The emergence of large-scale quantum computers threatens the security of classical public-key cryptosystems, making it essential to adopt post-quantum (PQ) security in Intelligent Transportation Systems (ITS). We introduce a framework that blends quantum-resilient cryptographic primitives with a sharded blockchain architecture. Each shard maintains a local ledger for its vehicle group, enabling real-time transactions and efficient certificate management without overloading any single chain. A lightweight global chain periodically anchors all shards, preserving system-wide consistency and blocking malicious revocations. Vehicles register or revoke PQ credentials via a lightweight Proof-of-Stake consensus, while roadside units (RSUs) handle signature verification to offload on-board computation. We further demonstrate practicality through a federated-learning case study in which vehicles exchange signed model updates over the same secure channel. SUMO/TraCI simulations with 2 000 vehicles and 10 shards show that despite PQ overhead the system sustains near real-time delays and high throughput. The framework thus offers a decentralized, quantum-resilient solution for secure Vehicle-to-Everything communications in next-generation ITS.

Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices, effectively addressing data privacy and computational concerns. however, FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server, limiting deployment in resource-constrained wireless networks. In this paper, we propose a three-fold strategy: firstly, an Adaptive Feature-Elimination Strategy to drop less important features while retaining high-value ones; secondly, Adaptive Gradient Innovation and Error Sensitivity-Based Quantization, which dynamically adjusts the quantization level for innovative gradient compression; and thirdly, Communication Frequency Optimization to enhance communication efficiency. We evaluated our proposed model's performance through extensive experiments, assessing accuracy, loss, and convergence compared to baseline techniques. The results show that our model achieves high communication efficiency in the framework while maintaining accuracy.

In vehicular networks, resource-constrained vehicles often face challenges in executing computationally intensive tasks due to limited local resources. Task offloading to nearby vehicles with sufficient resources provides an effective solution. However, in high-mobility scenarios, selecting the most appropriate vehicle for task offloading becomes a challenging and time-consuming process, leading to increased delays and degraded network performance. This paper proposes a novel deep learning-based task offloading technique to address this issue. The proposed approach operates in two stages. First, a deep learning model classifies nearby vehicles into eligible and unqualified nodes based on their ability to meet task requirements. Second, from the pool of eligible nodes, vehicles are ranked according to their credibility scores. Credibility scores are dynamically updated based on task completion within specified deadlines. By prioritizing vehicles with high credibility scores, the proposed technique ensures efficient and reliable task execution. Experimental results demonstrate that the proposed approach significantly improves the task execution success rate, reduces task offloading delays, and enhances the overall performance of vehicular networks.

Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. However, the highly dynamic nature of VANETs introduces challenges, such as unpredictable network conditions, high latency, energy inefficiency, and task failure. This research addresses these issues by proposing a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation. The framework leverages supervised models for predicting optimal offloading strategies, reinforcement learning for adaptive decision-making, and PSO for optimizing latency and energy consumption. Extensive simulations demonstrate that the proposed framework achieves significant reductions in latency and energy usage while improving task success rates and network throughput. By offering an efficient, and scalable solution, this framework sets the foundation for enhancing real-time applications in dynamic vehicular environments.

Post-quantum cryptography (PQC) is essential to secure vehicular ad-hoc networks (VANETs) against emerging quantum computing threats. However, selecting an appropriate Post-Quantum Key Encapsulation Mechanism (PQ-KEM) is challenging due to varying performance metrics such as key generation time, encapsulation/decapsulation latency, and ciphertext overhead. This issue becomes particularly critical in VANETs, where vehicles and roadside units (RSUs) must rapidly and securely exchange data under dynamic network conditions. Current methods typically overlook the initial key distribution phase, leaving communications vulnerable at the earliest interaction. To address these challenges, we created an extensive, open-source benchmark dataset that rigorously evaluates several candidate PQ-KEM algorithms based on performance factors relevant to vehicular environments. Leveraging this benchmark, we developed a lightweight deep learning model that dynamically selects the most suitable PQ-KEM algorithm by predicting optimal performance considering security requirements and real-time conditions such as message size and network congestion. Each recommended PQ-KEM algorithm is authenticated using Dilithium-2 post-quantum signatures, ensuring secure and quantum-resilient initial key distribution between vehicles and RSUs. Our comprehensive simulations demonstrate that our adaptive PQ-KEM selector significantly reduces end-to-end latency and ciphertext overhead without compromising security, thus enhancing secure, efficient communication in VANET scenarios.

