Emergency health care should be provided as carefully and efficiently as possible. We consider patients in a city who are in an emergency condition and need to be distributed across the city's hospitals using an ambulance service$\mathcal{A}$. Patients are characterized by the type of care they need and the city's area where they are located. The costs of hospitalization and transportation of a patient obviously depend on these two characterization elements. We present a method to optimally distribute patients, i.e. we choose which hospital each patient is sent to so that hospital and ambulance capacities are not exceeded and the total cost to be paid to$\mathcal{A}$and hospitals is minimal. A pricing model is developed to determine payments to hospitals and$\mathcal{A}$. Machine learning is suggested to predict the number of patients in each city's area. The management of our optimal patient distribution is carried out using a publicly reliable smart contract in blockchain. This ensures that patient transport and hospitalization, as well as corresponding payments, are recorded in blockchain in a secure, immutable, transparent and decentralized manner. Finally, we suggest how to make patient distribution robust by handling faulty behaviors of hospitals and$\mathbf{A}$and imperfect patient number predictions (PNPs) in city's areas. To this end, we develop a method to detect faulty behaviors and compute corresponding financial penalties, which is particularly difficult with imperfect PNPs.

Abdellah El Mekki, Muhammad Abdul-Mageed, ElMoatez Billah Nagoudi, Ismail Berrada, Ahmed Khoumsi. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Given a charging station (CS) infrastructure, we solve the problem of an electric vehicle (EV) that needs an efficient charging program that allows it to achieve a given objective which includes: get to its destination, and keep it as low as possible the overall cost and average waiting time of the EV near each CS. Besides developing a model and method to determine a charging program that allows the EV to meet its objective, we also propose a cryptocurrency-based mechanism to realize payments for the energy received. The determined charging program and corresponding payments are managed using a smart contract (SC) executed in the blockchain, to ensure that EV charging and payments comply with the rules and are recorded in a secure, immutable, transparent and decentralized manner.

Modern progress in edge caching has contributed significantly to the advancements of the Internet of Vehicles(IoVs). However, high mobility constraints, time-variant dynamics, and heterogeneous content requirements lead to challenging problematics for edge servers that strive to use their resources optimally. Deep Reinforcement Learning allows incorporating cognition and intelligence into the Internet of Vehicles and permits the intelligent allocation of resources to resolve such challenging problems with various features. We propose an inter-vehicle-enabled caching for IoVs based on Deep Reinforcement Learning to define the optimal caching resource allocation strategies. The framework objective is to encourage sharing caching resources while reducing the probability of under/over-estimation of caching requirements. Extensive simulation results with different system parameters demonstrate the efficiency of the proposed solution.

Abdellah El Mekki, Abdelkader El Mahdaouy, Ismail Berrada, Ahmed Khoumsi. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

We study failure prediction (or prognosis) in discrete event systems using decentralized architectures. A few years ago, researchers proposed an inference-based prognosis architecture that generalizes several existing decentralized prognosis architectures. In this paper, we first provide a precise interpretation of the predictions made by inference-based prognosis. Then, we propose an alternative inference-based prognosis that is applicable to more languages than the original inference-based prognosis. Finally, we show how the alternative inference-based prognosis can be easily adapted to predict failures in advance.

In big data environments with big number of users and high volume of data, we need to manage the corresponding huge number of security policies. Due to the distributed management of these policies, they may contain several anomalies, such as conflicts and redundancies, which may lead to both safety and availability problems. The distributed systems guided by such security policies produce a huge number of access logs. Due to potential security breaches, the access logs may show the presence of non-allowed accesses. This may also be a consequence of conflicting rules in the security policies. In this paper, we present an ongoing work on developing an environment for verifying and correcting security policies. To make the approach efficient, an access log is used as input to determine suspicious parts of the policy that should be considered. The approach is also made efficient by clustering the policy and the access log and considering separately the obtained clusters. The clustering technique and the use of access log significantly reduces the complexity of the suggested approach, making it scalable for large amounts of data.

