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With the growing elderly population, ensuring consistent and reliable healthcare monitoring has become increasingly important. This research introduces an innovative solution in the form of a smart glove designed specifically for elderly care. The glove is equipped with advanced features such as fall detection, emergency messaging via hand gestures, and vital signs monitoring, including oxygen saturation (SpO2) and heart rate. The smart glove integrates the MPU6050 and MAX30100 sensors with Arduino and ESP8266 modules to achieve these functionalities. Fall detection is accurately identified by the MPU6050 sensor, while the MAX30100 sensor monitors SpO2 and heart rate. Hand gestures are recognized and interpreted for sending emergency messages. Data from these sensors are transmitted through IFTTT, enabling alerts and notifications via SMS, email, and a secured website, thus facilitating real‐time remote monitoring and communication. This setup provides a comprehensive and reliable method for monitoring the health and safety of elderly individuals, ensuring prompt response and care in emergencies. The proposed system demonstrates significant potential in enhancing the quality of life for elderly individuals by offering a smart, integrated, and user‐friendly healthcare solution. [ABSTRACT FROM AUTHOR]

In IoT (Internet of Things) applications, there are many kinds of event flows. Without converging these flows based on their semantics, it is difficult to establish ultrascale‐connected IoT service systems. Event flow convergence may encompass event content aggregation, aggregated routing, and aggregated subscription matching. In our work, a semantic IoT service communication fabric is proposed to achieve this convergence, specifically for impedance matching between service computation and event communication, leveraging semantic knowledge technologies. Compared with classic semantic communication by abstracting the underlying network as a channel, our solution delves into the channel for impedance matching, that is, focusing on semantic event routing in the channel and semantic event content aggregation along routing paths. Multiple tenants are supported in the IoT service communication fabric, where each tenant can use virtual semantic interfaces to organize together its devices, IoT gateways, control centers, and data centers, and semantic event communication schemes and algorithms are proposed to map IoT services' goals into event routing. We finally conduct evaluations to show the effectiveness and applicability of our solution. [ABSTRACT FROM AUTHOR]

Soil nutrition assessment is a critical factor in improving both the quantity and quality of agricultural yields. The integration of Internet of Things (IoT) technology represents a groundbreaking advancement, allowing for real‐time measurements in smart farming. This article focuses on investigating and evaluating the current challenges in nutrient management within smart farming and explores the future potential of utilizing IoT for nutrient management. To gather data, bibliometric analysis and utilization of the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flowchart were conducted, searching on the Publish or Perish and Science direct search engines using the keywords "soil nutrient monitoring smart farming internet of things" without quotation marks. A total of 395 articles were initially identified, 20 of which were included in the analysis after verification with Covidence assessment tools. Our review of existing studies revealed that many nutritional management approaches are still in prototype and experimental design, without the use of experimental design agriculture standards, without validation through plant testing, and mainly focus on monitoring systems. This highlights the need for additional research into large‐scale studies, considering data security and the application of this technology in agricultural experimental design across a wide range of agricultural practices. Furthermore, future research on NPK IoT‐based sensors is expected to provide numerous benefits and accessibility to all farmers, ensuring agricultural profitability. [ABSTRACT FROM AUTHOR]

As the backbone of modern power systems, the smart grid is one of the most critical applications of the Internet of Things. The intelligent electricity system faces various types of unauthorized malicious access, that is, cyber‐attacks. With the development and application of information and communication technologies in traditional power systems, the improvement of physical‐cyber systems in the smart grid also increases. Nowadays, the deployment of defensive measures has surged in response to the growing number of attacks aimed at the smart grid. Therefore, in this paper, we investigate cyber‐attack identification using artificial intelligence‐based models and identify them by estimating the state vector of the electricity network. We accomplish simulations by extracting data from the five‐bus IEEE network to test the effectiveness of the proposed algorithm. False data attack vectors are then injected into the healthy measurements. In this way, to check the detection power of the proposed algorithms, 2,000 measurement samples have been taken from the five‐bus network, half of which are considered healthy data and the other half as manipulated data. After labeling healthy and false data, machine‐learning algorithms such as decision trees and k‐nearest neighbor (KNN) have been used to investigate and identify this type of attack. Comparative analysis of the two proposed algorithms against commonly used methods demonstrates significantly improved accuracy. Specifically, according to the best depth for the decision‐tree algorithm and k for the KNN algorithm, it is drawn with k = 3 and the decision‐tree algorithm with a depth of 9. According to the algorithms proposed in this article, the decision‐tree algorithm with a depth of 9 in p‐value of 0.45 and 0.64 and the nearest neighbor algorithm with k = 3 in p is equal to 0.72, 0.98, and 1 represent better accuracy. Also, the results indicate that the two proposed algorithms have performed much more favorably than other classification methods. Additionally, the detection accuracy increases with higher p‐values for these two algorithms. This problem shows that the detectors can detect false data injection attacks that cause more severe disturbances in the system. [ABSTRACT FROM AUTHOR]

