Electric Vehicle Charging Stations (EVCSs) are vital to smart grid infrastructure but remain exposed to privacy breaches and poisoning attacks in federated learning (FL). We present FLCMed-TAD, a defense-aware FL framework that integrates coordinate-wise median aggregation with temporal anomaly scoring at the fog layer to detect and suppress adversarial clients. Using the EV-specific CICEVSE2024 dataset, we evaluate FLCMed-TAD under both data and weight poisoning scenarios. While baseline FL aggregation collapses under 22% poisoned clients, yielding $\mathbf{R}^{2}$ below -2.5, our approach maintains positive generalization and recovers over $2.5 \ \mathbf{R}^{2}$ points compared to the unprotected case. These findings show that FLCMed-TAD safeguards model integrity while preserving forecasting accuracy in adversarial settings.

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

The Social Internet of Things (S-IoT) paradigm is focused on topic of the Internet of Things (IoT), which accelerates the object issues by working with the concept of social networks. Searching and finding a new object in the community are considered to manage the number of friends and complex relationships between them and affect the ability to navigate at the cloud-edge layer, and resources, such as battery lifetime of S-IoT devices and energy resources, are important challenges in this field. In the processing of social messages of remote devices, increasing the battery life of devices that require such requirements plays the most important role. In this research, a collaboration scenario is presented to consider object attributes, friend’s functions and intelligent friend selection among objects for group messaging. First, a general reference model is designed and presented to select a friend to access group message remote processing services and minimize cloud-edge resources. The simulation results show that, for the correct communication of friends at the edge of the network and in each service discovery, according to the length of the path in the network, it is possible to establish stable communication and make better service with the least possible. The results show that if we want to develop a method for friendship between objects in communication in cloud computing, the proposed method can greatly improve the effectiveness of providing reliable message processing types.

As the global transition towards renewable energy sources accelerates, solar power becomes an increasingly important solution. Identifying and understanding the current distribution of solar panel installations is crucial for future planning and decision-making process. This paper introduces SolarDetector, a transformer-based neural network model, which we developed and fine-tuned for the accurate detection of solar panels. It achieves 91.0% mIoU for the task of masking solar panels on SWISSIMAGE dataset.

Maps provide various sources of information. An important example of such information is textual labels such as cities, neighborhoods, and street names. Although we treat this information as facts, and despite the massive effort done by providers to continuously improve their accuracy, this data is far from perfect. Discrepancies in textual labels rendered on the map are one of the major sources of inconsistencies across map providers. These discrepancies can have significant impacts on the reliability of the derived information and decision-making processes. Thus, it is important to validate the accuracy and consistency in such data. Most providers treat this data as their propriety data and it is not available to the public, thus we cannot compare the data directly. To address these challenges, we introduce a novel computer vision-based approach for automatically extracting and classifying labels based on the visual characteristics of the label, which indicates its category based on the format convention used by the specific map provider. Based on the extracted data, we detect the degree of discrepancies across map providers. We consider three map providers: Bing Maps, Google Maps, and OpenStreetMaps. The neural network we develop classifies the text labels with an accuracy up to 93% in all providers. We leverage our system to analyze randomly selected regions in different markets. The studied markets are USA, Germany, France, and Brazil. Experimental results and statistical analysis reveal the amount of discrepancies across map providers per region. We calculate the Jaccard distance between the extracted text sets for each pair of map providers, which represents the discrepancy percentage. Discrepancies percentages as high as 90% were found in some markets.

We demonstrate MapsVision, a computer vision-based framework capable of identifying discrepancies across different map providers for similar geographical locations. In this study, we primarily focus on three map providers including: (a) Bing Maps, (b) Google Maps, and (c) OpenStreetMap. MapsVision detects textual data discrepancies such as: (1) missing location labels (2) misspelled or different keywords, (3) shifted labels, and (4) level of significance manifested by text or label font-size and color. For a given location, our MapsVision framework compares textual labels based on a ground truth entered manually to those that exist in the three map providers. We then use the results of the textual extraction to determine the accuracy of textual data appearing on map providers. Our framework intelligently identifies the set of techniques for each map providers' that can maximize the overall detection accuracy. MapsVision is composed of three main building blocks including: (a) a capturing module that captures map tiles from map providers, (b) an analysis tool that uses computer vision and text-analytic techniques, and (c) a rich visualization interface for displaying statistical and real-time analytics. The objective of MapsVision is to help map editors improve the textual quality of their maps compared to other map providers.

