Small and medium-sized enterprises (SMEs) face persistent challenges in maintaining industrial equipment due to a combination of factors such as limited resources, a shortage of skilled personnel, and inadequate IT infrastructure. Although equipment maintenance is a huge burden for SMEs, the literature lacks a tailored solution to be efficient, cost-effective, and sustainable. This study introduces a modular digital twin framework specifically designed for SME maintenance operations, integrating Microkernel Architecture, Service-Oriented Architecture, and a toolkit to identify the optimal features of digital twins based on organizational requirements. This approach ensures scalability, affordability, and adaptability in diverse SME contexts. Using three real-world SME cases in the agri-food sector, informed by expert interviews and evaluated through Monte Carlo simulation, the study finds that predictive and prescriptive modular digital twin strategies can substantially outperform reactive and preventive approaches under resource-constrained SME conditions, with scenario-based estimates indicating up to 83% reduction in average downtime-related cost and up to 42% reduction in average repair time. These quantitative gains are contingent on the simulated operating assumptions, particularly equipment criticality, technician response delays, and spare-part logistics. The findings provide a practical and scalable roadmap for SMEs seeking to adopt intelligent maintenance systems in a cost-effective manner. The proposed modular approach allows firms to begin with essential monitoring capabilities and gradually integrate predictive, prescriptive, and autonomous functions as their technological readiness evolves. This phased adoption strategy reduces investment risk, limits vendor lock-in, and supports incremental system upgrades, while remaining well suited to compliance-driven sectors such as food processing and agriculture.

Efficient routing protocols are required in opportunistic networks due to the inherent challenges of sporadic connectivity, transmission delays, and dynamic network topologies. In these networks, information is forwarded and disseminated among smart devices based on opportunistic contacts driven primarily by network dynamics and user mobility. This paper introducesLearnRouter, a dynamic reinforcement learning-based routing algorithm designed to improve the message delivery ratio while minimizing end-to-end delay in opportunistic networks. Existing routing protocols use static strategies or fixed replication schemes, leading to resource utilization and high message drop rates. LearnRouter addresses these challenges by incorporating the multi-armed bandit framework and utilizing the Upper Confidence Bound algorithm to make more intelligent and data-driven forwarding decisions. The proposed approach enables the protocol to adapt continuously to network changes while balancing the trade-off between exploration and exploitation. The simulation results show that LearnRouter surpasses end-to-end delay and message delivery ratio in comparison with Direct Delivery, Spray and Wait, CBR, SimRouter, RL-Prophet, and K-DQLR in opportunistic and dynamic environments.

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.

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.

In Industrial Internet of Things (IIoT) applications, data from a variety of sensors is collected continuously to monitor and manage industrial processes. However, missing data caused by network disruptions, sensor malfunctions, or hardware failures seriously affects the performance of datadriven models, leading to unreliable predictions and increased maintenance costs. To address this challenge, we propose Decayaware Transformer-enhanced GAIN (DT-GAIN), a novel imputation framework for multivariate time-series data in IIoT applications. DT-GAIN extends and enhances the Generative Adversarial Imputation Nets (GAIN) framework by integrating the Transformer architecture, which captures long-range dependencies essential for accurate imputation of multivariate timeseries industrial data. In addition, DT-GAIN incorporates a timedecay mechanism that accounts for temporal irregularities by weighing observations based on their recency, thereby improving the ability of the proposed method to handle varying intervals between observations and missing values. In our research, we thoroughly compare DT-GAIN with state-of-the-art imputation methods, including LSTM-based GAIN (L-GAIN), Transformerbased GAIN (T-GAIN), the original GAIN, and SAITS. Our experimental results indicate that DT-GAIN outperforms the methods under investigation in terms of Root Mean Squared Error (RMSE) across various missing rates, particularly excelling in high-missing-data scenarios.

