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Summary: This work presents a compact two‐element multi‐input‐multi‐output (MIMO) antenna for 5G‐enabled IoT devices. The antenna operates over a wide frequency range of 24.6 to 31.4 GHz (28‐GHz band) and 57.6 to 60.2 GHz (60‐GHz band). Each MIMO element consists of an inverted L‐shaped slotted radiator with a partial ground plane. The antenna offers a peak gain of 5.45 and 5.56 dBi across two operating bands. The minimum isolation between the two ports is −26.5 dB, reaching a maximum value of over −45 dB. The investigation of MIMO metrics like "envelope correlation coefficient (ECC)," "diversity gain (DG)," "mean effective gain (MEG)," "channel capacity loss (CCL)," and "total active reflection coefficient (TARC)" also show favorable characteristics. The antenna is fabricated on a 10 × 22 × 0.503 mm3 Rogers 5880 substrate. The experimental results are in close agreement with that of the simulation results. The distinguishing features of the proposed antenna such as its compact design, simple geometrical configuration, wide operating bandwidth, low ECC, and high isolation make it a strong candidate for 5G‐enabled IoT devices. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of insecure Internet of Things (IoT) devices, the security of Internet‐based applications and networks has become a prominent concern. One of the most significant security threats encountered in IoT environments is a Distributed Denial of Service (DDoS) attack. This attack can severely disrupt critical services and prevent smart devices from functioning normally, leading to severe consequences for businesses and individuals. It aims to overwhelm victims' resources, websites, and other services by flooding them with massive attack packets, making them inaccessible to legitimate users. Researchers have developed multiple detection schemes to detect DDoS attacks. As technology advances and other facilitating factors have increased, it is a challenge to identify such powerful attacks in real‐time. In this paper, we propose a novel distributed detection scheme for IoT network traffic‐based DDoS attacks by deploying it in a Kafka Streams processing framework named Kafka‐Shield. The Kafka‐Shield comprises two stages: design and deployment. Firstly, the detection scheme is designed on the Hadoop cluster employing a highly scalable H2O.ai machine learning platform. Secondly, a portable, scalable, and distributed detection scheme is deployed on the Kafka Streams processing framework. To analyze the incoming traffic data and categorize it into nine target classes in real time. Additionally, Kafka‐Shield stores each network flow with significant input features and the predicted outcome in the Hadoop Distributed File System (HDFS). It enables the development of new models or updating current ones. To validate the effectiveness of the Kafka‐Shield, we performed critical analysis using various configured attack scenarios. The experimental results affirm Kafka‐Shield's remarkable efficiency in detecting DDoS attacks. It has a detection rate of over 99% and can process 0.928 million traces in nearly 3.027 s. [ABSTRACT FROM AUTHOR]

The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the "security" of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device's keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required. [ABSTRACT FROM AUTHOR]

The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of the Internet of Things (IoT) in recent years, the number of IoT devices has surged exponentially. These devices collect and transmit vast amounts of data, including sensitive information. Encrypting data is a crucial means to prevent unauthorized access and potential misuse. However, the traditional cryptographic schemes offering robust security demand substantial device resources and are unsuitable for lightweight deployments, particularly in resource-constrained IoT devices. On the other hand, with the automotive industry making strides in autonomous driving, self-driving vehicles are beginning to integrate into people's daily lives. Ensuring the security of autonomous driving systems, particularly in preventing hacker infiltrations, is a paramount challenge currently facing the industry. An emerging lightweight sequence cipher—aiming to strike a balance between security and resource efficiency—has been proposed in this paper based on S-box substitution and arithmetic addition. The designed security threshold is 280. It has been verified that with a slight performance disadvantage, it can reduce memory usage while ensuring the security threshold. The key stream generated by this structure exhibits excellent pseudo-randomness. [ABSTRACT FROM AUTHOR]

In the world of connected devices, there is huge growth of less secure Internet of Things (IoT) devices, and the ease of performing sophisticated cyberattacks using these devices has posed a serious threat to the security of Internet-based services or networks. Distributed Denial of Service (DDoS) attack is one of the most significant cyberattacks. It aims to damage or exhaust victims' resources, services, or networks and make them unavailable to legitimate users. Several solutions are available in the literature to detect DDoS attacks. However, it is difficult to detect them in real-time due to today's high speed or high volume of attack traffic. Therefore, this paper proposes an Apache Storm-based distributed detection approach for IoT network traffic-based DDoS attacks, namely SDDA-IoT. SDDA-IoT is composed of two primary modules: model development and model deployment. In the case of model development, we created five distributed detection models by utilizing a Hadoop cluster and the extremely scalable H2O.ai machine learning platform. In the case of model deployment, we deploy an efficient distributed detection model on the Apache Storm stream processing framework for analyzing ingress streaming data and classifying it into seven classes in near-real-time. To create new models or update existing ones, this module also saves the highly discriminating input features of each network flow along with the predicted outcome in the Hadoop Distributed File System (HDFS). The effectiveness of the SDDA-IoT approach has been examined using a variety of configured scenarios. The experimental results show that the SDDA-IoT approach detects DDoS attacks faster than recent state-of-the-art methods and more accurately with 99%+ accuracy. [ABSTRACT FROM AUTHOR]