Elliptic‐curve cryptography (ECC) is a popular technique of public‐key cryptography used for secure communications in Internet of Things (IoT) applications. In this study, the implementation and analyze the Elliptic Curve Digital Signature Algorithm (ECDSA) with NIST‐256 on MicroBlaze processors based on RISC‐V architecture. This implementation aims to enhance secure communication and data exchange between IoT devices and also provides a detailed account of our ECDSA implementation on a RISC‐V processor and evaluates its performance, achieving a reduction of 11.8% in execution time compared to existing techniques. The implementation takes 483 clock cycles and utilizes approximately 4096 bytes of memory. These results demonstrate that ECDSA can be efficiently implemented on RISC‐V processors, making it a promising option for secure communication in IoT applications. Furthermore, the study highlights specific security considerations critical to implementing ECC on RISC‐V processors, ensuring robust and reliable security in IoT environments. [ABSTRACT FROM AUTHOR]

The increasing heterogeneity of traffic in the Internet of Things (IoT) service demands presents a challenge for computing nodes to meet the computing resources and link bandwidth required. To address this, IoT requests have been virtualized and organized in service function chaining (SFC), which requires a host among hybrid cloud‐fog computing nodes. To find a suitable host for each service function (SF) request, a typical migration algorithm has been used. However, this approach delayed the response due to propagation delays in searching for a valid nearby node. Mission‐critical service demands may not even be served at all. This article addresses this issue by proposing two novel approaches: nearest candidate node selection (NCNS) and fastest candidate node selection (FCNS). These approaches employ software‐defined network (SDN) controllers and the Markovian arrival process, Markovian service process, and single host (M/M/1) queuing model to monitor the maximum possible latency required to meet the SF service demand by each computing node and finally assigning into one with least latency. With the use of these methods, heuristic monitoring of computing resources is made possible, allowing the selection of the most suitable host computing nodes based on proximity or minimal round‐trip time. Moreover, priority‐based fastest candidate node selection (PB‐FCNS), an adaptation of FCNS, accounts for concurrent service requests using the general arrival process, general service process, and single host node (G/G/1) queuing model. Compared to traditional migration algorithms, NCNS and FCNS provide significant improvements in reducing round‐trip time by 5% and decreasing the probability of unsuccessful service function chains by 55%. Despite the cost of installation, employing these methods in conjunction with SDN controllers can reduce latency, maximize service success rates, and guarantee the delivery of heterogeneous service functions. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) and software‐defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. This study is aimed at reviewing the literature on SDN for IoT (SDN‐IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. The study articulates the challenges of managing and orchestrating IoT systems and how SDN can be used to address these challenges by enabling dynamic and flexible network configurations. It delineates not only the function of blockchain (BC) technology in securing and managing IoT networks but also how SDN can be utilized to incorporate BC‐based solutions. Additionally, the potential of SDN for mobile networks is explored, which are increasingly being used to support IoT devices. Finally, this study outlines the issues, challenges, and potential future research directions that may present opportunities for the researchers working in this field, underscoring the demand for more in‐depth investigation and advancement. [ABSTRACT FROM AUTHOR]