This paper proposes the Server–Client Machine Learning Intrusion Detection System (SC-MLIDS), a novel fusion framework designed to enhance security in Wireless Sensor Networks (WSNs), which are inherently vulnerable to various security threats due to their distributed nature and resource constraints . Traditional Intrusion Detection Systems (IDSs) often face challenges with high computational demands and privacy issues. SC-MLIDS addresses these problems by integrating Federated Learning (FL) with a multi-sensor fusion approach to implementing two layers of defence that operate independently of specific attack types. Moreover, this framework leverages a server–client architecture to efficiently manage and process data from sensor nodes , sink nodes, and gateways within the network. The core innovation of SC-MLIDS lies in its dual model aggregation algorithms at the gateway: one assesses model performance and weight, while the other uses majority voting to integrate predictions from both client and server models. As a result, this approach reduces redundant data transmissions and enhances detection accuracy, making it more effective than conventional methods in WSNs. Our proposed framework outperforms current state-of-the-art techniques, achieving F1-scores of 99.78% and 98.80% for the two aggregation algorithms, namely, Weighted Score and Majority Voting. This validation demonstrates the effectiveness of SC-MLIDS in providing accurate intrusion detection and robust data management.

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.

Deep Learning (DL) has revolutionized healthcare, particularly in disease prediction, medical imaging, and drug discovery. In ophthalmology, DL facilitates cell counting by detecting and quantifying retinal ganglion cells (RGCs) from microscopy images. Manual counting is labor-intensive and errorprone, leading to the development of automated DL systems. This paper compares three architectures, namely, Fully Convolutional Regression Network FCRN-A, FCRN-A-v2, and U-Net, using a synthetic dataset and a custom real dataset. The networks, trained through supervised learning, produce density maps, with model performance evaluated via 5-fold cross-validation based on mean absolute error (MAE) and standard deviation (STD). Results show U-Net outperforms the others, achieving the lowest MAE and STD in cell counting.

Federated Learning (FL) has emerged as a novel distributed Machine Learning (ML) approach, to tackle the challenges associated with data privacy and overload in MLbased intrusion detection systems (IDSs). Drawing inspiration from the FL architecture, we have introduced a hybrid ML IDS tailored for Wireless Sensor Networks (WSNs). This system is crafted to leverage ML for achieving a two-layer intrusion detection mechanism in WSNs free from constraints posed by specific attack types. The architecture follows a server-client model compatible with the configuration of sensor nodes, sink nodes, and gateways in WSNs. In this setup, client models located at sink nodes undergo training using sensing data while the server model at the gateway is trained using network traffic data. This two-layer training approach amplifies the efficiency of intrusion detection and ensures comprehensive network coverage. The results derived from our simulation experiments corroborate the effectiveness of the proposed hybrid ML IDS. It generates precise aggregation predictions and leads to a substantial reduction in redundant data transmissions. Furthermore, the system exhibits efficacy in detecting intrusions through a dual validation process.

Supervisory Control and Data Acquisition (SCADA) systems are vulnerable to traditional cyber-attacks, such as man-in-the-middle, denial of service, eavesdropping, and masquerade attacks, as well as future attacks based on Grover's and Shor's algorithm implemented in quantum hardware. This paper proposes a quantum-robust scheme based on entanglement and supersingular isogeny-based cryptography. The scheme employs a modified Supersingular Isogeny Key Encapsulation (SIKE) to generate shared secret keys, also authenticating BBM92, a quantum key distribution protocol to generate a symmetric key. The paper uses ASCON-128 and SHA-3 to encrypt and authenticate messages, and provides a comparative analysis of two entanglement-based quantum key distribution protocols. The proposed scheme is compared to the current SCADA standard, AGA-12, and is shown to provide confidentiality, integrity, intrusion resistance, message authentication, and scalability. The randomness of key pairs generated by our algorithm and RSA key pairs is 87.5% and 84.37%, respectively, addressing confidentiality and integrity. Using the BBM92 protocol, our proposed algorithm detects the presence of an adversary by generating an average error rate of 26.07% and information leakage of 76.01%. AGA-12 relies on SHA-1 hash function that Google has cracked recently. However, our algorithm includes SHA-3, a collision and quantum-resistant hash that provides message authentication.

