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IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.
Houssein Hellani; Layth Sliman; Abed Samhat; Ernesto Exposito. Computing Resource Allocation Scheme for DAG-Based IOTA Nodes. Sensors 2021, 21, 4703 .
AMA StyleHoussein Hellani, Layth Sliman, Abed Samhat, Ernesto Exposito. Computing Resource Allocation Scheme for DAG-Based IOTA Nodes. Sensors. 2021; 21 (14):4703.
Chicago/Turabian StyleHoussein Hellani; Layth Sliman; Abed Samhat; Ernesto Exposito. 2021. "Computing Resource Allocation Scheme for DAG-Based IOTA Nodes." Sensors 21, no. 14: 4703.
Data transparency is essential in the modern supply chain to improve trust and boost collaboration among partners. In this context, Blockchain is a promising technology to provide full transparency across the entire supply chain. However, Blockchain was originally designed to provide full transparency and uncontrolled data access. This leads many market actors to avoid Blockchain as they fear for their confidentiality. In this paper, we highlight the requirements and challenges of supply chain transparency. We then investigate a set of supply chain projects that tackle data transparency issues by utilizing Blockchain in their core platform in different manners. Furthermore, we analyze the projects’ techniques and the tools utilized to customize transparency. As a result of the projects’ analyses, we identified that further enhancements are needed to set a balance between the data transparency and process opacity required by different partners, to ensure the confidentiality of their processes and to control access to sensitive data.
Houssein Hellani; Layth Sliman; Abed Samhat; Ernesto Exposito. On Blockchain Integration with Supply Chain: Overview on Data Transparency. Logistics 2021, 5, 46 .
AMA StyleHoussein Hellani, Layth Sliman, Abed Samhat, Ernesto Exposito. On Blockchain Integration with Supply Chain: Overview on Data Transparency. Logistics. 2021; 5 (3):46.
Chicago/Turabian StyleHoussein Hellani; Layth Sliman; Abed Samhat; Ernesto Exposito. 2021. "On Blockchain Integration with Supply Chain: Overview on Data Transparency." Logistics 5, no. 3: 46.
Industry 4.0 is defined as a paradigm that integrates the latest technological inventions in Artificial Intelligence (AI), Communication, and Information technologies, among other domains. This integration is made to increase the levels of automation, efficiency, and productivity of production, in manufacturing and industrial processes. In particular, the actors of the production processes (Things, Data, People and Services) should autonomously be able to act and make decisions, to implement self-* properties, such as self-configuration, self-management, and self-healing. In that sense, the Industry 4.0 revolution introduces many new challenges and issues that need to be solved. Some of those challenges are related to the integration of the heterogeneous actors that carry out the manufacturing process's tasks. Moreover, it is crucial to determine how to permit the actors to self-manage the production processes. In this paper, we present a framework for the integration of autonomous processes based on the needs for coordination, cooperation, and collaboration. Notably, we define three autonomic cycles that allow the actors of manufacturing processes (Data, People, Things, and Services) to interoperate. These autonomic cycles can create a coordinated plan for self-configuration, self-optimization, and self-healing during the manufacturing process. In this way, the actors can be appropriately coordinated, oriented to autonomously manufacture Smart Products, detect failures, and recover from errors or failures, among other things.
Manuel Sanchez; Ernesto Exposito; Jose Aguilar. Autonomic computing in manufacturing process coordination in industry 4.0 context. Journal of Industrial Information Integration 2020, 19, 100159 .
AMA StyleManuel Sanchez, Ernesto Exposito, Jose Aguilar. Autonomic computing in manufacturing process coordination in industry 4.0 context. Journal of Industrial Information Integration. 2020; 19 ():100159.
Chicago/Turabian StyleManuel Sanchez; Ernesto Exposito; Jose Aguilar. 2020. "Autonomic computing in manufacturing process coordination in industry 4.0 context." Journal of Industrial Information Integration 19, no. : 100159.
