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The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures. To deal with this problem, communication networks consider fog computing as one of promising technologies that can improve overall communication performance. It brings on-demand services proximate to the end devices and delivers the requested data in a short time. Fog computing faces several issues such as latency, bandwidth, and link utilization due to limited resources and the high processing demands of end devices. To this end, fog caching plays an imperative role in addressing data dissemination issues. This study provides a comprehensive discussion of fog computing, Internet of Things (IoTs) and the critical issues related to data security and dissemination in fog computing. Moreover, we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing. Besides, this paper presents a number of caching schemes with their contributions, benefits, and challenges to overcome the problems and limitations of fog computing. We also identify machine learning-based approaches for cache security and management in fog computing, as well as several prospective future research directions in caching, fog computing, and machine learning.

Abstract Internet of Things (IoT) vision has astoundingly transcended environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines, and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real-world IoT scenarios relying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM), which is a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in TM field for different classes of IoT applications. It can provide tamper-proof data by enabling more reliable trust information and integrity verification, ultimately enhancing its availability and privacy when storing and sharing information. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain-based decentralized trust management systems (BCDTMS) for IoT. The contributions of this study are threefold; first, we provide the comprehensive and comparative analysis of state-of-the-art BCDTMS devised for different IoT classes such as Internet of Medical of Things (IoMT), Internet of Vehicles (IoV), Industrial IoT (IIoT), and Social IoT (SIoT). To make it an extensive study, we perform a detailed assessment of the existing BCDTMS in the literature in the aspects of Blockchain and TM. Second, we present requirements for developing Blockchain-based TM systems for IoT, and third we have highlighted the challenges in the context of using Blockchain for TM in various IoT applications.

Network-on-Chip (NoC) has been unfolded as a superior alternative for integrating a considerably greater extent of cores on a single chip. Recently, multi-core systems have become prevalent because of the increased processing demands for high-performance embedded applications. Application mapping techniques play a significant role in enhancing the extensive performance of such complex multi-core platforms. Developing and implementing efficient application mapping techniques are required for system design to meet the demand of such complicated multi-core systems. The paper primarily focuses on dynamic application mapping techniques, classifying them into a number of subcategories. It highlights such approaches and techniques that aim to enhance the performance of the NoC-based systems by optimizing them in terms of communication cost, latency, energy consumption, power, execution, and computational time. Future challenges, trends, and simulation tools have also been spotlighted.

The data breach and the integrity violation of remote data remain significant issues in the domain of information security. A provably-secure hash function aids in providing solutions to integrity-related issues. Nevertheless, the choice of a provably-secure hash function has to be made with caution from the perspective of security. This research study attempts to identify the weakness of contemporary keyless hash functions and proposes an algorithm called a provably secure subset hash function (PSSHF). The objectives of the studies are reinforcing the internal structure of random oracle (RO), intensifying stochastic deportment, presenting computationally infeasible conditions for reverse decoding, and forestalling block-level and differential attacks through subsets and polynomial functions. The avalanche response of PSSHF is 50.06% and is higher than that of its contemporary variants. Likewise, the Near-collision response of PSSHF is 49.94% and is the least among its other similitudes. The empirical analysis of the effect of avalanche proves the novel design modifies 93.78% of output symbols besides excelling its other counterparts on random behavior. The runtime response proves the PSSHF processes short messages with acceptable delay. Therefore, the proposed PSSHF can be considered a perfect replacement for its similitudes in respect of the short messages for higher security.

The phenomenal growth of smartphones and wearable devices has begun crowd-sourcing applications for the Internet of Things (IoT). E-healthcare is considered the essential service for crowd-sourcing IoT applications that help remote access or storage medical server (MS) data to the authorized doctors, patients, nurses, etc., via the public Internet. As the public Internet is exposed to various security attacks, remote user authenticated key exchange (AKE) has become a pressing need for the secure and reliable use of these services. This paper proposes a new resource-efficient AKE scheme for telecare medical information systems, called REAS-TMIS. It uses authenticated encryption with associative data (AEAD) and a hash function. AEAD schemes are devised specifically for encrypted communication among resource-constricted IoT devices. These features of AEAD make REAS-TMIS resource-efficient. Moreover, REAS-TMIS dispenses with the elliptic curve point multiplication and chaotic map that are computationally expensive operations. In addition, REAS-TMIS renders the functionality of session key (SK) establishment for future encrypted communication between MS and users after validating the authenticity of the user. The security of SK is corroborated employing the well establish random oracle model. Moreover, Scyther-based security corroboration is implemented to show that REAS-TMIS is secure, and informal security analysis is executed to show the resiliency of REAS-TMIS against various security attacks. Besides, a thorough analysis shows that REAS-TMIS, while accomplishing the authentication phase, requires less computational, communication, and storage resources than the related authentication protocol.

Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.

Abstract Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain.

The explosive growth and wide proliferation of mobile devices, the majority of which are smartphones, led to the inception of several novel and intuitive services, including on-the-go services, online customer services, and location-based services (LBS) [...]

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.

The wide use of mobile devices introduced several new services for the consumer market which are collectively called location-based services, the name being indicative of the significance of the consumer position. Consequently, a rich variety of positioning technologies have been adopted to provide and enhance user location information. The mass deployment of Wi-Fi access points (APs) and the ubiquity of the magnetic field data make them attractive candidates for indoor positioning. Additionally, the availability of embedded magnetic and Wi-Fi sensors in smartphones helps to achieve positioning without additional infrastructure. Even though Wi-Fi and magnetic field data offer complementary characteristics for enhancing positioning accuracy, several challenges for these technologies remain unresolved. However, the lack of publicly available datasets for the magnetic field and Wi-Fi makes it very difficult to extensively investigate these characteristics. Also, the proposed approaches cannot be tested on common benchmark datasets to analyze the results of the state-of-the-art approaches. To resolve these issues, this study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five different smartphones used by four different users, both female, and males. Different path geometries are followed in different multi-floor buildings which are physically separated, comprising both small and large areas. Besides, three different orientations of the smartphone are considered for data collection covering corridors, halls, and laboratories. The data from the stairs help to test ‘stairs up’ and ‘stairs down’ events and approaches aiming at multi-floor positioning can be tested with the provided dataset.

Fifth-Generation (5G) networks have adopted a multi-tier structural model which includes femtocells, picocells, and macrocells to ensure the user quality-of-service (QoS). To meet these QoS demands, the system requires optimization of different resources in different network dynamics carefully. However, if ignored, this will lead to long processing delays and high computational burdens. To avoid this, we proposed Deep Learning (DL) based resource allocation (RA) as a promising solution to meet the network requirements. DL is an effective mechanism where neural networks can learn to develop RA techniques. Thus, an optimized RA decision can be achieved using DL without exhaustive computations. Further, DL uses DL to achieve solutions for joint RA and remote-radio-head (RRH) association problems in multi-tier Cloud-Radio Access Networks (C-RAN). Initially, a summary of existing literature on DL-based RA techniques is provided, followed by a deep neural network (DNN) description, its architectures, and the data training method. Then, a supervised DL technique is presented to solve the joint RA and RRH-association problem. An efficient subchannel assignment, power allocation, and RRH-association (SAPARA) technique are used to generate the training data for the DNN model using the iterative approach where the seed data for the SAPARA technique is taken using a uniform power allocation and path-loss based association (UPA-PLBA) model. After training the DNN model, the accurateness of the presented model is tested. Simulation outcomes demonstrate that our proposed scheme is capable of providing an efficient solution in the considered scenario.

Network on chip (NoC) is a promising communication infrastructure for multiple cores on a chip to exchange data efficiently. In such NoC architecture, application mapping is a process of assigning tasks to the processing cores. An optimized application mapping technique enhances the performance of a chip and reduces the entire chip’s energy consumption. The optimization of application mapping is essential in the design of NoC. In this study, a greedy algorithm is utilized as the first technique to place the maximum communicating tasks together to give the main algorithm a head start. Then, a meta-heuristic Cuckoo Search via Lévy flight is employed further to optimize the placement of tasks on the NoC cores. The greedy algorithm furnishes a relatively pre-processed base to the cuckoo search optimization (CSO), which eventually helps in the fast convergence of the main algorithm. The analysis of the results shows that the proposed algorithm outperformed the state-of-the-art techniques in NoC application mapping in terms of various performance metrics, such as communication cost, energy consumption, and average packet latency.