Conventional decentralized control of discrete event systems consists in using local supervisors that observe locally the plant and compute local enabling/disabling decisions; the latter are transmitted to fusion modules that combine them to generate global decisions that are applied to the plant. We propose an unconventional approach where the local supervisors do not compute local decisions, they rather transmit all their local observations to fusion modules that combine them to generate global decisions. The proposed architecture is qualified as information loss-free (ILF), because all the information observed by the local supervisors is available to the fusion modules for making their global decisions. We justify the ILF control approach and prove that if the plant is slower than the control system, then: ILF control is equivalent to a combination of several centralized control architectures, and ILF control is more general than any conventional decentralized architecture. We also show that the slowness of the plant is necessary in any control architecture.

Decentralized control of discrete event systems consists in using local supervisors that observe locally the plant and compute local decisions to enable or disable events; those local decisions are transmitted to fusion modules that combine them to generate global enablement/disablement decisions that are actually applied to the plant. C&P and D&A controls are the two simplest systematic decentralized controls. Inference-based control is the most general systematic decentralized control, which generalizes significantly C&P and D&A controls. In this paper, we first propose a method that realizes a control objective C by an arborescent architecture (or tree). Each leaf of the tree is a decentralized control, and each node n is a disjunction or conjunction of the enabling/disabling decisions of the two children of n. We show that if the control objective C is realizable by inference-based control, then every leaf of the obtained tree is a C&P or D&A control. This means that by combining adequately C&P and D&A controls, we can realize every control objective that is realizable by inference-based control.

Decentralized diagnosis of discrete event systems consists in detecting faults in discrete event systems by using decentralized architectures. In particular, inference-based diagnosis is a decentralized architecture of interest, since it is more general than several other decentralized architectures. In this paper, we first propose a method that realizes a diagnosis objective D by an arborescent architecture (or tree). Each leaf of the tree is a decentralized diagnosis, and each node n is a disjunction or con- junction of the diagnosis decisions of the two children of n. Then, we show that if inference-based diagnosis is applicable to D, then all the leafs of the obtained tree are basic decentralized diagnosers. This implies that every inference-based diagnosis is realizable by a combination of basic decentralized diagnosers.

Some years ago, a decentralized architecture, qualified as inference-based, has been developed for supervisory control of discrete event systems. One of its essential principles is to associate ambiguity levels to local decisions. More recently, a decentralized control architecture, qualified as multi-decision, has been developed. Its main principle is to use several decentralized control architectures in parallel. So far, multi-decision control has been mostly studied as a solution to generalize inference-based control, by using several inference-based architectures in parallel. In the present study, we regard multi-decision control with a different perspective. Instead of using multi-decision control to generalize inference-based control, we will rather use it as an alternative to inference-based control. More precisely, our objective is to avoid using inference-based architectures, by using instead several simpler architectures running in parallel.

The main objectives of intelligent transportation systems (ITS) are to improve vehicle occupant safety and vehicle mobility. Another objective is to provide infotainment and communication services (ICS) to vehicle occupants. To achieve those three goals, ITS must integrate several wireless networks which should work in a cooperative/competitive way to satisfy communication requirements of different ITS services. In this paper, we consider ICS and study how to select the most appropriate wireless network for each ICS requested by vehicle occupants. We propose a procedure that synthesizes an automaton, which is used to select the network that best meets the service level desired by the user. In case no network can satisfy such a desired service level, the automaton can nevertheless be used to determine the network that provides the best service level. Another utility of the automaton is to detect networks which are never selected and those which are unavoidably selected.

We develop an Automotive Reaction System (ARS) framework to support cars by capabilities to react to various situations. With ARS, the states and actions of a car are designed as objects of a high level object-oriented language, called ARS-language. ARS permits also to design the reactions of a car to various situations by an ARS-specification consisting of rules “condition→action”. The ARS-objects and ARS-specification are implemented in a car to provide her with capabilities to function and react online. ARS permits also to model certain actions of a car at a high abstraction level by an ARS-model consisting of rules “condition→operation”. With ARS, we are confronted to conflicts (or feature interactions) which denote situations where an ARS-specification implies simultaneous executions of incompatible actions. We propose an approach to detect and resolve feature interactions.

In decentralized control of discrete event systems, two main agents contribute to the computation of decisions: local supervisors and fusion modules. The local supervisors process the information detected on the plant and its environment, and transmit their results to the fusion modules. The latter process what is received from the local supervisors in order to decide actions to be applied to the plant. In the existing decentralized control architectures, the local supervisors execute complex operations, while the fusion modules execute simple operations. In the present article, we propose to move the decision computation complexity from local supervisors to fusion modules, that is what we term: moving decisions closer to actions. We justify this movement of decision and develop a simple architecture based on it. With the proposed architecture, the local supervisors are simple local observers, while all decisions are computed by the fusion modules. We characterize the class of languages achievable with the new architecture and compare it with the classes of languages achievable with the existing decentralized architectures and the centralized architecture.

