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The reconfigurable intelligent surface (RIS) is widely recognized as an effective solution for enhancing security in wireless communications, owing to its passive reflective components and the ability to adjust signal phases. In light of this, this study investigates the physical layer security issues for a dual-hop RIS-aided system that makes use of both radio frequency (RF) and free space optical (FSO) connections, while taking into account three different eavesdropping scenarios: i) RF eavesdropping, ii) FSO eavesdropping, and iii) simultaneous RF and FSO eavesdropping. While Nakagami- $m$ distributed fading affects the RF link, Málaga turbulence with pointing error affects the FSO link. The main goal of this research is to guarantee the confidentiality of information, preventing unauthorized access or disclosure. To this end, closed-form expressions are developed for the average secrecy capacity, secrecy outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput. Monte Carlo simulations are used to verify the precision of these expressions. To further explore the suggested model, the asymptotic formulations of various performance metrics are produced. The impact of different factors, such as fading severity, atmospheric conditions, and detection techniques, on the secrecy performance is analyzed through simulations. Numerical results highlight the significant role of the proposed model in ensuring the security of confidential information and emphasize the substantial impact of key factors on its secrecy performance.

Recently, Blockchain-based applications have become immensely popular because of limited reliance on a single entity, unlike a centralized system. However, reaching a consensus among blockchain networks is a challenging and vital aspect of blockchain-based applications. There are various types of blockchain networks for different kinds of application scenarios. Among all of them, the consensus algorithm is the most crucial part of reaching an agreement in the complex blockchain network. Over the years, researchers have focused on dealing with the challenges like distributed computing, storage, transaction speed, security, validity, interoperability, and many more. However, only some of them are appropriate for all domains. Therefore, this paper presents an extensive study of different types of consensus protocols used in existing blockchain solutions with the strength and limitations of each algorithm. We also provide an inherent comparison among different algorithms to understand consensus protocol selection better. Moreover, we investigate operational and interoperability issues in existing blockchain-based applications to understand challenges and provide recommendations for future developers.

Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests. However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed. The parasite inflator acts as the shape increaser of the small, darker malaria parasites in the RBC images in order to detect malaria easily. CNN-DELM has achieved a sanguine result on every performance standard compared to CNN and CNN-ELM. The proposed CNN-DELM method has achieved 97.79% and 99.66% accuracy for the original version and the modified version, respectively. Hence, the proposed CNN-DELM model has also produced either comparable or better results when compared to other methods proposed in the literature, showing its robustness in detecting malaria.

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

In the era of industry 4.0, safety, efficiency and reliability of industrial machinery is an elementary concern in trade sectors. The accurate remaining useful life (RUL) prediction of an equipment in due time allows us to effectively plan the maintenance operation and mitigate the downtime to raise the revenue of business. In the past decade, data driven based RUL prognostic methods had gained a lot of interest among the researchers. There exist various deep learning-based techniques which have been used for accurate RUL estimation. One of the widely used technique in this regard is the long short-term memory (LSTM) networks. To further improve the prediction accuracy of LSTM networks, this paper proposes a model in which effective pre-processing steps are combined with LSTM network. C-MAPSS turbofan engine degradation dataset released by NASA is used to validate the performance of the proposed model. One important factor in RUL predictions is to determine the starting point of the engine degradation. This work proposes an improved piecewise linear degradation model to determine the starting point of deterioration and assign the RUL target labels. The sensors data is pre-processed using the correlation analysis to choose only those sensors measurement which have a monotonous behavior with RUL, which is then filtered through a moving median filter. The updated RUL labels from the degradation model together with the pre-processed data are used to train a deep LSTM network. The deep neural network when combined with dimensionality reduction and piece-wise linear RUL function algorithms achieves improved performance on aircraft turbofan engine sensor dataset. We have tested our proposed model on all four sub-datasets in C-MAPSS and the results are then compared with the existing methods which utilizes the same dataset in their experimental work. It is concluded that our model yields improvement in RUL prediction and attains minimum root mean squared error and score function values.