The characterization of aircraft in remote sensing satellite imagery has many armed and civil applications. For civil purposes, such as in tragedy and emergency aircraft searching, airport scrutiny and aircraft identification from satellite images are very important. This study presents an automated methodology based on handcrafted and deep convolutional neural network (DCNN) features. The presented aircraft classification technique consists of the following steps. The handcrafted features achieved from a local binary pattern (LBP) and DCNN are fused by feature fusion techniques. The DCNN features are extracted from Alexnet and Inception V3. Then we adopted a feature selection technique called principal component analysis (PCA). PCA removes the redundant and irrelevant information and improves the classification performance. Then, Famous supervised methodologies categorize these selected features. We chose the best classifier based on its highest accuracy. The proposed technique is executed on the multi-type aircraft remote sensing images (MTARSI) dataset, and the overall highest accuracy that we achieved from our proposed method is 96.8% by the linear support vector machine (SVM) classifier.

Abstract In a real network deployment, the diverse sensor applications generate a heterogeneous traffic pattern which may include basic sensing measurements such as temperature readings or high-volume multimedia traffic. In a heterogeneous traffic network, the two standardized objective functions (OFs), i.e., objective function zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF) for routing protocol for Low-Power and Lossy Networks (RPL) perform poor routing decisions by selecting an already congested parent node and cause more re-transmissions across the network. Therefore, careful consideration is required in designing a new OF for heterogeneous traffic scenarios. In this study, we examine the RPL protocol under a heterogeneous traffic pattern and proposed a new protocol based on queue and workload-based condition (QWL-RPL). The aim of the proposed protocol is to achieve a reliable path with better overall performance. The proposed OF model considers the link workload in addition to mapping the congestion status of the node using the packet queue. We implement the proposed routing model in the Contiki operating system (OS) Cooja environment to compare with the existing technique. The simulation results show that QWL-RPL can improve the performance of a heterogeneous traffic network as compared with both OF0 and MRHOF, specifically in terms of the amount of overhead, packets reception ratio (PRR), average delay, and jitter. Final results indicate that on average, there is a 5%–30% improvement in PRR, 25%–45% reduction in overheads, 12%–30% reduction in average delay, and 20%–40% reduction in jitter.

The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users' presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network's performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

The use of the magnetic field data to perform indoor positioning gained an accelerating interest during the last few years. The ubiquity of the magnetic field data makes it a potential candidate for indoor positioning and localization. Additionally, the availability of the embedded magnetic sensor in the smartphones makes it an infrastructure-less approach that requires no additional infrastructure to perform positioning. Despite that, the magnetic field-based positioning is relatively new than those of Wi-Fi, radio frequency identification, and ultra-wideband based approaches and faces many challenges and necessitates a comprehensive investigation of the associated problems for wide applicability. For example, smartphone heterogeneity, mutation of the magnetic field data due to change in the user's height, time, and device, and low discernibility are a few of the problems that require substantial investigation before the practical deployment of the magnetic field data for indoor positioning and localization. This study contributes by endeavoring a comprehensive analysis of the opportunities and challenges of using the magnetic field data for indoor positioning and localization using smartphones. The pros and cons of the magnetic field data are highlighted through the experiments conducted in various indoor environments using multiple smartphones. The research thus helps in understanding the practical considerations for deploying the magnetic field data for indoor positioning and localization.

Abstract One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment.

The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of channel observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, channel access delay, and fairness as performance metrics.

The Internet of Things (IoT) is one of the dominating paradigms of the new era with its abilities to provide ubiquitous intelligence and pervasive interconnections to diverse physical objects. With the advancements, such as new generation 5G communication and cloud/edge computing-based paradigms, the degree of domination is expected to further increase. Therefore, improvements for quality of service have become a critical issue. Traditional packets scheduling algorithms cannot meet the requirements of the large-scale IoT systems. In this paper, a novel scheduling approach is proposed for different data classes (types) to be exchanged between heterogeneous nodes of a generic IoT infrastructure. The possible ways of organizing these classes depending on different metrics, such as time latency, reliability, and data loss, are investigated. The proposed approach has the advantage of being able to tune the priorities and network characteristics to reach a specifically desired performance state. Since each type of packets is considered separately, it is possible to prioritize them, by tuning the related parameters, which changes the priorities between packets. The numerical results presented show that the new approach performs better than the existing typical scheduling approaches. The developed approach can be used in the various IoT applications with the support of 5G communication and edge computing, such as agriculture, wearables, connected cars, smart retail, and smart cities.

