contributions to advance the state of the art in network traffic anomaly detection. First, we study the effectiveness of PCA and Wavelet algorithms in detecting network anomalies from a labeled data set known as Kyoto2006+ - providing a useful baseline for future researchers. Second, we propose a novel anomaly detection approach based on a hybrid PCA-Haar Wavelet analysis methodology. The hybrid approach uses PCA to describe the data and Haar Wavelet filtering for analysis. Finally, we study the impact of applying the techniques solely to flow-based traffic summary data to detect network anomalies. The experimental results demonstrate an improved accuracy of the hybrid approach in comparison with the two algorithms individually.

Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward operation, suffered from the limitations of the max-flow min-cut theorem wherein sources transmitting information over bottleneck links had to compete for access to these links. With Network Coding, multiple sources are now able to transmit packets over bottleneck links simultaneously, increasing network capacity. While the majority of the contemporary literature has focused on the performance of Network Coding from a capacity perspective, the aim of this research has taken a new direction focusing on two Quality of Service metrics, Packet Delivery Ratio (PDR) and latency, in conjunction with Network Coding protocols in Mobile Ad-Hoc Networks (MANETs). Initial simulations will be performed on static environments to determine a Quality of Service baseline comparison between Network Coding protocols and traditional ad-hoc routing protocols. Additional simulations will then be performed for mobile scenarios to determine how the Network Coding protocols will compare to that of the standard ad-hoc routing protocols in the presence of mobility.

This paper addresses the Quality of Service (QoS) degradation problem of wireless relay node failures in multihop Worldwide interoperable Microwave Access (WiMAX) and Long Term Evolution (LTE) networks. In general, node protection in a communication network increases the reliability of the traffic flow from source to destination. For wireless networks, implementing the traditional 1+1 protection scheme increases the capital cost, as resources cannot be fully utilized. On the other hand, the design and implementation of the 1:N protection scheme in wireless networks for greater value of N (> 2) is very difficult, due to wireless network coverage and the physical layer design. To overcome the above drawbacks, a technique based on network coding is proposed for node protection in multihop wireless networks. In multihop wireless networks, few research efforts are concentrated on node protection schemes using network coding. Moreover, to best of our knowledge, no reports have been published on measuring the QoS performance for node protection in 4G wireless networks using network coding. In this paper, we first describe the node protection scheme using XOR network coding for multihop 4G wireless networks. Then, we measure the QoS performance, such as packet delivery ratio (PDR), latency and jitter, for different scenarios. The scenarios include a failure of a single relay node and failures of two relay nodes with and without our protection scheme and user's mobility. The simulation results show that the QoS performance with protection against a single relay node failure and failures of two relay nodes are very close to that of no failure scenario, while the network reliability increases due to the protection mechanism. Also, we report a decoding problem in multihop wireless networks using XOR network coding.

Clustering is one of the most energy-efficient methods to organize sensor nodes in Wireless Sensor Networks (WSNs). To perform clustering, location data are usually used for calculating the distance between sensor nodes. But location data may not always be available due to Global Positioning System (GPS) failures or may not be practical in consideration of cost. Alternatively, Received Signal Strength (RSS) or RSS Indicator (RSSI) is used as the distance estimator, but it has been showed that RSS or RSSI is unreliable in many studies. In order to mitigate these problems, we propose a hybrid clustering protocol - Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) - which uses both quantitative location data and binary qualitative connectivity data in clustering for WSNs. Our simulation results show that H-DHAC has a lower percentage of compromise in performance in terms of network life time and total transmitted data compared to similar approaches that use complete location data. However, H-DHAC still outperforms the well known clustering protocols, e.g., LEACH and LEACH-C.

This paper proposed a Distributed Address Resolution Protocol (DARP) for large layer 2 networks used in a data center. As data centers continue to grow in size, there is an increased amount of overhead required to resolve network addresses using the traditional Address Resolution Protocol (ARP). The DARP attempts to reduce this overhead for large data centers with thousands of nodes and allow for the resolution of network address with minimal strain on the underlying network infrastructure. By using Distributed Hash Tables (DHTs) as the core data structure technology to maintain address records, we attempt to create a decentralized and reliable service that trades the sporadic overhead associated with current approaches with a consistent and predictable overhead. To determine the viability of the protocol, a series of simulations were developed and run via the OPNET Modeler software package. The simulation results demonstrate that DARP outperforms ARP by reducing the number of messages. Data centersaddress resolution protocoldistributed hash tables.