Medicine has advanced significantly, but healthcare challenges remain, especially in rural areas. Telemedicine, supported by the Internet of Things (IoT), offers a solution through remote monitoring of cardiovascular diseases. However, its implementation faces obstacles such as the lack of healthcare professionals and facilities, and insufficient coverage and technological infrastructure for efficient and secure transmission of medical data. It is crucial to review the use of IoT devices in telemedicine for heart disease, highlighting trends and challenges in an updated state-of-the-art analysis for future proposals. Using the PRISMA methodological framework, 22 studies from Scopus, IEEE Xplore and ACM databases were included. The key factors for implementing these models were found to be economic and social. The most common devices are body temperature, electrocardiogram and heart rate sensors, which, together with technologies such as Wifi and Bluetooth, play important roles in hospital systems, such as real-time monitoring and decision making. This represents a move towards a more seamless and effective integration of emerging technologies in medicine, promoting more accurate and accessible medical care. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) services become closely coupled with machine learning and cloud computing, where the 5G network provides the connectivity for the IoT devices. The 5G network can be used not only for connecting the IoT devices to the cloud servers, but also for providing computing resources for 'edge computing'. In this paper, we propose to use the edge node resources of the 5G network for 'inferencing' and 'training' the deep neural network (DNN) models for massive IoT services. More specifically, two types of 5G edge nodes are utilized to this end: (i) the 'IoT controller', which functions as a 5G-UE (user equipment), (ii) the 'edge controller', which is collocated with 5G-UPF (user plane function) in the 5G core network. In the proposed scheme, the downsized DNN models are executed and trained at the IoT controllers. At the edge controller, a deep reinforcement learning (DRL) algorithm is executed to determine the downsizing configuration and the training configuration of the DNN models. The resource constraints of the IoT controllers are considered in these decisions. Extensive evaluations with various DNN models show the effectiveness of the proposed scheme. We show that the proposed scheme achieves proper load balancing even when the resource capacity of individual IoT controllers is very low. For example, fairly complex DNN models for computer vision can be effectively supported by using IoT controllers equipped with the resource capacity of NVIDIA Jetson Nano. [ABSTRACT FROM AUTHOR]

This study aims to structure the implementation of a governmental cloud of things (CoT), edge computing (EC), and fog computing in Iraq in the context of sustainable wireless communication. A base of literature was built that included any challenges, opportunities, and best practices relevant to these innovative technologies to set up the background for this paper. A concept model was created that included core components (cognitive technologies and fog computing), key processes (resource analysis, infrastructure design), and stakeholders (governments, industry, community). A strategic methodology made up of stakeholder involvement, capacity building, and pilot projects was used in the project. Concerning IoT planned deployment and services provision, network infrastructure was put in place to support the devices and a higher level of security measures were recommended. Using scenario hypothesis, MATLAB simulator was employed to simulate data value distribution as well as received power distribution based on different institutions for 12 months. Monitoring and evaluation should be followed to measure performance indicators and effects on this process. Continuously improvement strategies were the highlight of the session which further stimulated innovations. Acquainted projects will be put in the function to extend the range of activities by including additional government agencies, regions, or sectors. Reporting of the collected data and funding will be done with stakeholders to share and pool knowledge. [ABSTRACT FROM AUTHOR]

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch. [ABSTRACT FROM AUTHOR]

The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has shown incredible adaptability in recent years and has become an integral part of human life. The proliferation of IoT technology has made IoT devices more prone to severe security threats, such as Distributed Denial of Service (DDoS) attacks, which are dangerous threats to public systems and networks. Further, the frequency and complexity of IoT traffic-based DDoS attacks are increasing year-by-year. This article proposes an IoT traffic-based DDoS attack detection approach for classifying incoming IoT network traffic into 11 classes using multiclass machine learning techniques. The proposed approach comprises two phases: (i) designing and (ii) detection. In the designing phase, we employ the embedded feature reduction technique to create cost-effective and efficient classification models with a high feature reduction rate. Further, we evaluate these models using the K-fold cross-validation technique. While in the detection phase, we evaluate the performance of an efficient model by executing four different IoT traffic-based scenarios. A publicly available Bot-IoT dataset is employed to design and validate the proposed multiclass classification approach. The results show that the proposed approach provides an 84.4% feature reduction rate and approximately 5.19% higher classification accuracy than the existing approaches. [ABSTRACT FROM AUTHOR]

Health data collected from IoT devices can be utilised for predicting various medical information. The information collected is stored in centralised server and can be shared among various branches of the cooperative hospital network for analysis purposes. However, these communications are wireless which leads to various security threats like unauthorized access by intruders and misuse of health data. The existing work provides an authentication scheme that depends on trusted third party and centralised storage. This increases the computational burden and latency at the centralised server and may lead to a single-point failure. To solve this issue, a Blockchain-based authentication and key agreement scheme is proposed for sharing health data. The proposed scheme incorporates the concept of Blockchain that makes the system distributed, immutable, and transparent. An Elliptic Curve Cryptography based authentication scheme is proposed where all the information is stored in the Blockchain. In addition, a secret key is established to provide secure communication among patients, Hospital servers, and IoT devices. Hyperledger Fabric platform is used for Blockchain implementation that supports channel communication. There is a formal analysis using the ROR model that proves the security of the session key. The formal simulation using the Scyther tool shows that there is no attack within bound. Moreover, the informal analysis proves that the proposed work is resistant to various attacks and satisfies the security goals. The comparative study demonstrates that the proposed work is more effective as compared to others. The proposed work involves implementing a system of cooperative hospital servers that communicate through a shared channel. Hospital servers belonging to the same channel will be able to communicate securely and privately. This setup also facilitates the efficient transfer of health data among various hospital servers, eliminating the need for redundant computations during registration and authentication processes on each server. Additionally, the use of Blockchain reduces dependency on third party, makes the system distributed, provides immutable storage and increases confidentiality among members by using channel communication. [ABSTRACT FROM AUTHOR]