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 paper, we apply a machine learning classifier to the publicly available consumer Internet of Things (IoT) traffic traces to explore the nature and extent of any potential data exhaust. To this end, we propose two feature sets and compare them against the baseline flow feature set and the results from the previous works. Evaluations show the improvement in performance obtained using the proposed feature sets and the variety of information that can be extracted from the captured IoT traffic regardless of encryption.

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.

Industrial Internet of Things (IIoT), a branch of the Internet of Things (IoT) for the industrial sector, plays a vital role in integrating industrial equipment, monitoring equipment health, and improving the overall efficiency of industrial production process. Accurately predicting the remaining useful life (RUL) of IIoT equipment is a crucial task in prognostic health management (PHM), which analyzes the degradation trend of industrial equipment to schedule maintenance activi-ties in a timely manner. Artificial Intelligence (AI) techniques, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), have been widely used in RUL prediction. However, these techniques face challenges in incorporating long-sequence information to capture degradation trends and predicting long-term RUL values. In this paper, we propose an Informer-based method, Co-Informer, for long-term RUL prediction. Co-Informer utilizes a series of sensor data to provide the predicted RUL values during an upcoming time window. In our research, extensive experiments are carried out with C-MAPSS, a widely used turbofan engine degradation dataset provided by NASA. Our experimental results indicate that Co-Informer outperforms the state-of-the-art schemes for RUL prediction in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

Unhealthy lifestyle behaviors such as spending too many hours sitting and inadequate physical activity (PA) can contribute to different chronic diseases. Research has revealed the capabilities of digital technology interventions such as persuasive technologies (PTs) for providing health support and encouraging healthy behavior changes to assist people in preventing chronic diseases and having healthier lifestyles. Thus, the use of mobile technology to deliver PT interventions has dramatically increased, especially for promoting PA and reducing sedentary behavior (SB) by employing various persuasive strategies (PSs). This paper provides a systematic review of 16 years of research from 2006 to 2021. The review aims to (1) explore the various ways each strategy is implemented on mobile-based PTs for PA and SB, (2) evaluate the effectiveness of different ways of implementing the PSs on mobile-based PT interventions for PA and SB, (3) provide a comparison of the different ways of implementing each PS, (4) show the weaknesses and strengths of the interventions based on the strategies and implementations, (5) highlight the limitations and pitfalls of the existing research, and (6) give recommendations and directions for future research.

Supervisory Control and Data Acquisition (SCADA) networks play a vital role in industrial control systems. Industrial organizations perform operations remotely through SCADA systems to accelerate their processes. However, this enhancement in network capabilities comes at the cost of exposing the systems to cyber-attacks. Consequently, effective solutions are required to secure industrial infrastructure as cyber-attacks on SCADA systems can have severe financial and/or safety implications. Moreover, SCADA field devices are equipped with microcontrollers for processing information and have limited computational power and resources. This makes the deployment of sophisticated security features challenging. As a result, effective lightweight cryptography solutions are needed to strengthen the security of industrial plants against cyber threats. In this paper, we have proposed a multi-layered framework by combining both symmetric and asymmetric key cryptographic techniques to ensure high availability, integrity, confidentiality, authentication and scalability. Further, an efficient session key management mechanism is proposed by merging random number generation with a hashed message authentication code. Moreover, for each session, we have introduced three symmetric key cryptography techniques based on the concept of Vernam cipher and a pre-shared session key, namely, random prime number generator, prime counter, and hash chaining. The proposed scheme satisfies the SCADA requirements of real-time request response mechanism by supporting broadcast, multicast, and point to point communication.

Smart grids rely on SCADA (Supervisory Control and Data Acquisition) systems to monitor and control complex electrical networks in order to provide reliable energy to homes and industries. However, the increased inter-connectivity and remote accessibility of SCADA systems expose them to cyber attacks. As a consequence, developing effective security mechanisms is a priority in order to protect the network from internal and external attacks. We propose an integrated framework for an Intrusion Detection System (IDS) for smart grids which combines feature engineering-based preprocessing with machine learning classifiers. Whilst most of the machine learning techniques fine-tune the hyper-parameters to improve the detection rate, our approach focuses on selecting the most promising features of the dataset using Gradient Boosting Feature Selection (GBFS) before applying the classification algorithm, a combination which improves not only the detection rate but also the execution speed. GBFS uses the Weighted Feature Importance (WFI) extraction technique to reduce the complexity of classifiers. We implement and evaluate various decision-tree based machine learning techniques after obtaining the most promising features of the power grid dataset through a GBFS module, and show that this approach optimizes the False Positive Rate (FPR) and the execution time.

