Ramdane Maamri

IoT is an enormous network of interconnected devices that gather data and share it over the internet. Today, IoT suffers from malevolent behaviours and actions that aim to damage and compromise privacy and security. Moreover, these issues and the new IoT requirements are not effectively covered by current architectures. Thus, this paper is an extended work of the authors' previous paper, which suggested a new architecture adhering to the IoT needs that have evolved. It suggests a hybrid architecture that integrates our previous one with a Blockchain-based architecture, to combine their significant features. The hybrid architecture fulfils the emerged requirements of IoT and exploits the Blockchain to increase trust, security and privacy. Further, the hybrid architecture delivers a novel structure to build the policy header, which is an access control list used to regulate the local network and was inherited from the Blockchain-based architecture. The Cooja simulator is used to compare the two structures, revealing that the new structure can use 87% less storage and 83% less computing.

Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.

A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black holes. SSA is used to solve five well-known engineering design problems: three-bar truss, pressure vessel, tension/compression spring, welded beam, and gear train. The obtained results are compared to 16 state-of-the-art metaheuristic algorithms. They show that SSA is very competitive in solving the considered engineering problems. In addition, the performance of SSA is evaluated on the benchmarks CEC 2014 and CEC 2020. The experimental results are compared to 27 (12 for CEC 2014 and 15 for CEC 2020) metaheuristic algorithms. They demonstrate that SSA is very promising in finding efficient solutions.

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be NP-hard even with small numbers of robots and tasks. The field of survivors’ search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.

This paper presents a DSDEVS-based model "Dynamic Structure Discrete Event System specification" for modeling and simulating business processes with dynamic structure regarding to different contexts. Consequently, this model, formally, improves the reuse of configurable business processes. Thus, the proposed model allows the analysts to personalize their configurable business processes in a sound manner by verifying a set of structure properties, such as, the lack of synchronization and the deadlock by means of simulation. The implementation was done in DEVS-Suite simulator, which is based on DEVSJAVA models.

The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.