Wireless sensor nodes (WSNs) are widely used in implementation of the new concepts such as the Internet of Things (IoT) and smart homes and societies. These WSNs use batteries as the sole power source which has numerous problems such as limited shelf life, require charging points, deep discharge, and sensitivity to temperature, and require periodic replacement and recharging. Harvesting ambient energies such as hydrokinetic, vibration, and thermal from the surrounding of WSNs provide a promising solution to the power‐related problems. This research illustrates the design, development, and testing of a microplanar coil (MPC)‐based hydrokinetic‐based electromagnetic‐type energy harvester for WSNs applications. The developed MPC‐based hydrokinetic energy harvester (H‐EH) utilizes the flow of water in domestic pipelines such as in the kitchen and converts it into useful electrical energy. An indigenous setup was developed to test the prototype in‐lab. The H‐EH produces 548 μW at a flow rate of 7 L/min at an optimum load resistance of 9 Ω. This work opens up new possibilities for developing MPC‐based H‐EH that can enhance the efficiency of WSNs. The research will also help to achieve self‐sustained long‐term operation of WSNs in advanced technologies such as IoTs, smart homes, and smart cities. [ABSTRACT FROM AUTHOR]

Buffer‐aided cooperative nonorthogonal multiple access (NOMA) improves the effectiveness of the spectral by allowing more than one user to share the same resources to realize massive connectivity. This is notably attractive in the fifth generation (5G) and beyond systems, where a massive number of connections are essential such as the Internet of Things (IoT). Wireless‐powered communication through energy harvesting is another 5G promising solution for the future massively dense networks. To sufficiently utilize energy harvesting in a buffer‐aided cooperative NOMA network, the relay selection rule must consider the energy level of the selected relay in addition to its data content to avoid an outage. This paper proposes a relay selection rule for energy‐harvesting buffer‐aided cooperative NOMA networks. The proposed relay selection scheme considers the state of both the data buffer and the energy buffer. The simulations show that the proposed selection rule has improved the network outage probability and throughput as well. This enhancement is kept with changing the number of relays. The results also show that the impact of changing the energy buffer size is crucial, and the larger, the better. Furthermore, making the source transmitting power larger than the relay transmitting power is beneficial, especially with large enough energy buffer sizes. [ABSTRACT FROM AUTHOR]

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system. [ABSTRACT FROM AUTHOR]

Road accidents are a major cause of injuries and deaths worldwide. Many accident victims lose their lives because of the late arrival of the emergency response team (ERT) at the accident site. Moreover, the ERT often lacks crucial visual information about the victims and the condition of the vehicles involved in the accident, leading to a less effective rescue operation. To address these challenges, a new Internet of Things (IoT)-based system is proposed that uses on-vehicle sensors to detect and report the accident to rescue operator without any human involvement. The sensor data are automatically transmitted to a remote server to create a visual representation of the accident vehicles (which existing systems lack), facilitating the situation-based rescue operation. The system tackles any false reporting issue and also sends alerts to the victim's family. A mobile application has also been developed for eyewitnesses to manually report the accident. The proposed system is evaluated in a simulated environment using a remote-controlled car. The results show that the system is robust and effective, automatically generating visuals of accident vehicles to facilitate informed rescue operation. The system has the potential to aid the ERT in providing timely first aid and, thus, saving human lives. [ABSTRACT FROM AUTHOR]

Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children's monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the fundamental practical issues related to daily life are missing. As a result, the safety of babies against suffocation and falling hazards for employed parents or mothers involved in the workforce has been overlooked. Only a few studies exist that cover the age range of 0 months to 4 years, with further focus on the age range of 6 months to 4 years. In response to these gaps in previous research and existing technologies, this review work proposes a vision for a baby safety system that utilizes various sensors and technologies like machine learning and image processing to ensure a safer environment for babies. [ABSTRACT FROM AUTHOR]

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO's interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO. [ABSTRACT FROM AUTHOR]

Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, attacks against fog servers can take many forms, such as distributed denial of service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of fog computing-based SYN Flood DDoS attacks using an adaptive neuro-fuzzy inference system (ANFIS) and software defined networking (SDN) assistance (FASA). The simulation results show that the FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP-SYN floods and DDoS attacks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been used in smart cities, agriculture, weather forecasting, smart grids, and waste management. The IoT has huge potential but needs refinement. The paper focuses on lowering IoT sensor power consumption to improve network life. This work selects the best IoT cluster header (CH) to maximize energy consumption. The suggested technique uses particle swarm optimization (PSO) with artificial neural networks (ANNs). The optimal CH in an IoT network cluster was identified by taking into account the number of active nodes, the load, the residual energy, and the cost function. This work compares the suggested method with artificial bee colony, genetic, and adaptive gravity search algorithms. The hybrid solution beats conventional methods. [ABSTRACT FROM AUTHOR]