Distributed denial of service (DDoS) attacks are a major security threat against the availability of conventional or cloud computing resources. Numerous DDoS attacks, which have been launched against various organizations in the last decade, have had a direct impact on both vendors and users. Many researchers have attempted to tackle the security threat of DDoS attacks by combining classification algorithms with distributed computing. However, their solutions are static in terms of the classification algorithms used. In fact, current DDoS attacks have become so dynamic and sophisticated that they are able to pass the detection system thereby making it difficult for static solutions to detect. In this paper, we propose a dynamic DDoS attack detection system based on three main components: 1) classification algorithms; 2) a distributed system; and 3) a fuzzy logic system. Our framework uses fuzzy logic to dynamically select an algorithm from a set of prepared classification algorithms that detect different DDoS patterns. Out of the many candidate classification algorithms, we use Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), and Random Forest as candidate algorithms. We have evaluated the performance of classification algorithms and their delays and validated the fuzzy logic system. We have also evaluated the effectiveness of the distributed system and its impact on the classification algorithms delay. The results show that there is a trade-off between the utilized classification algorithms' accuracies and their delays. We observe that the fuzzy logic system can effectively select the right classification algorithm based on the traffic status.

Communication between nurses and other healthcare member is essential during the bedside medication administration stage to provide effective patient care and prevent medication errors. The nurse provides information to the physician and pharmacist when consultation regarding medication errors or concern is needed. This information is very critical as it affects the situation assessment and clinical judgment. Insufficient information can lead to failure in treatment and jeopardize patient health. The research to date has focused on improving tools for general communication between healthcare members. However, none of them have been customized to effectively fit the medication administration stage nor have considered the content of the communication. Therefore, in this paper, we propose a novel idea of customized communication that precisely applies to the medication administration stage. We developed the Medication Administration Communication (MAC) application that generates the essential content of communication between the nurse and other healthcare members. We evaluated the application by testing its usability from the nurses perspective.

Premature birth is the leading cause of death in children under 5 years. Furthermore, surviving children can have a lifetime of disability such as hearing and vision loss or learning difficulties. Research suggests that monitoring uterine contractions can help in evaluating the health and progress of pregnancy, and also determine if the pregnant woman is in labour, and consequently mitigate the effects of premature labour. In this paper, we propose a safe, simple and low-cost system to monitor pregnant women who are at high risk of premature labour. Our system consists of a wireless body sensor network to non-invasively monitor the uterine contractions and trigger a warning via a smartphone if the readings are outside the normal thresholds. We have designed a proof-of-concept prototype and tested it for reliability, performance and power consumption.

This paper provides an evaluation of the SingleTapBraille keyboard, designed to assist people with no or low vision in using touchscreen smartphones. This application allows blind users to input characters based on braille patterns. To assess SingleTapBraille, this study compares its performance with that of the commonly used QWERTY keyboard. We conducted an evaluation study with 7 blind participants to examine the performance of both keyboards on Android platforms. Overall, participants were able to quickly adjust to SingleTapBraille and type on touchscreen devices using their knowledge of Braille patterns within fifteen to twenty minutes of introduction to the system. The SingleTapBraille keyboard was better than the QWERTY keyboard in terms of both speed and accuracy, indicating that SingleTapBraille represents an improvement over existing alternatives in making touchscreen keyboards more accessible for blind users. Based on the evaluation results and the feedback of our participants, we discuss the strengths and weaknesses of previous keyboards that have been used by participants, as well as those of SingleTapBraille. In doing so, we consider possible design improvements for the future development of accessible keyboards for blind users.