In healthcare services, application development is considered the most complex and time-consuming phase. As it is difficult to plan and time-intense, it requires high maintenance. Healthcare applications need strict compliance and the scope of application is immense along with associates, classes in services, and classified system. Application designing in healthcare with the help of traditional approaches such as monolithic and service-oriented architecture (SOA) generate problems in different areas like service availability, remote access to services, service provisioning, scalability, healthcare systems integration with each other. That is why there is a need for less sophisticated and user-friendly healthcare systems, which are easy to plan and develop, inexpensive requirement maintenance, and agile testing. To overcome the aforesaid issues in the domain of healthcare application development, this paper develops a framework of micro services for the development of healthcare services using cloud computing infrastructure. Micro-service-based techniques provide lightly coupled and fine-grained methodology. With the use of micro services technique presented in this work, the efficiency, scalability, and performance are improved. In this research, an approach for development and deployment properly in the cloud for healthcare applications is developed. Thus, it contributes to the system design approach and system analysis. Quantitative and qualitative results are reported showing the advantages of micro services approach used.

In Radio Frequency (RF)-Free Space Optical (FSO) mixed links, secrecy capacity (SC) can be improved by exploiting spatial diversity (i.e., antenna diversity) in the RF path. In addition to atmospheric turbulence and point error of the FSO link, antenna correlation in the RF link can significantly deteriorate the secrecy performance. In this paper, a secrecy rate of wiretap channels with a single source, relay, destination, and eavesdropper is analyzed under practical environments with the aforementioned impairments. The RF hop (source-to-relay) and the FSO hop (relay-to-destination) are modeled utilizing arbitrarily correlated Nakagami-$m$ and Málaga ($\mathcal {M}$) distributions, respectively. The correlated signal branches of the RF hop are combined at the relay exploiting equal gain combining reception technique. We assume that the eavesdropper is capable of wiretapping via RF and FSO links separately. We derive novel closed-form expressions for secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) considering heterodyne detection (HD) and intensity modulation with direct detection (IM/DD) techniques in order to examine the impact of atmospheric scintillation, pointing error, fading, and correlation on the system's secrecy performance. It is shown that the HD technique exhibits a better performance than an IM/DD technique. In addition, similar to the pointing error and turbulent fading, the correlation imposes a detrimental impact on SC. Finally, Monte-Carlo simulation results are provided for validation of the derived expressions.

A surface plasmon resonance (SPR) based photonic crystal fiber (PCF) sensor having a milled microchannel, and an open D-channel has been proposed in this paper. The sensor shows good functionality in the wide sensing range of 1.14-1.36 Refractive Index Units (RIU) of the analyte, having the capability to detect low refractive index (RI). The Finite Element Method (FEM) based numerical investigations dictate that the proposed sensor has been able to gain a maximum wavelength sensitivity of 53,800 nm/RIU according to the wavelength interrogation technique. The amplitude interrogations show that the sensor has the highest amplitude sensitivity of 328 RIU-1. The highest FOM (Figure of Merit) has been found to be 105 RIU-1. The sensor evinces a minimum wavelength resolution of $1.86\times 10 ^{-6}$ RIU, which secures high detection accuracy. A circular perfectly matched layer (PML) is implemented in the sensor's outermost layer as a boundary condition to absorb surface radiations. Gold is the plasmonic metal, while TiO2 acts as the adhesive layer for gold attachment on silica. Due to the high sensitivity with a broad range of analyte detection, the sensor is well suited for practical biochemical detection purposes.