One of the fundamental components in cognitive radios (CRs) is spectrum sensing. For sensing the wide range of frequency bands, CRs need high sampling rate analog to digital converters (ADCs) which have to operate at or above the Nyquist rate. The high operating rate constitutes a major implementation challenge. Compressive sensing (CS) is a method that may overcome this problem. Sub-Nyquist rate can be used for CS recovery algorithms such as ℓ1-minimization. While boundary information of all frequency sub-bands is available, a more efficient recovery algorithm based on ℓ2/ℓ1-minimization can be used instead of ℓ1-minimization. In cognitive radio systems, network coding could be used for primary users (PUs) to increase packet transmissions. Furthermore, network coding provides a structure for vacant sub-bands of spectrum and makes the spectrum more predictable. Using this information that network coding provides us, we combine ℓ1-minimization and ℓ2/ℓ1-minimization algorithms with network coding for compressive spectrum sensing. Our methods require reduced signal sampling rate and result in improved false alarm (FA) and missed detection (MD) probabilities for idle band detection.

The problem of assignment of K identical servers to a set of N symmetric parallel queues is investigated in this paper. The parallel queueing system is considered to be time slotted and the connectivity of each queue to each server is varying randomly over time and following Bernoulli distribution with a given parameter. Each server is capable of serving at most one packet per time slot (if it is connected and assigned to a queue). At any time slot, each server can serve at most one queue and each queue can be served by at most one server. We assume that the service of a scheduled packet by a connected server fails randomly with a certain probability. The packet arrival processes to the queues are assumed to be i.i.d. and follow Bernoulli distribution with a fixed parameter. For such a symmetric system, i.e., with the same arrival, connectivity and service failure parameters for all the queues, we show that Maximum Weighted Matching (MWM) server assignment policy is delay optimal. More specifically, using stochastic ordering and dynamic coupling techniques we prove that MWM minimizes, in stochastic ordering sense, a broad range of stochastic cost functions of the queue lengths including total queue occupancy (or equivalently average queueing delay).

A Smart grid is characterized by two-way flows of power in electrical networks and information in communication networks [1]. The characteristics of communications in a smart grid vary for different applications that exist between control centers and power generating stations, distribution stations and consumer areas. For effective communications, proper communication network design and the selection of technology is essential. In this paper, we analyze the communication requirements of smart grids in electrical power distribution areas and in consumer places. As the communication in a Distribution Area Network (DAN) integrate the AMIs payload from the consumer area, it is necessary to analyze the data flow from the consumer area to control centers through a DAN. Few communication technologies are available to implement the DAN. Among that, WiMAX and LTE are more suitable than other technologies, as they satisfy the communication requirements and the cost. Little research has been conducted on analyzing and simulating the DAN by considering various applications that exist in a DAN. In this paper, we measure the smart metering capacity and the Quality of Service (QoS) performance (packet loss and latency) of DAN using WiMAX technology. From the obtained results, we also suggest that the 4G technologies are (WiMAX and LTE) more suitable candidate than existing cellular and wire line technologies for implementing the DAN in smart grids.

Selection of Call Admission Control (CAC) and packet scheduling are crucial for multihop WiMAX networks to satisfy the Quality of Service (QoS) for end users. In this paper, we propose an adaptive CAC method and two different scheduling schemes for multihop WiMAX networks. The proposed CAC in the multihop Base Station (BS) reserves some bandwidth (BW) for the mobile users and changes the BW reservation adaptively based on most recent requests from the handover users. When there are few or no handover users exist in a network, the remaining reserved BW is allocated to low priority Best Effort (BE) users for effective BW utilization. While admitting the New Calls (NCs) or Handover Calls (HCs), the BS verifies both BW and multihop delay requirements to satisfy the QoS of the call. Next, we propose two downlink scheduling algorithms (P+E) and (P+TB) for the BS in multihop networks. The (P+E) scheduler combines the Priority and Earliest Due Date (EDD) scheduling methods, while the (P+TB) scheduler combines the Priority and Token Bucket (TB) scheduling methods. When the network is lightly or moderately loaded, the (P+E) scheduler performs well for both single and multihop users but the performance of real time services are highly affected under high load conditions. On the other hand, the (P+TB) scheduler has very good QoS performance for real time services under high load conditions and also has closer QoS performance to (P+E) scheduler for multihop users under low and moderate load conditions. The simulation results for CAC show that the proposed CAC have lower Call Drop Probability (CDP) for HCs than existing fixed BW reservation policy. The CAC admits more NCs when fewer number of HCs arriving at the BS. The simulation results for scheduling show that the (P+E) scheduler outperforms the (P+TB) scheduler when the system is moderately loaded and the (P+TB) scheduler outperforms the (P+E) scheduler when the system is fully loaded.

