Intelligent transport systems (ITS) play a pivotal role in enhancing safety, efficiency, and sustainability in modern transportation. Deep learning, a subfield of machine learning, has emerged as a powerful tool for tackling complex problems in intelligent transport applications. In this paper, we first provide a comprehensive overview of the advancements and future prospects of deep learning in intelligent transport systems. It explores various deep learning techniques, their applications, challenges, and potential solutions, contributing to the development of efficient and intelligent transportation networks. We created a hybrid prediction model based on LSTM with a Random forest algorithm to inform drivers' real-time road situations. To evaluate our proposed model, we developed a web application in which the user should subscribe to access the different services and know the real-time situation of his current used road. In the final, we showed the performance of our model by calculating the different metrics of evaluation, which are accuracy and F1_Score.

The software supply chain is an unexpected revolutionary development in the software development and distribution field, paving the way for a new era of efficiency and creativity. Nonetheless, in this era of rapid technological progress, a significant absence of a complete study that encompasses the software supply chain. To fill this void, our paper introduced a survey about the important elements constituting this new paradigm. Within these pages, we unveil the nuances between the software supply chain with the distributed software paradigm, offering an unique and exhaustive exploration of its architecture, challenges, software lifecycle, security issues, and potential applications.

A software supply chain attack refers to an intrusion in the software development process that aims to inject malicious code into software components before they are deployed to end users. The objective of these attacks is to compromise the security of the end users or organizations using the software. This includes vulnerabilities introduced even during the critical design phase. Such attacks can occur at any stage of the software development process, from the initial design phase to the final delivery of the software to end-users. To address this problem, many security techniques and mechanisms were proposed to protect the software supply chain against the suspicious and malicious actors during the different stages. The scope of this survey is to give a comprehensive overview about the existing security solutions. Moreover, this paper introduces new and exhaustive criteria for attack classification to help identifying the features, capabilities, and limitations of software solutions, thereby enabling stakeholders to make informed decisions about the software products.

An important increase in the number of smart things, connected objects, smartphones, and sensors are founded every day, creating a huge amount of data called Big Data. Because of the dynamism of things and the limited capability of their resources and services to process and store this huge data, Cloud computing became an efficient, accessible, and reasonable solution. IoT in combination with Cloud functionalities provides a new phenomenon called the Cloud of Things (CoT) where many new possibilities are enabled. With the heterogeneity of different objects, the dynamism of their context, their distribution and the infinite cloud services, a middleware is a key solution. The main aim of this paper is to study middleware for self-adaptation of the Cloud of things environment into the context of objects. Toward this end, we first present some related features. Next, we compare some middlewares according to their characteristics. Then, we present some middlewares for static and dynamic adaptation and finally, we conclude this paper with a list of current challenges and issues in the design of a new middleware able to adapt dynamically and in execution time any cloud service into the context of the current object.

Technology is always improving, which raises standards of living and makes life easier for people. A significant portion of these advancements are aimed at healthcare systems, with a particular emphasis in recent years on assisting elderly people in maintaining their independence in their homes. Data is collected continuously from various sensors and is used for contextual anomaly detection. As a result, the amount of sensor contextual data grows exponentially. These sensor data’s ability to be shared, reused, and interpreted was, however, constrained by the lack of syntactic or semantic integrity. On the other hand, understanding the Quality of Context (QoC) is acknowledged as a crucial component for the health monitoring system’s performance. The QoC has a significant impact on how contextual data behaves in health monitoring systems. As a result, health monitoring systems need to manage the QoC they rely on carefully and effectively. To address these problems, the main goal of this study is to develop a Context and Quality of Context (CQoC) ontology, which offers a framework for handling and utilizing contextual data, any QoC criteria, as well as context interpretation in a health monitoring system. Another purpose of this paper is the development of a Semantic QoC-aware Health Monitoring System (SQoCHMS) for the elderly that assures their safety and issues early alerts to help them deal with minor everyday problems related to such disorders.

In recent years, Blockchain technology has proved its efficiency in many domains. A smart contract is a software component allowing exposing services via the Blockchain network. Smart contracts are small programs that automatically execute the terms of an agreement when predetermined terms and conditions are reached. With the increasing growth in the popularity of smart contracts, searching, retrieving and understanding smart contracts before executing them becomes a significant challenge. Nevertheless, the majority of these contracts are closed source contracts. Therefore, users cannot understand their functionality and their internal mechanism. In addition, their description lacks the expressiveness and it didn't cover the QOS parameters. In this paper, we propose a Uniform Description language for Smart Contract named UDL-SC. This proposal is an extension of USDL based on the MDA approach to promote trust and minimize the ambiguity between the user and the contract provider.

The miniaturization of computers, coupled with a constant increase in computing power, led to the emergence of new sources of context information. We are facing a new paradigm, the Internet of Things (IoT). Today, this latter improves the quality of life in multiple areas. However, the heterogeneity of objects used in such environments makes their interoperability difficult. In addition, the observations produced by context providers (connected objects) are generated with different vocabularies and data formats. This heterogeneity of technologies in the IoT world makes it necessary to adopt generic solutions. Therefore, it is important to transform the raw data from these context producers into knowledge and information based on ontologies. The use of ontologies solves the challenges of heterogeneity and interoperability of IoT systems. In this paper, we propose a semantic IoT model that aims to overcome the semantic interoperability challenges introduced by the variety of objects potentially used in IoT systems. Furthermore, we enhanced this ontology with quality of context meta-data. These meta-data helps in dealing with imperfection and inconsistency of the collected IoT data.