Today, people's lives are enriched by the integration of electronic maps via smartphones. Electronic maps are required for a variety of commercial activities, such as catering, movie viewing, and tourism. Route planning and navigation are particularly intrinsically linked to electronic maps. As a result, it is critical that the roads on the electronic map are complete and accurate. At the present time, there are discrepancies between the map roads of various providers. This paper evaluates the roads on various map providers' maps. Due to the varied terrain depicted on the map, assessing the road properties can be challenging. Additionally, roads of varying thicknesses exist within a tile image, making it difficult to quantify the map's road lengths. This paper proposes a method for extracting road segments using an image binarization technique and employs edge erosion to assist in automatically computing the length of roads within maps. Throughout the paper, we provide comparison and statistical analysis on using our proposed road length detection model across map providers. Results show that our detection model can identify road length accurately and hence provide an overall measure of quality of maps.

As more computing operations shift from the cloud to edge environments, the need for reliable and efficient resource allocation becomes inevitable. Unlike the cloud, edge computing environments often are equipped with limited computational capabilities which makes the task allocation process time consuming and challenging. When allocating resources, it is imperative to consider multi-criteria based on a number of factors including task requirements and the availability of existing edge-based computational capabilities. To this extent, we consider the resource allocation process across edge environments as an optimization problem that can be solved using multi-criteria decision analysis methods (MCDA). In this paper, we present an extension to our Edgify dynamic resource provisioning model that incorporates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for enhancing the decision making when provisioning edge-based resources across distributed edge or fog environments. We evaluate our proposed Edgify solution through multiple experiments which demonstrate the effectiveness of our proposed decision-making optimization approach.

Collecting data from smart Internet of Things (IoT) devices is becoming an increasingly essential part of many of the existing industrial applications. The importance of this data collection relies on the fact that it can uncover valuable insights and enable smarter, faster decision making. This enables organizations to quickly adapt to changes in the workflows, reduce downtime and expand the production capacity and enhance the overall operating efficiency. The problem, however, is that many of these Industrial IoT (IIoT) applications can considerably be influenced by the composition of RESTFul APIs and the microservices architecture they integrate. In addition, IIoT applications do not take into consideration the dynamism of service-based environments and requirements. To overcome these challenges, it is essential to consider the Quality of Service (QoS) characteristics of RESTful APIs and microservices particularly that these properties may fluctuate during their lifecycle. In this paper, we introduce a quality-aware microservices' architecture that continuously monitors the behavior of these services in delivering the required functionality. This paper presents experimental validation results and analysis of the presented ideas.

The detecting heartbeat abnormalities (i.e. arrhythmia) depends mainly on the examination of ECG signals over an adequate sampling period. This sampling period needs to contain sufficient data that can be extracted as features. Such features provide accurate measures for the diagnosis of heart arrhythmias. The problem, however, is that the analysis of ECG data requires to properly detect arrhythmias requires many ECG samples to be collected from patients and requires the extract of many features (e.g. temporal or morphological properties). In this paper, we introduce a neural network based solution that can detect heartbeat abnormalities with aim to minimize the feature-set required during the analysis process. Throughout the paper, we present results from testing our neural network using the MIT/BIH Arrhythmia database which show an accuracy rate of 98.70% success rate. We also provide insights to efficiently classify heartbeat rhythms as normal, bradycardia or tachycardia.

Addressing environmentally safe management of waste is becoming increasingly a challenging task. The predicament of the rate at which waste is generated due to increasing populations is also contributing to this challenge. One possible approach for effectively handling waste can be achieved by source reduction and recycling. The problem, however, improving the collection of waste can be costly particularly during the source separation process after waste is collected. It would be desirable if there exists a mechanism that can help municipalities, local governments or waste management companies to monitor in real-time sources of violations prior to the waste collection process. In this paper, we introduce recycle.io, an Internet of Things (IoT)-enabled waste management system that is based on a serverless architecture that can identify these sources of violations. Using recycle.io, it is then possible to track the violations geographically which can help local governments, for example, to improve or enforce tighter regulations for waste disposal. Our recycle.io system uses Microsoft Azure IoT Hub for device management. Throughout the paper, we demonstrate usefulness of using our approach for urban waste management in smart cities.

Identifying cardiac abnormalities has mainly been determined by the observation of electrocardiogram (ECG) signals. To collect ECG signals, it is often necessary to place ECG electrodes on the body for critical analysis of ECG data transmitted by such electrodes. By analyzing this collected data, it is then possible, for example, to examine the intervals between the heartbeats (or R-R intervals) to measure the heart rate variability (HRV). However, this process requires a multilayered setup for both hardware and software which can be costly and time consuming. To overcome these challenges, we introduce in this paper a real-time millimeter-wave radar-based, non-contact vital sign monitoring system that is capable of detecting the heart variability rate without the use of any heart rate sensors or wires required. Through this system, it is then possible to detect any heart rate abnormalities by analyzing the collected data. Throughout the paper, we present results for three individuals and compare our approach to heart rate monitoring devices and Apple Watch.

