The Internet of Things (IoT) is an extensive network of heterogeneous devices that provides an array of innovative applications and services. IoT networks enable the integration of data and services to seamlessly interconnect the cyber and physical systems. However, the heterogeneity of devices, underlying technologies and lack of standardization pose critical challenges in this domain. On account of these challenges, this research article aims to provide a comprehensive overview of the enabling technologies and standards that build up the IoT technology stack. First, a layered architecture approach is presented where the state-of-the-art research and open challenges are discussed at every layer. Next, this research article focuses on the role of middleware platforms in IoT application development and integration. Furthermore, this article addresses the open challenges and provides comprehensive steps towards IoT stack optimization. Finally, the interfacing of Fog/Edge Networks to IoT technology stack is thoroughly investigated by discussing the current research and open challenges in this domain. The main scope of this study is to provide a comprehensive review into IoT technology (the horizontal fabric), the associated middleware and networks required to build future proof applications (the vertical markets).

Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a thorough study we found that there currently exists no detailed comparative analysis of artificial intelligence-based techniques published in the last five years. Therefore, there is a need for this kind of work to be published, as there are many comparative analyses in other fields of cyber security that are available for readers to review.In this paper, we provide a comprehensive review of the latest and most recent literature, which introduces well-known machine learning and deep learning algorithms and the challenges they face in detecting zero-day attacks. Following these qualitative analyses, we present the comparative evaluation results regarding the highest accuracy, precision, recall, and F1 score compared to different datasets.

In today’s world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user’s social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the “News Headline Data set” for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.

Generally, various traffic requirements in wireless sensor network are mostly dependent on specific application types, that is, event-driven, continuous, and query-driven types. In these applications, real-time delivery is one of the important research challenges. However, due to harsh networking environment around a node, many researchers usually take different approach from conventional networks. In order to discuss and analyze the advantage or disadvantage of these approaches, some comprehensive survey literatures were published; however they are either out of date or compiled for communication protocols on single layer. Based on this deficiency, in this paper, we present the up-to-date research approaches and discuss the important features related to real-time communications in wireless sensor networks. As for grouping, we categorize the approaches into hard, soft, and firm real-time model. Furthermore, in all these categories, research has been focused on MAC and scheduling and routing according to research area or objective in second level. Finally, the article also suggests potential directions for future research in the field.

The deployment of wearable or body-worn devices is increasing rapidly, and thus researchers’ interests mainly include technical and economical issues, such as networking, interoperability, security, power optimization, business growth and regulation. To address these issues properly, previous survey papers usually focused on describing the wireless body area network architecture and network protocols. This implies that deployment issues and awareness issues of wearable and BAN devices are not emphasized in previous work. To defeat this problem, in this study, we have focused on feasibility, limitations, and security concerns in wireless body area networks. In the aspect of the economy, we have focused on the compound annual growth rate of these devices in the global market, different regulations of wearable/wireless body area network devices in different regions and countries of the world and feasible research projects for wireless body area networks. In addition, this study focuses on the domain of devices that are equally important to physicians, sportsmen, trainers and coaches, computer scientists, engineers, and investors. The outcomes of this study relating to physicians, fitness trainers and coaches indicate that the use of these devices means they would be able to treat their clients in a more effective way. The study also converges the focus of businessmen on the Annual Growth Rate (CAGR) and provides manufacturers and vendors with information about different regulatory bodies that are monitoring and regulating WBAN devices. Therefore, by providing deployment issues in the aspects of technology and economy at the same time, we believe that this survey can serve as a preliminary material that will lead to more advancements and improvements in deployment in the area of wearable wireless body area networks. Finally, we present open issues and further research direction in the area of wireless body area networks.