In recent years, a revolution named Industry 4.0 has arisen. Industry 4.0 is presented as the integration of new advances in areas such as Cyber-Physical Systems, the Internet of Things and Everything (IoE), Cloud computing, the Internet of Services, Big Data Analysis, Smart Factories, Augmented Reality, among others. Industry 4.0 is not only a new industrial revolution, but also a crucial integration challenge that involves several actors from the IoE, which are people, data, services, and things. This paper proposes an approach to analyze the integration challenges in the context of Industry 4.0 using five integration levels, which are connection, communication, coordination, cooperation, and collaboration (5 C). In that sense, this paper presents a state of the art of recent studies in Industry 4.0 from an integration perspective, categorized according to the 5 C integration levels versus the four actors of IoE. Specifically, this paper considers several works intended to solve problems of autonomic integration in Industry 4.0 at the highest levels of the 5 C integration stack (coordination, cooperation, and collaboration). Also, this paper presents a case study from an integration perspective, which contemplates autonomy, self-organizing, among other aspects, in order to turn a traditional industry into a smart factory regarding the Industry 4.0 concept.
Manuel Sanchez; Ernesto Exposito; Jose Aguilar. Industry 4.0: survey from a system integration perspective. International Journal of Computer Integrated Manufacturing 2020, 33, 1017 -1041.
AMA StyleManuel Sanchez, Ernesto Exposito, Jose Aguilar. Industry 4.0: survey from a system integration perspective. International Journal of Computer Integrated Manufacturing. 2020; 33 (10-11):1017-1041.
Chicago/Turabian StyleManuel Sanchez; Ernesto Exposito; Jose Aguilar. 2020. "Industry 4.0: survey from a system integration perspective." International Journal of Computer Integrated Manufacturing 33, no. 10-11: 1017-1041.
Industry 4.0 requires high levels of autonomy in order to guarantee the manufacturing processes to achieve production goals. For this, it is needed high levels of coordination, cooperation, and collaboration, such that the manufacturing process’ actors can communicate and interoperate. A previous paper proposed three autonomic cycles of data analytics tasks for self-coordination in manufacturing processes. In this paper, we implement one of these autonomic cycles, allowing self-supervising of the coordination process. This autonomic cycle is designed using the MIDANO’s methodology, and implemented and tested using an experimental tool that was developed to replay the production process event logs, in order to detect failures and invoke the autonomic cycle for self-healing when needed.
M. Sánchez; E. Exposito; J. Aguilar. Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context. Computers in Industry 2020, 121, 103247 .
AMA StyleM. Sánchez, E. Exposito, J. Aguilar. Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context. Computers in Industry. 2020; 121 ():103247.
Chicago/Turabian StyleM. Sánchez; E. Exposito; J. Aguilar. 2020. "Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context." Computers in Industry 121, no. : 103247.
Today we are living a new industrial revolution, which has its origin in the vertiginous deployment of ICT technologies that have been pervasively deployed at all levels of the modern society. This new industrial revolution, known as Industry 4.0, evolves within the context of a totally connected Cyber-Physic world in which organizations face immeasurable challenges related to the proper exploitation of ICT technologies to create and innovate in order to develop the intelligent products and services of tomorrow’s society. This paper introduces a semantic-driven architecture intended to design, develop and manage Industry 4.0 systems by incrementally integrating monitoring, analysis, planning and management capabilities within autonomic processes able to coordinate and orchestrate Cyber-Physical Systems (CPS). This approach is also intended to cope with the integrability and interoperability challenges of the heterogeneous actors of the Internet of Everything (people, things, data and services) involved in the CPS of the Industry 4.0.
Ernesto Exposito. Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0. Communications in Computer and Information Science 2019, 5 -17.
AMA StyleErnesto Exposito. Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0. Communications in Computer and Information Science. 2019; ():5-17.
Chicago/Turabian StyleErnesto Exposito. 2019. "Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0." Communications in Computer and Information Science , no. : 5-17.
The adoption of the Internet of Things (IoT) drastically witnesses an increase in different domains and contributes to the fast digitalization of the universe. Henceforth, next generation of IoT-based systems are set to become more complex to design and manage. Collecting real-time IoT-generated data unleashes a new wave of opportunities for business to take more precise and accurate decisions at the right time. However, a set of challenges, including the design complexity of IoT-based systems and the management of the ensuing heterogeneous big data as well as the system scalability, need to be addressed for the development of flexible smart IoT-based systems. Consequently, we proposed a set of design patterns that diminish the system design complexity through selecting the appropriate combination of patterns based on the system requirements. These patterns identify four maturity levels for the design and development of smart IoT-based systems. In this article, we are mainly dealing with the system design complexity to manage the context changeability at runtime. Thus, we delineate the autonomic cognitive management pattern, which is at the most mature level. Based on the autonomic computing, this pattern identifies a combination of management processes able to continuously detect and manage the context changes. These processes are coordinated based on cognitive mechanisms that allow the system perceiving and understanding the meaning of the received data to make business decisions, as well as dynamically discovering new processes that meet the requirements evolution at runtime. We demonstrated the use of the proposed pattern with a use case from the healthcare domain; more precisely, the patient comorbidity management based on wearables.