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.

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.

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 [...]

The inefficient assignment of spectrum for different communications purposes, plus technology enhancements and ever-increasing usage of wireless technology is causing spectrum scarcity. To address this issue, one of the proposed solutions in the literature is to access the spectrum dynamically or opportunistically. Therefore, the concept of cognitive radio appeared, which opens up a new research paradigm. There is extensive research on the physical, medium access control and network layers. The impact of the transport layer on the performance of cognitive radio ad hoc sensor networks is still unknown/unexplored. The Internet’s de facto transport protocol is not well suited to wireless networks because of its congestion control mechanism. We propose an opportunistic hybrid transport protocol for cognitive radio ad hoc sensor networks. We developed a new congestion control mechanism to differentiate true congestion from interruption loss. After such detection and differentiation, we propose methods to handle them opportunistically. There are several benefits to window- and rate-based protocols. To exploit the benefits of both in order to enhance overall system performance, we propose a hybrid transport protocol. We empirically calculate the optimal threshold value to switch between window- and rate-based mechanisms. We then compare our proposed transport protocol to Transmission Control Protocol (TCP)-friendly rate control, TCP-friendly rate control for cognitive radio, and TCP-friendly window-based control. We ran an extensive set of simulations in Network Simulator 2. The results indicate that the proposed transport protocol performs better than all the others.

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.

Recent innovation, growth, and deployment of internet of things (IoT) networks are changing the daily life of people. 5G networks are widely deployed around the world, and they are important for continuous growth of IoT. The next generation cellular networks and wireless sensor networks (WSN) make the road to the target of the next generation IoT networks. The challenges of the next generation IoT networks remain in reducing the overall network latency and increasing throughput without sacrificing reliability. One feasible alternative is coexistence of networks operating on different frequencies. However, data bandwidth support and spectrum availability are the major challenges. Therefore, cognitive radio networks (CRN) are the best available technology to cater to all these challenges for the co-existence of IoT, WSN, 5G, and beyond-5G networks.

Network-on-chip (NoC) architectures have become a popular communication platform for heterogeneous computing systems owing to their scalability and high performance. Aggressive technology scaling makes these architectures prone to both permanent and transient faults. This study focuses on the tolerance of a NoC router to permanent faults. A permanent fault in a NoC router severely impacts the performance of the entire network. Thus, it is necessary to incorporate component-level protection techniques in a router. In the proposed scheme, the input port utilizes a bypass path, virtual channel (VC) queuing, and VC closing strategies. Moreover, the routing computation stage utilizes spatial redundancy and double routing strategies, and the VC allocation stage utilizes spatial redundancy. The switch allocation stage utilizes run-time arbiter selection. The crossbar stage utilizes a triple bypass bus. The proposed router is highly fault-tolerant compared with the existing state-of-the-art fault-tolerant routers. The reliability of the proposed router is 7.98 times higher than that of the unprotected baseline router in terms of the mean-time-to-failure metric. The silicon protection factor metric is used to calculate the protection ability of the proposed router. Consequently, it is confirmed that the proposed router has a greater protection ability than the conventional fault-tolerant routers.

The Internet of Things (IoT) is a world of connected networks and modern technology devices, among them vehicular networks considered more challenging due to high speed and network dynamics. Future trends in IoT allow these inter networks to share information. Also, the previous security solutions to vehicular IoT (VIoT) much emphasize on privacy protection and security related issues using public keys infrastructure. However, the primary concern about efficient trust assessment, authorized users malfunctioning, and secure information dissemination in vehicular wireless networks have not been explored. To cope with these challenges, we propose a trust enhanced on-demand routing (TER) scheme, which adopts TrustWalker (TW) algorithm for efficient trust assessment and route search technique in VIoT. TER comprised of novel three-valued subjective logic (3VSL), TW algorithm, and ad hoc on-demand distance vector (AODV) routing protocol. The simulated results validate the accuracy of the proposed scheme in term of throughput, packet drop ratio (PDR), and end to end (E2E) delay. In the simulation, the execution time of the TW algorithm is analyzed and compared with another route search algorithm, i.e., Assess-Trust (AT), by considering real-world online datasets such as Pretty Good Privacy and Advogato. The accuracy and efficiency of the TW algorithm, even with a large number of vehicle users, are also demonstrated through simulations.