The usage of cloud-based grid computing services and Internet of Things (IoT) devices in medical diagnoses is increasing enormously. The cloud service provider's data centers store vast amounts of data without processing it. This big data need some intelligent technique to analyze and classify heart disease from the considerable volume of data; it is a challenging task. Many deep learning techniques are introduced earlier for heart disease diagnosis in the literature study. Still, all other classification techniques failed to achieve the minimum loss in heart disease classification with the highest accuracy and faster performance. This research introduces a new classification approach to overcome these issues: elephant herding optimizer turned restricted Boltzmann machine EHO-RBM network. The optimizer is used in this network to optimize the number of neuron utilization during the learning process by updating the network weight without compromising the loss. The previous research proves that the optimizer is performed well in reaching global minima efficiently. Therefore, the new classifier incorporates the optimizers instead of the classical stochastic gradient descent optimizer to improve the network performance by minimizing the global minima faster with less loss in predicting heart disease. The simulation result of the new heart disease classification framework shows that the elephant herding optimizer-trained classification model has reduced the loss rate and maximized the accuracy rate up to 0.0027 then the comparison method. As a result, the new classifier has obtained a maximum accuracy of up to 99.96%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is growing rapidly in various domains, including smart city applications. In many cases, IoT data in smart city applications have time constraints in which they are relevant and acceptable to the task at hand—a window of validity (WoV). Existing algorithms, such as ex post facto adjustment, data offloading, fog computing, and blockchain applications, generally focus on managing the time-validity of data. In this paper, we consider that the functional components of the IoT devices' decision-making strategies themselves may also be defined in terms of a WoV. We propose an aspect-oriented mechanism to supervise the execution of the IoT device's strategy, manage the WoV constraints, and resolve invalidated functional components through communication in the multi-agent system. The applicability of our proposed approach is considered with respect to the improved cost, service life, and environmental outcomes for IoT devices in a smart cities context. [ABSTRACT FROM AUTHOR]

The emergence of the infectious diseases, such as the novel coronavirus, as a significant global health threat has emphasized the urgent need for effective treatments and vaccines. As infectious diseases become more common around the world, it is important to have strategies in place to prevent and monitor them. This study reviews hybrid models that incorporate emerging technologies for preventing and monitoring infectious diseases. It also presents a comprehensive review of the hybrid models employed for preventing and monitoring infectious diseases since the outbreak of COVID-19. The review encompasses models that integrate emerging and innovative technologies, such as blockchain, Internet of Things (IoT), big data, and artificial intelligence (AI). By harnessing these technologies, the hybrid system enables secure contact tracing and source isolation. Based on the review, a hybrid conceptual framework model proposes a hybrid model that incorporates emerging technologies. The proposed hybrid model enables effective contact tracing, secure source isolation using blockchain technology, IoT sensors, and big data collection. A hybrid model that incorporates emerging technologies is proposed as a comprehensive approach to preventing and monitoring infectious diseases. With continued research on and the development of the proposed model, the global efforts to effectively combat infectious diseases and safeguard public health will continue. [ABSTRACT FROM AUTHOR]

The search for new energy sources in the 21st century is a crucial topic with an essential economic and societal meaning. Today, energy from electromagnetic fields (EMFs) is considered a promising new energy source for ultra-low-power consumption devices, such as wearable devices and Internet of Things (IoT) sensors. The research goal of this study was to experimentally evaluate the electric field (E-field) inside a compact car for several realistic wireless communication scenarios and to explore the possibility of using these EMFs in energy-harvesting applications. For each scenario, we performed measurements of E-fields in an urban area, in two cases: when the car was in an open space without a direct line of sight to a base station, and when the car was in underground parking. The results show that the highest measured value of the electric field appeared during the voice calls via the GSM network. Moreover, the maximum measured values of the electric field during a UMTS, LTE and 5G voice call were five to six times lower than those in the GSM network. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle's nature to the "vehicle management system." C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction. [ABSTRACT FROM AUTHOR]

Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the article introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed. [ABSTRACT FROM AUTHOR]

