Amir Taherkordi

Mobile Edge Computing: A Survey

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

Towards Independent In-Cloud Evolution of Cyber-Physical Systems

The capabilities of Cyber-Physical Systems (CPSs) are increasingly being extended towards new composite services deployed across a range of smart sensing and controlling devices. These services enable the emergence of multiple end-to-end cyber-physical scenarios, formed dynamically based on their demands, e.g., disaster recovery systems. In such scenarios, each cyber-physical flow may be composed of a large number of physical services with composition challenges such as high dynamism of CPS platforms, rapid development and scalability, and real-time and reliable processing and controlling tasks. Cloud computing enables new perspectives in the design and operation of CPSs, including consolidating and sharing physical services among different applications, auto-scaling computing and communication, and designing and maintaining multiple in-Cloud CPSs dynamically at the same time. In this paper, we present a new architectural approach to address the key concerns of a new generation of CPS services whose functionalities reside in-Cloud (cyber), and on devices and systems (physical). In particular, this design space is focused on principles that allow in-Cloud evolution of CPS services, including dynamic in-Cloud service composition and distribution, virtualization of physical services and devices, and the dynamic creation of CPS ecosystems. In this design model, the in-Cloud cyber part may evolve independently, while the on-device cyber and physical platform still work closely together and provide the basic CPS services.

Enhancing Dependability of Cloud-Based IoT Services through Virtualization

As Internet of Things (IoT) technology moves forward, more and more IoT provided services are being pushed toward clouds. Since the operation of IoT services runs the risk of failures due to lossy communication links and error prone nature of physical objects, cloud providers (offering such services) should provide suitable platforms supporting two desired service dependability features -- i.e., reliability and availability. This issue has so far been addressed for specific application scenarios and often at the network layer. In this paper, we therefore aim at proposing a generic, model-based approach for enhancing these two important features at the application layer of cloud-based IoT systems. Following the principle of dependability by design, we build a framework based on the concept of virtualized IoT services, promising a service abstraction model to efficiently and simultaneously meet the dependability requirements of multiple cloud-based IoT applications. The proposed virtualization approach supports a variety of different dependability patterns and implements them according to the demands of the target application. We implemented the virtualization framework using the SicthSense cloud platform with satisfactory evaluation results on dependability metrics, such as maximum availability and the probability of failure on demand.

From IoT big data to IoT big services

The large-scale deployments of Internet of Things (IoT) systems have introduced several new challenges in terms of processing their data. The massive amount of IoT-generated data requires design solutions to speed up data processing, scale up with the data volume and improve data adaptability and extensibility. Beyond existing techniques for IoT data collection, filtering, and analytics, innovative service computing technologies are required for provisioning data-centric and scalable IoT services. This paper presents a service-oriented design model and framework for realizing scalable and efficient acquisition, processing and integration of data-centric IoT services. In this approach, data-centric IoT services are organized in a service integrating tree structure, adhering to the architecture of many large-scale IoT systems, including recent fog-based IoT computing models. A service node in the tree is called a Big Service and acts as an integrator, collecting data from lower level Big Services, processing them, and delivering the result to higher level IoT Big Services. The service tree thereby encapsulates required data processing functions in a hierarchical manner in order to achieve scalable and real-time data collection and processing. We have implemented the IoT Big Services framework leveraging a popular cloud-based service and data platform called Firebase, and evaluated its performance in terms of real-time requirements.

An architecture for using commodity devices and smart phones in health systems

The potential of patient-centred care and a connected eHealth ecosystem can be developed through socially responsible innovative architectures. The purpose of this paper is to define key innovation needs. This is achieved through conceptual development of an architecture for common information spaces with emergent end-user applications by supporting intelligent processing of measurements, data and services at the Internet of Things (IoT) integration level. The scope is conceptual definition, and results include descriptions of social, legal and ethical requirements, an architecture, services and connectivity infrastructures for consumer-oriented healthcare systems linking co-existing healthcare systems and consumer devices. We conclude with recommendations based on an analysis of research challenges related to how to process the data securely and anonymously and how to interconnect participants and services with different standards and interaction protocols, and devices with heterogeneous hardware and software configurations.