With the rapid evolution of communication technologies, high-speed optical wireless applications under the water surface as a replacement or complementary to the conventional radio frequency (RF) and acoustic technologies are attracting significant attention from the researchers. Since underwater turbulence (UWT) is an inevitable impediment for a long distance underwater optical wireless communication (UOWC) link, mixed RF-UOWC is being considered as a more feasible solution by the research community. This article deals with the secrecy performance of a variable gain relay-based mixed dual-hop RF-UOWC framework under the intercepting attempt of a potential eavesdropper. The RF link undergoes Generalized Gamma (GG) fading distribution, whereas the UOWC link is subjected to mixture Exponential Generalized Gamma (mEGG) distribution. The eavesdropper is capable of wiretapping via a RF link that also experiences the GG fading. The secrecy analysis incorporates the derivations of closed-form expressions for strictly positive secrecy capacity, average secrecy capacity, and exact as well as lower bound of secrecy outage probability in terms of univariate and bivariate Meijer's G and Fox's H functions. Based on these expressions, impacts of heterodyne and intensity modulation/direct detection techniques along with weak, moderate, and severe UWT conditions due to air bubbles, temperature, and salinity gradients are quantified. To the best of authors' knowledge, the proposed model is the first of its kind that addresses the secrecy analysis of a temperature gradient RF-UOWC system along with air bubbles, as opposed to the existing models that considered thermally uniform scenarios only. Finally, the derived expressions are verified via Monte-Carlo simulations.

Establishing eye contact is the fundamental key to begin any interaction between human-human and robot-human. Two approaches are available to develop an eye contact mechanism for robot-human interaction, such as simplex and duplex. The two most critical tasks: gaze crossing and gaze awareness, are prerequisite to implementing an active eye contact mechanism in any approach. However, most past robot-human interaction studies implemented a gaze crossing function to develop eye contact in the simplex mode where a robot holds for the human to initiate the communication. However, implementing gaze crossing alone is inadequate to create an active eye contact episode; the gaze awareness function also essential to achieve. This paper aims to develop a mechanism of duplex eye contact for robot-human inter-communication satisfying both functions. This work proposes a conceptual model of a duplex eye contact mechanism considering two cases: human initiative (where the human starts communication with the robot) and robot initiative (where the robot starts the communication with the participant) to achieve a duplex eye contact mechanism. Moreover, a simple robotic system is developed consisting of four software constituents: face detection module, gaze detection and tracking module, gaze awareness module, and robot response and control module to implement the conceptual model of duplex eye contact. Several preliminary experiments are performed to extract necessary cues for designing the duplex eye contact mechanism’s behavioural protocol and present their results to show the usefulness of extracted cues. Moreover, the robotic framework results in a scenario (e.g., reading the book) with the proposed duplex eye contact mechanism are presented. The results show that the proposed scheme achieved 92% and 86% accuracy for human initiative case and robot initiative case, respectively in making eye contact.

Increasing concerns regarding wireless systems’ security are leading researchers to exploit the physical properties of a medium while designing any secured wireless network. The secrecy performance of a mixed radio frequency-free space optical (RF-FSO) system with a variable gain relaying scheme is investigated in this paper under the attempt of wiretapping by an eavesdropper. We assume that the eavesdropper can intrude the target data from the RF link only. Both the RF links (main and eavesdropper) undergo the $\alpha -\mu $ fading statistics and the FSO link experiences the exponentiated Weibull fading statistics. Exploiting the amplify-and-forward (AF) relaying scheme while considering two detection techniques (i.e. heterodyne detection and intensity modulation/direct detection) with pointing error impairments, the mathematical formulations of the unified probability density function and cumulative distribution function are performed for the equivalent signal-to-noise ratio of the considered dual-hop RF-FSO link. Closed-form analytical expressions for average secrecy capacity, secrecy outage probability, and the probability of non-zero secrecy capacity are derived in terms of Meijer’s $G$ and Fox’s $H$ functions to quantify the system performance. Capitalizing on these expressions, the secrecy performance is further analyzed for various channel parameters of RF links, aperture sizes of the receiver, pointing errors, and atmospheric turbulence severity. The results reveal that aperture averaging can improve the secrecy performance remarkably by suppressing the effects of turbulence. Monte Carlo simulations are provided to justify the accuracy of the proposed model.

