Dangerous collisions happen at traffic intersections and cause dire effects. So, numerous systems based on Vehicular ad hoc networks (VANET) have been introduced to prevent collisions and enhance ...users’ safety at intersections. However, these systems may engender several problems since they do not store data in appropriate databases, while VANET cannot currently guarantee the reception of all messages. To improve the safety in the intersections without stop signs and traffic lights, we propose in this paper a new system, entitled cooperative intersection collision avoidance persistent system (CICAPS). This system is based on real-time databases (RTDB), the notion of Quality of Data (QoD) and a hybrid architecture using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The introduction of RTDB makes CICAPS able to manage real-time data, which are characterized by very rapid evolution and limited temporal validity, and to leverage these data to better control intersection situations. Moreover, it helps CICAPS respect the time constraints of transactions, knowing that late orders are likely to cause accidents. As for the notion of QoD, it allows decreasing the numbers of data updates and exchanged messages, and so the risk of congestion of messages and loss of data. Regarding the hybrid architecture, it allows implementing both a central driving control and a distributed driving control, and so increasing the operational reliability and the failure tolerance. Finally, CICAPS can deal with different forms of intersections and not only four-way perfect-intersections. Simulations of various driving scenarios at urban intersections, under the vehicles in network simulation (VEINS) framework, confirm that CICAPS ensures a robust and efficient intersection management safety.
Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the ...four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for "OWL 2 Web Ontology Language" and JSON stands for "JavaScript Object Notation" ). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for τJOWL, named τSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.
Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not ...provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: “insert”, “replace”, “evolve”, and “delete”. Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF.
Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web ...environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named τJOWL (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.
To remain competitive, companies must face the changes occurring in their environment and adapt their business processes accordingly. Those processes are implemented in business process management ...systems (BPMS), which mostly support manual adaptations. That means that the process users have to detect what changes in the environment require process adaptation, and what adaptation operations have to be performed. Such manual adaptations of processes are costly, time-consuming and error prone tasks. For this reason, some contributions of the literature have tried to address the issue of self-adaptations of processes. But these contributions suffer from shortcomings: isolated use of adaptation techniques, non-coverage of the process dimensions and of the adaptation types, etc.; the adaptation issue remains partially addressed. Thus we recommend in this paper a hybrid approach to ensure autonomic adaptations of running processes. According to this approach, the
Plan
component tries to find an appropriate model version of the concerned process. Then, if such a version does not exist, it looks to reuse an adaptation case that was applied in the past under a similar situation (context). Finally, if necessary, it applies rules, as an artificial intelligence planning technique, to define an ad hoc adaptation. Moreover, the recommended approach takes advantage of the IBM MAPE-K (
Monitor
,
Analyze
,
Plan
,
Execute
—
Knowledge
) control loop from autonomic computing, recognized as a prominent solution for self-adaptation at run-time. More precisely the paper addresses the resolution of adaptation needs while covering three process dimensions and all adaptation types and ensuring the separation of concerns for better portability and wide usability through the BPMN standard. It presents both the required Knowledge and the
Plan
component of the control loop for this resolution. It also shows the effectiveness of the approach by illustrating self-adaptation of a process from the crisis domain, and demonstrates its feasibility by reporting about its implementation and qualitative and quantitative evaluation.
Systems which manipulate important volumes of data need to be managed with Real-Time (RT) databases. These systems are subject to several temporal constraints related to data and to transactions. ...Thus, their design remains a complex task. To remedy this complexity, it is necessary to integrate design methods to support data and transactions temporal constraints. Among the design methods, those based on patterns have been widely used in several fields. However, despite their advantages, these patterns present some shortcomings. Indeed, they do not manage efficiently the patterns variability and they do not specify the pattern elements when they are instantiated. To overcome these limitations, we propose, in this paper, a new UML profile to (i) express the variability in patterns and (ii) to identify the pattern elements in its instance. Besides, in order to well-capture the knowledge of the domain, the proposed profile extends UML with concepts related to real-time databases and integrates OCL (Object Constraint Language) to enforce the variation points consistency. Finally, we give an example of a RT pattern that illustrates these UML extensions, where we implement the proposed profile and we validate the pattern diagrams using the constraints we have proposed.
Domain specific design patterns capture domain knowledge and provide solutions of non trivial design problems in a specific domain. Their application improves considerably the quality of software ...design. In order to benefit from these advantages and to reinforce the application of these patterns, we provide, in this paper, new processes and tools for the development and the instantiation of domain specific design patterns, especially those intended for real-time domain.
Initially, we propose a pattern development process that guides pattern developers in the construction of patterns. The proposed process defines unification rules that apply a set of comparison criteria on various applications in the pattern domain. This process is illustrated through the design of the controller pattern. Moreover, we propose a process guiding the application designers in pattern instantiation based on model transformation. Finally, the proposed RT patterns and their development process are evaluated by calculating quality metrics and comparing the applications designed with our RT patterns and others developed by experts without the use of our patterns.
Abstract
Overtaking maneuvers in roads cause a large number of accidents. To prevent collisions, numerous cooperative overtaking assistance systems, based on Vehicular Ad hoc Networks (VANET), have ...been proposed. However, these systems may meet problems in case of data congestion or data loss. To improve overtaking safety, this paper studies the use of Vehicle-To-Vehicle (V2V) communications and Real-Time DataBases (RTDB). More precisely, we suggest a new integrated system, called Cooperative Overtaking Assistance Persistent System (COAPS), to better avoid potential collisions during overtaking maneuvers. Each vehicle stores its information in a local RTDB, along with the adjacent vehicles’ information. While detecting an overtaking intention, the vehicle COAPS queries its database and selects the preceding vehicle(s) and the oncoming one(s) from the other direction to make decision about the overtaking. It controls the overtaking in case of positive decision or reduces the vehicle speed to avoid a collision ahead, in the other case. Simulations of different driving scenarios, under the Vehicles In Network Simulation (VEINS) framework, show that COAPS provides a high level of overtaking safety due to the V2V communications, the RTDB and the notion of Quality of Data used to reduce the number of data freshness transactions.
Summary
Coverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the ...performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.
Coverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSN). In this paper, a hybrid of particle swarm optimization and iterated local search (PSO‐ILS) is proposed to deploy the minimum the number of sensor and relay nodes while ensuring the full coverage of target points and the connectivity of the network. The experiment results prove the superiority of PSO‐ILS in terms of the selected potential positions.
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