Service Virtualization for Self-adaptation in Mobile Cyber-Physical Systems

Mobile Cyber-Physical Systems (mCPS) consist of cooperating units that often operate in an unpredictably changing environment. Thus, they need to adapt quickly to varying spatial and temporal conditions during operation, e.g., to avoid collisions. The control software of the mobile units has to reflect this complex dynamics, and traditional device-level adaptation models are usually not efficient enough to engineer them smoothly. We address this challenge by proposing a Virtual Adaptation Services Framework (VASF). It provides a virtualized application-level view to adaptation requirements, enabling adaptation coordination between cooperative mCPS devices. In particular, VASF allows us to describe the contextual conditions of mCPS by abstract rules that are analyzed at runtime by the tool-set BeSpaceD. Based on this analysis, the control systems of the involved mCPS units are automatically reconfigured using the OSGi framework. The approach is demonstrated with DiddyBorg robots that are operated by Raspberry Pi boards.

Data-Centric IoT Services Provisioning in Fog-Cloud Computing Systems: Poster Abstract

Fog computing is mainly proposed for IoT applications that are geospatially distributed, large-scale, and latency sensitive. This poses new research challenges in real-time and scalable provisioning of IoT services distributed across Fog-Cloud computing platforms. Data-centric IoT services, as a dominant type of IoT services in large-scale deployments, require design solutions to speed up data processing and notification, and scale up with the data volume. In this paper, we propose a service-oriented design architecture which is particularly focused on provisioning and processing data-centric IoT services over Fog-Cloud systems. In the proposed architecture, data-centric IoT services are organized in a service integrating tree structure, adhering to the hierarchical fog-based IoT computing models. A service node in the tree is empowered with features for real-time service data notification, local data processing and multi-level IoT data access. The initial results show that, along the design advantages of the proposed model, it does not impose any additional overhead as compared to state-of-the-art solutions.

Self-adaptive control in cyber-physical systems: the autonomous train experiment

Autonomous systems have become more and more important in todays transport sector. They often operate in dynamic environments in which unpredictable events may occur at any time. These events may affect the safe operation of vehicles, calling for highly efficient control software technologies to reason about and react on their appearance. A crucial efficiency parameter is timeliness as vehicles often operate under high speed. The contribution of this paper is the presentation and analysis of design aspects of dynamic control software in the context of an autonomous train experiment. This is achieved through a self-adaptation software framework intended for autonomous trains and built on a demonstrator using Lego Mindstorms. The main mission of the framework is to collect context information, reason about it, and adapt the train behavior accordingly. The adaptation framework is implemented using the development tool Reactive Blocks and tested on the demonstrator. The evaluation results provide useful insights into the performance of the framework, particularly about the time needed to reason about the context and to carry out reconfigurations.

Tokenit: Designing State-Driven Embedded Systems through Tokenized Transitions

The development of resource-constrained embedded systems that are naturally state-driven is still a challenging issue, especially in industrial applications -- developed on a bare-bone style runtime system with basic programming features. This is because of the complexity of state-driven design in embedded applications, such as parallel and complicated event-based activity flows, and complicated constraints for transitioning between program states. State machines are considered a systematic approach for such needs. However, existing approaches, in this area, either do not satisfactorily address the above complexity aspects, or force the developer to write code intermingling state handling logic with the functional code. To tackle these issues, we propose TOKEN IT, a state machine-based development framework for resource-constrained embedded systems. Using TOKEN IT, the programmer models the application as a set of parallel processes, where each process consists of sequenced activities with state constraints such as delayed transitions or interdependency between the states of parallel processes. TOKEN IT, then, processes the obtained model and associates a token to each sequential flow of activities, synthesizing them and executing state transitions according to the constraints expressed in the TOKEN IT model. The evaluation results show that TOKEN IT reduces significantly the complexity of state-driven programming in embedded systems at an acceptable memory cost and with no extra processing overhead.