With the evolution of the fifth-generation (5G) wireless network, the Internet of Things (IoT) has become a revolutionary technique that enables a diverse number of features and applications. It can able a diverse amount of devices to be connected in order to create a single communication architecture. As it has significantly expanded in recent years, it is fundamental to study this trending technology in detail and take a close look at its applications in the different domains. It represents an enabler of new communication possibilities between people and things. The main asset of this concept is its significant influence through the creation of a new world dimension. The key features required for employing a large-scale IoT are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications. This research work is presented in four main sections, including a general overview of IoT technology, a summary of previous correlated surveys, a review regarding the main IoT applications, and a section on the challenges of IoT. The purpose of this study is to fully cover the applications of IoT, including healthcare, environmental, commercial, industrial, smart cities, and infrastructural applications. This work explains the concept of IoT and defines and summarizes its main technologies and uses, offering a next-generation protocol as a solution to the challenges. IoT challenges were investigated to enhance research and development in the fields. The contribution and weaknesses of each research work cited are covered, highlighting eventual possible research questions and open matters for IoT applications to ensure a full analysis coverage of the discussed papers.

The Internet of Things (IoT) is shaping the current and next generation of the Internet. The vision of IoT is to embed communication capabilities with a highly distributed, ubiquitous and dense heterogeneous devices network. This vision includes the adaptation of secure mobile networks, anytime, anywhere, by anyone or anything with new intelligent applications and services. Many efforts have been made to review the literature related to the IoT for the benefit of IoT development. However, many issues need to be addressed to overtake the full potential of the IoT. Therefore, this paper aims to classify and standardize IoT research areas by considering review papers that were published between 2010 and 2019. This paper analyzes a total of 95 related reviews, which were manually selected from databases based on 6 chosen areas. This paper presents the trends and classification of IoT reviews based on 6 research areas, namely, application, architecture, communication, challenges, technology, and security. IoT communication research has been dominating the trends with 21% of total reviews and more than 100% research growth in the last 10 years. Hence, this paper can provide useful insights into specific emerging areas of IoT to assist future research.

The Internet of Things (IoT) is transforming how we live and work, and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail. Considering its wide range of applications, addressing the security challenges arising from IoT devices’ massive collection and transmission of user data is vital. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research needs a comprehensive summary of deep learning-based IDS in IoT. This paper introduces intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

Internet of Things (IoT) is proliferating in our real world, and it is a promising technology that serves a very comfortable service to the users. IoT's underlying technology is to connect to central Cloud Computing (CC), which is a huge data center collecting the generated data by IoT devices, and it is located in different areas on demand. However, cloud computing lacks data transmission because of the infrastructure and limitations of networks which enormously decrease its performance. Therefore, a new paradigm has been founded to act as a middleware between the Cloud and IoT, termed Fog Computing (FC) Technology. Considering Fog as a cloud extension that provides computing service at the edge of the network, Fog Computing placement enables this technology to deal with numerous data locally. In this study, we surveyed Fog computing with in-depth analysis and covered the latest studies to address and overcome the existing challenges in FC. We reviewed fog computing technology conceptually and defined it based on the existing studies in the literature, together with its architecture, applications, advantages, and open issues with optimization methods being performed to obtain the optimal services.

Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.