Radio Frequency Identification (RFID) technology is rapidly making its way to next generation automatic identification systems. Despite encouraging prospects of RFID technology, security threats and privacy concerns limit its widespread deployment. Security in passive RFID tag based systems is a challenge owing to the severe resource restrictions. In this paper, we present a lightweight anonymity / mutual authentication protocol that uses a unique choice of pseudorandom numbers to achieve basic security goals, i.e. confidentiality, integrity and authentication. We validate our protocol by security analysis.

This paper proposes a hybrid encryption technique that generates a key dynamically, along with integrity check parameters. Our approach generates the key stream using a chained approach, beginning with an initial preshared key. Subsequent keys are derived using logical operations on intermediate cipher texts and intermediate keys generated in each stage. This is an improvement over chaining techniques, which use a cipher text to derive successive keys. We validate our algorithm by proof-of-concept implementation and security analysis.

Mobile cloud computing is an emerging area in the cloud computing paradigm, comprising several modes of communication that are governed by varying security standards. WBAN (Wireless Body Area Networks), RFID (Radio Frequency IDentification) and VANET (Vehicular Ad-hoc NETworks) are three example applications that could be based on mobile cloud computing. Considering the fact that the security mechanisms in different applications are highly heterogeneous while the cloud server is common to these applications, we devised a context-adaptive security framework that could be deployed at the cloud premises to provide an additional security layer to mobile cloud computing systems. Furthermore, the framework provides varied techniques to improve the quality of service and reliability of mobile cloud computing. Technically, this multicomponent context-adaptive framework accepts the traffic in different communication modes, prevents attacks by randomly choosing pre-defined algorithms, learns from previous attacks using cognitive model, and rearranges the cloud service model as a self-healing system.

The security of data transmission is of paramount importance to mobile cloud computing. For security purposes, the data transmission in mobile cloud computing typically requires a mutually-authenticated environment for mobile devices and cloud servers. SSH (secure shell) could be used to satisfy the requirement. However, it makes the authentication process computationally expensive for mobile devices because it involves public key cryptosystem and mobile devices are relatively restricted in terms of computation capacity. This places the onus of establishing and maintaining secure communication sessions on the resourceful cloud servers. We propose a novel mutual authentication scheme, “Message Digest and Location based Authentication (MDLA)”, which involves symmetric key operations. In addition to computational simplicity, MDLA achieves integrity through message digest, and ensures the unpredictability of keys using location vector and timestamp.

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

With the development of the World Wide Web (WWW) and virtualization technologies, cloud computing has started to play a key role in the new computation era. Cloud computing can be used to serve a wide range of applications, from personal to organizational, by means of various infrastructure, software, and platform services. However, the ease of access from terminal devices to powerful processing units and information-rich databases makes the cloud susceptible to a variety of different attacks. The widespread use of cloud computing brings with it a hoard of security, privacy and integrity issues. In this paper, we propose an innovative authentication scheme for mobile cloud computing, MDA. The proposed scheme only uses existing hardware and platforms to prevent most of the potential attacks during the authentication process between a mobile device and the cloud. Technically, the encrypted hashed message (i.e. message digest) is employed by MDA to achieve secure authentication. The performance of MDA is evaluated via protocol simulation and security analysis.

Controlled access to confidential information and resources is a critical element in security systems. Role-based access control (RBAC) has gained widespread usage in modern enterprise systems. Extensions have been proposed to RBAC for incorporating spatial constraints into such systems. Several solutions have been proposed for such models and many researchers are now focusing on enforcing system policies. In this paper we propose a security framework for RBAC systems with spatial constraints based on identity-based encryption. In our framework, we use identity-based encryption with zero knowledge proof (ZKP) to provide authentication and information security. We also show how Near Field Communication (NFC) can be used to establish the integrity of a user's proof of location. Simulation results in Java validate our model. Furthermore, security analysis has been done to show how our framework protects against well-known attacks.