The emergence of wearable medical devices and wireless body area networks (WBANs) has enabled continuous, real-time patient monitoring. These systems generate large volumes of health data, requiring low-latency and reliable processing for timely interventions. However, local processing is often inefficient due to the energy and computational limitations of mobile devices. Offloading tasks to edge computing and cloud resources offers a promising alternative. Nonetheless, optimizing offloading decisions in dynamic healthcare scenarios remains challenging due to heterogeneous task requirements and varying computational resources. This paper presents a hierarchical actor-critic task offloading approach (HACTO), a deep-reinforcement-learning framework designed to enhance the efficiency and adaptability of task offloading in healthcare scenarios. By introducing a hierarchical decision structure, HACTO reduces complexity and improves learning performance. The problem is modeled as a Markov decision process and solved using the deep deterministic policy gradient algorithm. HACTO jointly optimizes task offloading with respect to three objectives: meeting task deadlines, minimizing the energy consumption of mobile devices, and reducing resource usage costs. Our experimental results show that HACTO outperforms traditional and deep-reinforcement-learning-based offloading strategies, making it a promising solution for intelligent task offloading in resource-constrained WBAN environments.

In the Internet of Medical Things (IoMT), specifically in the field of medical image classification—particularly for skin cancer detection—traditional methods face challenges related to data privacy, heterogeneity, and the need for personalization across institutions. This research proposes a personalized federated learning (PFL) framework Meta-XPFL that addresses these challenges through a decentralized approach, allowing institutions to collaboratively train models without sharing raw data. The framework integrates meta-learning for adaptability, and self-supervised learning to leverage unlabeled data and secure multiparty computation (SMPC). Adversarial training improves model robustness, while attention mechanisms enhance the focus on relevant image features. The use of explainable AI techniques ensures interpretability, which is crucial in clinical settings. To validate the proposed framework, experiments were conducted on the HAM10000 dataset for skin cancer classification, demonstrating significant improvements in model accuracy, privacy preservation, and robustness against adversarial attacks compared to traditional methods. The results indicate that the framework not only enhances scalability and diagnostic accuracy but also offers a privacy-preserving solution that can be extended to various types of medical images, making it adaptable for broader applications in IoMT.

Federated learning (FL) is a cutting-edge approach to machine learning where multiple clients (or nodes) collaboratively train a model while keeping their data localized. This method addresses significant privacy concerns and reduces data centralization risks. However, a key challenge in FL is efficiently selecting which clients contribute to the model and determining how often their updates should be aggregated. This process is crucial for enhancing model performance and maintaining data integrity. This paper introduces the Trust-Based Dynamic Node Selection and Aggregation Frequency Optimization methodology to tackle this challenge using a Deep Q-Network (DQN). We focus on dynamically selecting clients based on a trust metric that evaluates their reliability and the quality of their data contributions. This metric incorporates factors like historical accuracy, frequency of successful contributions, and consistency in participation. Furthermore, we optimize the frequency of aggregating client updates to improve learning efficiency and model accuracy. By integrating these elements, our approach aims to maximize the effectiveness of federated learning, ensuring that reliable and relevant data significantly influences the model, thereby enhancing its overall performance and trustworthiness.

Content Delivery Networks (CDNs) are designed to reduce user-perceived waiting times and alleviate backbone bandwidth pressure. Since CDN cache servers have limited storage capacity, effective cache replacement policies are needed. However, existing CDN cache replacement policies mainly focus on improving content hit rates. As a result, some content with long origin fetch latency may not be cached, resulting in the long tail latency and degrading user experience. In this paper, we present Hierarchical Sketch, an efficient, scalable, and latency-aware cache replacement algorithm. Our approach leverages hierarchical slicing and voting mechanisms on a modified sketch to optimize content caching, reducing sorting complexity from O(log n) to O(1) with minimal loss of hit rate. Extensive simulations on synthetic and real-life industry CDN traces demonstrate that Hierarchical Sketch outperforms other algorithms in four different scenarios, with up to a 15% improvement.

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.