The aim of this work is to measure and analyze the impact of aspect-oriented programming on software performance. Thus we hypothesized as follow: adding aspects to the original base program will affect its performance because of the overhead caused by the control flow switching, and that incremental effect on performance is more obvious as the number of join points increases. To confirm (or reject) our hypotheses we carried out a case study of two concurrent software architectures: Half-Sync/Half-Asyn (HS/HA) and Leader/Followers (LFs). Aspects were extracted and encapsulated, and the aspect-enabled program was compared to the base program for performance. Our results show that aspect-oriented approach does not have significant effect on the performance and that in some cases, aspect-oriented program even outperform the non-aspect program. Additionally, introduction of a large number of joint points does not have significant effect on the performance.

Accurate identification of P2P traffic is critical for efficient network management and reasonable utilization of network resources, as P2P applications have been growing dramatically. Fuzzy clustering is more flexible than hard clustering and is practical for P2P traffic identification because of the natural treatment of data using fuzzy clustering. Fuzzy c-means clustering (FCM) is an iteratively optimal algorithm normally based on the least square method to partition data sets, which has high computational overhead. This paper proposes modifications to the objective function and the distance function that greatly reduces the computational complexity of FCM while keeping the clustering accurate. The proposed FCM clustering technology can be incorporated into a Fuzzy Inference System (FIS) to implement real-time network traffic classification by updating the training data set continuously and efficiently.

Providing strong security is necessary for any wireless access networks. The latest broadband access network implementations are based on WiMAX and LTE, since they support high data rate and mobility. The WiMAX network has well structured QoS mechanisms and security architecture to support all kinds of fixed, mobile and multihop network users. Even though the existing fixed WiMAX network has well defined security architecture, it has many security issues like rouge Base Station (BS), Denial of Service (DoS) and etc. The rouge BS issue was solved in mobile WiMAX network, but the other security issues in fixed WiMAX network and the issues related to mobility like handover latency issues still exist. Most of the existing security issues in fixed and mobile WiMAX networks are solved in the upcoming international mobile telecommunication (IMT) - Advanced WiMAX network. But there are still some security issues due to high mobility support and advanced Medium Access Control (MAC) functionalities. On the other hand, Internet service providers may use the Internet Protocol Security (IPSec) for their wireless access due to its popularity in wired network. But IPSec may affect the throughput performance, since the IPSec header in each packet consumes additional bandwidth. Little research based on real experiments has been reported comparing WiMAX standard security and IPSec. In this paper, the security supported by the standards and IPSec for fixed WiMAX network is evaluated using testbed experiments. From the experimental results and existing research efforts, the security level and QoS support of theoretical and practical security schemes are analyzed.

In this paper, we characterize the stability region of multi-queue multi-server (MQMS) queueing systems with stationary channel and packet arrival processes. Toward this, the necessary and sufficient conditions for the stability of the system are derived under general arrival processes with finite first and second moments. We show that when the arrival processes are stationary, the stability region form is a polytope for which we explicitly find the coefficients of the linear inequalities which characterize the stability region polytope.