As the number of cloud services continues to increase, selecting services of interest across one or more cloud service environments using existing service selection methods raises a number of concerns such as performance, efficiency, end-to-end reliability and most importantly quality of search results. Clients often spend a considerable amount of time manually reading cloud providers' documentation to determine services that can meet their objectives and satisfy the application's requirements. Furthermore, cloud service providers' Quality of Service (QoS) claims for published services might not always be trustworthy and current cloud service selection methods do not take into consideration the dynamism of cloud environments as they are constantly changing. In addressing these challenges, we developed the Cloud Application Management (CAM), a multilayered framework that employs a meta-heuristic approach that is based on QoS for cloud services (QSCS) for enabling clients to effectively manage and control the quality of their applications deployed in the cloud. CAM supports the self-adaptive nature of the service selection process and adapts to the changes in clients' requirements and interests.

In recent years, more emphasis on how to predict traffic of web pages has increased significantly and prompted the need for exploring various methods on how to effectively forecast future values of multiple times series. In this paper, we apply a forecasting model for the purpose of predicting web traffic. In particular, we use existing Web Traffic Time Series Forecasting dataset by Google to predict future traffic of Wikipedia articles. Predicting web traffic can help web site owners in many ways including: (a) determining an effective strategy for load balancing of web pages residing in the cloud, (b) forecasting future trends based on historical data and (c) understanding the user behavior. To achieve the goals of this research work, we built a time-series model that utilizes RNN seq2seq model. We then investigate the use of symmetric mean absolute percentage error (SMAPE) for measuring the overall performance and accuracy of the developed model. Finally, we compare the outcome of our developed model to existing ones to determine the effectiveness of our proposed method in predicting future traffic of Wikipedia articles.

The proliferation of cloud computing and web-based technologies have made it possible for universities to expand their academic networks reaching a wider range of students. In an effort to expand offered services while reducing costs, universities began exploring the use of distributed software platforms and middleware infrastructures to make laboratories accessible over the Internet. However, there are major challenges in the way these platforms enable students to effectively interact with a remote laboratory environment. In this paper, we introduce a remotely-controlled middleware infrastructure called Lab-as-a-Service (LaaS) that is based on cloud computing and service-oriented architecture (SOA) concepts. LaaS aims at providing educators with the necessary tools to deploy course lab components over the Internet more efficiently while enabling students to productively complete lab material remotely. Throughout the paper, we discuss the overall architecture of LaaS, use cases and implementation details. We further use our LaaS framework to provide insights on improving the remote laboratory experience and interactive online learning models.

Computer education is in the midst of a major transformation as a result of the rapid emergence of new technologies, intersection of computing with other disciplines and growing interest among students in computing courses. Such transformation prompted educators to redefine the way computing education is delivered and experiment a wide range of learning approaches. One of these approaches is the integration of hardware prototyping platforms such as Arduino, Raspberry Pi and Intel Internet of Things as part of project-based learning. These platforms have become in recent years instrumental tools for improving students' learning process and promoting proactive thinking. The aim of this study is to investigate the usefulness of integrating low cost open-source hardware platforms into engineering and computer science courses. In particular, we explore the use of an experiential learning approach using these kits for improving the students' learning experience in computer science and engineering courses. Throughout this paper, we discuss the outcome of our investigation and provide insights on how to make classrooms more experiential using the hardware prototyping paradigm.

Abstract Web service interfaces can be discovered through several means, including service registries, search engines, service portals, and peer‐to‐peer networks. But discovering Web services in such heterogeneous environments is becoming a challenging task and raises several concerns, such as performance, reliability, and robustness. In this paper, we introduce the Web Service Broker (WSB) framework that provides a universal access point for discovering Web services. WSB uses a crawler to collect the plurality of Web services disseminated throughout the Web, continuously monitor the behavior of Web services in delivering the expected functionality, and enable clients to articulate service queries tailored to their needs. The framework features ranking algorithms we have developed which are capable of ranking services according to Quality of Web Service parameters. WSB can be seamlessly integrated into the existing service‐oriented architectures. Copyright © 2010 John Wiley & Sons, Ltd.