Federated learning (FL) is a promising approach for training AI models across multiple clients in Edge Computing (EC), without sharing raw local data. By enabling local training and aggregating updates into a global model, FL maintains privacy while facilitating collaborative learning. Nevertheless, FL encounters several challenges, including trustworthy client participation, inefficient model aggregation due to client with malicious or less accurate model. In this paper, we propose a trustworthy FL method incorporating Q-learning, trust, and reputation mechanisms, enhancing model accuracy and fairness. This method promotes client participation, mitigates malicious attacks' impact, and ensures fair model distribution. Inspired by reinforcement learning, the Q-learning algorithm optimizes client selection using the Bellman equation, enabling the server to balance exploration and exploitation for improved system performance. Furthermore, we explored the advantages of peer-to-peer FL settings. Extensive experimentation demonstrates our proposed trustworthy FL approach's effectiveness in achieving high learning accuracy while ensuring fairness across clients and maintaining efficient client selection. Our results reveal significant improvements in model performance, convergence speed, and generalization.

The convergence of artificial intelligence (AI) and the space industry is a crucial step towards advancing humanity. It has the potential to create innovative technologies and services, revolutionize existing practices, and provide valuable insights to space researchers. Many private investors and government organizations possess high demand for satellite images for various reasons, including weather forecasting, global disaster management (e.g., floods, earthquakes, and tsunamis), space object location, land records, geolocations, and international shipment management. However, the traditional centralized economic model for space business is costly to implement in terms of infrastructure, deployment, security risks, ownership, and marketing of the data. Additionally, the initial acquisition of satellite images is usually retrieved in a raw format without any authentication or interpretation. Consequently, the integration of AI and the space industry has the potential to revolutionize the existing model by unlocking new avenues to monetize satellite data by introducing automated processes that are more secure and cost-effective. In this paper, we aim to use deep learning models that are capable of automatically analyzing and classifying raw satellite images into valuable and actionable formats. Furthermore, we are inspired to design a blockchain-based solution to make satellite image datasets available to private and government agencies through a user-friendly and secure marketplace. The proposed solution will focus on a decentralized socio-economic model using blockchain to increase the security of transactions. Blockchain networks facilitate the fair exchange of transactions using smart contracts that will execute and enforce agreement rules among the blockchain participants. The proposed research will mainly focus on the efficient design of smart contracts for satellite data blockchain. In addition, our integrated image quality assessment method is built and tested to accurately group high-quality satellite images into valid categories to make the trading and marketing process more efficient.

The emergence of social media platforms and the increase of users leads to a rise in a huge amount of shared information. Some applications may opportunistically sense and store the users’ data, including personal information, which raises concerns about privacy and security. In response, we developed an application called Slinker, which employs double encryption to protect users’ messages as they are transmitted through the network. The application allows end-users to set their own encryption key and settings and send it through social media platforms, which ensures double encryption from the end user and the providers. The GUI was designed using NetBeans and involved modifying and combining two encryption algorithms to achieve a double-encrypted code. The application was deeply tested using random keys and effectively encrypts and decrypts the transmitted data.

Abstract Arrhythmia is a fatal cardiac clinical condition that risks the lives of millions every year. It has multiple classes with variable prevalence rates. Some rare arrhythmia classes are equally critical as common ones, yet are very hard to detect due to limited training samples. While several methods accurately detect Arrhythmia's multi-class, minority class accuracy remains low and these methods are resource-intensive. Therefore, most of the existing detection systems ignore minority classes in their classification or focus on binary classification. In this study, we introduce RL-ECGNet, a resource-efficient reinforcement learning-based optimization for multi-class arrhythmia detection, encompassing minority classes, through ECG signal analysis. RL-ECGNet uses raw ECG signals, processes them to extract the temporal ECG features, and utilizes Reinforcement Learning (RL) to optimize the training and network hyperparameters of the Deep Learning (DL) models while reducing resource consumption. For evaluation, four DL models, namely, MLP, CNN, LSTM, and GRU, are trained and optimized. Moreover, time and memory usage are minimized to optimize resource consumption. Throughout the evaluation of the four DL models, the proposed RL model achieved accuracies ranging from 88.45% to 96.41% for all 9 arrhythmia classes, including minority classes. In addition, the proposed RL method improved performance by a factor ranging from 1.28 to 1.39 in terms of accuracy. Moreover, the optimized DL models had reduced training time, as well as minimized memory usage. The proposed method achieved resource consumption reduction ranging from 1.36 to 1.925 times for training time, and from 1.179 to 1.815 times for memory usage.