Emna Mezghani; Ernesto Exposito; Khalil Drira. An Autonomic Cognitive Pattern for Smart IoT-Based System Manageability. ACM Transactions on Internet Technology 2019, 19, 1 -17.
AMA StyleEmna Mezghani, Ernesto Exposito, Khalil Drira. An Autonomic Cognitive Pattern for Smart IoT-Based System Manageability. ACM Transactions on Internet Technology. 2019; 19 (1):1-17.
Chicago/Turabian StyleEmna Mezghani; Ernesto Exposito; Khalil Drira. 2019. "An Autonomic Cognitive Pattern for Smart IoT-Based System Manageability." ACM Transactions on Internet Technology 19, no. 1: 1-17.
David Tchoffa; Nicolas Figay; Parisa Ghodous; Ernesto Exposito; Kouami Seli Apedome; Abderrahaman El Mhamedi. Dynamic manufacturing network – from flat semantic graphs to composite models. International Journal of Production Research 2019, 57, 6569 -6578.
AMA StyleDavid Tchoffa, Nicolas Figay, Parisa Ghodous, Ernesto Exposito, Kouami Seli Apedome, Abderrahaman El Mhamedi. Dynamic manufacturing network – from flat semantic graphs to composite models. International Journal of Production Research. 2019; 57 (20):6569-6578.
Chicago/Turabian StyleDavid Tchoffa; Nicolas Figay; Parisa Ghodous; Ernesto Exposito; Kouami Seli Apedome; Abderrahaman El Mhamedi. 2019. "Dynamic manufacturing network – from flat semantic graphs to composite models." International Journal of Production Research 57, no. 20: 6569-6578.
Fannia Pacheco; Ernesto Exposito; Mathieu Gineste; Cedric Baudoin; Jose Aguilar. Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey. IEEE Communications Surveys & Tutorials 2018, 21, 1988 -2014.
AMA StyleFannia Pacheco, Ernesto Exposito, Mathieu Gineste, Cedric Baudoin, Jose Aguilar. Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey. IEEE Communications Surveys & Tutorials. 2018; 21 (2):1988-2014.
Chicago/Turabian StyleFannia Pacheco; Ernesto Exposito; Mathieu Gineste; Cedric Baudoin; Jose Aguilar. 2018. "Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey." IEEE Communications Surveys & Tutorials 21, no. 2: 1988-2014.
Service-oriented architecture (SOA) has emerged as a dominant architecture for interoperability between applications, by using a weakly coupled model based on the flexibility provided by web services, which has led to a wide range of applications, which is known as cloud computing. On the other hand, multi-agent system (MAS) is widely used in the industry, because it provides an appropriate solution to complex problems, in a proactive and intelligent way. Specifically, intelligent environments (smart city, smart classroom, cyber-physical system, and smart factory) obtain great benefits by using both architectures, because MAS endows intelligence to the environment, while SOA enables users to interact with cloud services, which improve the capabilities of the devices deployed in the environment. Additionally, the fog computing paradigm extends the cloud computing paradigm to be closer to the things that produce and act on the intelligent environment, allowing to deal with issues like mobility, real time, low latency, geo-localization, among other aspects. In this sense, in this article we present a middleware, which not only is capable of allowing MAS and SOA to communicate in a bidirectional and transparent way, but also it uses the fog computing paradigm autonomously, according to the context and to the system load factor. Additionally, we analyze the performance of the incorporation of the fog computing paradigm in our middleware and compare it with other works.
Manuel Sánchez; Jose Aguilar; Ernesto Exposito. Fog computing for the integration of agents and web services in an autonomic reflexive middleware. Service Oriented Computing and Applications 2018, 12, 333 -347.
AMA StyleManuel Sánchez, Jose Aguilar, Ernesto Exposito. Fog computing for the integration of agents and web services in an autonomic reflexive middleware. Service Oriented Computing and Applications. 2018; 12 (3-4):333-347.
Chicago/Turabian StyleManuel Sánchez; Jose Aguilar; Ernesto Exposito. 2018. "Fog computing for the integration of agents and web services in an autonomic reflexive middleware." Service Oriented Computing and Applications 12, no. 3-4: 333-347.