Modern computer systems for distributed service computing become highly complex and difficult to manage. A self-adaptive approach that integrates monitoring, analyzing, and actuation functionalities has the potential to accommodate to a dynamically changing environment. The main objective of this paper is to develop an architecture-based self-adaptive framework to improve performance and resource efficiency of a server while maintaining reliable services. The target problem is distributed and concurrent systems. This paper proposes a Self-Adaptive Framework for Concurrency Architecture (SAFCA) that includes multiple concurrency architectural patterns or alternatives. The framework has monitoring and managing capabilities that can invoke another architectural alternative at run-time to cope with increasing demands or for reliability purpose. Two control mechanisms have been developed: SAFCA-Q and SAFCA-R. With SAFCA-Q, the system does not need to be statically configured for the highest workloads; hence, resource usage becomes more efficient in normal conditions and the system still is able to handle busty demands. SAFCA-R is used to improve reliability in the case of a failure by conducting a switchover to another software architecture. Experiment results demonstrate that the performance of SAFCA-Q is better than systems using only standalone concurrency architecture and resources are also better utilized. SAFCA-R also shows fast recovery in the face of a failure.

In publish/subscribe systems, XML message filtering performed at application layer is an important operation for XML message multicast. As a specific case of content-based multicast in application layer, XML message multicast depends on the data filtering and matching processes and the forwarding and routing schemes. As the XML data emerges in transition, XML message filtering and matching becomes more and more desirable. BFilter, proposed in this paper, conducts the XML message filtering and matching by leveraging branch points in both the XML document and user query. It evaluates user queries that use backward matching branch points to delay further matching processes until branch points match in the XML document and user query. In this way, XML message filtering can be performed more efficiently as the probability of mismatching is reduced. A number of experiments have been conducted and the results demonstrate that BFilter has better performance than the well-known YFilter for complex queries.

Availability of multiple channels and multiple radio interfaces can lead to substantial improvements in the performance of wireless access networks. The optimal allocation of available channels to the users with multiple interfaces however is not a trivial problem especially in a dynamically varying system. In this paper, we will consider the problem of channel and radio interface management in multi-channel multi-interface wireless access networks. In our model, there is a set of orthogonal channels which is shared among several users. Furthermore, each user is equipped with a fixed number of radio interfaces through which it can communicate with the access point. We also consider exogenous stochastic packet arrivals for each user which may be queued for future transmissions. We introduce a general modelling for multi-channel multi-interface wireless access networks for which we propose a throughput optimal channel/interface allocation policy that stabilizes the system for all the arrival rates strictly inside the stability region. We show that the optimal policy determination is equivalent to finding the maximum weighted matching in a bipartite graph at every time slot. Finally, we characterize the stability region for specific cases of the proposed model. Simulation is used to compare the performance of the optimal policy with some other policies in terms of average total queue occupancy.

Maintaining per-flow information and state is a crucial topic in network monitoring. Tracking per-flow state is a relatively new area. Two main approaches have been proposed for tracking state: Binned Duration Flow Tracking (BDFT) and Fingerprint-Compressed Filter Approximate Concurrent State Machine (FCF ACSM). BDFT which uses Bloom filters is time efficient, whereas FCF ACSM using d-left hash tables has near-perfect memory efficiency but has higher computational cost. This paper presents a hybrid method (BDFT-H) by employing the best features of BDFT and FCF ACSM to achieve both time and space efficiency. Performance analysis and comparisons are conducted for BDFT, FCF ACSM, and BDFT-H. These methods are all intended for implementation on high-speed routers where resources such as memory and CPU time are limited. For the computational performance of the three schemes, we find that based on analysis, d-left hashing may require substantially more computational resources than Bloom filters. We also conduct simulations to compare the accuracy of these three schemes and the results show that all three methods can achieve over 99% accuracy on traces of real traffic. The proposed approach provides the best overall tradeoff between time and space efficiency.

Network capacity region of multi-queue multi-server queueing system with random connectivities and stationary arrival processes is studied in this paper. Specifically, the necessary and sufficient conditions for the stability of the system are derived under general arrival processes with finite first and second moments. In the case of stationary arrival processes, these conditions establish the network capacity region of the system. It is also shown that AS/LCQ (Any Server/Longest Connected Queue) policy stabilizes the system when it is stabilizable. Furthermore, an upper bound for the average queue occupancy is derived for this policy.

Physical-Layer Network Coding (PLNC) has recently emerged as a promising new communications paradigm that has the ability to greatly increase the capacity of wireless networks. Current research has also demonstrated that PLNC can decrease the eavesdropping region of an external entity. In this paper we show how it is possible to mitigate passive eavesdropping in the presence of co-located nodes in a PLNC environment.

