Efficiently balancing Quality of Service (QoS) requirements with energy conservation is essential for the sustainability of Internet of Things (IoT) sensor networks in industrial applications. This paper introduces a novel algorithm designed to optimize QoS and energy efficiency in Time-Slotted Channel Hopping (TSCH) networks for heterogeneous IoT sensor systems. The proposed approach meets application-specific QoS requirements, such as delay and packet loss, while reducing energy consumption through a strategic duty cycle optimization technique. Implemented in MATLAB and tested in a co-simulation environment, the algorithm was evaluated across various sensor network topologies and industrial QoS scenarios aligned with the ISA SP100 standard. The results demonstrate that the algorithm supports the delay and packet loss requirements for “open-loop” and “monitoring” services in networks of 16 to 36 nodes. For larger topologies of up to 64 nodes, it meets delay requirements but slightly exceeds packet loss thresholds by an average of 0.5%. Importantly, the duty cycle reduction strategy significantly enhances energy efficiency, lowering duty cycles by approximately 80% for networks of 16 to 36 nodes and by 15% for networks with 64 nodes. This method thus provides a robust solution for ensuring QoS compliance in industrial IoT systems, balancing high performance with optimized energy usage.

Test case prioritization (TCP) is essential for improving testing efficiency in large-scale continuous integration (CI) environments by reducing feedback time and efficient resource usage. Machine learning (ML) has shown promise in enhancing TCP, however, demonstrating its effectiveness in production environments remains a challenge. Using the IBM Open Liberty dataset, we developed and validated an ML-based TCP framework, showing how we identified the best-performing model step by step-from feature extraction and model training to hyperparameter tuning. After validating the framework in a research setting, we deployed it in IBM's live production system. The practical implications of this study are as follows. The production results closely mirrored the research outcomes, with models trained on recent data consistently outperforming older models and non-prioritized approaches. Specifically, prioritized builds achieved a mean Average Percentage of Faults Detected (APFD) value 50% higher than that of non-prioritized builds, leading to a substantial improvement in early fault detection. The consistent improvement of models trained on newer data (M-2023) over those trained on older data (M-2022) underscores the importance of regular model updates in maintaining optimal performance. This paper comprehensively compares research and production data, illustrating how our ML-driven TCP framework ensures optimal performance and detailing the steps necessary for successful implementation in dynamic CI environments.

Locks play a crucial role in multi-threaded applications, offering an effective solution for synchronizing shared resources. Yet, mishandling locks and threads can result in contention, leading to performance deterioration and compromising the scalability of software applications. In this study, several machine learning models were evaluated on how well they could detect the Java lock contention anti-pattern that caused the lock contention fault based on run time performance data. We trained the machine learning models with performance data generated from the execution of eight Java lock contention anti-patterns and tested the prediction of the models against 30% of the training data as well as performance data from six applications in the Dacappo benchmark that exhibit lock contention. Our results show that we can accurately identify the lock contention anti-pattern based on runtime performance data with an accuracy close to 90%.

Continuous integration pipelines execute extensive automated test suites to validate new software builds. In this fast-paced development environment, delivering timely testing results to developers is critical to ensuring software quality. Test case prioritization (TCP) emerges as a pivotal solution, enabling the prioritization of fault-prone test cases for immediate attention. Recent advancements in machine learning have showcased promising results in TCP, offering the potential to revolutionize how we optimize testing workflows. Hyperparameter tuning plays a crucial role in enhancing the performance of ML models. However, there needs to be more work investigating the effects of hyperparameter tuning on TCP. Therefore, we explore how optimized hyperparameters influence the performance of various ML classifiers, focusing on the Average Percentage of Faults Detected (APFD) metric. Through empirical analysis of ten real-world, large-scale, diverse datasets, we conduct a grid search-based tuning with 885 hyperparameter combinations for four machine learning models. Our results provide model-specific insights and demonstrate an average 15% improvement in model performance with hyperparameter tuning compared to default settings. We further explain how hyperparameter tuning improves precision (max = 1), recall (max = 0.9633), F1-score (max = 0.9662), and influences APFD value (max = 0.9835), indicating a direct connection between tuning and prioritization performance. Hence, this study underscores the importance of hyperparameter tuning in optimizing failure prediction models and their direct impact on prioritization performance.

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold $\mathbf{Q}$ value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization Technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots.