Nowadays, statistical based feature extraction approaches are commonly used in the knowledge discovery field with Machine Learning. These features are accurate and give relevant information of the samples; however, these approaches consider some assumptions, such as the membership of the signals or samples to specific statistical distributions. In this work, we propose to model statistical computation through linear regression models; these models will be divided by classes, in order to increase the inner-class identification likelihood. In general, an ensemble of linear regression models will estimate a targeted statistical feature. In an online deployment, the pool of LR models of a given targeted statistical feature will be evaluated to find the most similar value to the current input, which will be as the estimated of the feature. The proposal is tested with a real world application in traffic network classification. In this case study, fast classification response has to be provided, and statistical based features are widely used for this aim. In this sense, the statistical features must give early signs of the status of the network in order to achieve some objectives such as improve the quality of service or detect malicious traffic.
Fannia Pacheco; Ernesto Exposito; Jose Aguilar; Mathieu Gineste; Cedric Baudoin. A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression. 2018 International Joint Conference on Neural Networks (IJCNN) 2018, 1 -8.
AMA StyleFannia Pacheco, Ernesto Exposito, Jose Aguilar, Mathieu Gineste, Cedric Baudoin. A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression. 2018 International Joint Conference on Neural Networks (IJCNN). 2018; ():1-8.
Chicago/Turabian StyleFannia Pacheco; Ernesto Exposito; Jose Aguilar; Mathieu Gineste; Cedric Baudoin. 2018. "A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression." 2018 International Joint Conference on Neural Networks (IJCNN) , no. : 1-8.
SOA (Service Oriented Architecture) ha emergido como una arquitectura dominante para la interoperabilidad entre aplicaciones, por medio de un modelo de acoplamiento débil basado en la flexibilidad que proveen los servicios web, esto ha dado lugar a una amplia gama de aplicaciones, en lo que se conoce hoy en día como computación en la nube. Por otro lado, los MAS (Multiagent System, por sus siglas en inglés) son usados ampliamente en la industria, ya que brindan soluciones apropiadas a problemas complejos, de forma proactiva e inteligente. En particular, los Ambientes Inteligentes (AmI) educativos se benefician de estás dos arquitecturas, ya que por un lado los MAS dotan al AmI de inteligencia, mientras que SOA permite a los usuarios interactuar con servicios académicos en la nube. El propósito de este artículo es proponer una arquitectura de integración bidireccional SOA-MAS para AmI educativos. La solución propuesta aprovecha las ventajas de ambas tecnologías (SOA-MAS), y resuelve problemas de integración planteados en investigaciones previas.
Manuel Sánchez; Jose Aguilar; Ernesto Exposito. Integración SOA-MAS en Ambientes Inteligentes. DYNA 2018, 85, 268 -282.
AMA StyleManuel Sánchez, Jose Aguilar, Ernesto Exposito. Integración SOA-MAS en Ambientes Inteligentes. DYNA. 2018; 85 (206):268-282.
Chicago/Turabian StyleManuel Sánchez; Jose Aguilar; Ernesto Exposito. 2018. "Integración SOA-MAS en Ambientes Inteligentes." DYNA 85, no. 206: 268-282.
The combination of multiple functions having different and complementary capabilities enables the emergence of Autonomous Vehicles. Their deployment is limited by the level of complexity they represent together with the challenges encountered in real environments with strong safety concerns. Thus a major concern prior to massive deployment is on how to ensure the safety of autonomous vehicles despite likely internal (e.g. malfunctions) and external (e.g., aggressive behaviors) disturbances they might undergo. This paper presents the challenges that undergoes the design and development of autonomous vehicles with respect to their functional architecture and adaptive behaviors from a safety perspective. For the purpose of the rationales, we define needs and requirements that lead to the formulation of an architectural framework. Our approach is based on paradigms and technologies from non-automotive domains to address non-functional system properties like safety, reliability and security. The notion of micro-services is also introduced for the self-safety of autonomous vehicles. These are part of the proposed framework that should facilitate the analysis, design, development and validation for the adequate composition and orchestration of services aimed to warrant the required non-functional properties, such as safety. In the present paper, we introduce the structural and behavioral adaptations of the framework to offer a holistic and scalable vision of the safety over the system.