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson’s disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

In this paper,a polarization-maintaining single-mode rectangular-shaped hollow-core waveguide with four segmented air cladding in the terahertz (THz) regime is presented for detecting various toxic industrial chemicals. A new type of injection moldable cyclic olefin copolymer, commercially named as TOPAS is used as the base fiber material for its high optical transmission and high resistance to other chemicals. The finite element method with a perfectly matched layer as the boundary condition is employed for numerical explorations. The proposed sensor exhibits ultra-high relative sensitivity of 99.73% and ultra-low effective material loss of 0.007 cm−1 at 1.6 THz frequency for Toluene in y polarization. This sensor also evinces a high birefringence of $4.16\times 10^{-3}$ at 1.6 THz frequency. A maximum V parameter of 2.224 has been found at 2.2 THz which ensures the single-mode propagation of light. The sensor shows a very low confinement loss of $3.2\times 10^{-11}$ dB/m and a high numerical aperture of 0.3574 at 1.6 THz frequency for Hydrogen Sulfide. This paper also concentrates on other important design parameters such as bending loss, mode field radius, beam divergence and effective area for serviceability of the sensor in the THz region. This sensor can be a very good candidate for various chemical detection as well as other applications in the terahertz regime.

We study the secrecy transmission of uplink non-orthogonal multiple access (NOMA) with single antenna and multi-antenna users in presence of an eavesdropper. Two phases are required for communications during each time frame between the users and the base station in cellular networks. We study the case where an eavesdropper overhears the relay and direct links from the users to the base stations. In terms of the secure performance analysis, we focus on two main metrics including secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) with the assumption that the eavesdropper is able to detect the signals. Analytical closed-form expressions for the SOP and SPSC are derived to evaluate the system secure performance achieved by the proposed schemes. Furthermore, the asymptotic analysis is presented to gain further insights. The analytical and numerical results indicate that the proposed schemes can realize better secrecy performance once we improve the channel condition and signal-to-noise ratio (SNR) at the base station. Our results confirms that the secrecy performance gaps exist among the two users since different power allocation factors are assigned to these users.

This work deals with the secrecy performance analysis of a dual-hop RF-FSO DF relaying network composed of a source, a relay, a destination, and an eavesdropper. We assume the eavesdropper is located close to the destination and overhears the relay’s transmitted optical signal. The RF and FSO links undergo ( $\alpha -\kappa -\mu $ )-shadowed fading and unified Málaga turbulence with pointing error. The secrecy performance of the mixed system is studied by deriving closed-form analytical expressions of secure outage probability (SOP), strictly positive secrecy capacity (SPSC), and intercept probability (IP). Besides, we also derive the asymptotic SOP, SPSC, and IP upon utilizing the unfolding of Meijer’s $G$ function where the electrical SNR of the FSO link tends to infinity. Finally, the Monte-Carlo simulation is performed to corroborate the analytical expressions. Our results illustrate that fading, shadowing, detection techniques (i.e. heterodyne detection (HD) and intensity modulation and direct detection (IM/DD)), atmospheric turbulence, and pointing error significantly affect the secrecy performance. In addition, better performance is obtained exploiting the HD technique at the destination relative to IM/DD technique.

This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham’s approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.

Abstract Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Nanoscale devices, also called nanomachines, form communication networks and cooperate with each other so that they can be used to perform complicated tasks. Such networks of nanodevices, named nanonetworks, envisioned to serve the functionality and performance of today’s Internet. This paper presents a comparative review of the state-of-the-art electromagnetic (EM) nanonetworks highlighting their potentials and challenges in a comprehensive manner. We first introduce the promising areas of applications of nanonetworks; therein, we explain how it can be useful in biomedical fields, environmental domains, consumer products, military systems, and on-chip wireless communications. Then, the survey focuses on the basic principles of fundamental physical layer issues enabling nanonetworks; the discussion includes frequency bands, modulation and demodulation, and EM properties of nanoparticles. Subsequently, the study provides an overview of transmission characteristics including channels, channel coding, and energy constraint nature of nanonetworks. Furthermore, we provide an in-depth discussion on nanoantenna highlighting its variants and their characteristics; give an overview of network layer issues; and discuss the security issues in EM nanonetworks. The study argues that despite the significant recent development of EM communication as one of the most desired modes of nano communications, the limited capabilities of nanomachines introduce a new set of challenges and unique requirements and pose unseen characteristics that need to be deliberately addressed. On that, the review finally provides a critical discussion on the applicability of EM mode for nano communication networks highlighting the future challenges with a set of perspectives of possible solutions.