With the immersive growth of the Internet of Things (IoT) and real-time adaptability, quality of life for people is improving. IoT applications are diverse in nature and one crucial aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). It generates a massive volume of data with different characteristics and requirements than the IoT. The real-time deployment scenarios vary from smart traffic monitoring to smart hospitals. Hence, Timely delivery of IoMT data and decision making is critical as it directly involves the safety of human beings. In this paper, we present a brief overview of IoMT and future research directions. Afterward, we provide an overview of the accepted articles in our special issue on the IoMT: Opportunities, Challenges, and Solutions.

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.

In recent years, the increase in vehicular traffic has led to a drastic increase in road accidents, thus requiring adequate vehicle safety measures. The intelligent transportation system is a branch of modern technology responsible for delivering such road safety services. The wireless access vehicular environment standard defines how these intelligent transportation system applications can be incorporated in highly varying vehicular ad hoc network environments. Wireless access vehicular environment comprises the IEEE 802.11p and IEEE 1609.4 standards responsible for handling medium access control and transmission mechanisms in vehicular ad hoc networks. However, these standards still face medium access control and physical layer challenges, which must be addressed for reliable quality of service. Several research studies have been proposed and are still underway into improving medium access control protocols in the wireless access vehicular environment stack model defined by IEEE 1609.4 multichannel operation. These proposed medium access control protocols refine the quality of service and reliability of certain performance parameters and have also added some limitations. The working of such wireless access vehicular environment medium access control protocols can be enhanced by defining their limitations and improving them, without causing any performance tradeoffs. Finally, this article works toward reviewing and analyzing such limitations and tradeoffs to provide insight into possible improvements for future medium access control research deployments.

Information dissemination in current Vehicular Sensor Networks (VSN) depends on the physical location in which similar data is transmitted multiple times across the network. This data replication has led to several problems, among which resource consumption (memory), stretch, and communication latency due to the lake of data availability are the most crucial. Information-Centric Networking (ICN) provides an enhanced version of the internet that is capable of resolving such issues efficiently. ICN is the new internet paradigm that supports innovative communication systems with location-independent data dissemination. The emergence of ICN with VSNs can handle the massive amount of data generated from heterogeneous mobile sensors in surrounding smart environments. The ICN paradigm offers an in-network cache, which is the most effective means to reduce the number of complications of the receiver-driven content retrieval process. However, due to the non-linearity of the Quality-of-Experience (QoE) in VSN systems, efficient content management within the context of ICN is needed. For this purpose, this paper implements a new distributed caching strategy (DCS) at the edge of the network in VSN environments to reduce the number of overall data dissemination problems. The proposed DCS mechanism is studied comparatively against existing caching strategies to check its performance in terms of memory consumption, path stretch ratio, cache hit ratio, and content eviction ratio. Extensive simulation results have shown that the proposed strategy outperforms these benchmark caching strategies.

Internet of Things (IoT) is rapidly growing and contributing drastically to improve the quality of life. Immense technological innovations and growth is a key factor in IoT advancements. Readily available low cost IoT hardware is essential for continuous adaptation of IoT. Advancements in IoT Operating System (OS) to support these newly developed IoT hardware along with the recent standards and techniques for all the communication layers are the way forward. The variety of IoT OS availability demands to support interoperability that requires to follow standard set of rules for development and protocol functionalities to support heterogeneous deployment scenarios. IoT requires to be intelligent to self-adapt according to the network conditions. In this paper, we present brief overview of different IoT OSs, supported hardware, and future research directions. Therein, we provide overview of the accepted papers in our Special Issue on IoT OS management: opportunities, challenges, and solution. Finally, we conclude the manuscript.

In cognitive radio sensor networks, the routing methods including multiple relays have been extensively studied to achieve higher throughput and lower end-to-end delay. As one of innovative approaches, the opportunistic routing scheme was proposed. In this paper, the effectiveness of the opportunistic transmission in terms of reliability and delay of transmission is verified with an analytical way. For the analysis, we establish the probabilistic model with respect to distance and the number of relay nodes under the Rayleigh fading channels including path loss effects. Under this model, we develop a generic Markov chain model to obtain the analytical results and verify the effectiveness of the statistical analysis. The results show that an opportunistic transmission approach is better than traditional multihop transmissions in terms of successful data delivery with fewer transmissions. Consequently, it can provide an energy efficient transmission mechanism for cognitive radio sensor networks.