This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Next Generation 5G Wireless Networks: A Comprehensive Survey

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

This article is focused on the importance and influence of Industry 4.0 and consequently the Internet-connected technologies for the creation of value added for organizations and society. The contribution of the article is mainly conceptual. With the development of the Internet, the Internet of things that is central to the new industrial revolution has led to “Industry 4.0.” The aim of this article is to synthesize the known theory and practices of Industry 4.0, and to investigate the changes that will result from Industry 4.0 and with the development of the Internet of things.

https://journals.sagepub.com/doi/pdf/10.1177/2158244016653987

A Complex View of Industry 4.0

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

An automated irrigation system was developed to optimize water use for agricultural crops. The system has a distributed wireless network of soil-moisture and temperature sensors placed in the root zone of the plants. In addition, a gateway unit handles sensor information, triggers actuators, and transmits data to a web application. An algorithm was developed with threshold values of temperature and soil moisture that was programmed into a microcontroller-based gateway to control water quantity. The system was powered by photovoltaic panels and had a duplex communication link based on a cellular-Internet interface that allowed for data inspection and irrigation scheduling to be programmed through a web page. The automated system was tested in a sage crop field for 136 days and water savings of up to 90% compared with traditional irrigation practices of the agricultural zone were achieved. Three replicas of the automated system have been used successfully in other places for 18 months. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.

/pdf/wsn-based-automated-irrigation-system-using-gsm-gprs-107bshwa2z.pdf

WSN based Automated Irrigation System using GSM/GPRS Technology

This paper is to design a new hybrid deep learning model for stock market prediction. Initially, the collected stock market data from the benchmark sources are pre-processed using empirical wavelet transform (EWT). This pre-processed data is subjected to the prediction model based on hybrid deep learning approach by adopting Resnet and recurrent neural network (RNN). Here, the fully connected layer of Resnet is replaced with the RNN. In both the Resnet and RNN structures, the parameter is optimized using the probabilistic spider monkey optimization (P-SMO) for attaining accurate prediction. When analyzing the proposed P-SMO-ResRNN, it secures 6.27%, 12.26%, 15.13%, 13.61%, and 14.10% more than RNN, DNN, NN, KNN, and SVM, respectively, regarding the MASE analysis. Hence, the proposed model shows enhanced performance. With the elaborated model and estimation of prediction term based on several analyses, this work supports the stock analysis research community.

Ch. Ramesh Babu

Papers

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

WSN based Automated Irrigation System using GSM/GPRS Technology

Meta-Heuristic-Based Hybrid Resnet with Recurrent Neural Network for Enhanced Stock Market Prediction