Smart street LED lighting systems have received much attention driven by the need to save energy and the dramatic advances in the Internet of Things. This work proposes a new smart street lighting system that adaptively changes the street lights' intensity based on traffic and weather conditions and provides a platform for monitoring road conditions and detecting lamp faults. The system transfers the data using the UDP protocol over NBIoT radio technology. It also maintains two-way communication between the luminaires and the central node. In order to ensure real-time response to traffic and avoid dimming delays, each light is locally controlled by a microcontroller based on the sensed traffic and weather data. The measurements of each luminaire are also sent to the central control node to locate lamp faults, detect emergency situations, and, if needed, broadcast on/off messages to the whole network's luminaires. The system was implemented in a suburban street in Ras Al Khaimah. Evaluations proved that the system can locate and detect faulty lamps and vary the light intensity in real time based on traffic. It also resulted in energy savings of up to 55% compared to a normal LED street light network. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay-minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision-free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay. [ABSTRACT FROM AUTHOR]

Nowadays, with the advent of various communication technologies such as the internet of things (IoT), a large volume of data is produced that needs to be processed in real-time. Fog computing is an appropriate solution to address the requirements of different types of IoT applications. In most cases, IoT applications consist of a set of dependent tasks that can be separately processed in a heterogeneous fog environment. Scheduling these tasks in a fog environment is an NP-hard problem that needs a vast amount of time and computation resources to solve, making it infeasible for real-time applications. In addition, reducing response time and energy consumption in fog computing is an essential issue that should be taken into account in task scheduling algorithms. To address these challenges, we aim to propose a multiobjective task scheduling model to jointly improve energy efficiency and response time. To solve the model, we also propose an intelligent solution named IETIF which combines and leverages the benefits of simulated annealing and NSGA-III algorithms. Simulation results show that IETIF outperforms the state-of-the-art methods in terms of energy consumption, response time, and speedup. [ABSTRACT FROM AUTHOR]

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts' domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) has emerged as a technology that automates industrial processes. In IIoT networks, data are collected from various nodes and sent to a base station for managerial purposes. However, in the industrial environment, network reliability and delay are significant challenges due to the high likelihood of packet loss in radio networks. Anycast is a link layer mechanism that increases reliability and reduces delay by allowing multiple receivers to be connected to a sender, and a single packet is simultaneously sent to all receivers. The receivers decode the packet based on their priorities, and transmission succeeds if at least one receiver can decode the packet. Moreover, mechanisms exist to limit the number of duplicates. This paper proposes a novel centralized anycast aware scheduling algorithm (AASA), which implements anycast based on the 802.15.4e-time-slotted channel hopping (TSCH) standard and in the stack of the 6TiSCH protocol. The goal of AASA is to improve IIoT networks more reliable and reduce end-to-end delay. To do this, upon a link failure, AASA chooses an alternative link and the packet is sent without any delay via that link through the same time slot. We implemented AASA in 6TiSCH simulator and carried out different scenarios to investigate its efficiency under various conditions. Results from simulations show that AASA effectively increases reliability by reducing repetitive packet transmissions and, thus, decreasing the delay in packet delivery. [ABSTRACT FROM AUTHOR]

This research article designs and develops a planar small-size antenna design for smart Internet of Things (IoT) communication with long-range technology (LoRa). The proposed system is best suited for transceiver systems in this automation and sensing era. In the proposed antenna, the ground, the radiating element, and the stub feed are designed on the same side of the substrate, keeping in mind that it can print the LoRa module. The design consists of a meandered monopole, a dipole structure as a ground, and a stub feed. A different design approach is employed to get an optimized result. The antenna is made up of a rectangular feed stub to which a connecting wire is attached. The overall dimension of the antenna is 55 m × 55 m × 1.6 mm. To verify the proposed design, an antenna was fabricated and measured, which covers the LoRa frequency band at 868 MHz, providing a sufficient bandwidth of 10 MHz and a gain of more than 0.5 dB in the operating band. A designed antenna is implemented for sensor data communication with the LoRa module device and device interface Arduino platform. The antenna is connected as a transmitter and receiver one by one to verify its performance with machine-to-machine communication using the LoRa module. The size, bandwidth, and radiation efficiency of this antenna are better than the antennas in the literature. The designed antenna is successfully implemented with LoRa connectivity and communicates the data up to 8 km in line-of-sight communication, more than 1 km in urban environments, and approximately 250 m of connectivity in building areas. [ABSTRACT FROM AUTHOR]