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage the processing and analytics are moved close to where the data is located (e.g., the Edge). Data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, can mislead clinical judgments and cause incorrect actions, if not managed properly. In this paper, inspired by Federated Learning (FL), we propose a novel approach using Federated Data Quality (FDQ) Profiling to assess DQ at the edge considering a global quality profile aggregated based on local profiles. We conducted experiments to evaluate the effect of FDQ profiling on DQ improvement considering the assessment of outlier detection and unbalanced data. The results demonstrated that the improved DQ has a positive impact on the accuracy of four conventional machine learning models.

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model’s training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient’s data similarity at the edge; and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models.

The traffic of modern data centers exhibits long-tail distribution, in which massive delay-sensitive short flows and a small number of bandwidth-hungry long flows co-exist. These two types of flows could share same bottleneck links in the data center networks but request different or even opposite network requirements. Existing solutions try to realize a trade-off between the requirements of different flows by either prioritizing short flows or limiting the buffer used by long flows at switches or end-hosts. However, they do not consider the dynamic traffic change and suffer from performance degradation, resulted from severe queueing delay and massive packet drops for short flows under current First-In-First- Out (FIFO) queueing mechanism. In this paper, we propose a novel buffer management scheme at switches, called Cut-in Queue (CQ), to achieve both low latency for short flows and high throughput for long flows. Based on network status in real time, CQ prioritizes short flows by dynamically cutting the short flows’ packets into the head of long flows or evicting some enqueued long flows’ packets and enables high throughput for long flows in most of the cases. Evaluation of both DPDK testbed and NS2 simulations show that CQ outperforms state-of-the-art buffer management schemes by reducing flow completion time by up to 73%.

Facial emotion detection and expressions are vital for applications that require credibility assessment, evaluating truthfulness, and detection of deception. However, most of the research reveal low accuracy in emotion detection mainly due to the low quality of images under consideration. Conducting intensive pre-processing activities and using artificial intelligence especially deep learning techniques are increasing accuracy in computational predictions. Our research focuses on emotion detection using deep learning techniques and combined preprocessing activities. We propose a solution that applies and compares four deep learning models for image pre-processing with the main objective to improve emotion recognition accuracy. Our methodology includes three major stages in the data value chain, pre-processing, deep learning and post-processing. We evaluate the proposed scheme on a real facial data set, namely Facial Image Data of Indian Film Stars for our study. The experimentation compares the performance of various deep learning techniques on the facial image data and confirms that our approach enhanced significantly the image quality using intensive pre-processing and deep-learning, improves accuracy in emotion prediction.

Extending workflow orchestration to embrace monitoring and adaptation within cloud environment is perceived to be a challenging activity. It has to consider different resources, require heavy processing to adapt to the dynamic nature of cloud environment. In this paper, we propose a multi-model framework for workflow resource monitoring, prediction, and adaptation. The framework supports continuous monitoring of several workflow runtime cloud entities and detect diverse types of violations (e.g. resource saturation). Moreover, collected logs resulted from monitoring are used as a training dataset for predicting resource shortage. Furthermore, two adaptation strategies are proposed to cope with environment resources changes and avoid violations: 1) monitoring-based adaptation and 2) prediction-based adaptation. Both adaptation schemes perform the necessary actions to adapt resources according to workflow required quality levels. To evaluate our monitoring and adaptation approaches we used a real cloud environment where we perform a couple of experimental scenarios. Experiments results showed that our framework and proposed monitoring, prediction and adaptation schemes are efficient in detecting violations, accurately predicting cloud resource shortages and accordingly issuing the proper adapting decisions.