Matthieu Carre; Ernesto Exposito; Javier Ibanez-Guzman. Challenges for the Self-Safety in Autonomous Vehicles. 2018 13th Annual Conference on System of Systems Engineering (SoSE) 2018, 181 -188.
AMA StyleMatthieu Carre, Ernesto Exposito, Javier Ibanez-Guzman. Challenges for the Self-Safety in Autonomous Vehicles. 2018 13th Annual Conference on System of Systems Engineering (SoSE). 2018; ():181-188.
Chicago/Turabian StyleMatthieu Carre; Ernesto Exposito; Javier Ibanez-Guzman. 2018. "Challenges for the Self-Safety in Autonomous Vehicles." 2018 13th Annual Conference on System of Systems Engineering (SoSE) , no. : 181-188.
Built upon the Internet of Things (IoT), the Internet of Everything (IoE) acknowledges the importance of data quality within sensor-based systems, alongside with people, processes and Things. Nevertheless, the impact of many technologies and paradigms that pertain to the IoE is still unknown regarding Quality of Observation (QoO). This paper proposes to study experimental results from three IoE-related deployment scenarios in order to promote the QoO notion and raise awareness about the need for characterizing observation quality within sensor-based systems. We specifically tailor the definition of QoO attributes to each use case, assessing observation accuracy within Smart Cities, observation rate for virtual sensors and observation freshness within post-disaster areas. To emulate these different experiments, we rely on a custom-developed integration platform for the assessment of QoO as a service called iQAS. We show that QoO attributes should be used to specify what is an observation of “good quality”, that virtual sensors may have specific and limiting capabilities impacting QoO and that network QoS and QoO are two complementary quality dimensions that should be used together to improve the overall service provided to end-users.
Antoine Auger; Ernesto Exposito; Emmanuel Lochin. Towards the internet of everything: Deployment scenarios for a QoO-aware integration platform. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) 2018, 499 -504.
AMA StyleAntoine Auger, Ernesto Exposito, Emmanuel Lochin. Towards the internet of everything: Deployment scenarios for a QoO-aware integration platform. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). 2018; ():499-504.
Chicago/Turabian StyleAntoine Auger; Ernesto Exposito; Emmanuel Lochin. 2018. "Towards the internet of everything: Deployment scenarios for a QoO-aware integration platform." 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) , no. : 499-504.
The sensor web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, sensor web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any sensor web should provide support for quality of observation (QoO) requirements. In practise, however, only few sensor webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner. This survey aims to study why and how observation quality should be addressed in sensor webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within sensor webs. Second, it proposes a QoO-oriented review of 29 sensor web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware sensor web solutions.
Antoine Auger; Ernesto Exposito; Emmanuel Lochin. Survey on Quality of Observation within Sensor Web systems. IET Wireless Sensor Systems 2017, 7, 163 -177.
AMA StyleAntoine Auger, Ernesto Exposito, Emmanuel Lochin. Survey on Quality of Observation within Sensor Web systems. IET Wireless Sensor Systems. 2017; 7 (6):163-177.
Chicago/Turabian StyleAntoine Auger; Ernesto Exposito; Emmanuel Lochin. 2017. "Survey on Quality of Observation within Sensor Web systems." IET Wireless Sensor Systems 7, no. 6: 163-177.
Fannia Pacheco; Ernesto Exposito; Mathieu Gineste; Cedric Budoin. An autonomic traffic analysis proposal using Machine Learning techniques. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 2017, 1 .
AMA StyleFannia Pacheco, Ernesto Exposito, Mathieu Gineste, Cedric Budoin. An autonomic traffic analysis proposal using Machine Learning techniques. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18. 2017; ():1.
Chicago/Turabian StyleFannia Pacheco; Ernesto Exposito; Mathieu Gineste; Cedric Budoin. 2017. "An autonomic traffic analysis proposal using Machine Learning techniques." Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 , no. : 1.
With the growth of the Internet of Things (IoT), sensor-based systems deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Since network QoS is rarely sufficient to expertise Quality of Observation (QoO), managing such diversity at the application level is a very complex task and requires high levels of experience from application developers. Given this statement, this paper proposes a generic framework for QoO-based autonomic adaptation within sensor-based systems. An abstract architecture is first introduced, intended to bridge the gap between sensors capabilities and application needs thanks to the Autonomic Computing paradigm. Then, the framework is instantiated and practical considerations when implementing an autonomous sensor-based system are given. We illustrate this instantiation with concrete examples of sensor middlewares and IoT platforms.