While several service discovery protocols and standards have been proposed for supporting service discovery from mobile devices, this remains a challenging problem. In many cases, mobile clients may discover services which they consider relevant but soon realize that such services are not completely usable on their mobile devices due to compatibility and interoperability issues. Without integrating device capabilities into the discovery process, or a device-aware mobile service discovery, it becomes extremely difficult to determine whether discovered services may or may not function properly within the device's constraints. This paper introduces a solution to this problem and proposes MobiEureka, a mobile device- aware system for enhancing the discovery of mobile services using mobile devices. This paper presents experimental validation, results, and analysis of the presented ideas.

Articulating proper services search queries has been a challenging task for clients when searching for relevant Web services. Service discovery search results will not be improved unless we determine ways for correctly understanding client discovery goals. In this paper, we introduce a solution to this problem and perform a key study for understanding service discovery goals. We introduce the concept of Quality of Web Service (QWS) for our quality-driven ranking mechanism. Based on our study, we determine that service discovery goals can be defined as exploratory or informational. We further use these findings to demonstrate how the knowledge of service discovery goals are beneficial in improving the way clients perform service search queries. Results from our experiments are intriguing and show that the performance of informational service queries in terms of precision improves the querying process by 36.26% and 40.39% when compared to Google's PageRank and Yahoo, respectively. We further use our findings to provide insights on improving the service retrieval process.

Differentiating between Web services that share similar functionalities is becoming a major challenge into the discovery of Web services. In this paper we propose a framework for enabling the efficient discovery of Web services using Artificial Neural Networks (ANN) best known for their generalization capabilities. The core of this framework is applying a novel neural network model to Web services to determine suitable Web services based on the notion of the Quality of Web Service (QWS). The main concept of QWS is to assess a Web service's behaviour and ability to deliver the requested functionality. Through the aggregation of QWS for Web services, the neural network is capable of identifying those services that belong to a variety of class objects. The overall performance of the proposed method shows a 95% success rate for discovering Web services of interest. To test the robustness and effectiveness of the neural network algorithm, some of the QWS features were excluded from the training set and results showed a significant impact in the overall performance of the system. Hence, discovering Web services through a wide selection of quality attributes can considerably be influenced with the selection of QWS features used to provide an overall assessment of Web services.

Searching for Web service access points is no longer attached to service registries as Web search engines have become a new major source for discovering Web services. In this work, we conduct a thorough analytical investigation on the plurality of Web service interfaces that exist on the Web today. Using our Web Service Crawler Engine (WSCE), we collect metadata service information on retrieved interfaces through accessible UBRs, service portals and search engines. We use this data to determine Web service statistics and distribution based on object sizes, types of technologies employed, and the number of functioning services. This statistical data can be used to help determine the current status of Web services. We determine an intriguing result that 63% of the available Web services on the Web are considered to be active. We further use our findings to provide insights on improving the service retrieval process.

Growth and propagation of the Internet has been a contributing factor for information overload which acts as a deterrent for quick and easy discovery of information. As Web services proliferate, the same dilemma perceived in the discovery of Web pages will become tangible. Currently, the automatic discovery of Web services, an important capability of service- oriented architecture (SOA), is mainly achieved by performing inquiries to business registries such as the UDDI or ebXML. The ability to discover Web services across multiple heterogeneous registries is becoming a challenging task and raises several issues such as performance, reliability, and robustness. In this paper, we introduce the Web Service Repository Builder (WRSB) that serves as an integrated SOA registry and repository for managing the proliferation of Web services and system artifacts. Specifically, the proposed framework actively captures and navigates among multiple service registries and provides a unified environment for the discovery of Web services. The WSRB framework is compatible with, and can be integrated seamlessly into, the existing infrastructure without any modifications to the existing environments.

Major research challenges in the next generation of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end quality of service (QoS), supporting high data rates over wide area and enabling users to specify their personal preferences. The integration and interoperability of this multitude of available networks will lead to the emergence of the fourth generation (4G) of wireless technologies. 4G wireless technologies have the potential to provide these features and many more, which at the end will change the way we use mobile devices and provide a wide variety of new applications. However, such technology does not come without its challenges. One of these challenges is the user's ability to control and manage handoffs across heterogeneous wireless networks. This paper proposes a solution to this problem using artificial neural networks (ANNs). The proposed method is capable of distinguishing the best existing wireless network that matches predefined user preferences set on a mobile device when performing a vertical handoff. The overall performance of the proposed method shows 87.0 % success rate in finding the best available wireless network. To test for the robustness and effectiveness of the neural network algorithm, some of the features were removed from the training set and results showed a significant impact on the overall performance of the system. Hence, managing vertical handoffs through user preferences can be significantly affected with the selection of features used to provide the closest match of the available wireless networks.