Internet is becoming a part of our lifestyle; however, the usage rate and application of the Internet are disparate in different parts of the world. In many emerging countries, the Internet is yet to penetrate ordinary households. The present study focuses on how IoT adoption impacts the banks' customer relationship management (CRM) in an emerging market context. Furthermore, the moderating roles of gender, age, and bank ownership type on the relationship between the adoption of IoT and CRM have also been tested. Cost, convenience, social context, and privacy were studied as the predicting variables of IoT adoption, while IoT adoption was investigated as the antecedent of CRM. The CRM variable has been operationalized as a second-order latent construct consisting of three first-order latent variables: responsiveness, satisfaction, and assurance. A cross-sectional, non-probability-based survey was conducted from 467 bank customers of three public and three private sector banks in Aligarh city of India. Two CFA models were run to ensure reliability, validity, and model fit. Hypotheses were tested using structural equation modeling (SEM) on AMOS software, while PROCESS Macro v4.0 by Hayes (2009) was used to test the moderating effect of gender on the relationship between IoT adoption and CRM. The results indicate that cost, convenience, social context, and privacy are positively influencing IoT adoption, which in turn positively affects CRM. Gender and age were found to have a negative moderation effect on the path between IoT adoption and CRM, while bank ownership type positively moderated this link. [ABSTRACT FROM AUTHOR]

The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained. [ABSTRACT FROM AUTHOR]

With the development of cloud services and the Internet of Things, the integration of heterogeneous systems is becoming increasingly complex. Identity management is important in the coordination of various systems, and public key infrastructure (PKI) is widely known as an identity management methods. In PKI, a certificate authority (CA) acts as a trust point to guarantee the identity of entities such as users, devices, and services. However, traditional CAs that delegate the operations to a specific organization are not always suitable for heterogeneous services, and a new methodology is required to enable multiple stakeholders to securely and cooperatively operate a CA. In this study, we introduce the concept of a consortium CA and propose a distributed public key certificate-issuing infrastructure that realizes a consortium CA. The proposed infrastructure enables multiple organizations to cooperatively operate a CA suitable for services involving multiple stakeholders. We identify four requirements for the cooperative operation of a consortium CA and design the proposed infrastructure with distributed ledger technology. Furthermore, we present the implementation of smart contracts with Hyperledger Fabric and prove that the proposed infrastructure satisfies the four requirements. Finally, we confirm that certificate issuance and verification are stable at approximately 4 and 3 ms, respectively. [ABSTRACT FROM AUTHOR]

The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device's data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes' fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real-time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self-organization of these devices, power-aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension. [ABSTRACT FROM AUTHOR]

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

IoT security detection plays an important role in securing the IoT ecosystem. The current detection systems suffer from poor fault tolerance and inefficient detection results. To address the IoT security vulnerability, the paper designs a multifeature fusion-based IoT security detection model to simulate an attacker sending test commands to IoT nodes. Firstly, the data collection algorithm is introduced, and the collected dataset is analyzed by three neural network models, namely, RNN, LSTM, and GRU, respectively. The best scoring model is selected as the classifier to identify vulnerabilities and achieve IoT security detection. [ABSTRACT FROM AUTHOR]

Field-Programmable Gate Arrays (FPGAs) can be found in an increasing number of application domains, such as the telecom industry, the automotive electronics sector, or automation technology as well as in the area of reconfigurable computing. In recent years, it can be observed that the open-source idea which is known from the software domain for a long time also became popular in the world of hardware and FPGA design. In the era of the Internet of Things, many of today's electronic devices implement some kind of network interface with Ethernet being known as one of the most widely used network standards. Thus, there is consequently a high demand on available Ethernet implementations for FPGA platforms. The goal of this work is to survey available open-source Ethernet MAC IP cores, evaluate existing designs in terms of performance, resource utilization, code quality, or maturity, and to present and summarize the evaluation results herein. Furthermore, advantages of commercial solutions and related publication work are discussed. To the authors' best knowledge, this is the first publication that evaluates and compares existing open-source Ethernet MAC IP cores on a large scale. This work should help designers to select an appropriate open-source Ethernet MAC for an FPGA design and shows possible pitfalls and things to pay attention when using an open-source IP core in general. Finally, the authors would like to show that the open-source community can be also very helpful in the world of hardware in terms of design reuse or time to market. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT) technology, massive heterogeneous equipment involving all walks of life has been connected to networks. However, many current authentication methods have poor robustness and cannot ensure the safety of access equipment due to the complexity and uncertainty of the network environment. To deal with this problem, a hierarchical authentication system was proposed in this paper. First, an equipment identification method (EIM), using stack denoising autoencoders (SDAs), is developed to weaken the uncertainty to recognize the type of access equipment. Second, an equipment authentication method (EAM), based on the identification result and similarity theory, is designed to improve the verification accuracy to guarantee the credibility of access equipment. Third, the proposed hierarchical authentication system was tested in a real access platform. The experimental results demonstrated the satisfactory performance of this proposed hierarchical authentication system. [ABSTRACT FROM AUTHOR]

The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT. [ABSTRACT FROM AUTHOR]

Federated learning has been popularly studied with people's increasing awareness of privacy protection. It solves the problem of privacy leakage by its ability that allows many clients to train a collaborative model without uploading local data collected by Internet of Things (IoT) devices. However, there are still threats of privacy leakage in federated learning. The privacy inference attacks can reconstruct the privacy data of other clients based on GAN from the parameters in the process of iterations for global models. In this work, we are motivated to prevent GAN-based privacy inference attacks in federated learning. Inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the private information of victims while maintaining the accuracy of the global model. Extensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks. In addition, based on the experimental results, we propose a norm-based metric to assess the performance of privacy-preserving. [ABSTRACT FROM AUTHOR]

In big data analytics, Jaccard similarity is a widely used block for scalable similarity computation. It is broadly applied in the Internet of Things (IoT) applications, such as credit system, social networking, epidemic tracking, and so on. However, with the increasing privacy concerns of user's sensitive data for IoT, it is intensively desirable and necessary to investigate privacy-preserving Jaccard similarity computing over two users' datasets. To boost the efficiency and enhance the security, we propose two methods to measure Jaccard similarity over private sets of two users under the assistance of an untrusted cloud server in this paper. Concretely, by leveraging an effective Min-Hash algorithm on encrypted datasets, our protocols output an approximate similarity, which is very close to the exact value, without leaking any additional privacy to the cloud. Our first solution is under a semihonest cloud server, and our enhanced solution introduced the consistency-check mechanism to achieve verifiability in malicious model. For efficiency, the first solution only need about 6 minutes for billion-element sets. Furthermore, as far as we know, the consistency-check mechanism is proposed for the first time to achieve an effective verifiable approximate similarity computation. [ABSTRACT FROM AUTHOR]

In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH's selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are used in almost every part of our life, so it is important to protect the sensitive data and information that is transmitted over wireless networks such as images and documents. The IoT devices have limited computational resources; they are called limited devices due to their limited processors and memory size. Traditional encryption methods require a lot of computing power; therefore, it is difficult to implement traditional cryptographic algorithm on IoT processor. Finally, a new, lightweight encryption method based on the DNA sequence is proposed to suit the IoT devices in a way to make an easy and secure the communications among the IoT devices. DNA sequences are very random, so we have used it to make a strong secret key that is hard for attackers to break. The proposed method has an advantage in terms of efficiency and strength. Experiments and security tests show that the proposed encryption system not only has a good encryption effect and can withstand known attacks, but it is also fast enough for real-world use. The DNA key is used to encrypt files using two simple and reliable methods such as substitution and transposition procedures that meet IoT computational requirements. In addition, when compared with other encryption algorithms, the experimental results shows that the key size, encryption time, and distortion preparation are all superior. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.'s scheme which reduces the execution time by one third for security levels of λ = 128. Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with λ = 128 bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool. [ABSTRACT FROM AUTHOR]