With its flexibility in deployment and data/lifecycle management, radio frequency identification (RFID) technology has potential for application in a wide variety of areas. However, RFID tags suffer from severe resource restrictions. This makes systems employing such tags vulnerable to several attacks, often resulting in the loss of privacy of the tag owner, and misuse of tags. A minimal requirement for a secure RFID environment is the authentication between the tag and the server. This paper presents a mutual authentication scheme, based on the concepts of genetic mutation and gene transfer that is coupled with a key generation/management scheme for encryption of data on the tag. The novelty of the proposed scheme is in the independent generation of keys at the server and the tag in such a system, and the mutual authentication that such a set up can be used to achieve. The proposed scheme is validated by simulation studies and security analysis.

Wireless Sensor Networks (WSNs) are wireless ad-hoc networks of tiny battery-operated wireless sensors. They are usually deployed in unsecured, open, and, harsh environments where it is difficult for humans to perform continuous monitoring. Due to its nature of deployment it is very crucial to provide security mechanisms for authenticating data. Key management is a pre-requisite for any security mechanism. Due to memory, computation, and communication constraints of sensor nodes, distribution and management of key in WSNs is a challenging task. Because of its lightweight feature, symmetric crypto-systems are a natural choice for key management in WSNs. However, they often fail to provide a good trade-off between resilience and storage. On the other hand, Public Key Infrastructure (PKI) is infeasible in WSNs because of its continuous availability of trusted third party and heavy computational requirements for certificate verification. Pairing-Based Cryptography (PBC) has paved a way for how parties can agree on keys without any interaction. It has relaxed the requirement of expensive certificate verification on PKI system. In this paper, we propose a new hybrid ID based non-interactive key management protocol for WSNs, which leverages the benefits from both symmetric key based cryptosystems and PBC by combining them together. The proposed protocol is very flexible and suits many applications. We also provide mechanisms for key refresh when the network changes.

There has been a tremendous interest in recent years in the design of mobile ad hoc networks (MANETs) because of their dynamic topology, self-organization and ease of deployment. However, the lack of security in their routing protocols make MANETs vulnerable to a variety of routing intrusions that can compromise the data or the entire network itself. This paper presents a novel scheme for the detection and prevention of intrusions on the Optimized Link State Routing (OLSR) protocol for MANETs. This scheme is patched to the OLSR implementation and runs independently on each MANET node. The mechanism verifies specifications in control messages sent by intruder nodes in the network and addresses a unique vulnerability in the implementation of the OLSR protocol. Furthermore, this technique helps each MANET node maintain a reliable routing table which is very important to prevent intrusions.

Denial of Service (DoS) attacks on 802.11 wireless LANs can be caused by management frames sent by rogue access points. Unfortunately, such attacks can be successful even if the wireless network is protected by a high-level security protocol such as WiFi Protected Access Version 2 (WPA2). We present a novel client-based scheme for the prevention of such intrusions. By using a Medium Access Control (MAC) filtering mechanism, the “smart” client is able to differentiate between legitimate and forged management frames. The proposed mechanism is non-cryptographic, has low overheads and can be deployed in existing IEEE 802.11 WLANs. We have built and tested a prototype of our scheme. We demonstrate that our mechanism can protect wireless clients against management frame DoS attacks launched at the MAC layer.

We investgate weighted shortest path routing mechanisms based on Dijkstra’s algorithm to find optimal paths for periodically scheduled burst flows in optical burst switching networks. Our objective is to reduce the flow blocking rate. We propose three dynamic weight functions to estimate the path level flow blocking probability based on wavelength utilization. We evaluate the performance of these weight functions using simulation. Results show that the weight function that incorporates both wavelength utilization and the number of inbound traffic sources has the best performance.

Link adaptation is one of the key technologies used in high speed wireless networks such as HSDPA and mobile WiMAX. The dynamic feature of mobile users' channel capacities brings challenges to the call admission control algorithm deployed for such networks. Our aim in this paper is to develop an admission control scheme that handles the intra-cell mobility issue in the downlink of broadband wireless networks with link adaptation. When the cell is decomposed into "rings", the intra-cell mobility can be modeled as a BCMP queuing chain network. Additionally, a change detection system is employed to track the non-stationary parameters. Our cell mobility-based admission control algorithm can provide efficient resource allocation by predicting min-guaranteed resource consumption on a per cell basis.

