The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.

The Internet of Things (IoT) has become a reality. As the IoT is now becoming a far more common field, the demand for IoT technologies to manage the communication of devices with the rest of the world has increased. The IoT is connecting various individual devices called things and wireless sensor networks is also playing an important role. A thing can be defined as an embedded device based on a micro controller that can transmit and receive information. These devices are extremely low in power, memory, and resources. Therefore, the research community has recognized the importance of IoT device operating systems (OSs). An adequate OS with a kernel, networking, real-time capability, and more can make these devices flexible. This review provides a detailed comparison of the OSs designed for IoT devices on the basis of their architecture, scheduling methods, networking technologies, programming models, power and memory management methods, together with other features required for IoT applications. In addition, various applications, challenges, and case studies in the field of IoT research is discussed.

A vision of the future Internet is introduced in such a fashion that various computing devices are connected together to form a network called Internet of Things (IoT). This network will generate massive data that may be leveraged for entertainment, security, and most importantly user trust. Yet, trust is an imperative obstruction that may hinder the IoT growth and even delay the substantial squeeze of a number of applications. In this survey, an extensive analysis of trust management techniques along with their pros and cons is presented in a different context. In comparison with other surveys, the goal is to provide a systematic description of the most relevant trust management techniques to help researchers understand that how various systems fit together to bring preferred functionalities without examining different standards. Besides, the lessons learned are presented and the views are argued regarding the primary goal trust is likely to play in the future Internet.

The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.

Advanced metering infrastructure (AMI) is becoming a vital part of utility distribution networks, allowing the development of smart cities. AMI consists of smart electric, gas, and water meters, and the devices are very limited in terms of battery, processing power, and memory. The deployment and operational needs of energy-constrained network infrastructures in smart water and gas metering systems require the use of routing mechanisms that consider energy consumption, minimize energy use, and prolong network lifetime. An efficient routing metric is needed for energy-constrained devices. In this paper, we propose an energy- and congestion-aware routing metric for smart meter networks to be deployed in smart cities. The proposed metric is an adaptive parent node selection mechanism that considers the residual energy and queue utilization of neighboring nodes. Minimizing power consumption will enhance network lifetime. The proposed scheme was evaluated with the Cooja Simulator 3.0 using random and grid topology. The simulation results show greater network performance in terms of average power consumption and packet delivery ratio.