Data-Centric Edge Federation: A Multi-Edge Architecture for Data Stream Processing of IoT Applications

Emerging Internet of Things (IoT) applications demand data stream processing with low latency and high processing power. Although the cloud naturally provides huge processing capacity, high latency to move data to the datacenter is prohibitive. Edge computing is a recent paradigm where part of computing and storage resources are pushed from the cloud to the edge of the network. In edge computing, edge providers manage their resources near to IoT devices to meet low latency application requirements and reduce the network core bandwidth. To reach the maximum potential of edge computing, a big challenge is to promote the cooperation between edge providers. Currently, edge computing architectures are severely limited for providing cooperation mechanisms between distinct edge providers. In this paper, we propose a edge federation to leverage the cooperation between different edge providers. The edge federation uses interest information propagated in data streams that travel between edge providers to allow an stakeholder to react to inefficient resource allocation and service provision. The main objective of the federation is to create a consortium of edge providers to provide cooperation mechanisms and define and standardize the application interests. The proposed edge federation is (i) data-centric, since edge providers can share common interests and data and, thus, establish cooperation to increase the capacity to provide services for applications; (ii) distributed, since no assumption is made concerning the geo-location of the edge providers and their logical connections; (iii) opportunistic, because an edge provider can react dynamically to the environment change ; (iv) scalable, since the edge provider has the ability to analyze a data flow passing by its infrastructure and make decisions to increase network performance locally, which impacts the global performanc

A Systematic Literature Review on Distributed Machine Learning in Edge Computing

Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies

An Architecture for Distributed Video Stream Processing in IoMT Systems

In Internet of Multimedia Things (IoMT) systems, Internet cameras installed in buildings and streets are major sources of sensing data. From these large-scale video streams, it is possible to infer various information providing the current status of the monitored environments. Some events of interest that have occurred in these observed locations produce insights that might demand near real-time responses from the system. In this context, the event processing depends on data freshness, and computation time, otherwise, the processing results and activities become less valuable or even worthless. An encouraging plan to support the computational demand for latency-sensitive applications of largely geo-distributed systems is applying Edge Computing resources to perform the video stream processing stages. However, some of these stages use deep learning methods for the detection and identification of objects of interest, which are voracious consumers of computational resources. To address these issues, this work proposes an architecture to distribute the video stream processing stages in multiple tasks running on different edge nodes, reducing network overhead and consequent delays. The Multilevel Information Fusion Edge Architecture (MELINDA) encapsulates the data analytics algorithms provided by machine learning methods in different types of processing tasks organized by multiple data-abstraction levels. This distribution strategy, combined with the new category of Edge AI hardware specifically designed to develop smart systems, is a promising approach to address the resource limitations of edge devices

Self-Adaptive Middleware for Wireless Sensor Networks: A Reference Architecture

International audienceWireless Sensor Networks (WSN) are networks composed by tiny devices equipped with sensing, processing, storage, and wireless communication capabilities. WSN are used in highly dynamic environments. Applications for WSN should have an autonomous behavior to adapt their operation and achieve the best network performance. Such adaptation should preferably be performed by a middleware layer tailored to the limited resources of WSN. In this paper, we introduce a Reference Architecture (RA) of a self-adaptive middleware for WSN to contribute for the development of solutions enabling autonomic behavior in WSN. Our RA follows an autonomic computing model (MAPE-K) proposed by IBM and it was specified using a formal description language (pi-ADL) that enables the specification of dynamic architectures. ProSA-RA was used to systematize the design, representation and evaluation of our RA

A Platform for Integrating Physical Devices in the Internet of Things

International audienceThe Internet of Things (IoT) has emerged as a paradigm in which smart things actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high-level tasks. IoT environments are typically characterized by a high degree of heterogeneity, thus encompassing devices with different capabilities, functionalities, and network protocols. In such a scenario, it is necessary to provide abstractions for physical devices and services to applications and end-users, as well as means to manage the interoperability between such heterogeneous elements. In this context, we introduce EcoDiF (Web Ecosystem of Physical Devices), a Web-based platform for integrating heterogeneous physical devices with applications and users in order to provide services to support real-time data control, visualization, processing, and storage. In this paper, we present the main features of EcoDiF and detail its architecture and implementation, which is based on well-known Web technologies such as HTTP, REST, EEML, and EMML. Furthermore, we present a preliminary evaluation of an EcoDiF prototype through proof-of-concept applications from different domains as well as a performance analysis of the platform

On the Development of Systems-of-Systems based on the Internet of Things: A Systematic Mapping

International audienceThe Internet of Things (IoT) has emerged as a paradigm in which smart things actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high level tasks. These things can be engaged in complex relationships including the composition and collaboration with other independent and heterogeneous systems in order to provide new functionalities, thus leading to the so-called systems-of-systems (SoS). In the context of integrating IoT-based systems in order to compose complex, large-scale SoS, this paper presents a systematic mapping aimed to discuss current scenarios and approaches in the development of IoT-based SoS, as well as some challenges and research opportunities in this context

A Systematic Survey of Service Identification Methods

International audienceOne of the major challenges for the adoption of the service-oriented architecture (SOA) is the service identification phase that aims to determine which services are appropriate to be implemented. In the last decade, several service identification methods (SIMs) were proposed. However, the service identification phase still remains a challenge to organizations due to the lack of systematic methods and comprehensive approaches that support the examination of the businesses from multiple perspectives and consider service quality attributes. This work aims to provide an overview of existing SIMs by detailing which service’s perspectives, stated as relevant by the industry, are addressed by the SIMs and also by synthesizing the identification techniques used by them. We have performed a systematic survey over publications about SIMs from 2002 to June 2013, and 105 studies were selected. A detailed investigation on the analyzed SIMs revealed that the identification techniques applied by them have a correlation on how they address many of the service’s perspectives. In addition, they are supporting the SOA adoption by handling many perspectives of the OASIS’ reference architecture for SOA. However, most of them do not explicitly address service quality attributes and few studies support the evaluation of both. Therefore, future research should follow the direction toward hybrid methods with mechanisms to elicit business and service’s quality attributes

A Web Platform for Interconnecting Body Sensors and Improving Health Care

International audienceThe Internet of Things (IoT) is a paradigm in which smart objects actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high-level tasks for the benefit of end-users. In the e-health scenario, these communicating smart objects can be body sensors that enable a continuous real-time monitoring of vital signs of patients. Data produced by such sensors can be used for several purposes and by different actors, such as doctors, patients, relatives, and health care centers, in order to provide remote assistance to users. However, major challenges arise mainly in terms of the interoperability among several heterogeneous devices from a variety of manufacturers. In this context, we introduce EcoHealth (Ecosystem of Health Care Devices), a Web middleware platform for connecting doctors and patients using attached body sensors, thus aiming to provide improved health monitoring and diagnosis for patients. This platform is able to integrate information obtained from heterogeneous sensors in order to provide mechanisms to monitor, process, visualize, store, and send notifications regarding patients’ conditions and vital signs at real-time by using Internet standards. In this paper, we present blueprints of our proposal to EcoHealth and its logical architecture and implementation, as well as an e-health motivational scenario where such a platform would be useful