Roll-to-roll (R2R) printing methods are well known as additive, cost-effective, and ecologically friendly mass-production methods for processing functional materials and fabricating devices. However, implementing R2R printing to fabricate sophisticated devices is challenging because of the efficiency of material processing, the alignment, and the vulnerability of the polymeric substrate during printing. Therefore, this study proposes the fabrication process of a hybrid device to solve the problems. The device was created so that four layers, composed of polymer insulating layers and conductive circuit layers, are entirely screen-printed layer by layer onto a roll of polyethylene terephthalate (PET) film to produce the circuit. Registration control methods were presented to deal with the PET substrate during printing, and then solid-state components and sensors were assembled and soldered to the printed circuits of the completed devices. In this way, the quality of the devices could be ensured, and the devices could be massively used for specific purposes. Specifically, a hybrid device for personal environmental monitoring was fabricated in this study. The importance of environmental challenges to human welfare and sustainable development is growing. As a result, environmental monitoring is essential to protect public health and serve as a basis for policymaking. In addition to the fabrication of the monitoring devices, a whole monitoring system was also developed to collect and process the data. Here, the monitored data from the fabricated device were personally collected via a mobile phone and uploaded to a cloud server for additional processing. The information could then be utilized for local or global monitoring purposes, moving one step toward creating tools for big data analysis and forecasting. The successful deployment of this system could be a foundation for creating and developing systems for other prospective uses. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the antenna is thus very important and must not be neglected. Considering the high demand of IoT applications, there is a constant need to improve antenna technologies, including new antenna designs, in order to increase the performance level of WSNs (Wireless Sensor Networks) and enhance their efficiency by enabling a long range and a low error-rate communication link. This paper proposes a new antenna design that is able to increase the performance level of IoT applications by means of an original design. The antenna was designed, simulated, tested, and evaluated in a real operating scenario. From the obtained results, it ensured a high level of performance and can be used in IoT applications specific to the 868 MHz frequency band.By inserting two notches along x axis, we find an optimal structure of the microstrip patch antenna with a reflection coefficient of −34.3 dB and a bandwidth of 20 MHz. After testing the designed novel antenna in real IoT operating conditions, we concluded that the proposed antenna can increase the performance level of IoT wireless communications. [ABSTRACT FROM AUTHOR]

This paper studies the anti-jamming problem of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) communication networks in the presence of a jammer under the accurate probabilistic line-of-sight (LoS) model. Our goal is to maximize the information collection throughput of the system under the assumption that only the jammer's approximate location is known. To this end, we formulate a throughput maximization problem by optimizing the UAV trajectory, the IoT devices' transmit power, and communication scheduling under the accurate real-time probabilistic LoS channel. However, the proposed optimization problem is non-convex and coupled, and hence intractable to be solved. In order to tackle the problem, a robust iterative algorithm is proposed by leveraging the block coordinate descent (BCD) method, the successive convex approximation (SCA) technology, the difference of convex (D.C) programming approach, and the S -procedure. Extensive simulation results show that our proposed algorithm significantly improves the system throughput while achieving a practical anti-jamming effect compared with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Real-time monitoring of objects is being applied in bridges, tunnels, water, and others. Hence, the monitoring of the concrete properties using the maturity method and monitoring the electrical resistivity is crucial for its evaluation. This paper evaluates the influence of construction material density on the performance of data transmission from IoT (internet of things) devices in a laboratory. Tests were carried out regarding signal strengths of IoT devices and the reliability of the temperature measurement and its relative error. The results show that for the devices evaluated, the density of the material in which the IoT device is inserted directly influences the RSSI (Received Signal Strength Indication) and the IoT connection. However, there is no change in the quality of data collected in the field, resulting in high reliability in data gathering by IoT devices and thus leading to correct analysis and decision making by the stakeholders. • Construction material with density lower than 1945 kg/m3 do not inhibit data transmission. • As the density increases, the RSSI decreases. • The RSSI intensity do not influence the data quality and errors. • Employing an external antenna increases the RSSI. [ABSTRACT FROM AUTHOR]

The activities of daily living (ADL) ability level of an elderly patient is an important indicator in determining the patient's degree of degenerative brain disease and is mainly evaluated through face-to-face interviews with doctors and patients in hospitals. It is impossible to determine the exact ADL ability of a patient through such a temporary interview, and the pursuit of accurate ADL ability evaluation technology is a very important research task worldwide. In this paper, in order to overcome the limitations of the existing ADL evaluation method mentioned above, first of all, a self-organized IoT architecture in which IoT devices autonomously and non-invasively measure a patient's ADL ability within the context of the patient's daily living place was designed and implemented. Second, a remote rehabilitation treatment concept for enhancing the patient's ADL ability we call an "e-coaching framework", in which a doctor remotely gives an instruction in a specific ADL scenario, and the patient's ability to understand and perform the instruction can be measured on-line and in real time, was additionally developed on top of the self-organized IoT architecture. In order to verify the possibility of remote rehabilitation treatment through the proposed architecture, various remotely directed ADL scenarios were performed and the accuracy of the measurements was verified. [ABSTRACT FROM AUTHOR]

Lightweight ciphers are often used as the underlying encryption algorithm in resource-constrained devices. Their cryptographic security is a mandatory goal for ensuring the security of data transmission. Differential cryptanalysis is one of the most fundamental methods applicable primarily to block ciphers, and the resistance against this type of cryptanalysis is a necessary design criterion. ANU-II is an ultra-lightweight block cipher proposed in 2017, whose design offers many advantages such as the use of fewer hardware resources (logic gates), low power consumption and fast encryption for Internet of Things devices. The designers of ANU-II claimed its resistance against differential cryptanalysis and postulated that the design is safe enough for Internet of Things devices. However, as addressed in this article, the security claims made by designers appear not to be well grounded. Using mixed-integer linear programming–like techniques, we identify one-round differential characteristic that holds with probability 1, which is then efficiently employed in mounting the key recovery attack on full-round ANU-II with only 22 chosen plaintexts and 262.4 full-round encryptions. The result shows that the designers' security evaluation of ANU-II against differential cryptanalysis is incorrect and the design rationale is flawed. To remedy this weakness, we provide an improved variant of ANU-II, which has much better resistance to differential cryptanalysis without affecting the hardware and/or software implementation cost. [ABSTRACT FROM AUTHOR]