In this paper, we study the current security standards in multihop WiMAX networks and their security issues. For secured communications, hop-by-hop authentication is necessary for any multihop wireless networks [5][6]. WiMAX multihop networks provide default hop-by-hop authentication in a distributed security mode only. Apart from this, the multihop standards should consider the existing security issues in mobile WiMAX standard [4]. The new multihop standard IEEE 802.16m has improved functionalities and security support. It provides the solution for medium access control (MAC)-control message issues. At the same time, network coding is used for enhanced-multicast broadcast service (E-MBS) retransmission to improve the performance of MBS. However, the standard IEEE 802.16m/D4 fails to consider the security threats for network coding, multihop support and initial ranging. For the above issues, we propose a distributed security architecture using the Elliptic Curve Diffie-Hellman (ECDH) key exchange protocol. Our proposed architecture solves the network coding and other multihop security issues with the help of neighbor authentication/security association (SA), distributed security architecture and ECDH protocol.

The primary concern of broadband wireless technologies is to provide the end-to-end Quality of Service (QoS) for integrated real-time and non real-time applications. The main focus of the IEEE 802.16d/e MAC layer is to manage the radio recourse in an efficient way. The basic functional blocks of the QoS model are addressed by the standards to support five different types of service classes. However, the detailed admission control, radio resource management and scheduling are left for implementation perspective and many research efforts are on going to assure the QoS for end customers. In this paper we propose a hybrid uplink scheduling algorithm (P+E) for subscriber station (SS), which is the combination of priority and Earliest Due Date (EDD) scheduling methods to maintain QoS and utilize the radio resource allocated by the BS in an efficient manner. Simulation results demonstrate the advantages of the proposed hybrid scheduling algorithm.

An XML publish/subscribe system is based on filtering XML message streams for a large number of subscriptions expressed in XPath. A major issue on an XML-based publish/subscribe system is its performance. As the number of XML documents and XPath-based subscriptions increases in the system, to provide XML filtering efficiently becomes a challenging problem. Hence, there is an urgent need for optimization techniques to meet this challenge. There are many existing approaches on designing efficient XML filtering engine. Most existing research efforts focus on efficient filtering algorithms for achieving a high system performance or supporting more complex XPath syntax. Each proposed scheme has its advantages and limitations. Not much research, however, has considered using caching in the context of XML filtering. In this paper, we propose two caching schemes to be used in conjunction with an XML filtering engine. First, we present a complete message caching algorithm that is a strict caching policy to reduce the computation cost that accrues from multiple filtering of the same messages, by reusing results of previously processed messages. Second, we investigate a structure-based caching method that is an approximate caching policy for messages sharing the same structure. Performance evaluation for synthetic data and real data both show that complete message caching and structure-based caching schemes are able to achieve significantly better filtering performance (up to 80% for both caching schemes for the message streams experimented with).

Results of experimental studies of a teleoperator system with projection-based force reflection algorithms in the presence of communication constraints are presented. It is demonstrated that, using the projection-based force reflection algorithms, the admissible force reflection gain can be substantially increased without loosing the overall stability, which confirms the earlier theoretical results. It is also shown that this improvement is achieved without transparency deterioration.

In this paper, we propose a practical implementation of a location identification approach. In this approach, only the basic assumptions that are realistic in most types of ad hoc/sensor networks are required, which means that this system is implementable in most kinds of ad hoc/sensor networks. By introducing the idea of cell-based location identification method into the system, some drawbacks to the TDOA (time difference of arrival) system are resolved. More importantly, by employing the new theory, the location estimation accuracy of the proposed system is improved without costing extra resources. Finally, simulations show that the proposed Hybrid TDOA (HTDOA) location estimation system performs better in different environments compared with the current TDOA method.

Two complementary classes of approaches exist to protect high security mobile ad hoc networks (MANETs), prevention-based approaches, such as authentication, and detection-based approaches, such as intrusion detection. Most previous work studies these two classes of issues separately. In this paper, we propose a framework of combining intrusion detection and continuous authentication in MANETs. In this framework, multimodal biometrics are used for continuous authentication, and intrusion detection is modeled as sensors to detect system security state. We formulate the whole system as a partially observed Markov decision process considering both system security requirements and resource constraints. We then use dynamic programming-based hidden Markov model scheduling algorithms to derive the optimal schemes for both intrusion detection and continuous authentication. Extensive simulations show the effectiveness of the proposed scheme.