Obesity has been recognized to be among the principal causes of many chronic diseases such as diabetes, cholesterol, hypertension, and other cardiovascular diseases. Therefore, monitoring, controlling, and preventing obesity will mitigate the risks generated from the complications of these diseases. Comprehensive preventive measures are essential to control the spread of obesity, while healthcare systems should be organized on the basis of locally derived data to provide adequate and affordable care to the increasing groups of overweight and obese people. In this paper, we propose a hybrid model that relies on both data collected from sensors and participatory data collected from a social network community established to provide value-added obesity awareness, monitoring, and prevention. The model encompasses some key smart features including tracking food intake, lifestyle, and exercise activities, generating warnings and recommendations, and triggering interventions whenever needed. Our model also mines the collected data to produce statistical analysis that can be used by health authorities to have a clear picture of the health status of the population and might help in making rational and informed decisions. Moreover, we implement a prototype of our model as a set of Web services using the SOA paradigm and lightweight protocols. Promising results of our prototype are reported and analyzed.

Monitoring heart diseases often requires frequent measurements of electrocardiogram (ECG) signals at different periods of the day, and at different situations (e.g., traveling, and exercising). This can only be implemented using mobile devices in order to cope with mobility of patients under monitoring, thus supporting continuous monitoring practices. However, these devices are energy-aware, have limited computing resources (e.g., CPU speed and memory), and might lose network connectivity, which makes it very challenging to maintain a continuity of the monitoring episode. In this paper, we propose a mobile monitoring solution to cope with these challenges by compromising on the fly resources availability, battery level, and network intermittence. In order to solve this problem, first we divide the whole process into several subtasks such that each subtask can be executed sequentially either in the server or in the mobile or in parallel in both devices. Then, we developed a mathematical model that considers all the constraints and finds a dynamic programing solution to obtain the best execution path (i.e., which substep should be done where). The solution guarantees an optimum execution time, while considering device battery availability, execution and transmission time, and network availability. We conducted a series of experiments to evaluate our proposed approach using some key monitoring tasks starting from preprocessing to classification and prediction. The results we have obtained proved that our approach gives the best (lowest) running time for any combination of factors including processing speed, input size, and network bandwidth. Compared to several greedy but nonoptimal solutions, the execution time of our approach was at least 10 times faster and consumed 90% less energy.

Global demographic trends clearly point out that the world population is ageing owing to a combination of dropping mortality rates and increasing life expectancy. The global community is looking for ways to address the pressing societal challenge of providing effective and efficient healthcare to the elderly. It is difficult to achieve satisfactory results merely by relying on scaling up of conventional healthcare infrastructure as the conventional techniques will not be sufficient to assist the elderly to independently live in house especially if they are suffering from chronic diseases, thus require continuous health monitoring. It is imperative to exploit the advances in emerging technologies such as biosensors, mobile devices, and communication networks to provide remote health monitoring services along with the physical infrastructural facilities. Remote/continuous monitoring of patients with chronic diseases is being considered as an efficient and cost-effective solution, which will reduce the burden on the elderly as well as on the health authorities and the government's expenses. While considerable research and development is being undertaken in this field, most of the current state of the art reflects a lack of a concerted and cohesive approach to develop an integrated remote health monitoring system. The present paper proposes a novel healthcare monitoring system based on an integrated and scalable architecture which provides flexibility and enables interoperability between myriads of healthcare monitoring devices and products.

Nowadays, mobile applications/devices have become the trends, especially, when they were gradually shifted from basic communication services to supporting more sophisticated service provisioning. Mobile applications are usually very light, are nowadays likely to be often connected to the Internet, and can be used quite easily. However, these applications exhibit some challenges related to limited resources they have access to, including limited processing power, memory, storage size, battery power, and intermittent network connection. In fact, these considerations have to be taken seriously into consideration when developing mobile applications especially if those applications will be used for critical services, for example, to collect and report vital health data over a long period of time. In this paper, we study the use of mobile applications for monitoring patient's vital. Mobile devices, through an application, are connected to body-strapped biosensors to collect and synchronize these parameters with information systems. This synchronization should be done in such a way that the cost of synchronization is kept low and urgent readings are delivered as soon as possible. To optimize the synchronization process and reduce its cost, we propose and validate cost-oriented algorithms. A case study is developed to illustrate the applicability and effectiveness of our innovative techniques in making continuous monitoring an efficient process.

