An agent can play one or more roles. A role is a specific behavior to be played by an agent, defined in terms of permission, responsibilities, activities, and of its interactions with other roles. An agent plays a role by actualizing the behavior in terms of services to be activated and de-activated in dependence of specific pre-condition and post-conditions. So the need to develop a model representing formally role of an agent and its interaction with other agent is valuable. The need for a role model that expresses how agent assumes and change roles is essential and AUML is not completely supportive. The core parts of AUML are interaction protocol diagrams and agent class diagrams, which are extensions of UML's sequence diagrams and class diagrams, respectively. Agents are assigned to roles, belong to classes and an Interaction Protocol diagram shows interactions between these agents roles along a timeline. So, the majority of AUML problems are the lack of: (1) agent role definition methodology, (2) formal semantics to AUML role diagrams, (3) responsibilities (internal) agent role definition (only the external role is defined by sequence diagram), and the agent role control over the time. In this paper, we propose a solution to the above problems. We start by analyzing some significant actual approaches to agent role modeling, we introduce an enhancement to AUML by an agent role definition methodology, and we end by comparing our contribution with similar works.

Many research studies have designed intelligent traffic light scheduling algorithms. Some researchers rely on specialized sensors and hardware to gather real-time traffic data at signalized road intersections. Others benefit from artificial intelligence techniques and/or cloud computing technologies. The technology of vehicular networks has been widely used to gather the traffic characteristics of competing traffic flows at signalized road intersections. Intelligent traffic light controlling systems aim to fairly liberate competing traffic at signalized road intersections and eliminate traffic crises. These algorithms have been initially developed without focusing on the consequences of security threats or attacks. However, the accuracy of gathered traffic data at each road intersection affects its performance. Fake and corrupted packets highly affect the accuracy of the gathered traffic data. Thus, in this work, we aim to investigate the aspects of security and confidentiality of intelligent traffic light systems. The possible attacks on the confidentiality of intelligent traffic light systems are examined. Then, a confidential traffic light control system that protects the privacy of traveling vehicles and drivers is presented. The proposed algorithm mainly prevents unauthorized traceability and linkability attacks that threaten people’s lives and violate their privacy. Finally, the proposed algorithm is evaluated through extensive experiments to verify its correctness and benefits compared to traditional insecure intelligent traffic light systems.

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters.

Software reuse has been claimed to be one of the most promising approaches to enhance programmer productivity and software quality. One of the problems to be addressed to achieve high software reuse is organizing databases of software experience, in which information on software products and processes is stored and organized to enhance reuse. We present a system to define and construct such databases called the Reuse Description Formalism (RDF). The formalism is a generalization of the faceted index approach to classification in the sense that it provides facilities to define facets, terms, and object descriptions. Unlike the faceted approach, objects in RDF can be described in terms of different sets of faceted and in terms of other object descriptions. This allows a software library to contain different classes of objects, to represent various types of relations among these classes, and to refine classification schemes by adding more detail supporting a growing application domain and reducing the impact of initial domain analysis Author(s) Houhamdi, Z. Comput. Sci. Inst., Univ. of Biskra, Algeria Ghoul, S.