Recent proliferation of CPS and IoT devices has led to an increasing demand for analyzing performance and timing of event-driven computational activity, especially interrupts and exceptions. However, these devices typically lack hardware resources, power, and system-software infrastructure for profiling/monitoring such events. Even when feasible, the profiling/monitoring activity itself can perturb the performance and timing of the timing-sensitive activity to be analyzed, therefore producing misleading results. Thus, we present Primer, a novel approach for profiling interrupts. Primer leverages existing unintentional (side-channel) electromagnetic emanations of the profiled/monitored device to identify its asynchronous execution (e.g., interrupt handlers). Primer leaves the monitored system (and its behavior) completely unchanged, requires no system resources or support, and introduces neither overheads nor perturbation in the monitored system. We validate Primer by analyzing signals that correspond to five different types of interrupts on an IoT device (ARM Cortex-M), achieving 99.5% accuracy (with no false positives), and on an MSP430 microcontroller-based device with even better accuracy. We also demonstrate the effectiveness of Primer in analyzing page faults and network interrupts when executing real-world applications on a more sophisticated embedded device (ARM Cortex-A8), and show that the results provided by Primer can provide useful insights about an application's interaction with the system's virtual memory and network-oriented services. [ABSTRACT FROM AUTHOR]

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices. [ABSTRACT FROM AUTHOR]

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases. [ABSTRACT FROM AUTHOR]

The rapid evolution of Internet of Things (IoT) applications, such as e-health and the smart ecosystem, has resulted in the emergence of numerous security flaws. Therefore, security protocols must be implemented among IoT network nodes to resist the majority of the emerging threats. As a result, IoT devices must adopt cryptographic algorithms such as public-key encryption and decryption. The cryptographic algorithms are computationally more complicated to be efficiently implemented on IoT devices due to their limited computing resources. The core operation of most cryptographic algorithms is the finite field multiplication operation, and concise implementation of this operation will have a significant impact on the cryptographic algorithm's entire implementation. As a result, this paper mainly concentrates on developing a compact and efficient word-based serial-in/serial-out finite field multiplier suitable for usage in IoT devices with limited resources. The proposed multiplier structure is simple to implement in VLSI technology due to its modularity and regularity. The suggested structure is derived from a formal and systematic technique for mapping regular iterative algorithms onto processor arrays. The proposed methodology allows for control of the processor array workload and the workload of each processing element. Managing processor word size allows for control of system latency, area, and consumed energy. The ASIC experimental results indicate that the proposed processor structure reduces area and energy consumption by factors reaching up to 97.7% and 99.2%, respectively. [ABSTRACT FROM AUTHOR]

Background: Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device's topology varies due to the shift in users' orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role.Objective: In this paper, the energy efficient optimized heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson's health monitoring system.Method: The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson's fitness based on case study analysis.Results: The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods. [ABSTRACT FROM AUTHOR]

This document is the result of the INV DIS 2575 project of the internal call for projects at the Military University of Nueva Granada (UMNG); With emphasis on cognitive learning and particularly in the line of learning styles; invloving authors such as Kolb, Grasha - Riechmann and Alonso - Gallego - Honey, applied to a distance learning community (B - Learning). The study provides the possible relationship between the categories proposed by these authors through the application of their questionnaires to two hundred and nine (209) students of distance industrial engineering at UMNG. The information is tabulated, classified and analyzed through the statistical correlation that allows to relate the categories of learning styles and determine their value, in addition to ghaphing the result to obtain the characteristics that relate the learning styles of the students with the styles of the teachers of the program. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) devices has increased exponentially in recent years. Consequently, the security risks and vulnerabilities related to these unsecured IoT devices are also continuously increasing. Among the significant challenges facing the IoT environment is the threat of Distributed Denial of Service (DDoS) attacks. Several solutions are available in the literature to detect DDoS attacks. However, these detection mechanisms can easily be evaded by attackers using advanced tools and techniques, posing difficulty in detecting such lethal attacks in real time. Therefore, this paper proposes a novel distributed ensemble method for detecting lethal IoT traffic-based DDoS attacks. This method comprises two key stages: first, developing a distributed ensemble method using the breathtaking capabilities of the H2O.ai distributed machine learning platform and the ensemble learning technique. Secondly, this method was deployed on the Apache Storm stream processing framework, to swiftly analyze incoming network streams and categorize them into eleven distinct classes, including benign traffic and ten types of attacks, in near real time. The proposed method accurately identifies specific target categories within a multi-attack classification scenario by utilizing the expertise of various models. Ultimately, the prediction for a target class is determined based on the model with the highest detection rate. The effectiveness of this method has been examined using different configured scenarios. The experimental results show that our method can identify various attack categories more accurately with 99%+ accuracy and 8.45 s quicker than non-ensemble methods. [ABSTRACT FROM AUTHOR]