Various and independent studies are showing that an exponential increase of chronic diseases (CDs) is exhausting governmental and private healthcare systems to an extent that some countries allocate half of their budget to healthcare systems. To benefit from the IT development, e-health monitoring and prevention approaches revealed to be among top promising solutions. In fact, well-implemented monitoring and prevention schemes have reported a decent reduction of CDs risk and have narrowed their effects, on both patients' health conditions and on government budget spent on healthcare. In this paper, we propose a framework to collect patients' data in real time, perform appropriate nonintrusive monitoring, and propose medical and/or life style engagements, whenever needed and appropriate. The framework, which relies on service-oriented architecture (SOA) and the Cloud, allows a seamless integration of different technologies, applications, and services. It also integrates mobile technologies to smoothly collect and communicate vital data from a patient's wearable biosensors while considering the mobile devices' limited capabilities and power drainage in addition to intermittent network disconnections. Then, data are stored in the Cloud and made available via SOA to allow easy access by physicians, paramedics, or any other authorized entity. A case study has been developed to evaluate the usability of the framework, and the preliminary results that have been analyzed are showing very promising results.

The new concepts of ubiquitous computing are being integrated in healthcare technologies and led to the emergence of pervasive healthcare systems. This development is shifting the care that patients get to the comfort of their own homes. This paper surveys existing pervasive healthcare solutions including those developed in academia as well as commercial solutions from the industry sector. This paper also develops a set of criteria, which were used to classify and compare these solutions. These criteria are: integrity, confidentiality, mobility and context-awareness. We discuss some drawbacks of existing solutions, and propose future directions in pervasive healthcare. Finally, the paper draws some guidelines and best practices, which are predicted to shape future pervasive healthcare systems.

Despite the rapid growth of the mobile technology, mobile devices are still considered as resource constrained with limited battery. Same computations are awkward to be undertaken on these devices with limited processing capabilities. Other processes are costly in terms of battery consumption. Ideally, mobile applications will have the possibility to decide either to do a computation locally or remotely depending on the current device capabilities status. Making such decision is very challenging as many interrelated factors are to be considered (e.g. network connection, battery level, and processing capabilities). In this paper, we propose a framework that supports developers in implementing such smartness fitness within their mobile applications. This solution provides approaches in form of algorithms to instrument code of mobile applications to behave in smart way. Incorporating these algorithms will allow for on-the-fly decision of local versus remote computation using a calculated cost function. We conducted some experimental scenarios to evaluate the usability and effectiveness of our decision-based algorithms. The results we have obtained prove that for the same computation, depending on the size of data, the network status and the device status, the decision of the engine may differ.

Standard specifications of Web Services are mainly concerned with Web Service publishing and discovery. However, there is no standard regarding Web Service selection, which is very crucial for providers and clients. It will support clients in selecting Web Services based on required Quality of Web Service (QoWS) and support providers to remain competitive. Most of solutions on Web Service selection are very often based on a central component to make the selection decision, do not scale to the growing number of clients and Web Service providers, and/or lack trustworthiness. The objective of our approach is to support clients in selecting, and monitoring appropriate Web Services while increasing scalability, trust and reliability of Web Service selection. We propose a framework based on a federation of cooperative brokers. Each broker in the federation manages Web Services within its domain of expertise, and cooperates with its peers to select appropriate Web Services. We describe the federation management operations and our cooperative brokers QoS-aware selection algorithm. We propose certification and monitoring of Web Services as well as monitoring of brokers. The implementation and the experiments we have conducted evaluated the performance of our approach. The obtained results show that: the load is shared and distributed among brokers, a large number of client requests with different QoWS requirements were served, the architecture can scale up in order to include other brokers and/or other Web Services, and selection is guaranteed, trustworthy as it is supported by monitoring.

Mobility-aware Web services (MWS) should become nowadays an important research area as the number of powerful mobile devices proliferates and their usage for daily business transaction increases. Quality of Web Service (QoWS) assurance for MWS is very crucial for mobile users, however, it is highly affected by the available resources on mobile devices consuming these services and the performance of MWS. In this paper, we propose an architecture for MWS selection based on QoWS and resources requirements. The main purpose of the architecture is to support the client in selecting MWS based on desired QoWS as well as on its device resources availability. This architecture requires a concise description of QoWS and resources requirements. For this purpose, the architecture proposes a verification scheme to verify the conformity of claimed MWS QoWS and required device resources compared to the published one. A set of validation test cases are executed to measure, for each specific MWS operation: the required battery consumption, memory, CPU, and network throughput. The verification is used as input to a three-tier selection process in which selected MWS are those who passed the verification test cases. As proof of concept, a prototype has been implemented to evaluate the verification scheme and show its importance in selecting the best MWS while supporting the QoWS and the required device resource availability.