The continuous Integration (CI) process runs a large set of automated test cases to verify software builds. The testing phase in the CI systems has timing constraints to ensure software quality without significantly delaying the CI builds. Therefore, CI requires efficient testing techniques such as Test Case Prioritization (TCP) to run faulty test cases with priority. Recent research studies on TCP utilize different Machine Learning (ML) methods to adopt the dynamic and complex nature of CI. However, the performance of ML for TCP may decrease for a low volume of data and less failure rate, whereas using existing data with similar patterns from other domains can be valuable. We formulate this as a transfer learning (TL) problem. TL has proven to be beneficial for many real-world applications where source domains have plenty of data, but the target domains have a scarcity of it. Therefore, this research investigates leveraging the benefit of transfer learning for test case prioritization (TCP). However, only some industrial CI datasets are publicly available due to data privacy protection regulations. In such cases, model-based transfer learning is a potential solution to share knowledge among different projects without revealing data to other stakeholders. This paper applies TransBoost, a tree-kernel-based TL algorithm, to evaluate the TL approach for 24 study subjects and identify potential source datasets.

Locks are essential in multi-threaded applications as they provide a solution to synchronization of shared resources. However, improper management of locks and threads can lead to contention and surface as run-time performance degradation in the application. Nowadays, performance engineers use legacy tools and their experience to determine causes of lock contention but it takes significant expertise to use these tools. In this paper, a data clustering approach is presented to help identify lock contention faults. The classifier is trained leveraging run-time performance data acquired from a catalog of lock contention Java anti-patterns and code smells. The K-means unsupervised classifier algorithm was used to create the classification model and the results show that lock contentions can be classified into three clusters that can be identified into those caused by a) threads spending too much time inside the critical section, b) threads blocked because of high frequency access requests, and c) threads with a low contention. This classifier is intended to be used to help tailor recommendations to the developer based on the lock contention anti-patterns and type of lock contention.

The Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard has been proposed as a MAC protocol for industrial sensor networks as it provides a reliable media access control protocol under harsh conditions. This paper presents an algorithm for the optimization of a TSCH schedule based on a combined determination of interference free transmissions and Differential Evolution (DE) algorithm. Because of the inconsistent number of transmissions that a TSCH schedule can support, the standard mutation and cross-over steps of the DE algorithm had to be modified and a unique approach is presented that accommodates for this inconsistency. The feasibility of the optimization algorithm is demonstrated by showing an improvement in the throughput of a sensor network with heterogeneous sensors rates as the DE optimizer iterates through the population of schedules.

Prolonging sensor and network life is a significant concern for wireless sensor networks and in this paper we present a novel optimization technique for determining the superframe slot allocation for a beacon-enabled 802.15.4 superframe operating with sensors with different data acquisition periods. The optimization approach is based on a differential evolution algorithm that leverages a flat cluster network topology to constrain the contention free period (CFP) and the contention access period (CAP) of the superframe. In this work we also define a multi-superframe structure to help conserve energy in the network. A sensitivity analysis of the optimization shows that the optimized results are not too sensitive to the clustering algorithm but are very sensitive to the original network configuration, such as number of nodes and their locations and transmission range, and therefore is better suited for static sensor networks. We also demonstrate that the optimized superframe results in a 5% gain in energy conservation over other approaches that optimize the superframe based on the ratio of the beacon order (BO) to superframe order (SO).

The widespread use of applications through Wireless Sensor Network (WSN) demands an adaptive management system. One of the issues in autonomous WSN is finding the optimal solution for minimizing energy consumption while taking into account the application data rate requirements. This trade-off is done by modifying the size and composition of the MAC superframe. In this paper, an optimized application-aware superframe scheduling for clustered WSN is presented that optimizes the trade-off between power consumption and sensor data acquisition rate, by utilizing a differential evolution algorithm to find the optimal size and composition of the MAC superframe. The approach presented also leverages a cluster network topology in the design of the network. It also considers multi-superframe configurations that have not been previously leveraged in the literature. Simulation comparisons leveraging LEACH-C show that our approach can optimize the 802.15.4 superframe size and composition for particular sensor data rates and improve energy consumption in the network compared to using a standard single superframe.

The paper analyses the performance of a software defined wireless sensor network (SD-WSN) to a more conventional ad-hoc wireless sensor network (WSN). Recently SD-WSN has increased in popularity due to the demand of managed and service oriented sensor networks. In this analysis, we perform several simulation experiments comparing convergence time, packet reception ratio, and data delivery time for the SDN-WISE SD-WSN system and an ad-hoc WSN based the RPL routing protocol. The experimental results confirm the expected overhead required in a SD-WSN compared to an ad-hoc approach and exposes some design flaws in the SDN-WISE architecture.

