Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems

The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds.Peer ReviewedPostprint (author's final draft

Sofie: Smart Operating System For Internet Of Everything

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Applications such as cloud offloading, smart home, and smart city are idea area for Edge computing to achieve better performance than cloud computing. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. However, there are still some challenges for applying Edge computing in our daily life. The missing of the specialized operating system for Edge computing is holding back the flourish of Edge computing applications. Service management, device management, component selection as well as data privacy and security is also not well supported yet in the current computing structure. To address the challenges for Edge computing systems and applications in these aspects, we have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating System For Internet Of Everything. SOFIE is the operating system specialized for Edge computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection between cloud and Edge device to handle the data transportation between gateway and Edge devices; to provide service management and data management for Edge applications; to protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices. Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently. To solve the component selection problem in Edge computing paradigm, SOFIE also include our previous work, SURF, as a model to optimize the performance of the system. Finally, we deployed the design of SOFIE on an IoT/M2M system and support semantics with access control

Combining edge and cloud computing for mobility analytics

Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things. This poster describes the preliminary results from our experimental prototype developed for supporting transit systems, in which edge and cloud computing are combined to process transit data streams forwarded from fog nodes into a cloud. The motivation of this research is to understand how to perform meaningfulness mobility analytics on transit feeds by combining cloud and fog computing architectures in order to improve fleet management, mass transit and remote asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of Mobile Things, Edge Fog Fabri

An SDN-based architecture for security provisioning in Fog-to-Cloud (F2C) computing systems

The unstoppable adoption of cloud and fog computing is paving the way to developing innovative services, some requiring features not yet covered by either fog or cloud computing. Simultaneously, nowadays technology evolution is easing the monitoring of any kind of infrastructure, be it large or small, private or public, static or dynamic. The fog-to-cloud computing (F2C) paradigm recently came up to support foreseen and unforeseen services demands while simultaneously benefiting from the smart capacities of the edge devices. Inherited from cloud and fog computing, a challenging aspect in F2C is security provisioning. Unfortunately, security strategies employed by cloud computing require computation power not supported by devices at the edge of the network, whereas security strategies in fog are yet on their infancy. Put this way, in this paper we propose Software Defined Network (SDN)-based security management architecture based on a master/slave strategy. The proposed architecture is conceptually applied to a critical infrastructure (CI) scenario, thus analyzing the benefits F2C may bring for security provisioning in CIs.Peer ReviewedPostprint (published version