The secrecy performance of realistic wireless multicast scenarios can be significantly deteriorated by the simultaneous occurrence of multipath and shadowing. To resolve this security threat, in this work an opportunistic relaying-based dual-hop wireless multicast framework is proposed in which the source dispatches confidential information to a bunch of receivers via intermediate relays under the wiretapping attempts of multiple eavesdroppers. Two scenarios, i.e. non-line of sight (NLOS) and line of sight (LOS) communications along with the multiplicative and LOS shadowing are considered where the first scenario assumes $\eta -\mu $ and $\eta -\mu $ /inverse Gamma (IG) composite fading channels and the latter one follows $\kappa -\mu $ and $\kappa -\mu $ /IG composite fading channels as the source to relay and relay to receiver’s as well as eavesdropper’s links, respectively. Secrecy analysis is accomplished by deriving closed-form expressions of three familiar secrecy measures i.e. secure outage probability for multicasting, probability of non-zero secrecy multicast capacity, and ergodic secrecy multicast capacity. We further capitalize on those expressions to observe the effects of all system parameters which are again corroborated via Monte-Carlo simulations. Our observations indicate that a secrecy tradeoff between the number of relays and number of receivers, eavesdroppers, and shadowing parameters can be established to maintain the admissible security level by decreasing the detrimental influences of fading, shadowing, the number of multicast receivers and eavesdroppers.

Vehicular networks aim to support cooperative warning applications that involve the dissemination of warning messages to reach vehicles in a target area. Due to the high mobility of vehicles, imperative technologies such as software-defined network (SDN) and edge computing (EC) have been proposed for the next-generation vehicular networks. The SDN separates the control plane from data plane entities and executes the control plane software on general purpose hardware. On the other hand, EC aims to reduce the network latency and packet loss rate by pushing the computations to the edge of the network. Nevertheless, the current solutions that integrate SDN and EC could not satisfy the latency requirements for data dissemination of vehicle-to-everything (V2X) services. To bridge the gap between the two technologies, the conventional EC is enhanced to multi-access edge computing (MEC) by collocating the edge computing servers with the radio access networks. In order to improve the latency for V2X services, we propose in this paper, an SDN-based multi-access edge computing framework for the vehicular networks (SDMEV). In the proposed solution, two main algorithms are implemented. First, a fuzzy logic-based algorithm is used to select the head vehicle for each evolved node B (eNB) collocated with road-side unit (RSU) for the purpose of grouping vehicles based on their communication interfaces. Afterward, an OpenFlow algorithm is deployed to update flow tables of forwarding devices at forwarding layers. In addition, a case study is presented and evaluated using the object-oriented modular discrete event network (OMNeT++) simulation framework which includes the INET framework-based SDN. Simulation results depict that the data dissemination based-SDN supported by multi-access edge computing over SDMEV can improve the latency requirements for V2X services.

The emergence of an array of new wireless networks has led researchers to evaluate the prospect of utilizing the physical properties of the wireless medium in order to design secure systems. In this paper, the physical layer secrecy performance of a mixed radio frequency-free space optical (RF-FSO) system with variable gain relaying scheme is investigated in the presence of an eavesdropper. We assume that the eavesdropper can wiretap the transmitted confidential data from the RF link only. It is further assumed that the main and eavesdropper RF links are modeled as generalized Gamma (GG) fading channel, and the free space optical (FSO) link experiences Málaga turbulence with pointing error impairment. Our primary concern is to protect this confidential information from being wiretapped. Besides pointing error, the atmospheric turbulence and two types of detection techniques (i.e. heterodyne detection and intensity modulation with direct detection) are also taken into consideration. Utilizing amplify-and-forward (AF) scheme, the novel mathematical closed-form expressions for average secrecy capacity, lower bound of secrecy outage probability, and strictly positive secrecy capacity are derived. As both the links (RF and FSO) undergo generalized fading channels, the derived expressions are also general. We present a unification of some existing works utilizing the proposed model to better clarify the novelty of this work. Finally, all the derived expressions are justified via Monte-Carlo simulations.

