A model for trustworthy orchestration in the internet of things

Embedded systems such as Cyber-Physical Systems (CPS) are typically designed as a network of multiple interacting elements with physical input (or sensors) and output (or actuators). One aspect of interest of open systems is fidelity, or the compliance between physical figures of interest and their internal representation. High fidelity is defined as a stable mapping between actions in the physical domain and intended or expected values in the system domain and deviations from fidelity are quantifiable over time by some appropriate informative variable. In this paper, we provide a model for designing such systems based on a framework for trustworthiness monitoring and we provide a Jason implementation to evaluate the feasibility of our approach. In particular, we build a bridge between a standard publish/subscribe framework for CPS called MQTT and Jason to enable automatic reasoning about trustworthines

A model for trustworthy orchestration in the internet of things

Embedded systems such as Cyber-Physical Systems (CPS) are typically designed as a network of multiple interacting elements with physical input (or sensors) and output (or actuators). One aspect of interest of open systems is fidelity, or the compliance between physical figures of interest and their internal representation. High fidelity is defined as a stable mapping between actions in the physical domain and intended or expected values in the system domain and deviations from fidelity are quantifiable over time by some appropriate informative variable. In this paper, we provide a model for designing such systems based on a framework for trustworthiness monitoring and we provide a Jason implementation to evaluate the feasibility of our approach. In particular, we build a bridge between a standard publish/subscribe framework for CPS called MQTT and Jason to enable automatic reasoning about trustworthines

Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

Continuous Deployment of Trustworthy Smart IoT Systems.

While the next generation of IoT systems need to perform distributed processing and coordinated behaviour across IoT, Edge and Cloud infrastructures, their development and operation are still challenging. A major challenge is the high heterogeneity of their infrastructure, which broadens the surface for security attacks and increases the complexity of maintaining and evolving such complex systems. In this paper, we present our approach for Generation and Deployment of Smart IoT Systems (GeneSIS) to tame this complexity. GeneSIS leverages model-driven engineering to support the DevSecOps of Smart IoT Systems (SIS). More precisely, GeneSIS includes: (i) a domain specific modelling language to specify the deployment of SIS over IoT, Edge and Cloud infrastructure with the necessary concepts for security and privacy; and (ii) a [email protected] engine to enact the orchestration, deployment, and adaptation of these SIS. The results from our smart building case study have shown that GeneSIS can support security by design from the development (via deployment) to the operation of IoT systems and back again in a DevSecOps loop. In other words, GeneSIS enables IoT systems to keep up security and adapt to evolving conditions and threats while maintaining their trustworthiness.The research leading to these results has received funding from the European Commission’s H2020 Programme under grant agreement numbers 780351 (ENACT)

Taming the cloud: Safety, certification and compliance for software services - Keynote at the Workshop on Engineering Service-Oriented Applications (WESOA) 2011

The maturity of IT processes, such as software development, can be and is often certified. Current trends in the IT industry suggest that software systems in the future will be very different from their counterparts today, with an increasing adoption of the Service-Oriented Architecture (SOA) design pattern and the deployment of Software-as-a-Service (SaaS) on Cloud infrastructures. In this talk we discuss some issues surrounding engineering Software Services for Cloud infrastructures and highlight the need for enhanced control, service-level agreement and compliance mechanisms for Software Services. Cloud Infrastructures and Service Mash-ups

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Model-based Continuous Deployment of SIS

This chapter is organized as follows. Section 4.2 provides an overview of the current state of the art and of the practice for the automatic deployment of SIS. Section 4.3 introduces our solutions for the automatic deployment of SIS, first describing how they can be integrated in order to form a coherent deployment bundle and then detailing each our two enablers: GENESIS and DivENACT. Section 4.4 focus on the support offered by our solutions to ensure the trustworthiness deployment of SIS. Finally, Section 4.5 draws some conclusions.publishedVersio