This paper discusses the issue with scaling SIP/RTP services in a container orchestration cluster such as Kubernetes. In order to run SIP/RTP services in the Kubernetes platform, we require that state and affinity between the two ends, be maintained by the container orchestration cluster. Currently Kubernetes primarily supports stateless services like HTTP. The paper explains the challenge of the Kubernetes overlay network for SIP/RTP services by presenting four failure scenarios with the objective to validate the failure scenarios and present viable solutions. Two of the four failure scenarios have been validated and our approach to counter these failures are presented. One proposed solution is based on a SIP back-to-back user agent or proxy integrated with the Kubernetes environment and a second that leverages the Kubernetes Ingress architecture and services. A third probable solution discussed are the service meshes.

Containers are a method of virtualization. Advantages of containers include increasing utilization of bare-metal resources, and adding security isolation properties to various types of systems. These advantages make containers well-suited for applicable Internet-of-Things devices. This paper defines and describes our ‘kill switch’ Internet-of-Things container architecture pattern, aimed at enforcing multiple modes of application functionality. To our knowledge, this is a new and novel pattern that could lead to further research in the area. An example implementation that modifies the Docker engine, and an implementation using a Go script demonstrate that implementing the kill switch is feasible and achieves desired functionality.

Control algorithms are essential in the modern world to tackle with ever-changing environmental disturbances while meeting adequate safety standards. Control systems must be appropriately tuned for each application to ensure reliability and safety. This paper applies a multi-objective optimization method, Generalized Differential Evolution (GDE3), to tune a Linear Quadratic Regulator (LQR) while enabling posteriori decision-making. The utilized case study is the aircraft pitch control. We compare the results of GDE3 with Non-Dominated Sorting Genetic Algorithm (NSGA-II) and conventional tuning. The current findings show that the GDE3 performs better than the other methods. This paper sheds light on how trade-off and condition-specific based optimization can enhance real-time control systems.

In this paper, we study the effect of the dedicated short range communication (DSRC) system on the stability of an articulated heavy vehicle that is using active trailer steering to improve the lateral stability under a double lane change maneuverability test. Several design parameters of the communication system such as modulation, quantization, channel estimation algorithm and transmit diversity have been studied and the effect of each parameter on the active control system is illustrated through simulations. The numerical evaluations show that, by adopting proper configurations, the wireless communication system can perform as good as the wired system it replaces in terms of successfully stabilizing the vehicle.

The Collection Tree Protocol (CTP) is widely used for data collection in wireless sensor network applications due to its simplicity and its capability to dynamically adapt to ad-hoc changes in the network even though it is designed for static networks. With the increasing deployment of mobile wireless sensor networks, the performance of this scheme in mobile scenarios becomes extremely important. The motivation in using collection style protocols in mobile sensor networks is that the sensors primarily send data to key collecting nodes, and often there is no need for the more complex Mobile and Ad hoc routing protocols that are typically used in MANETs or the formation of clusters. Our objective in this paper is to provide a detailed analysis of the shortcomings of CTP in mobile scenarios, and to propose changes to CTP to improve its performance in mobile scenarios. This is done by a comprehensive simulation study of CTP's performance in mobile sensor environments, and identify root causes for its performance degradation in mobile scenarios. We also study the impact of introducing a mix of mobile and fixed nodes in a CTP network and evaluate how their presence improves network performance. Our results show that the performance of CTP in mobile environments. We conclude with proposed changes to CTP that improve its performance in mobile scenarios.

Recent advances in brain-computer interfaces (BCI) have enabled them as affordable consumer-grade devices for nonmedical purposes such as academic research, marketing, and entertainment. We report on the possibility of using BCIs to classify passwords into two classes—one class may be deemed as memorable and the other one as non-memorable—based on electroencephalogram (EEG) potentials collected by the BCI upon presenting the passwords to human participants. The memorable set consists of the most commonly used passwords, also known as "worst passwords lists", while the non-memorable set consists of randomly generated strings of characters, symbols, and numbers. When classifying passwords as memorable vs. nonmemorable, a classification accuracy of 76.5% was achieved. We found a positive correlation between password EEG features and password recall. We also report on users' choice of passwords, where 74% of participants were found to inadvertently choose the password with higher elicited voltage, when presented with two passwords to choose from.