Precision medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics and the growing availability of health data have set a new pace of the research and impose a set of new requirements on the different stakeholders. Some studies are available that discuss about the different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer with the technological perspective, in relation to the applications and challenges, have been mostly ignored. In this context, this paper surveys the advances in PM from the informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms, which include big data, artificial intelligence, and the internet of things, can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for a seamless implementation and a wide-scale deployment of PM based on the identified open issues and the associated challenges. As a result, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research work in an appropriate context.

In this paper, we investigate a secure dual-hop radio frequency-free space optical (RF-FSO) mixed variable gain relaying framework in the presence of a single eavesdropper. The RF and FSO links are modeled with hyper Gamma (HG) and Gamma-Gamma (ΓΓ) fading channels, respectively. We assume that the eavesdropper utilizes another HG fading channel to wiretap the transmitted confidential data from the RF link. Our key concern is to defend this information against passive eavesdropping. We carry out the secrecy measurements by deriving closed-form mathematical expressions of average secrecy capacity (ASC), secure outage probability (SOP), and strictly positive secrecy capacity (SPSC), all in terms of Meijer's G function. Capitalizing on the derived expressions, we analyze the impacts of atmospheric turbulence and pointing errors on the secrecy capacity and outage performance of the proposed scenario. For gaining more insights, we also analyze the asymptotic outage behaviour for high signal-to-noise ratio. Two detection techniques i.e. heterodyne (HD) and the intensity modulation with direct detection (IM/DD) are taken into consideration and our results demonstrate that HD technique notably outperforms the IM/DD scheme. The supremacy and novelty of the model is demonstrated via utilizing generic properties of the HG fading channel. Finally, we provide a justification of the derived expressions via Monte-Carlo simulations.

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today's medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

Where traditional genetic algorithms tend to prematurely converge on local optima, adaptive strategies aim to maintain a healthy level of population diversity by introducing randomness to the population. Often times this is done through adjusting control parameters according to diversity measurements. While these approaches introduce diversity, they do not aid in focusing or directing the search effort. Meanwhile, other works in the literature propose creating individuals designed to improve the population's health and quality but their effectiveness is limited outside of general problems. This article proposes novel sequence-wise approach to designing and editing genotypes for ordered problems. A Markov model based similarity guide matrix (MSGM) is used to determine the relationships between gene nodes in order to produce new genotypes that focus on improving fitness and increasing population diversity. The proposed MSGM based approach is implemented in a balanced-evolution genetic algorithm framework in order to investigate its characteristics with encouraging results demonstrating its effectiveness when solving combinatorial ordered optimisation problems.

Energy consumption in residential, commercial and industrial buildings is one of the major contributors to global warming. Due to the increase in the latter, and growing global energy crisis, more attention is being paid to renewable energy resources (RES). The use of innovative concepts in existing buildings is gaining popularity to provide reduction in energy requirements for electricity, heating and cooling. In this paper, an electricity, heating and cooling cooperation mechanism among neighboring buildings with RES is proposed. It relies on adjusting the RES tariff with a mutual agreement between the neighboring buildings, with an aim to minimize the operational costs. For this purpose, a mathematical model is developed for joint energy cooperation, where surplus energy in one of the buildings is shared with others, thereby reducing dependency on the grid. The optimization structure of the environment friendly energy cooperation is nonlinear, which is linearized using the McCormick envelopes. A scenario for the city of Islamabad, Pakistan, is considered by utilizing its environmental data obtained from public domain websites. The simulation results show more than twenty percent energy cost savings with the proposed cooperation model.

Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can achieve higher spectral efficiency by exploiting the power domain multiplexing. In this paper, we investigate the performance of NOMA in a two-tier heterogeneous network (HetNet) with non-uniform small cell deployment, where critical performance metrics like coverage probability and achievable rate are analyzed. First, a NOMA-based HetNet model is established, where users are paired based on the proposed user pairing scheme. Then the distribution of the order statistics for the distances between different NOMA users and the serving base station (BS) is presented considering the channel qualities from the NOMA users to the BSs. On this basis, we analytically demonstrate the impact of various network parameters on the coverage probability and achievable rate of NOMA users, such as signal-to-interference-plus-noise ratio threshold and BS density. Furthermore, an analysis is presented to provide insight into the energy efficiency of the considered system. Finally, extensive simulation and comparisons are conducted, which validate the advantages of NOMA over OMA in the considered HetNet environment.

Heterogeneity is one of the key features that characterizes the future generation of cellular networks, 5G and beyond. However, streaming high-quality bandwidth-hungry multimedia contents over bandwidth-constrained 5G heterogeneous networks (5G HetNets) involves various significant challenges, including long video start time, video start failures, frequent buffering and stalling, and low quality of experience (QoE). Traditional multimedia streaming technologies, however, do not pay attention to either the available network bandwidth or the interaction between content characteristics and resources. To reduce network strain and improve QoE, we propose “Context-Aware Streaming over 5G HetNets (CASH)”that allows us to achieve a tradeoff between content-context and network-context. The proposed CASH fundamentally works in a multi-step process. First, the CASH comes with an integrated architecture that includes a media server, a flow scheduler, and a single radio controller (SRC). The SRC and the user equipment (UE) of interest cooperatively prepare a metadata file that contains the network-context. Second, based on the metadata file, which can be accessed from the SRC in the media preparation server, we analyze and cluster the contents based on the content-context, e.g., the actual bitrate of each scene. The metadata file is then updated by adding the content-context information. Third, the flow scheduler basically controls the flow of the clusters of the contents in the server-push mode and conveys that to the appropriate radio access technology (RAT) conforming to the bitrate of the clusters and bandwidth delivered by RATs. Finally, the UE will aggregate the received packets and will play-back the content. We analytically show the validity of CASH. Also, extensive simulations are performed to demonstrate that CASH offers substantial performance improvements compared with exiting works in terms of peak data rate, latency, users' experiences, and spectral efficiency.

In this work, a comparative investigation of chaotic flow behavior inside multi-layer crossing channels was numerically carried out to select suitable micromixers. New micromixers were proposed and compared with an efficient passive mixer called a Two-Layer Crossing Channel Micromixer (TLCCM), which was investigated recently. The computational evaluation was a concern to the mixing enhancement and kinematic measurements, such as vorticity, deformation, stretching, and folding rates for various low Reynolds number regimes. The 3D continuity, momentum, and species transport equations were solved by a Fluent ANSYS CFD code. For various cases of fluid regimes (0.1 to 25 values of Reynolds number), the new configuration displayed a mixing enhancement of 40%–60% relative to that obtained in the older TLCCM in terms of kinematic measurement, which was studied recently. The results revealed that all proposed micromixers have a strong secondary flow, which significantly enhances the fluid kinematic performances at low Reynolds numbers. The visualization of mass fraction and path-lines presents that the TLCCM configuration is inefficient at low Reynolds numbers, while the new designs exhibit rapid mixing with lower pressure losses. Thus, it can be used to enhance the homogenization in several microfluidic systems.

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

The recent technology of human voice capture and interpretation has spawned the social robot to convey information and to provide recommendations. This technology helps people obtain information about a particular topic after giving an oral query to a humanoid robot. However, most of the search engines are keyword-matching mechanism-based, and the existing full-text query search engines are inadequate at retrieving relevant information from various oral queries. With only predefined words and sentence-based recommendations, a social robot may not suggest the correct items, if items retrieved along with the information are not predefined. In addition, the available conventional ontology-based systems cannot extract precise data from webpages to show the correct results. In this regard, we propose a merged ontology and support vector machine (SVM)-based information extraction and recommendation system. In the proposed system, when a humanoid robot receives an oral query from a disabled user, the oral query changes into a full-text query, the system mines the full-text query to extract the disabled user's needs, and then converts the query into the correct format for a search engine. The proposed system downloads a collection of information about items (city features, diabetes drugs, and hotel features). The SVM identifies the relevant information on the item and removes anything irrelevant. Merged ontology-based sentiment analysis is then employed to find the polarity of the item for recommendation. The system suggests items with a positive polarity term to the disabled user. The intelligent model and merged ontology were designed by employing Java and Protégé Web Ontology Language 2 software, respectively. Experimentation results show that the proposed system is highly productive when analyzing retrieved information, and provides accurate recommendations.