The way in which consumers engage with, utilise, or discard the technologies in their lives is constantly being reassessed and changed. This paper questions what role the emergent "right to repair" could play in resolving issues posed by the increasing ubiquity of the Internet of Things (IoT). The right gives consumers the ability and freedom to fix their devices, or to fair access to appropriate services that can carry out repair on their behalf. In this paper, firstly we establish the problem space surrounding consumer IoT – i.e., devices that are interconnected via the internet, enabling them to send and receive data. We reflect on hardware, software, and data components that pose legal and policy challenges for data protection, security, and sustainability. Through a literature review we then reflect on the current socio-legal developments that support or oppose changes in the consumer IoT market in regards to repair. We then highlight gaps in the existing literature that should inform future research trajectories in this area. This includes exploring disparities between environmental and consumer autonomy approaches, assessing consistency in regulatory developments, and market prioritisation. Finally, the paper concludes with a series of key insights and recommendations from our analysis including: recognition of the growing e-Waste problem and the inequalities it exacerbates and perpetuates; the need for identification and argumentation for different formulations of "repair" and how these may impact the implementation of a right going forward; the need for identification of the reasoning behind disparities in governmental approaches to the right to repair; and the need to practically translate better IoT design practices into reality. [ABSTRACT FROM AUTHOR]

Knowledge Distillation (KD) has shown great promise in improving the performance of tiny neural networks. Most existing KD methods have the large teacher–student discrepancy, thus, students hardly learn useful knowledge and may not achieve effective distillation. In this paper, we focus on the construction and training of homogeneous teachers and propose the novel Buffer Knowledge Distillation (BKD), which reduces the teacher–student discrepancy in terms of network architecture and distilled knowledge. Particularly, we first construct a series of homogeneous networks with larger capacity based on the student. A width-by-width fine-tuning mechanism is developed to reduce training costs of homogeneous networks, and the one with the highest accuracy is selected as the teacher. Furthermore, we propose BKD to reduce the learning difficulty, in which teacher and student features are fused into buffer features by our new multi-scale feature fusion module. Extensive experiments for image classification have been conducted to verify the homogeneous teacher based BKD, which consistently outperforms many existing KD methods. The results show that our method achieves up to 4.75% accuracy improvement on CIFAR-100, and the width-by-width fine-tuning mechanism incurs 33.58% and 36.08% less training time on CIFAR-100 and CIFAR-10, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic and information technology, Internet of Things (IoT) devices have become extensively utilised in various fields. Increasing attention has been paid to the performance and security analysis of IoT-based services. Dynamic instrumentation is a common process in software analysis for acquiring runtime information. However, due to the limited software and hardware resources in IoT devices, most dynamic instrumentation tools do not support IoT-based services. In this paper, we provide an analysis tool, IoTDIT, to solve the current problem of runtime detection in IoT-based services. IoTDIT employs static analysis and ptrace system calls to obtain dynamic firmware information, which can aid in firmware performance analysis and security detection. We perform experiments to verify the performance and effectiveness of the proposed instrumentation tool. [ABSTRACT FROM AUTHOR]

Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain. This article is categorized under:Application Areas > Science and TechnologyApplication Areas > InternetTechnologies > Machine LearningApplication Areas > Industry Specific Applications [ABSTRACT FROM AUTHOR]

The extensive use of Internet of things (IoT) devices demonstrates an increasing demand for secure frameworks. Position altering attacks are amongst the most severe vulnerabilities in two‐way time of arrival distance measurement technique, which is the most common in distance measurements by IoT devices. The overshadowing attack as a distance enlargement attack replays the authentic signal with a certain amount of delay and sensible power amplification. Therefore, the attack detection is difficult and requires a precise model to estimate its behaviour. The contribution of this study is a location estimation method based on the overshadowing attack probabilistic model that estimates the attack outcome delay and corrects the erroneous measurements. The overshadow delay, which is the delay between the authentic signal and the attacked one, plays a key role in this process. The method has been evaluated by three attack scenarios and compared to the conventional maximum likelihood (CML) and mixture secure location of things (M‐SLOT) methods. The results of the proposed method demonstrate root mean squared error (RMSE) of 0.48 in position estimation. With the same dataset, RMSEs of M‐SLOT and CML methods are 4.69 and 9.84, respectively, which confirm the better performance of the proposed method. [ABSTRACT FROM AUTHOR]

Educational Data management is a critical task for the researchers due to mammoth data generated by sensors and IoT (Internet of Things) devices. Managing this huge volume of data, cleaning this data from impurities is an inherent need. DF (Data Fusion) processes combine data from multiple sources based on their similarity for an easy management. DF processes focus on many factors like nature of data and application that uses that data. Many DFAs (Data Fusion approaches) have been proposed without detailing on the context for integrating data in fusion tasks. This work attempts to cover this gap of context's relevance by proposing a technique CDFT (Context aware Data Fusion technique). In this research work, initially data from IoT devices will be gathered and pre-processed to make it clear for the fusion processing. In this work, boundary based noise reduction algorithm is introduced for data pre-processing which attempts to label the unlabelled attributes in the data's that are gathered, so that data fusion can be done accurately. After pre-processing Context aware data fusion is performed which will combine the data's from multiple IoT devices together with the concern of context. Finally this combined data will be learnt using the convolution neural network for data fusion performance checking. The proposed CDFT is simulated on Matlab whose results prove that the proposed technique obtains optimal outcomes. [ABSTRACT FROM AUTHOR]

