Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

The development of flexible and efficient communication mechanisms is of paramount importance within the context of the Internet of Things (IoT) paradigm. IoT has been used for industrial, commercial, and residential applications, and the IEEE 802.15.4/ZigBee standard is one of the most suitable protocols for this purpose. This protocol is now frequently used to implement large-scale Wireless Sensor Networks (WSNs). In industrial settings, it is becoming increasingly common to deploy cluster-tree WSNs, a complex IEEE 802.15.4/ZigBee-based peer-to-peer network topology, to monitor and control critical processes such as those related to oil or gas, mining, or certain specific chemicals. The remote monitoring of critical events for hazards or disaster detection in large areas is a challenging issue, since the occurrence of events in the monitored environment may severely stress the regular operation of the network. This paper proposes the Dynamic REconfiguration mechanism of cluster-Tree WSNs (DyRET), which is able to dynamically reconfigure large-scale IEEE 802.15.4 cluster-tree WSNs, and to assign communication resources to the overloaded branches of the tree based on the accumulated network load generated by each of the sensor nodes. A complete simulation assessment demonstrates the proposed mechanism’s efficiency, and the results show that it can guarantee the required quality of service level for the dynamic reconfiguration of cluster-tree networks.

RESUMO: Uma forma viável de sistematização e personalização da intervenção delineada em Análise do Comportamento Aplicada (ABA), com estudantes com Transtorno do Espectro Autista (TEA), pode ser por meio de recursos computacionais. Assim sendo, este estudo teve como objetivo elaborar um ambiente digital denominado como mTEA para aplicação de programas de ensino por tentativas discretas, fundamentados na perspectiva comportamental, assim como avaliar o uso do mTEA, em relação à elaboração e à aplicação das atividades por duas profissionais com estudantes com TEA. Participaram duas profissionais e cinco crianças com TEA. O procedimento foi organizado da seguinte forma: desenvolvimento do sistema; na sequência, as duas profissionais elaboraram e aplicaram as atividades com cinco estudantes com TEA e responderam a um questionário sobre o uso do mTEA. Os resultados foram agrupados em relação ao desempenho dos estudantes com TEA nas atividades e ao uso do sistema pelas profissionais. Os resultados identificaram que o mTEA atingiu o objetivo proposto para personalizar as atividades propostas em cada currículo de ensino de cada estudante, apesar de ainda carecer de melhorias a serem implantadas futuramente.