Towards edge intelligence in smart spaces

After more than two decades of existence, the internet of things has been revolutionizing the way we interact with the world around us. Although, in its origins, the adoption of a cloud computing paradigm supported this ubiquitous computing model, the increasing complexity of IoT systems has led to the gradual fading of the traditional hierarchical model of cloud computing. The search for solutions to the problems of latency, scalability and privacy has, in recent years, driven the movement of data processing and storage, from the cloud, to the edge of the network (edge computing). Starting from the particular case of edge computing that keeps the focus on extending the boundaries of artificial intelligence to the edge of the network - Edge intelligence - a survey of the current state of the art is carried out, culminating into the specification of an architecture to support edge intelligence applications. In order to validate the proposed architecture, two scenarios are presented. In the scope of waste management and energy recycling, a system for used cooking oil classification in a national domestic collection network is presented. With the local classification of the trustworthiness of each deposit, it was possible to significantly shorten the response times, with a direct impact on energy consumption levels. Aimed at smart cities, a second application scenario, proposes an approach based on computer vision and deep learning, for local detection of pedestrians on crosswalks. In this context, an edge intelligence paradigm allowed to overcome privacy related issues, as well as reducing response times by more than 80 times, when compared to a cloud computing based solution.Após mais de duas décadas de existência, a internet das coisas, tem vindo a revolucionar a forma como interagimos com o mundo que nos rodeia. Apesar de, nas suas origens, a adoção de um paradigma de computação em nuvem ter servido de suporte a este modelo de computação ubíqua, a crescente complexidade dos sistemas IoT tem conduzido ao paulatino esvanecer do tradicional modelo hierárquico da computação em nuvem. A procura por soluções para os problemas de latência, escalabilidade e garantia de qualidade de serviço tem, nos últimos anos, impulsionado a deslocação do processamento e armazenamento de dados, da nuvem, para a periferia da rede (computação periférica). Partindo do caso particular de computação periférica que mantém o foco no alargar das fronteiras da inteligência artificial para a periferia da rede - Periferia inteligente - um levantamento do atual estado da arte é levado a cabo, culminando na especificação de uma arquitetura de suporte a cenários de periferia inteligente. Com vista à validação da arquitetura proposta, dois cenários são apresentados. No âmbito da gestão de resíduos e reciclagem energética, um sistema para classificação de óleo alimentar usado, numa rede nacional de recolha doméstica é apresentado. Com classificação local da veracidade de cada depósito foi possível encurtar significativamente os tempos de resposta, com impacto direto nos níveis de consumo energético. Direcionado às cidades inteligentes, um segundo cenário de aplicação, propõe uma abordagem baseada em visão computacional e aprendizagem profunda, para deteção local de peões em passadeiras. Neste contexto, um paradigma de periferia inteligente permitiu ultrapassar questões relativas à privacidade na transmissão de dados, assim como reduzir em mais de 80 vezes os tempos de resposta, quando comparado com uma solução de computação em nuvem

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces

With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates (MPAT). The detection position was repositioned according to the estimated speed of target by optical flow method, and the alternate template was stored with a template update mechanism, which were all computed at the edge. Experimental results on large-scale public benchmark datasets showed the effectiveness of the proposed method in comparison with state-of-the-art methods

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond