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Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. [ABSTRACT FROM AUTHOR]

Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of ( 93.26 % ), whereas, LSTM, RNN, SVM achieved the precision value of ( 89.13 % ), ( 90.43 % ), ( 88.23 % ), respectively. In addition, the precision value of the PW-NB is ( 97.15 % ), which is also higher as compared to KNN ( 93.25 % ) and DT ( 96.91 % ). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency. [ABSTRACT FROM AUTHOR]

In the last couple of years, massive growth has been seen in the Internet of Things (IoT). It is being adopted by all the application areas worldwide and playing a vital role in making smart environments. In IoT based paradigms, millions of sensors and intelligent devices are linked with each other and produce a massive volume of data. For data computation and analysis, we need computing resources that will able to provide the services at the edge levels. These computing resources may also be capable of dealing with bandwidth, storage, and latency related issues. The computing technologies in IoT may offer the services at the end-user levels, which will increase the system's overall performance with higher throughput in real-time. The developing rate of IoT based smart devices or sensors needs mobility with extensive geographical distribution, which is only possible by computing technologies. In this paper, we have discussed the emerging opportunities of Cloud, Fog, and Mist computing in IoT paradigms and also described the layer-wise architecture of these computing technologies. Apart from this, various characteristics and significance toward the problem-solving in the IoT paradigm have also been deeply discussed. [ABSTRACT FROM AUTHOR]

The rising popularity of cloud computing is now widely adopted by many industries including resource-restricted data owners, i.e., with the smart sensors in the Internet of things (IoT) to store their data in the cloud. Considering the untrusted nature of cloud server, the data collected by smart sensors should be encrypted before offloading them to the cloud. This unfortunately raises a concern on how to perform search functionality on encrypted data in the cloud. To tackle this challenge, an identity-based encryption scheme with authorized equivalence test (IBE-AET) is proposed in this paper to achieve simultaneously encryption and search functionality over outsourced data in cloud-assisted IoT. In IBE-AET, an authorized cloud server is allowed to carry out the equivalence test of two messages encrypted using the same identity as well as also those messages encrypted in different identities. In addition, the authorization mechanism in IBE-AET is versatile such that it enables a user to delegate the testing capability to the cloud server in a fine-grained manner. In the random oracle model, the proposed IBE-AET is formally proved to be equivalent to the bilinear Diffie–Hellman (BDH) assumption. The practicability of the suggested scheme is demonstrated by both the theoretic analysis and experiment simulation. [ABSTRACT FROM AUTHOR]

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency. [ABSTRACT FROM AUTHOR]

By introducing networking technologies and services into healthcare infrastructures (e.g., multimodal sensors and smart devices) that are deployed to supervise a person's health condition, the traditional healthcare system is being revolutionized toward knowledge-centric connected healthcare (KCCH), where persons will take their own responsibility for their healthcare in a knowledge- centric way. Due to the volume, velocity, and variety of healthcare supervision data generated by these healthcare infrastructures, an urgent and strategic issue is how to efficiently process a person's healthcare supervision data with the right knowledge of the right guardians (e.g., relatives, nurses, and doctors) at the right time. To solve this issue, the naming and routing criterion of medical knowledge is studied. With this offloaded medical knowledge, we propose an edge learning as a service (EdgeLaaS) framework for KCCH to locally process health supervision data. In this framework, edge learning nodes can help the patient choose better advice from the right guardians in real time when some emergencies occur. Two application cases: 1) fast self-help and 2) mobile help pre-calling are studied. Performance evaluations demonstrate the superiority of KCCH and EdgeLaaS, respectively. [ABSTRACT FROM AUTHOR]

While applications of the heterogeneous Internet of Things (IoT) proliferate, their performance requirements in terms of latency and jitter become more stringent and difficult to be met by the traditional cloud computing paradigm. As a result, a new paradigm called multi-access edge computing has emerged in recent years. It complements the centralized cloud platform by providing additional resources at the edge of radio access networks, and provides better support of IoT applications. One of the challenges of supporting IoT applications in mobile edge computing (MEC) is security. In this article, we will scrutinize the security issues of two IoT applications that can exploit the benefits of MEC. The first one is an environment perception system based on an industrial IoT network. The second one, which is gaining momentum fast, is mobile IoT based on a network of unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

The MEC vision leverages the availability of powerful and low-cost middleboxes, statically deployed at suitable edges of the network and acting as local proxies for the centralized cloud backbone; this potentially enables, among other things, better scalability and better reactivity in the interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application scenarios, and also mutual benefits due to the integration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of traditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct living lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effectiveness of the proposed techniques in elastically scaling the load at edge nodes according to runtime provisioning needs. [ABSTRACT FROM PUBLISHER]

Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information and communication advancements will empower better administration of accessible resources. The eventual fate of the world's betterment lies in its urban environment advancement. The fast influx of individuals creates possibility, yet it additionally causes challenges. Creating sustainable, reasonable space in the world's steadily extending cities is tested confronting governments worldwide. The model of the smart cities rise, where the rights and well-being of the smart city citizens are assured, the industry is in action, and the assessment of urban planning from an environmental point of view. This paper presents a survey on analyzing future technologies and requirements for future smart cities. We provide extensive research to identify and inspect the latest technology advancements, the foundation of the upcoming robust era. Such technologies include deep learning (DL), machine learning (ML), internet of things (IoT), mobile computing, big data, blockchain, sixth-generation (6G) networks, WiFi-7, industry 5.0, robotic systems, heating ventilation, and air conditioning (HVAC), digital forensic, industrial control systems, connected and automated vehicles (CAVs), electric vehicles, product recycling, flying Cars, pantry backup, calamity backup and vital integration of cybersecurity to keep the user concerns secured. We provide a detailed review of the existing future smart cities application frameworks. Furthermore, we discuss various technological challenges of future smart cities. Finally, we identify the future dimensions of smart cities to develop smart cities with the precedence of smart living. • Analyze future smart cities' characteristics based on state-of-the-art technologies. • Identifies and inspects the latest advancements in technology. • Analyze the most feasible frameworks and their proficiency for FSC. • Provide technology challenges identified in the literature. • Provide future directions are identified to develop smart cities. [ABSTRACT FROM AUTHOR]

Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called Interactive, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%. [ABSTRACT FROM AUTHOR]