The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. Research institutions and scientific groups are continuously working to deliver solutions and products using IoT to address different domains of agriculture. This paper presents a systematic literature review (SLR) by conducting a survey of IoT technologies and their current utilization in different application domains of the agriculture sector. The underlying SLR has been compiled by reviewing research articles published in well-reputed venues between 2006 and 2019. A total of 67 papers were carefully selected through a systematic process and classified accordingly. The primary objective of this systematic study is the collection of all relevant research on IoT agricultural applications, sensors/devices, communication protocols, and network types. Furthermore, it also discusses the main issues and challenges that are being investigated in the field of agriculture. Moreover, an IoT agriculture framework has been presented that contextualizes the representation of a wide range of current solutions in the field of agriculture. Similarly, country policies for IoT-based agriculture have also been presented. Lastly, open issues and challenges have been presented to provide the researchers promising future directions in the domain of IoT agriculture.

It is predicted that by 2025, all devices will be connected to the Internet, subsequently causing the number of devices connected with the Internet to rise

Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices' diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted.

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5 ^th generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3 ^rd generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

Impressive technological advancements over the past decades commenced significant advantages in the maritime industry sector and elevated commercial, operational, and financial benefits. However, technological development introduces several novel risks that pose serious and potential threats to the maritime industry and considerably impact the maritime industry. Keeping in view the importance of maritime cyber security, this study presents the cyber security threats to understand their impact and loss scale. It serves as a guideline for the stakeholders to implement effective preventive and corrective strategies. Cyber security risks are discussed concerning maritime security, confidentiality, integrity, and availability, and their impact is analyzed. The proneness of the digital transformation is analyzed regarding the use of internet of things (IoT) devices, modern security frameworks for ships, and sensors and devices used in modern ships. In addition, risk assessment methods are discussed to determine the potential threat and severity along with the cyber risk mitigation schemes and frameworks. Possible recommendations and countermeasures are elaborated to alleviate the impact of cyber security breaches. Finally, recommendations about the future prospects to safeguard the maritime industry from cyber-attacks are discussed, and the necessity of efficient security policies is highlighted.

Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract features from state-of-the-art speech emotional datasets. Subsequently, a correlation-based feature selection technique is applied to the extracted features to select the most appropriate and discriminative features for SER. For the classification of emotions, we utilize support vector machines, random forests, the k-nearest neighbors algorithm, and neural network classifiers. Experiments are performed for speaker-dependent and speaker-independent SER using four publicly available datasets: the Berlin Dataset of Emotional Speech (Emo-DB), Surrey Audio Visual Expressed Emotion (SAVEE), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and the Ryerson Audio Visual Dataset of Emotional Speech and Song (RAVDESS). Our proposed method achieves an accuracy of 95.10% for Emo-DB, 82.10% for SAVEE, 83.80% for IEMOCAP, and 81.30% for RAVDESS, for speaker-dependent SER experiments. Moreover, our method yields the best results for speaker-independent SER with existing handcrafted features-based SER approaches.