In wireless sensor networks (WSNs), hierarchical network structures have the advantage of providing scalable and resource efficient solutions. Thus, finding an efficient way to generate clusters is an important topic in WSNs. To achieve this goal, this paper adapts the well-understood hierarchical agglomerative clustering (HAC) algorithm by proposing a distributed HAC (DHAC) algorithm. DHAC provides a bottom-up clustering approach by grouping similar nodes together before the cluster head (CH) is selected. DHAC can accommodate both quantitative and qualitative information types. With automatic CH rotation and rescheduling, DHAC avoids reclustering and achieves uniform energy dissipation through the whole network lifetime. Simulation results in the NS2 platform demonstrate the longer network lifetime of the DHAC than the better-known clustering protocols, LEACH and LEACH-C.

Two complementary classes of approaches exist to protect high security mobile ad hoc networks (MANETs), prevention-based approaches, such as authentication, and detection-based approaches, such as intrusion detection. Most previous work studies these two classes of issues separately. In this paper, we propose a framework of combining intrusion detection and continuous authentication in MANETs. In this framework, multimodal biometrics are used for continuous authentication, and intrusion detection is modeled as sensors to detect system security state. We formulate the system and use dynamic programming- based algorithms to derive the optimal schemes for both intrusion detection and continuous authentication. Simulation examples show the effectiveness of the proposed scheme.

A general stability result for force-reflecting teleoperator systems with projection-based force reflection algorithms is established. It is shown that the closed-loop system’s gain can be assigned arbitrarily by an appropriate choice of certain weighting function of the projection-based force reflection algorithm. In particular, this allows to achieve stability of the force-reflecting teleoperator system in presence of timevarying irregular delays for arbitrarily large force-reflecting gain and arbitrarily low damping and stiffness of the master. The proposed approach solves, to some extent, the trade-off between stability, manoeuvrability, and high force reflection gain in force-reflecting teleoperator system with network-induced communication constraints.

File sizes have grown tremendously over the past years for music/video applications and the trend is still growing. As a result, large ISPs are facing increasing demand for bandwidth from the growth of file sizes. A main contribution to this bandwidth demand problem is inefficient use of bandwidth due to many ISP customers downloading the same large files multiple times. This paper first reports real experiments conducted on Carletonpsilas Internet backbone by using the large file caching technique. Various cache replacement algorithms are then simulated and compared using traces of large file transfers. The results reveal that least recently used (LRU) performs better than others.

Abstract As the front line of defense, user authentication is crucial for integrity and confidentiality. Mobile ad hoc networks (MANETs) impose a number of non‐trivial challenges to user authentication such as lack of central coordination and limited resources. In high security MANETs, continuous authentication is desirable so that a system can be monitored for the duration of the session to reduce the vulnerability. Biometrics provides some possible solutions to the authentication problem in MANETs, since it has direct connection with user identity. In this paper, we introduce some biometric technologies and their applications in the authentication problem. Multimodal biometrics can be used to exploit the benefits of one biometric while mitigating the inaccuracies of another. We propose an optimal multimodal biometric‐based continuous authentication scheme in MANETs. Some numerical results show the effectiveness of the proposed scheme. Copyright © 2007 John Wiley & Sons, Ltd.

Most wireless ad hoc networks research assumes a common communication channel. Using multiple channels can increase network capacity by transmitting traffic on different channels in an interference area. Channel assignment is a critical issue for the multi-channel scheme. Proactive receiver-based channel assignment (RCA) which is a network layer multichannel solution has shown better performance compared to single common channel solutions. However, channel collision cannot be resolved in some scenarios and a large number of orthogonal channels are needed with the RCA scheme. This paper proposes an on-demand negotiation-based channel assignment (NCA) that determines the communicating channel using the network topology and the dynamic traffic. We extend the OLSR (optimized link state routing) protocol to support NCA. Simulation results with NS-2 indicate considerable performance improvement on throughput and end-to-end delay of NCA over RCA for most scenarios.