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.

In this paper, a novel interference free dual-hop device-to-device (D2D) aided cooperative relaying strategy (CRS) based on spatial modulation (SM) (termed D2D-CRS-SM) is proposed. In D2D-CRS-SM, two cellular users (e.g., a near user (NU) and a relay-aided far user (FU)) and a pair of D2D transmitter (D1)-receivers (D2) are served in two time-slots. Two different scenarios are investigated considering information reception criteria at the NU. Irrespective of the scenarios, the base station (BS) exploits SM to map information bits into two sets: modulation bits and antenna index, in phase-1. In the first scenario, the BS maps FU information as the modulation bits and NU information as antenna index, whereas modulation bits correspond to NU information and the antenna index carries FU’s information in scenario-2. The iterative-maximum ratio combining (i-MRC) technique is then used by NU and D1 to de-map their desired information bits. During phase-2, D1 also exploits SM to forward FU’s information received from BS and its own information bits to the D2D receiver D2. Then, FU and D2 retrieve their desired information by using i-MRC. Due to exploiting SM in both phases, interference free information reception is possible at each receiving node without allocating any fixed transmit power. The performance of D2D-CRS-SM is studied in terms of bit-error rate and spectral efficiency considering M-ary phase shift keying and quadrature amplitude modulation. Finally, the efficiency of D2D-CRS-SM is demonstrated via the Monte Carlo simulation.

Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks’ challenges handled by SDN-based vehicular networks.

In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today’s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user’s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm’s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.

We propose and analyse a medium access control (MAC) protocol for low energy critical infrastructure monitoring (LECIM) networks. As the packet drop probability plays crucial role in LECIM applications, we propose framed slotted aloha based MAC for LECIM using linearly increased contention window size to reduce the packet drop probability. We present a mathematical model of our proposed MAC under both saturated and nonsaturated traffic scenarios. We use probabilistic approach to find the performance metrics such as collision probability, packet drop probability, throughput, and energy consumption. Also, we obtain the probability-generating function of the head-of-line (HoL) delay of packet. The analytical results match with simulations. Our results can be used in the design of a system by providing the optimum system parameters for endpoints satisfying the given quality of service requirements on packet drop probability, energy consumption, and delay.

Energy efficiency is a primary requirement in a wireless sensor network (WSN). This is a major design parameter in medium access control (MAC) protocols for WSN due to limited resources in sensor nodes that include low battery power. Hence a proposed MAC protocol must be energy efficient by reducing the potential energy wastes. Developing such a MAC protocol has been a hot research area in WSN. To avoid wasting the limited energy, various energy saving mechanisms are proposed for MAC protocols. These mechanisms have a common design objective—to save energy to maximize the network lifetime. This paper presents a survey on various energy saving mechanisms that are proposed for MAC protocols in WSN. We present a detailed discussion of these mechanisms and discuss their strengths and weaknesses. We also discuss MAC protocols that use these energy saving mechanisms.

This paper presents an empirical investigation on the performance of body implant communication using radio frequency (RF) technology. In body implant communication, the electrical properties of the body influence the signal propagation in several ways. We use a Perspex body model (30 cm diameter, 80 cm height and 0.5 cm thickness) filled with a liquid that mimics the electrical properties of the basic body tissues. This model is used to observe the effects of body tissue on the RF communication. We observe best performance at 3cm depth inside the liquid. We further present a simulation study of several low-power MAC protocols for an on-body sensor network and discuss the derived results. Also, the traditional preamble-based TMDA protocol is extended towards a beacon-based TDMA protocol in order to avoid preamble collision and to ensure low-power communication.