In recent years, our life has become broader and faster by adapting to the Internet of Things (IoT). In IoT, the devices distributed globally that are connected to the Internet improve productivity in various sectors. The network plays an important role for transferring data to the sink node by collecting from all other nodes in IoT. The IoT requires energy saving since it is connected to resource-constrained devices. Energy preservation is a difficult challenge to improve network lifetime in IoT. Clustering is one of the key techniques to extend the network's life. In that, cluster head selection is one of the promising techniques to extend the lifespan of the IoT network. Many researchers proposed various cluster head (CH) selection techniques in IoT. However, inappropriate CH selection quickly degrades a network battery and creates an energy-hole problem in the network. This paper proposes a novel sandpiper optimization algorithm (SOA) to select CH among the networks. Later, the cluster is formed by using the Euclidean distance. The proposed SOA's accomplishments are compared to fitness value-based improved grey wolf optimization (FIGWO), particle swarm optimization (PSO), artificial bee colony-SD (ABC-SD), and improved artificial bee colony (IABC). The proposed SOA extends the network lifespan by 3-18% and increases the throughput by 6-10%. Thus, the proposed SOA increases the network lifetime and throughput and decreases the energy consumption among the nodes in the network. [ABSTRACT FROM AUTHOR]

We propose and experimentally validate a hybrid energy harvester embedded in a wearable system used to measure real-time information, such as body temperature, heartbeat, blood oxygen saturation (SpO2), and movement (or acceleration) of human body in real time. This hybrid energy harvester, or in short eMeD, has a unique design that can improve the energy efficiency of the overall wearable system and extract more energy from ambient sources. Specifically, the wearable system is integrated with a hybrid photovoltaic-radio frequency (RF) energy harvester as the power source to prolong its lifetime and reduce the dependence on battery energy. Experimentally, the current consumption of the wearable system with load switching and event management algorithm improved from 31 mA to 18.6 mA. In addition, the maximum conversion efficiency is 14.35%. The experimental results illustrate a sustainable and long-term monitoring operation for Internet of Medical Things systems. [ABSTRACT FROM AUTHOR]

Aiming at the network security problem of power system cable trench control industrial Internet system, we studied an intrusion detection method applied to the embedded industrial Internet of Things gateway. This method extracts rules from the DBN-DNN deep neural network to obtain intrusion detection models that are conducive to integration into embedded systems. We first use the DBN network to reduce the dimensionality of the data, then use the DNN to train the classification model, and extract the rules from the DNN's neurons to form a rule tree for intrusion detection. The KDD CUP99 training database is used to verify the feasibility of the method, and the test is carried out in the embedded gateway. The results show that the detection method based on rule extraction used in this paper can ensure detection efficiency and accuracy compared to the traditional detection methods. At the same time, it saves more computing resources and is more conducive to integration in embedded gateway systems. [ABSTRACT FROM AUTHOR]

The Internet of Things allows vehicles to communicate with their surroundings and send various traffic and road conditions to other vehicles, making driving better. Data received from other vehicles sometimes need to be processed. In this state, the data should be sent to the Roadside unit for processing and fog if necessary. The source and destination must know each other's identities to send and receive information, and various attacks can threaten source and destination authentication. The paper presents secure authentication scheme based on fog-cloud for the Internet of Vehicles. Formal and informal security analyses verify that the SAIFC demonstrates resistance to famous attacks. The SAIFC is compared with another scheme regarding security features, computing, and communication costs. The SAIFC simulated with the NS3 tool and compared several routing protocols in packet loss, packet delivery, end-to-end delay, throughput, and MAC/PHY overhead. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, the combination of mobile robots and IoT technology has realized the complementary advantages of the IoT and robots, which has brought new challenges and opportunities for the development and application of the IoT and mobile robots. In this study, a mobile robot positioning system based on the wireless positioning technology of the IoT is first designed, and an adaptive federated filtering algorithm based on the confidence of the sensor measurement data is given, and the data fusion processing is carried out for the positioning system to achieve the accurate positioning of the mobile robot. Then, we embed the intelligent control system of the ring lamp and propose an adaptive fuzzy PI double closed loop equalization control strategy, which can automatically identify the number of interlaced parallel and ensure its equalization effect. The experimental results show that the equalization strategy designed in this study has a good equalization effect in the loop interleaving parallel topology, strong adaptive parameter adjustment ability, good robustness, and anti-interference. [ABSTRACT FROM AUTHOR]