Network measurement at 10+Gbps speeds imposes many restrictions on the resource consumption of the measurement application, making any filtering of input data highly desirable. Symmetric connection detection (SCD) is a method of filtering TCP sessions, passing only those sessions which become fully established. SCD can benefit network monitoring applications that are only interested fully established TCP connections by reducing processing requirements. Incomplete connection attempts, such as port scanning attempts, simply waste resources in many applications if they are not filtered. SCD filters out unsuccessful connection attempts using a combination of Bloom filters to track the state of connection establishment for every flow passing through a network device. Unsuccessful flows can be filtered out to a very high degree of accuracy, depending on the size of the Bloom filter and traffic rate, 99.5% is typical. Resource consumption, both memory and CPU is low. The core SCD algorithm is designed to work in high-speed routers, in real-time, and at line speed. Using an upper bound of 32 k bytes of RAM our experimental results indicate 99+% accuracy with 900,000 active flows.

The problem of stable force-reflecting teleoperation is addressed where the communication between the master and the slave is subject to multiple time-varying, discontinuous, and possibly unbounded communication delays. A new force reflection algorithm is proposed which improves the stability of the system without decreasing its transparency. Based on an estimate of the human forces provided by a high-gain input observer, the proposed algorithm restricts the reflected force in such a way that it eliminates the motion of the master induced by the force reflection signal without changing the human perception of the environmental force. It is shown that the force reflection algorithm proposed allows to achieve stability of the system for arbitrarily high force-reflection gain and arbitrarily low damping/stiffness of the master manipulator.

Software architectural design has an enormous effect on downstream software artifacts. Decomposition of function for the final system is one of the critical steps in software architectural design. The process of decomposition is typically conducted by designers based on their intuition and past experiences, which may not be robust sometimes. This paper presents a study of applying the clustering technique to support system decomposition based on requirements and their attributes. The approach can support the architectural design process by grouping closely related requirements to form a subsystem or module. In this paper, we demonstrate our experiments in applying the approach to an industrial communication protocol software system and comparing several clustering algorithms. The result obtained from WPGMA (weighted pair-group method using arithmetic averages) shows closer resemblance than other clustering methods to the one developed by the designer.

The problem of stable force-reflecting teleoperation with time-varying communication delay is addressed. A new force reflection algorithm is presented, where the environmental force reflected on the master side can be altered depending on the forces applied by the human operator. This alteration is not felt by the human operator, however, it makes the force reflection safe in the sense it does not destroy the stability of the teleoperator. In particular, using IOS small gain approach, it is shown that the overall stability in the teleoperator system with the force-reflecting algorithm proposed can be achieved theoretically for arbitrarily low damping on the master side and arbitrarily high force-reflection gain. The simulation results are presented that confirm that the proposed scheme allows to decrease master damping significantly, and thus improve the transparency of force-reflecting teleoperation, without sacrificing the overall stability

The problem of force-reflecting teleoperation over Internet protocol networks is addressed. The existence of time-varying communication delay and the possibility of data losses are taken into consideration. Since significant data loss may result in discontinuity of the reference trajectory transmitted through the communication channel, the proposed control scheme includes a filter that provides a smooth approximation of a possibly discontinuous reference trajectory. The stability of the overall system is guaranteed by a version of the input-to-output stable small-gain theorem for functional differential equations. If the communication delay in the forward channel is an "approximately smooth" function of time, the proposed scheme guarantees that the slave manipulator tracks the delayed trajectory of the master within a prescribed small error.

QoS routing solutions can be classified into two categories, state-dependent and time-dependent, according to their awareness of the future traffic demand in the network. Compared with representative state-dependent routing algorithms, a time-dependent variation of WSP - TDWSP - is proposed in this paper to study the role of traffic forecasting in QoS routing, by customizing itself for a range of traffic demands. Our simulation results confirm the feasibility of traffic forecasting in the context of QoS routing, which empowers TDWSP to achieve better routing performance and to overcome QoS routing difficulties, even though completely accurate traffic prediction is not required. The case study involving TDWSP further reveals that even a static forecast can remain effective over a large area in the solvable traffic demand space, if the network topology and the peak traffic value are given. Thus, the role of traffic forecasting in QoS routing becomes more prominent.