Antoine Auger; Ernesto Exposito; Emmanuel Lochin. A Generic Framework for Quality-Based Autonomic Adaptation Within Sensor-Based Systems. Computer Vision 2017, 21 -32.
AMA StyleAntoine Auger, Ernesto Exposito, Emmanuel Lochin. A Generic Framework for Quality-Based Autonomic Adaptation Within Sensor-Based Systems. Computer Vision. 2017; ():21-32.
Chicago/Turabian StyleAntoine Auger; Ernesto Exposito; Emmanuel Lochin. 2017. "A Generic Framework for Quality-Based Autonomic Adaptation Within Sensor-Based Systems." Computer Vision , no. : 21-32.
In the domain of the service composition, the failure of a service generates error propagation in the other services, and therefore, it can generate the failure of the entire system. Usually, these failures cannot be detected and corrected only with local information. Normally, it is required the development of architectures that enable the diagnosis and correction of faults, both locally (elementary service) as well as globally (service composition). This paper presents a reflexive middleware architecture based on autonomic computing, which allows the distributed diagnosis of faults in the service composition, called ARMISCOM. This middleware has not a central diagnoser, instead the diagnosis of failures is carried out through the interaction of local diagnosers present in each service of the composition. These local diagnoses use a distributed chronicle approach proposed in previous works, which allows the recognition of fully distributed patterns of the classic failures in the SOA systems. In addition, the repair strategies are defined through consensus of the repairers, equally distributed between the services of the composition. The repair strategies use the concept of “equivalent regions” defined in this paper, for the fault correction in a SOA application.
J. Vizcarrondo; Jose Aguilar; E. Exposito; A. Subias. ARMISCOM: self-healing service composition. Service Oriented Computing and Applications 2017, 11, 345 -365.
AMA StyleJ. Vizcarrondo, Jose Aguilar, E. Exposito, A. Subias. ARMISCOM: self-healing service composition. Service Oriented Computing and Applications. 2017; 11 (3):345-365.
Chicago/Turabian StyleJ. Vizcarrondo; Jose Aguilar; E. Exposito; A. Subias. 2017. "ARMISCOM: self-healing service composition." Service Oriented Computing and Applications 11, no. 3: 345-365.
Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloud-based IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.
Antoine Auger; Ernesto Exposito; Emmanuel Lochin. Sensor observation streams within cloud-based IoT platforms: Challenges and directions. 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) 2017, 177 -184.
AMA StyleAntoine Auger, Ernesto Exposito, Emmanuel Lochin. Sensor observation streams within cloud-based IoT platforms: Challenges and directions. 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN). 2017; ():177-184.
Chicago/Turabian StyleAntoine Auger; Ernesto Exposito; Emmanuel Lochin. 2017. "Sensor observation streams within cloud-based IoT platforms: Challenges and directions." 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) , no. : 177-184.
While reducing costs and improving sustainability, a common goal for Smart Cities is to become more “liveable” for their citizens. By taking advantage of new information sources offered by the Internet of Things (IoT), cities can rely on sensing platforms to improve their service offer. These sensing platforms, however, raise new research challenges, in particular regarding Quality of Information (QoI). To cope with this issue, common platforms generally provide quality-oriented internal mechanisms. Nevertheless, the configuration of such platforms is complex, especially for Smart City stakeholders that may have various skill levels and different areas of expertise. As a result, QoI assessment is often delegated to end applications where developers have to implement their own adaptation mechanisms. This paper proposes and describes iQAS, an integration platform for QoI Assessment as a Service for Smart Cities. iQAS is autonomic, extensible and configurable, allowing Smart City stakeholders to collaboratively assess and improve (when possible) QoI in real-time. While the platform development is at its early stages, we illustrate within a concrete case study the need for QoI assessment and the benefits to implement adaptation mechanisms.
Antoine Auger; Ernesto Exposito; Emmanuel Lochin. iQAS: An integration platform for QoI assessment as a service for smart cities. 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) 2016, 88 -93.
AMA StyleAntoine Auger, Ernesto Exposito, Emmanuel Lochin. iQAS: An integration platform for QoI assessment as a service for smart cities. 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). 2016; ():88-93.
Chicago/Turabian StyleAntoine Auger; Ernesto Exposito; Emmanuel Lochin. 2016. "iQAS: An integration platform for QoI assessment as a service for smart cities." 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) , no. : 88-93.