Information systems must be controlled by security policies to protect them from undue accesses. Security policies are often designed by rules expressed using informal text, which implies ambiguities and inconsistencies in security rules. Our objective in this paper is to develop a formal approach to design and analyze security policies. We propose a procedure that synthesizes an automaton which implements a given security policy. Our automata-based approach can be a common basis to analyze several aspects of security policies. We use our automata-based approach to develop three analysis procedures to: verify completeness of a security policy, detect anomalies in a security policy, and detect functional discrepancies between several implementations of a security policy. We illustrate our approach using examples of security policies for a firewall.

Kumar and Takai have proposed an architecture for the decentralized prognosis of discrete-event systems (DES), where several local prognosers cooperate to predict failures in a DES. In this paper, we first present the proposition (Kumar and Takai, "Decentralized prognosis of failures in discrete event systems," IEEE Trans. Autom. Control, vol. 55, no. 1, pp. 48-59, Jan. 2010) as a disjunctive architecture, and then we develop a conjunctive architecture which is dual and complementary to the disjunctive one. We also propose a mixed architecture that combines and generalizes the disjunctive and conjunctive architectures. We finally show that our work can be easily extended to predict a failure at least k steps before its occurrence, for a given k >; 1.

We address the problem of modeling and adapting JPEG to the energy requirements of visual sensor networks (VSN). For JPEG modeling purposes, we develop a simplified high-level energy consumption model for each stage of JPEG-like scheme, which can be used to roughly evaluate the energy dissipated by a given visual sensor. This model is based on the basic operations needed at each stage of JPEG, and it does not take into account the complexity of implementation. For JPEG adaptation, we propose to process only a reduced part of each block of 8times8 DCT coefficients of the target image, which minimizes the dissipated energy and maximizes the system lifetime, while preserving an adequate image quality at the sink.

With Visual Sensor Networks (VSN), designers must respect strict constraints on energy consumption, which make compression standards, such as JPEG, not energy-beneficial to VSN. Our approach for tackling this constraint problem consists in adapting JPEG by exploiting the DCT energy compaction property. This exploitation is performed by processing only a portion of each block of 8 times 8 DCT coefficients of the captured image. This approach induces two conflicting effects. Indeed, reducing the size of the portion of DCT block presents the advantage of reducing the energy consumed for processing and transmitting an image, but it also presents the drawback of reducing the quality of the image received at the sink.We propose two methods to solve this conflict: a global method and a local method. In the global method, an optimal size is computed for all portions of DCT blocks of a whole image.

We propose a method for coordinating local components that observe a distributed discrete-event system R and execute actions depending on the current state of R. Coordination is achieved by allowing the components to communicate with one another. This communication helps the components to distinguish slates of R when necessary. This paper is illustrated in distributed supervisory control

This paper deals with testing distributed software systems. In the past, two important problems have been determined for executing tests using a distributed test architecture: controllability and observability problems. A coordinated test method has subsequently been proposed to solve these two problems. In the present article: 1) we show that controllability and observability are indeed resolved if and only if the test system respects timing constraints, even when the system under test is non-real-time; 2) we determine these timing constraints; 3) we determine other timing constraints which optimize the duration of test execution; 4) we show that the communication medium used by the test system does not necessarily have to be FIFO; and 5) we show that the centralized test method can be considered just as a particular case of the proposed coordinated test method.

We present a method for testing real-time distributed systems. More precisely, we propose: (1) a model for specifying distributed real-time systems, (2) a simple and practical test architecture, and (3) a procedure for distributing test sequences.

In Kant et al. (1992), a method of protocol synthesis, using basic LOTOS (BL) as a specification language, is proposed. In the present paper, we generalize this method. We propose an extended basic LOTOS (EBL) to specify the service and the protocol. With EBL, events are associated with enabling conditions and transformation functions that depend on global variables. Next, we propose a method to synthesize protocols using EBL as a specification language. This method is inspired by the concept of transactions.