The problem of force-reflecting teleoperation over IP networks is addressed. The existence of time-varying communication delay and possibility of data packets dropouts are taken into consideration. Since significant data dropouts may result in discontinuity of the reference trajectory transmitted through the communication channel, the proposed control scheme includes a filter that provides a smooth approximation of a possibly discontinuous reference trajectory. The stability of the overall system is guaranteed by a version of the IOS small gain theorem for functional-differential equations. It is also shown that, in the case of reliable communication protocols, the proposed scheme guarantees that the slave manipulator tracks the delayed trajectory of the master with a prescribed small error.

This report presents a reflection on software architecture practices based on our past ten year’s industrial experiences, particularly in the area of telecommunications. The report summarizes the methods, tools, and techniques that we have used on various projects. We also discuss, based on our experiences, what methods are useful, what remains to be validated, and what the gaps are between the state of practices and our wishes.

Models can help to understand the performance aspects of a computer system from the software architecture and its configurations, but ease of model creation is critical. A compositional model-building approach is described here, in which component submodels are generated from the scenarios they participate in. Submodel classes are derived from an analysis of behaviour patterns as the scenarios traverse the software components. Then submodels are instantiated and combined in the overall system model. The approach is particularly effective in peer-to-peer systems in which subsystems inherit most of their behaviour from a few shared patterns, termed "behaviour-inheriting peer" (BIP) systems. A model-building algorithm is described, and is demonstrated on a prototype emulator for a network of routers. The emulator, called CGNet, can be configured for its deployment and for traffic patterns and routes. An automatic model-generator uses this information to build a model which represents the overall system configuration. The approach is quite general and can be used to model component-based systems in which the components themselves are created in many configurations.

Software architectures are often claimed to be robust. However, there is no explicit and concrete definition of software architecture robustness. This paper gives a definition of software architecture robustness and presents a set of architecture metrics that were applied to real-time telecommunications software for the evaluation of robustness. The purpose of this study is to provide a structured method to support software architecture evaluations and downstream software implementations. The study also expands the software architecture research to quantitative and measurable evaluations as opposed to qualitative assessments. In addition, this paper presents an empirical case study of applying the metrics. The approach and the metrics data provide insights into software architecture sensitivity analysis on system qualities and trade-off analysis among a set of design alternatives to support product evolution.

Software architectural analysis is critically important before a system is built or for system evolution. The article presents a methodology that the author has been developing and testing in evaluating the sensitivity of software architectures with respect to changes in key customer values and future requirements at Nortel's SEAL. The approach consists of a framework for modeling various types of relevant information and a set of architectural views for re-engineering, analyzing, and comparing software architectures. The article highlights the approach by applying it to the common problem of this panel. The information that one needs to add to the architecture for the analysis is described. The article also presents some potential results, lessons learned from empirical studies, and the current research works in progress.

Current domain modeling techniques encounter a similar barrier that the traditional requirements modeling methods have suffered: lack of enough flexibility to support potential reuse. Domain analysis is proposed to facilitate reuse across different applications in the same domain. But domain analysis is a complex and time-consuming task, furthermore, there are similarities among different application domains. The results of domain modeling should also be reused for different but analogous domains to receive high-payoff. This paper expands the current domain modeling methods by incorporating some concepts reported in the analogy research discipline. The expanded approach include four main models: object model, functional model, relational model, and dynamic model. Relational modeling and systems goals in dynamic modeling are adopted from analogical studies and experiments and are integrated into the approach. The expanded view will not only help better understand the domain, but also facilitate reasoning and mapping of existing knowledge or software to different yet analogous application domains.

Software reuse has drawn much attention in computing research. Domain analysis is considered a prerequisite to effective reuse of existing software. Several approaches and methodologies have been proposed for domain analysis or domain modeling, but not many case studies have been reported in the literature. The first objective of this paper is to present the concept and practical experiences of a domain analysis approach in discrete-event simulation in manufacturing — generic /specific modeling. A second objective of this paper is to present a meta-model based on the generic/ specific approach from the software engineering perspective. The steps and knowledge required to build the model are described. Domain analysis lessons learned from the generic/specific approach in discrete-event simulation are discussed. Classification of this domain modeling approach was conducted through the Wartik and Prieto-Diaz criteria. The classification will facilitate the comparison with other domain analysis approaches. Similar modeling concepts or techniques may be beneficial to other researchers in their own application domains.