Discovering Web services using keyword-based search techniques offered by existing UDDI APIs (i.e. Inquiry API) may not yield results that are tailored to clients' needs. When discovering Web services, clients look for those that meet their requirements, primarily the overall functionality and Quality of Service (QoS). Standards such as UDDI, WSDL, and SOAP have the potential of providing QoS-aware discovery, however, there are technical challenges associated with existing standards such as the client's ability to control and manage discovery of Web services across accessible service registries. This paper proposes a solution to this problem and introduces the Web Service Relevancy Function (WsRF) used for measuring the relevancy ranking of a particular Web service based on client's preferences, and QoS metrics. We present experimental validation, results, and analysis of the presented ideas.

This paper addresses issues relating to the efficient access and discovery of Web services across multiple UDDI Business Registries (UBRs). The ability to explore Web services across multiple UBRs is becoming a challenge particularly as size and magnitude of these registries increase. As Web services proliferate, finding an appropriate Web service across one or more service registries using existing registry APIs (i.e. UDDI APIs) raises a number of concerns such as performance, efficiency, end-to-end reliability, and most importantly quality of returned results. Clients do not have to endlessly search accessible UBRs for finding appropriate Web services particularly when operating via mobile devices. Finding relevant Web services should be time effective and highly productive. In an attempt to enhance the efficiency of searching for businesses and Web services across multiple UBRs, we propose a novel exploration engine, the Web Service Crawler Engine (WSCE). WSCE is capable of crawling multiple UBRs, and enables for the establishment of a centralized Web services' repository which can be used for large-scale discovery of Web services. The paper presents experimental validation, results, and analysis of the presented ideas.

In order for Web services to truly become a standard approach for just-in-time application integration, we need to enhance the discovery mechanism by assisting clients to select relevant Web services of interest. In recent years, there have been several standards that regulate how services can be published, discovered, or used (i.e. UDDI, WSDL, SOAP). Many of these standards have the potential of enhancing the discovery process, however, there are major technical challenges associated with these standards. One of these challenges is the client’s ability to control and mange the discovery process for finding Web services of interest. Clients should be able to find relevant services much more efficiently. To address this issue, we propose a Web Service Ranking (WSR) algorithm for measuring the relevancy of Web services to particular clients’ requirements. This paper presents experimental validation, results and analysis of the presented ideas.

As Web services proliferate, size and magnitude of UDDI Business Registries (UBRs) are likely to increase. The ability to discover Web services of interest then across multiple UBRs becomes a major challenge specially when using primitive search methods provided by existing UDDI APIs. Clients do not have the time to endlessly search accessible UBRs for finding appropriate services particularly when operating via mobile devices. Finding services of interest should be time effective and highly productive. This paper addresses issues relating to the efficient access and discovery of Web services across multiple UBRs and introduces a novel exploration engine, the Web Service Crawler Engine (WSCE). WSCE is capable of crawling multiple UBRs, and enables for the establishment of a centralized Web services repository that can be used for discovering Web services much more efficiently. The paper presents experimental validation, results, and analysis of the proposed ideas.

Major research challenges in discovering Web services include, provisioning of services across multiple or heterogeneous registries, differentiating between services that share similar functionalities, improving end-to-end Quality of Service (QoS), and enabling clients to customize the discovery process. Proliferation and interoperability of this multitude of Web services have lead to the emergence of new standards on how services can be published, discovered, or used (i.e. UDDI, WSDL, SOAP). Such standards can potentially provide many of these features and much more, however, there are technical challenges associated with existing standards. One of these challenges is the client.s ability to control the discovery process across accessible service registries for finding services of interest. This work proposes a solution to this problem and introduces the Web Service Relevancy Function (WsRF) used for measuring the relevancy ranking of a particular Web service based on QoS metrics and client preferences. We present experimental validation, results, and analysis of the presented ideas.

In this paper we present SmartCon, a context-aware system for the discovery and selection of mobile services using Artificial Neural Networks (ANNs). The solution we have developed is a mobile agent-enabled system that adaptively and iteratively learns to select the best available mobile service derived from the extraction of a series of features utilizing contextual information such as the Composite Capabilities/Preferences Profile (CC/PP), service-specific, and non-uniform user-specific features which are supplied to a backpropagation neural network. Based on the features provided, the neural network classifies the most relevant mobile service. In the present work, the system is also capable through iterative learning to generalize and gather information using cognitive feedback based on user's decisions and interactivity with a mobile device. SmartCon is evaluated using a series of preliminary empirical data and results show an 87% success rate in the discovery and selection of the best or most relevant mobile service.