In this era of research and technology, Internet of things (IoT) takes a prominent part in the evolution of applications of the various field like health, education, smart cities, homes, agriculture etc. This paper provides a survey of the IoT ecosystem. All the components of IoT and their significance has been elaborated. The smart sensors collaborate through wireless communication and internet, with zero human activity, to deliver automated intelligent applications. In this internet world, machine-to-machine (M2M) technologies are the first phase of the IoT. As IoT is expanding, it is bringing together vast technologies as in Big Data, Artificial Intelligent, Machine Learning to tackle the huge data and devices. This paper starts by providing an overview of the taxonomy of the IoT ecosystem. Then, it provides a technical overview of IoT enabling architectures, devices, gateways, operating systems (OS), middleware, platforms, data storage, security, communication protocols and interfaces for the data flow in an ecosystem. This paper also discusses the key hurdles that need to be tackled for expanding IoT. A relation between IoT and new technologies like big data, cloud and fog computing has been briefed. Finally, it presents the growing applications that IoT delivers. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in political mayhem is visible during the election process where no stone is kept unturned in order to gain the power. Further, due to poll violence and waiting in the long queues to cast their votes, numbers of common citizens avoid the voting process completely in order to evade the chaos. In order to reduce these issues or to increase the voting rate, number of smart based or IoT enabled voting applications has been projected by various authors. However, IoT devices can be easily compromised or hacked by the experts (intruders) in order to gain their own access. Therefore, to ensure an enhanced secure and transparent polling process, Blockchain plays a vital role in ensuring the legit votes and their counting without their manipulation in any way. It is also needed in today's times where the world is available to people in their IoT or smart devices to also give them the opportunity to register their votes hassle free via their smart devices without having to worry about the system getting hacked. Therefore, in this paper, the proposed layout will be based on an enhanced security through Blockchain in E-voting application using IoT devices where the user will create an account with proper verification done via voter id and other biometric methods on a smart device. Every transaction done via this account which in this case will be registering vote will be validated by a miner. The proposed Blockchain mechanism for E-polling using IoT devices is again validated and verified through several parameters such as response time, resource utilization and request processed. [ABSTRACT FROM AUTHOR]

Nowadays, the technological innovations affect all human activities; also the agriculture field heavily benefits of technologies as informatics, electronic, telecommunication, allowing huge improvements of productivity and resources exploitation. This manuscript presents an innovative low cost fertigation system for assisting the cultures by using dataprocessing electronic boards and wireless sensors network (WSN) connected to a remote software platform. The proposed system receives information related to air and soil parameters, by a custom solar-powered WSN. A control unit elaborates the acquired data by using dynamic agronomic models implemented on a cloud platform, for optimizing the amount and typology of fertilizers as well as the irrigations frequency, as function also of weather forecasts got by on-line weather service. [ABSTRACT FROM AUTHOR]

The Industry 4.0 and smart city solutions are impossible to be implemented without using IoT devices. There can be several problems in acquiring data from these IoT devices, problems that can lead to missing values. Without a complete set of data, the automation of processes is not possible or is not satisfying enough. The aim of this paper is to introduce a new algorithm that can be used to fill in the missing values of signals sent by IoT devices. In order to do that, we introduce Shepard local approximation operators in Riesz MV-algebras for one variable function and we structure the set of possible values of the IoT devices signals as Riesz MV-algebra. Based on these local approximation operators we define a new algorithm and we test it to prove that it can be used to fill in the missing values of signals sent by IoT devices. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the rising networking standards that gap between the physical world and the cyber. Energy conservation of IoT devices becomes a fundamental challenge for extending the life time of the network. As a solution to this challenge, cluster head selection can be used. This paper intends to adopt a hybrid model with both Moth Flame Optimization and Ant Lion Optimization (ALO) to improve the performance of cluster head selection among IoT devices in WSN–IoT network. The particular simulation approach not only preserves energy of the sensor node by maintaining distance and delay but also balances the temperature and load of IoT devices for attaining the optimal cluster head selection in WSN–IoT network. Further, it compares the performance of the proposed hybrid model over the traditional models like Artificial Bee Colony, Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, ALO, MFO and Adaptive GSA. The simulation analysis considers the convergence, sustainability of alive nodes, normalized energy, load, and temperature. Thus the proposed simulation results are more efficient for prolonging the life time of the network. [ABSTRACT FROM AUTHOR]