As a novel network security scheme, the zero trust model can effectively improve the traditional role-based access control model in the public network by continuous trust evaluation and dynamic authorization. As a private network for collecting the health status data of the power equipment, the terminals in the power Internet of Things (IoT) have the characteristics of fixed and controllable hardware, software, and users, and the main network security threat of the power IoT comes from the risk of terminals being damaged or forged. Therefore, based on the analysis of the key connotation of the zero trust and the security characteristics of the power IoT, this paper proposes a novel zero trust model and framework to synthetically evaluate the security status of the power IoT based on the behaviour and characteristics of the terminals. The analysis based on the stochastic Petri net and simulation results show that the proposed model can effectively address network security problems and significantly improve the level of security protection of the power IoT. [ABSTRACT FROM AUTHOR]

IoT is one of the most popular technologies in recent years due to the interconnection of various infrastructures, physical devices, and software. To guarantee the security of Internet of Things (IoT) pervasiveness, lightweight cryptographic solutions are needed and this requires lightweight cryptographic primitives. The choice of S-box in light block ciphers plays an important role in characterizing the security-performance trade-off. The choice of the 4 × 4 S-box for the lightweight constructions results in compact hardware, speeding up the computational capability of the security algorithm unlike the 8 × 8 S-box. This work presents efficient algebraic S-boxes for a fast image cryptosystem based on a strong nonlinear function which is expanded by a biological technique depending on DNA. The robustness of the proposed S-boxes is analysed and tested against various standard attack criteria such as interpolation attacks, avalanche effect, and nonlinearity. The great advantage of introducing S-boxes is that its DSAC is the ideal value which is equal to zero. Also, other tests executed on these S-boxes guaranteed its robustness and excellent security performance. Moreover, the experiments are applied with full description in two different modes; RGB and gray images. The results of all tests proved to have fast and strong effective S-boxes. [ABSTRACT FROM AUTHOR]

Blockchain can effectively deal with the security and trust issues in Internet of Things (IoT) due to its salient features including decentralization, immutability, traceability, openness, and transparency. However, most IoT devices have too limited computing, storage, and bandwidth resources to maintain the complete operation of a blockchain system. To this end, we propose a hierarchical blockchain framework called HLOChain for IoT scenarios. First, according to computing and storage capabilities, the IoT devices are classified into three levels, i.e., high, medium, and low. They are deployed on different layers. In this way, a hierarchical blockchain architecture is designed. Second, we propose a lightweight proof of random (PoR) consensus mechanism to provide low-energy block mining, so that even the medium nodes can participate in the consensus task. Third, in order to reduce the ledger storage overhead, we design a blockchain storage optimization strategy based on the account model. Finally, the security analysis demonstrates that our HLOChain is secure against double-spend attack, Sybil attack, and so on. The experimental evaluation shows that our HLOChain achieves better performance in ledger storage cost, consensus computing cost, throughput, and transaction confirmation latency. [ABSTRACT FROM AUTHOR]

The terminal guidance tracking always has high speed rotating and scale changing. It makes a large margin of error in current tracking algorithms, which can transmit wrong position in the wireless sensor networks of the Internet of Things. In this paper, we propose a selector based on Fourier-Mellin transformer, which can accurately track the target with high speed rotating. Firstly we use existing detector to get all proposal target per frame. Then, we use Fourier-Mellin matching to select the best target in the new frame. Our dataset is the ship video which is created by HRSC2016 in rotating. Experimental results show that the accuracy of this method is up to 89.8%. Compared with other traditional tracking algorithms, it has a leap forward. The FMT selector can be applied to the back end of the detector, and it can achieve a feasible effect in the field of terminal guidance tracking. [ABSTRACT FROM AUTHOR]

Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance's applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources. [ABSTRACT FROM AUTHOR]