The wireless packet scheduler is a key element of HSDPA that determines the overall behavior of the system. We propose a novel QoS-guaranteed wireless packet scheduling scheme for a mixture of real-time and non-real-time services in HSDPA networks. By implementing a periodic non-work-conserving scheduling scheme, in contrast to the traditional work-conserving schemes, our scheme can enhance channel usage efficiency while satisfying the QoS requirements of real-time users. Simulation results of comparison with other popular scheduling schemes indicate that our scheduling algorithm can be used to maximize the channel capacity with guaranteed QoS provision for real-time users.

The next generation wireless network is an overlay network, in which AAA (authentication, authorization and accounting) is performed across domains. The current IETF AAA architecture causes long authentication delays during handoffs. In this paper, we propose a new network trust model to simplify the key management among networks. Based on this, a protocol is designed to shorten the delays.

The design of micro-mobility management protocols stands out as an important challenge in integrating wireless networks into the IP-based Internet, especially when such networks are deployed for real-time multimedia applications. We present a new load balancing hierarchical model for micro-mobility management for the wireless access network. The scheme includes a novel anchor selection algorithm. The hierarchical model and the anchor selection algorithm enable load balancing by distributing the number of mobile hosts managed by each anchor. In addition to load balancing, the proposed scheme has the advantages of QoS (quality of service) support, robustness, scalability, and fast handoff. Simulation results of our model indicate that it provides good load balancing performance in the presence of multiple QoS classes of applications.

Connection preemption plays an important role in multiclass QoS-aware networks. Most of the preemption research has focussed on the algorithms for choosing the appropriate set of preempted connections with minimal cost, in either a centralized or decentralized manner. In this paper, we introduce a new backward preemption policy for connection preemption. This policy reduces the number of redundant preemptions. We show how it can be integrated into the MPLS-TE (multiprotocol label switching-traffic engineering) framework. The policy can also be applied to other types of network control protocols with a two-way signaling process for setting up constraint-based routes with resource reservation. The proposed policy is also independent of the preemption decision algorithm and can work in both centralized and decentralized approaches. We examine the performance of the proposed approach by simulation studies.

Security is a crucial aspect in multicasting, the lack of which is currently preventing the large-scale deployment of group-oriented applications. Traditional IP multicast does not provide a secure framework for authentication, integrity and privacy for multicast sessions. This paper presents results of an ongoing subproject within the Secure Active VPN Environment (SAVE) project in active networks at Dalhousie University. Our subproject focuses on building a novel architecture for secure multicasting on virtual private networks (VPNs) using the concept of active networking. In any secure multicasting environment, group management becomes an important aspect for achieving privacy and integrity of the multicast session. In this paper, we propose group management strategies to address these objectives. Our approach combines cryptographic and active network techniques to provide a more powerful and flexible control for managing multicast groups.

A generalized architecture is presented for reconfigurable m-ary tree structures, where m is any integer >1. The approach is based on a generalized multistage interconnection network (MIN), which is a generalization of the augmented shuffle-exchange MIN introduce by the authors previously (1990) for obtaining reconfigurable binary tree structures. The generalized architecture with m/sup k/ processing elements or nodes (where k is any integer >1) is implemented with a k-stage MIN. A single control code issued to the MIN establishes a distinct m-ary tree configuration among the nodes. The favorable features of the architecture include fast reconfiguration, simplified hardware in the nodes and the MIN, and simple routing control. The reconfigurability of the architecture is proved, and the results of the analysis are utilized to provide a procedure to synthesize the m-ary tree configuration that is generated for any given control code. Considerations for implementing the switching elements of the MIN are discussed.>

A novel approach to the design of a reconfigurable tree architecture is presented. The architecture is implemented with an augmented shuffle-exchange multistage interconnection network and is capable of assuming N distinct binary tree configurations, where N is the number of processing elements (PEs) in the system. The novel features of the architecture include fast switching from one configuration to another, simplified hardware in the PEs and the switching network, and simple routing control.>