Internet of Things (IoT) aims to minimize human intervention and to adapt the behavior of systems to the user's needs. Achieving this vision involves the abstraction of IoT objects resources as services and the composition of these services. A major obstacle to the use of services is the discovery and selection of these services. Due to the excessive number of connected objects searching and discovering specific services offered by these objects is becoming a challenging task. The use of traditional solution of service discovery is non-practical for IoT due to its scale and complexity. Enabling an easy and seamless discovery of these IoT services is crucial for the success of IoT. Resource discovery in IoT has captured much attention in both academia and industry. Therefore, many architectures and frameworks have been proposed to provide solutions for its issues. In this paper, we present a state-of-the-art study of service discovery methods in IoT systems, classifying them according to their search approaches and providing a comparison of the selected works. Equally, we identify and discuss key challenges and future research directions that will support the success of IoT.

Summary With the rapid spread of services in the Cloud computing environment, it is difficult for users to find the right service. Therefore, the necessity of a search engine with semantic focused crawler becomes a fundamental requirement. However, the huge size and varied functionalities of Cloud services on the Web, together with the lack of standardized and coherent description of services available, have a great effect for crawlers in order to provide effective Cloud services. To solve these issues, we propose a Cloud service discovery crawler that employs a two‐level semantic similarity measure based on both TF‐IDF and LDA models. Moreover, in order to automatically discover and categorize Cloud services, we present a Cloud Service Ontology (CSOnt) that contains a set of concepts defining Cloud service categories. Experimental results show that the proposed method enhances the performance of the focused crawlers and presents an efficient way to parse the Web and collect Web pages relevant to Cloud services.

This paper proposes a framework for composing and formally verifying adaptable semantic Web services. This framework enables, on one hand, the service provider to describe and publish adaptable semantic Web services. On the other hand, it allows users to compose and verify Web services according to their contexts. By our verification process, two important properties for the composition of adaptable semantic Web services namely the liveness and the adaptability are verified. Unlike previous works, our framework permits to take into accounts context information throughout the whole Web services life cycle.

This paper proposes a context-aware semantic planning graph technique for Web services composition. We first use an ontology based context model for extending Web services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build a set of best composed Web services based on user’s context. The construction of the planning graph is based on semantic context-aware Web service discovery. This allow, for each step of the construction, to add most suitable Web services in terms of semantic compatibility between the services parameters, and their context similarity with the user’s context. In the backward search step, semantic and contextual similarity scores are used to find composed Web services list. Finally, in the ranking step, a score is calculated for each candidate solution and a set of ranked solutions is returned to the user.

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. In this paper, we propose SABPEL, an extension to the BPEL language allowing dynamic adaptation of composite service. SABPEL include dynamic context and reflection computation. This later allows the service composite to create dynamically adaptable business processes. We use a case study to demonstrate the feasibility and effectiveness of our approach.

In this paper, we introduce a new registry that provides publishing adaptable composition Web services (CAC-WSR). Our registry allows to publish and to find composition Web services not only through the functional criterion but also through users contexts and profiles.

With the growing number of web service available in web, selecting web service that satisfies the users need is become more difficulties. So to deal with the problem, numerous approaches are proposed. Some approaches are interested to integrate the Quality of Service (QoS) to select the suitable service. The QoS plays an important role in the discovery and selection process of web services within many similar functional services. Other approaches are interested to integrate the notion of adaptation to select services that satisfies the users context.Finally other approaches are interested to satisfy the goal of users by integrate the notion of goal. In our opinion, these three approaches are complementary. Such as the satisfaction of intentions user is meaningful only if it meets in user context and the service possesses a better QoS. In this paper we propose a quality and context awareness intention web service ontology.This ontology extends the OWL-S by the three notion intention, context and QoS.

Since the emergence of Web services and the diversity of user's devices, an emerging need for adapted web services appeared. In fact, users aim to find web services meeting their requests, contexts and desired QoS. In order to achieve this goal, we propose in this paper a framework to find and select adaptable web services. Our framework is named QoS-ASF and it is based on a multi-agent system.

A Web Service is a software component allowing to expose services via Internet. It insures interactions between heterogeneous applications and systems. The explosion of the Internet users number has led to an important diversity of their profiles. Nevertheless, existing Web Services offer the same result regardless the user profile. Therefore, we are interested in defining adaptation solutions that deals with different steps on the service life cycle. In a previous work we were interested in the design step and proposed and detailed an UML (Unified Modeling Language) extension named AWS-UML (Adaptive Web Service Unified Modeling Language) that describes the different allowed users profiles at the metamodel level according to the MDA (Model Driven Architecture) principles and standards. As we modified the design of the Web Service and so the resulting implementation code, we need also to extend the service description in order to support the additional adaptation informations. Thus we introduce in the current paper an extension of the standard WSDL (Web Service Description Language) used to describe the service offered functionalities. Our proposal is named AWS-WSDL (Adaptive Web Service Web Service Description Language).