Policy-Based Management Systems (PBMS) are becoming a critical component of Wireless Sensor Networks (WSNs). Due to hardware resource constraints, policy-based management applications on WSNs can store only a limited number of policies in the local memory of a sensor node and must recycle them when additional policies are required. To tackle these challenges, a new distributed policy-based management framework named TinyPolicy has been developed, which can store, locate, access, and execute policies in a WSN. This framework uses a P2P policy storage and deployment mechanism, named PolicyP2P, which is designed to take advantage of available memory in the network and replication thus resulting in a policy system that is more robust against node failure, and single points of failure.

Most of the recent proposed approaches for sen-sor(mote) localization are focused on 2-D environments with limited functionalities. This is mostly due to the nature of problem which is non-linear, large-scale, and has limited hardware resources. The micro-evolutionary algorithms (MEAs) utilize a small-size population to solve optimization problems. Therefore, such algorithms require much less processing time and memory than standard evolutionary algorithms (EA), suitable for implementation on embedded systems. In this paper, a novel protocol for localization of motes in 3-D environments is proposed, simulated, and discussed. The localization problem is modeled as an optimization problem. The proposed model is based on a realistic approach to the localization problem, where possible errors and noises in the localization procedure such as signal strength detection are addressed. To present a suitable approach to solve the proposed optimization model, a comparative study on performance of the micro-differential evolution (MDE) algorithms is performed and the results are discussed.

Internet was designed for network services without any intention for secure communication. Exponential growth of internet and its users have developed an era of global competition and rivalry. Denial of service attack by multiple nodes is capable of disturbing the services of rival servers. The attack can be for multiple reasons e. g. extortion or to beat the rivals. Peer to peer and cloud computing offer content services and content delivery networks and require reliable communication medium and servers for the clients. IBM, Amazon and Microsoft are offering peer to peer, cloud computing using port to port addressing and priority level identification and filtering mechanism by embedded Java and XML technologies. Cloud computing also involves various other technologies however analysis of every aspect of clouding is beyond scope of this paper. Oversim, OMNET, OPNET, ns2/ns3, GlomoSIM, Cisco packet tracer etc. are test bed simulation tools to analyze and generate reliable data. We have used Gia network within Oversim simulator to simulate a virtual overlay network DDoS attack based on work conducted by Srivatsa et. al. [9] and Naoumov et. al. [8].

In this paper we model the data traffic congestion scenario in a vehicular ad-hoc network, and we apply the Adaptive PI Rate Controller (APIRATE) to deal with the congestion. Derived from classical control theory, the APIRATE controller performs AQM (Active Queue Management) by controlling the source rate based on the instantaneous queue length of the congested node. We then study the performance of this controller in a mobile rural scenario, and show that it has better performance for streaming traffic when compared with other existing controller.

Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the adaptive proportional integral rate controller, a congestion control algorithm designed for the Internet, to deal with the problem of vehicle traffic congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data traffic congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle traffic congestion in a VANET.

In this paper, we establish a hidden 802.11 wireless channel, with the masking of the channel achieved by inserting intentional errors in the frame check sequence (FCS). We design a frame handler module to provide a proof-of-concept model of the side-channel using MATLAB and Simulink with communication toolbox. We justify using MATLAB over the other simulation tools because of its existing functions: physical layer IEEE 802.11 wireless local area networking (WLAN) standard, existing modular channel fading models, the MAC layer cyclic redundancy checksum (CRC) generator, the CRC Syndrome detector, and the capability of modifying fields in a frame. These existing functions allow for the creation of a frame handler which generates frames, according to our design, to be inserted as erroneous frames and recovers frames from normal 802.11 traffic. Herein we provide the design and details of the implementation of the channel. Our design offers the ability to introduce error detection and correction capabilities, and protection against passive monitoring defences. This simulation framework is a step towards the development of more sophisticated environments including multi-node simulations that maintain robust and reliable side-channel communication.

Vehicular ad-hoc networks (VANET) are a mobile adhoc networking technology to facilitate vehicle-to-vehicle and vehicle-to-roadside communication. A vehicle in VANET is considered to be an intelligent mobile node capable of communicating with its neighbors and other vehicles in the network. As in a mobile ad-hoc network (MANET) it is necessary to identify or address each vehicle in the vehicular ad-hoc network with a unique address. The current addressing mechanisms in VANET do not succeed in configuring the vehicle with a unique address. Furthermore, there is a need for address reconfigurations depending on the mobility patterns. In order to deal with these problems, we have presented a centralized addressing scheme for VANET using DHCP (Dynamic Host Configuration Protocol). Results obtained in our approach are compared against the results presented in one of the existing addressing mechanism in VANET. It is observed that our approach is efficient and feasible for vehicular ad-hoc networks.

