A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end‐devices. Mist computing is a new paradigm that attempts to make use of the more resource‐rich nodes that are closer than Edge nodes to end‐users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive‐scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning‐to‐rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource‐rich nodes, while considering the selection of other nodes.publishedVersio

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

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

Fog Computing Architecture for Indoor Disaster Management

Most people spend their time indoors. Indoors have a higher complexity than outdoors. Moreover, today's building structures are increasingly sophisticated and complex, which can create problems when a disaster occurs in the room. Fire is one of the disasters that often occurs in a building. For that, we need disaster management that can minimize the risk of casualties. Disaster management with cloud computing has been extensively investigated in other studies. Traditional ways of centralizing data in the cloud are almost scalable as they cannot cater to many latency-critical IoT applications, and this results in too high network traffic when the number of objects and services increased. It will be especially problematic when in a disaster that requires a quick response. The Fog infrastructure is the beginning of the answer to such problems. This research started with an analysis of literature and hot topics related to fog computing and indoor disasters, which later became the basis for creating a fog computing-based architecture for indoor disasters. In this research, fog computing is used as the backbone in disaster management architecture in buildings. MQTT is used as a messaging protocol with the advantages of simplicity and speed. This research proposes a disaster architecture for indoor disasters, mainly fire disasters