Quality of Web Service (QoWS) support for Mobility-aware Web services (MWS) is critical for mobile users since it relies on the available resources on mobile devices consuming these services. In this paper, we propose a selection model for MWS based on QoWS and device resources requirements. The main purpose of the model is to support the client in selecting MWS based on desired QoWS as well as on its device resources availability. We propose a verification scheme to verify the conformity of claimed MWS QoWS and required device resources compared to the published one. The verification is used to support selection of MWS. The implementation of our model is discussed and the importance of our verification scheme is highlighted.

Web Services are considered an attracting distributed approach of application/services integration over the Internet. As the number of Web Services is exponentially growing and expected to do so for the next decade, the need for categorizing and/or classifying Web Services is very crucial for their success and the success of the underlying SOA. Categorization aims at systematizing Web Services according to their functionalities and their Quality of Service attributes. Communities of Web Services have been used to connect Web Services based on their functionalities. In this paper, we augment the community approach by defining a new community to monitor Web Services operating in any Web Services community. This will be of prime importance for communities' creators/managers willing to protect and sustain their communities. This paper defines the overall architecture of the monitoring community and the basic services it offers to its various classes of customers.

Discovering and accessing services while on the road is an important component in the architecture of future vehicular ad hoc networks, and for a successful deployment of services. Several studies have focused on the design and development of new routing and dissemination techniques that allow vehicles to communicate with each other and with road side units. However, detecting and reaching available services in a vehicular network remains problematic due to the amount of wireless traffic generated when service queries or advertisements are flooded across the network. In this paper, we propose a cooperative hybrid service discovery scheme for discovering services provided by mobile vehicles. This scheme is achieved through cooperating vehicles using store-and-forward approach and by sharing collected service information. We also propose to study the performance of the scheme by varying its degree of reactiveness and proactiveness. It is integrated with a caching mechanism which substantially improves the performance of the service discovery in terms of reduction of the traffic generated, and minimization of the response time while increasing the discovery success rate.

Service computing has become a very promising area of research and development for business-to-business integration, enterprise service migration and communication on the Internet. It provides the potential of communicating businesses online, and expands to become a large scale organization. Service market is growing continuously and need for service innovation is associated with demands for service research and education. Initiatives for service computing curriculum started in addressing/incorporating service courses within graduate programs for instance computer science, software engineering, and web engineering. Ideas for developing a program for service computing is in its initial state and some initiatives are ongoing. In this paper, we describe a case study of Master of Science in Service Computing proposal. We propose the curriculum structure that consists of key core courses, business process modeling and implementation courses, in addition to specialized key courses on service computing. We finally highlight the implementation of this curriculum and the possible issues that might be faced.

Quality of service (QoS) support in Web services plays a great role for the success of this emerging technology. In this paper, we present a QoS broker-based architecture for Web services. The main goal of the architecture is to support the client in selecting Web services based on his/her required QoS. To achieve this goal, we propose a two-phase verification technique that is performed by a third party broker. The first phase consists of syntactic and semantic verification of the service interface description including the QoS parameters description. The second phase consists of applying a measurement technique to compute the QoS metrics stated in the service interface and compares their values with the claimed one. This is used to verify the conformity of a Web service from the QoS point of view (QoS testing). A methodological approach to generate QoS test cases, as input to QoS verification is used. We have implemented a prototype that includes the verification and certification components of the broker. We performed experiments to evaluate the importance of verification and certification features in the selection process using real Web services.

Current object-oriented (OO) software systems must satisfy new requirements that include quality aspects. These, contrary to functional requirements, are difficult to determine during the test phase of a project. Predictive and estimation models offer an interesting solution to this problem. This paper describes an original approach to build rule-based predictive models that are based on fuzzy logic and that enhance the performance of classical decision trees. The approach also attempts to bridge the cognitive gap that may exist between the antecedent and the consequent of a rule by turning the latter into a chain of sub rules that account for domain knowledge. The whole framework is evaluated on a set of OO applications.