Controlling access in pervasive environments is crucial and a significant challenge because users and devices can connect from anywhere which results in users and resources becoming available at any point of time and location depending on the situation. Access control policies for this type of environment are required to conform to high-level business notions. In pervasive environments, these high-level notions refer to contexts of the situation which can change unpredictably and must be interpreted semantically to maintain proper access control. Therefore, it is necessary to have a formal representation that represents semantics of the contexts, reflects the change of the situation, and can be shared and understood by a policy system. This paper addresses these issues by introducing a context management system that uses a semantic web approach as an underlying mechanism to model and represent semantics of the contexts. The system stores current contexts in a semantic knowledge base which is used by a semantic access control system in order to form access control policies and evaluate policies at run time. The approach is validated through a proof of concept implementation that includes performance results of the context management system as it responds to a change of the situation.

The DSRC standard gives the provision to process messages in XML format instead of the default binary encoding scheme. Although, use of XML encoding provides flexibility, it also introduces bandwidth communication overhead since the message size will increase and end to end communication delay since processing time will also increase. There are many proposed XML compression algorithms to decrease message size but few have been tested for processing and communication delays for inter vehicle communication. In this paper, performance measurements for various compression algorithms on DSRC messages are presented.

Coalition access control models are required in order to properly manage access to resources among different collaborating organizations. When these relationships are long term inter-organizational agreements and policies can be established that can satisfy appropriate access to the resources owned by those entities. When these coalitions are spontaneous access rights to resources among the parties in the coalition need to be specified by users and must be context dependant. A good example of this is in ad-hoc collaborative scenarios. Controlling access to private services being shared within the collaborative group is a challenge in these scenarios. This paper presents a semantic Web approach in order to represent context that can be shared and used by a policy engine to form dynamic groups based on the context of the situation, as well as manage access to the private Web services that each group introduces to the ad-hoc collaborative environment. The approach leverages the distributed policy framework (Rein) built on top of a rule-base reasoner (CWM)

No abstract available.

An approach that integrates service discovery with the Internet Engineering Task Force session initiation protocol to support service discovery of private services across a collaborative session is presented. Embedding the service discovery protocol within a session protocol facilitates the discovery of private services in a safe and controlled manner. Access to services is managed by defining local scopes that are shared among participants in the collaborative session. The fundamentals of the approach are presented using a simple two-party scenario and further extended by describing the approach using both central and peer-to-peer collaborative scenarios. Finally, the feasibility of the approach is demonstrated through a proof-of-concept demonstration of the sharing of Bluetooth services located in a private wireless personal area network across a telephone conversation

A significant deterrent to the ability to connect in a spontaneous manner in cross-enterprise collaborative applications is the difficulty in users from different domains being able to access resources or services located and owned by other entities. Coalition access control encompasses control mechanisms dealing with access between users of two or more different security domains. In this paper we present an approach to add contextual information to the distributed role based access control (dRBAC) model to support spontaneous coalition. The dRBAC model is a relatively new approach for coalition access control based on a delegation model but has been targeted towards pre-arranged delegations among distributed enterprises. A delegation architecture is presented that leverages SIP communication sessions to discover delegation security managers that can automatically exchange roles and delegations based on location and communication session contexts.

This paper presents an approach for the integration of Web Services with presence-based communication services for the purpose of facilitating pervasive computing. A principal goal for pervasive computing is to be able to have easy and secure access to services based on the context of the situation. Service discovery protocols are a key component in achieving this goal but the ability to successfully discover the appropriate services using service discovery is minimal. For many communication and collaborative sessions pushing of services to participants of the sessions is more effective. The approach presented integrates Web services description language (WSDL) with presence-based communications in order to advertise services connected a communication session.

This article introduces a representation known as Bayesian attributed hypergraphs (BAHGs) that are based on the integration of Bayesian networks and attributed hypergraphs. BAHGs are an augmentation to attributed hypergraphs that allow for the management of uncertainty, using Bayesian theory, and can reason about formations from the sensory data using simple graph operators. They allow for the creation of multiple instantiations of Bayesian networks while maintaining single instantiation of nodes that represent the same event. This unification of uncertainty management and attributed hypergraphs removes the need of maintaining and synchronizing between a representation for managing uncertainty and another to manage declarative knowledge. A formalism for the construction of a BAHG for image understanding is presented based on the decomposition by parts methodology and the use of geometric constraints among feature sets. An example is presented that performs perceptual grouping among fragmented 3-D surfaces in an attempt to group the surfaces into corners and continuous surfaces.

NRC publication: Yes