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With recent advancement in cyber-physical systems and technological revolutions, internet of things is the focus of research in industry as well as in academia. IoT is not only a research and technological revolution but in fact a revolution in our daily life. It is considered a new era of smart lifestyle and has a deep impact on everyday errands. Its applications include but are not limited to smart home, smart transportation, smart health, smart security, and smart surveillance. A large number of devices connected in all these application networks generates an enormous amount of data. This leads to problems in data storage, efficient data processing, and intelligent data analytics. In this chapter, the authors discuss the role of big data and related challenges in IoT networks and various data analytics platforms, used for the IoT domain. In addition to this, they present and discuss the architectural model of big data in IoT along with various future research challenges. Afterward, they discuss smart health and smart transportation as a case study to supplement the presented architectural model.

The goal of the work is to propose architectural and information model for assessing the human condition on the basis of microservice architecture in medical cyber-physical system, which, in contrast to the known models for assessing the human condition, can simultaneously provide scaling, fault tolerance and increase the speed of human condition assessment. The theoretical substantiation and the new decision of an actual scientific problem of development and research means of an estimation of a human condition in medical cyber-physical system have been considered. These means involve the parallel processing of data on vital signs of the human condition, organizing the means of information processing into separate independent logical elements — microservices, in comparison with other existing medical cyber-physical systems. An architectural model based on microservice architecture has been proposed.

Abstract The aim of developing urban forests for steep areas is to prevent erosion. Erosion can be caused by stemflow and throughfall. The difference in stemflow and throughfall is thought to be due to differences in the tree architecture model. The study investigates the effects of several tree architectural models on the amount of stemflow and throughfall. It is hoped that data and information of this research can be taken into consideration in selecting tree species for the benefit of soil and water conservation in urban forest areas that have the potential for erosion and sedimentation. The collection and processing of data comprised the rainfall data obtained from Meteorological Climatological and Geophysical Agency, measurement of leaf area index using a hemispherical photograph and Hemiview 2.1 software, measurement of stemflow and throughfall in five tree architectural models (Massart, Aubreville, Koriba, Rauh, and Troll). Afterward, the relationship between the dependent and independent variables is known through multiple linear regression analysis using Minitab 16 software. The result showed that the tree architectural model influences stemflow and throughfall. The tree architectural model with the highest stemflow and throughfall is Rauh, and the lowest belongs to the Massart architectural model. The tree architectural model that can be used for land and water conservation is Massart; the species is Diospyros discolor Willd.

The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

Pervasive collaborative computing within the Internet of Things (IoT) has progressed rapidly over the last decade. Nevertheless, emerging architectural models and their applications still suffer from limited capacity in areas like power, efficient computing, memory, connectivity, latency and bandwidth. Technological development is still in progress in the fields of hardware, software and wireless communications. Their communication is usually done via the Internet and wireless via base stations. However, these models are sometimes subject to connectivity failures and limited coverage. The models that incorporate devices with peer-to-peer (P2P) communication technologies are of great importance, especially in harsh environments. Nevertheless, their power-limited devices are randomly distributed on the periphery where their availability can be limited and arbitrary. Despite these limitations, their capabilities and efficiency are constantly increasing. Accelerating development in these areas can be achieved by improving architectures and technologies of pervasive collaborative computing, which refers to the collaboration of mobile and embedded computing devices. To enhance mobile collaborative computing, especially in the models acting at the network’s periphery, we are interested in modernizing and strengthening connectivity using wireless technologies and P2P communication. Therefore, the main goal of this paper is to enhance and maintain connectivity and improve the performance of these pervasive systems while performing the required and expected services in a challenging environment. This is especially important in catastrophic situations and harsh environments, where connectivity is used to facilitate and enhance rescue operations. Thus, we have established a resilient mobile collaborative architectural model comprising a peripheral autonomous network of pervasive devices that considers the constraints of these resources. By maintaining the connectivity of its devices, this model can operate independently of wireless base stations by taking advantage of emerging P2P connection technologies such as Wi-Fi Direct and those enabled by LoPy4 from Pycom such as LoRa, BLE, Sigfox, Wi-Fi, Radio Wi-Fi and Bluetooth. Likewise, we have designed four algorithms to construct a group of devices, calculate their scores, select a group manager, and exchange inter- and intra-group messages. The experimental study we conducted shows that this model continues to perform efficiently, even in circumstances like the breakdown of wireless connectivity due to an extreme event or congestion from connecting a huge number of devices.