The purpose of this article is to analyze some security methods used on the Internet of Things (IoT), taking into account their characteristics, operation, benefits and cuttingedge schemes, as well such as good security practices in IoT devices, generating control and protection strategies, in hardware and software technologies. Bearing in mind that "at the present time every computer attack is a challenge in the field of information security" [1], that is why in the hectic environment of technologies, and with the implementation of everyday objects such as houses, vehicles, appliances among others that are connected to the internet, allows the information that is handled through these are affected to attacks in various situations, and with the lack of controls and measures for the management of information and security mechanisms For information on homes, companies, corporations and even government entities in most cases, preventive actions must be taken that assume the characteristics and functionalities of the security mechanisms of (IoT) presented in the article in review mode bibliographic, will serve to analyze and identify security problems in devices connected to the Internet of things, and some security mechanisms that provide greater information security in IoT environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time, and in any place, to each other and to the Internet. One of the major problems associated with the IoT is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly, in the areas of security and privacy. Specifically, security testing and analysis of IoT devices is considered a very complex task, as different security testing methodologies, including software and hardware security testing approaches, are needed. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed is aimed at testing all types of IoT devices, with different software/hardware configurations, by performing standard and advanced security testing. Advanced analysis processes based on machine learning algorithms are employed in the testbed in order to monitor the overall operation of the IoT device under test. The architectural design of the proposed security testbed along with a detailed description of the testbed implementation is discussed. The testbed operation is demonstrated on different IoT devices using several specific IoT testing scenarios. The results obtained demonstrate that the testbed is effective at detecting vulnerabilities and compromised IoT devices. [ABSTRACT FROM AUTHOR]

The need for reconfigurable lightweight cryptographic processors with low power consumption for securing the Internet of Things (IoT) with adaptive functionalities is getting attracted due to security properties, such as data fidelity, provenance, and privacy concerns. This paper presents RECO-ASCON, a reconfigurable security processor that supports both cryptographic Hash and Hasha ASCON algorithms. The proposed design is implemented in Chisel hardware construction language, an open-source hardware library. It has been evaluated in various hardware platforms such as FPGAs, embedded systems and ASICs with various technology nodes and design flows. In the PYNQ-Z1 board, RECO-ASCON can be mapped to five processors in total with a Micro-Blaze soft-core processor for streaming data to all of them while running at 200 MHz without producing any timing violations. Moreover, mapping the proposed RECO-ASCON processor on the Nexsys4-DDR Xilinx Kit (ARTIX-7 chip) would cost ∼ two times fewer resources and run 1.25 × faster than the existing implementation of just one of the ASCON hash functions on the same family of the chip. We implement the proposed processor in 28 nm industry technology with commercial design flow, it achieves a maximum frequency of 1.12 GHz while achieving highest energy efficiency compared to the state-of-the-art implementations. The total area is only 9170. 82 um 2 in 28 nm. To further evaluate the design, we also implement it with all open-source design tools and process technology libraries in 45 nm, it achieves 1 GHz maximum frequency and consumes only 18225 um 2 in 45 nm. Overall, the proposed reconfigurable processor is compact and more energy efficient, making it a perfect candidate to be deployed in wide range of IoT systems. • RECO-ASCON is a reconfigurable processor that supports multiple hashing modes. • The design is implemented in Chisel and has been evaluated in various platforms. • RECO-ASCON achieves better energy efficiency and the highest maximum frequency. • The design has been evaluated with both commercial and open-source EDA tools. [ABSTRACT FROM AUTHOR]

Basketball players need to frequently engage in various physical movements during the game, which puts a certain burden on their bodies and can easily lead to various sports injuries. Therefore, it is crucial to prevent sports injuries in basketball teaching. This paper also studies basketball motion track capture. Basketball motion capture preserves the motion posture information of the target person in three-dimensional space. Because the motion capture system based on machine vision often encounters problems such as occlusion or self occlusion in the application scene, human motion capture is still a challenging problem in the current research field. This article designs a multi perspective human motion trajectory capture algorithm framework, which uses a two-dimensional human motion pose estimation algorithm based on deep learning to estimate the position distribution of human joint points on the two-dimensional image from each perspective. By combining the knowledge of camera poses from multiple perspectives, the three-dimensional spatial distribution of joint points is transformed, and the final evaluation result of the target human 3D pose is obtained. This article applies the research results of neural networks and IoT devices to basketball motion capture methods, further developing basketball motion capture systems. • It is crucial to prevent sports injuries in basketball teaching. • Basketball motion capture preserves the motion posture information in 3D space. • Designs a multi perspective human motion trajectory capture algorithm framework. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in artificial intelligence promote the development of deep neural networks (DNNs)-based intelligent applications in the Internet of Things (IoT). However, these powerful DNNs have considerable model parameters, computational complexity, and storage usage, which impose heavy costs on resource-constrained IoT devices and incur unendurable latency in smart applications. To overcome these challenges, numerous works aim to design lightweight DNNs, but existing works encounter two severe bottlenecks: one is the acute accuracy degradation when reducing model parameters, and the other is the failure to balance the accuracy and resource usage under dynamic workloads. In this paper, we investigate the problems of designing and deploying DNNs on resource-constrained IoT devices. Specifically, we first design a novel multi-branch scalable neural network (MBSNN) architecture, which features multiple subnets to provide optional intelligent services of different accuracy. Then, we propose a stepwise incremental training technique and a learning rate dual decay strategy that can efficiently train MBSNN and increase accuracy. Afterwards, we develop a threshold selection-based adaptive inference mechanism, which can wisely select thresholds for MBSNN to achieve optimal accuracy under given timing constraints. Extensive experiments show that compared to benchmarking methods, our MBSNN improves accuracy by up to 1.63%, reduces computational complexity by up to 32.7%, and decreases model parameters by up to 41.9%. [ABSTRACT FROM AUTHOR]