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In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process. [ABSTRACT FROM AUTHOR]

This paper presents the realization and assessment of a compact metamaterial (MTM)-integrated wireless information and power transfer (WIPT) system. Developed WIPT is tailored to facilitate numerous Internet of Things (IoT) sensors operating at the low-power regime. Generally, WIPT is accomplished with a system possessing two operating bands. Therefore, the dual-band system working at frequencies of 405 MHz and 900 MHz was proposed. WIPT is achieved by coupling two defected ground structure-based resonators that are designed by cascading two single-band resonating structures. Then, MTM with the double negative characteristics at WIPT's operating frequencies was incorporated, which led to the performance enhancement. The power transfer efficiency of WIPT was increased by 14% and 7% at 405 MHz and 900 MHz, respectively, when the resonators were coupled at 20 mm. It is worth mentioning that WIPT and MTM share the same area of 25 × 25 mm2 to keep the compactness of the overall system. The WIPT system was evaluated in terms of charging time for two low-power IoT sensors. The powering time was approximated considering two scenarios: using only one band and two bands simultaneously. The provided analysis in both cases reveals that MTM-integrated WIPT aids in the significant reduction of a charging cycle. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

The emergence of IoT has introduced new security concerns, particularly the detection of large-scale network attacks initiated by compromised IoT devices. The diverse nature of IoT devices adds complexity to attack detection, necessitating the classification of devices based on their unique characteristics and behavior patterns. This paper presents a novel device classification method grounded in traffic differentials before and after IoT devices succumb to compromise. The study defines the concept of attack sensitivity and designs a method for calculating the value. The K-means algorithm is used to classify devices into predefined categories based on their attack sensitivity values. The effectiveness of this method is demonstrated through experiments on a public dataset. Additionally, a multi-class classification model is developed to identify newly added devices to the IoT environment, with good results in accuracy, precision, recall, F1 score, and FPR (0.999, 0.997–1,0.997–1,0.998–1,0–0.001). Overall, The device classification method proposed in this paper, based on attack sensitivity, not only enables a rational definition of device categories but also ensures precise classification for devices newly integrated into the IoT environment. This lays the foundation for achieving more precise attack detection and protection, advancing the field towards enhanced cybersecurity measures. [ABSTRACT FROM AUTHOR]

Empowered by Internet of Things (IoT) and cloud computing platforms, the concept of smart cities is making a transition from conceptual models to development and implementation phases. Multiple smart city initiatives and services such as Smart Grid and Smart Meters have emerged that have led to the accumulation of massive amounts of energy big data. Big data is typically characterized by five distinct features namely, volume, velocity, variety, veracity, and value. To gain insights and to monetize big data, data has to be collected, stored, processed, analyzed, mined, and visualized. This paper identifies the primary layers of a big data architecture with start-of-the-art communication, storage, and processing technologies that can be utilized to gain meaningful insights and intelligence from big data. In addition, this paper gives an in-depth overview for research and development who intend to explore the various techniques and technologies that can be implemented for harnessing big data value utilizing the recent big data specific processing and visualization tools. Finally, a use case model utilizing the above mentioned technologies for Smart Grid is presented to demonstrate the energy big data road map from generation to monetization. Our key findings highlight the significance of selecting the appropriate big data tools and technologies for each layer of big data architecture, detailing their advantages and disadvantages. We pinpoint the critical shortcomings of existing works, particularly the lack of a unified framework that effectively integrates these layers for smart city applications. This gap presents both a challenge and an opportunity for future research, suggesting a need for more holistic and interoperable solutions in big data management and utilization. [ABSTRACT FROM AUTHOR]

Agriculture must overcome escalating problems in order to feed a growing population while preserving the environment and natural resources. Recently, it has become clear that sensors and the Internet of Things (IoT) are effective tools for boosting agricultural sustainability and food security. This study provides insights into the global market size for smart agriculture in future years from 2021 to 2030, In addition, this research offered four levels of the IoT architecture for smart agriculture: the perception or sensing and actuator layer, the network layer, the cloud layer, and the application layer. The state of the art in IoT and sensor technologies for agriculture is examined in this review paper, along with some of their potential uses, including 1) irrigation monitoring systems, 2) fertilizer administration, 3) crop disease detection, 4) monitoring (yield monitoring, quality monitoring, processing monitoring logistic monotoring), forecasting, and harvesting, 5) climate conditions monitoring, and 6) fire detection. Additionally, this review offers a number of sensors for agriculture that can detect parameters like soil NPK, moisture, nitrate, pH, electrical conductivity, CO2, temperature, humidity, light, weather station, water level, livestock, plant disease, smoke, flame, flexible wearable. Subsequently, this study highlights the advantages of IoT in smart agriculture, including superior efficiency, expansion, reduced resources, cleaner method, agility, and product quality improvement. However, there are still issues that need to be resolved in order for IoT technology to be used in agriculture where covered in this paper, and also provide insights into future research directions and opportunities. This study will contribute to helping future readers and researchers to better understand the state of academic achievement in this subject. [ABSTRACT FROM AUTHOR]

Poor management of waste electronic and electrical equipment (e-waste) has resulted in serious challenges to the environment and, consequently, to living organisms. Increasing consumer willingness to return household e-waste is a practical solution for better e-waste management. In this paper, for the first time, a circular supply chain network is structured by applying internet of things (IoT) technology to increase consumer willingness to return household e-waste and to manage the network more effectively. For this purpose, we formulate a mixed-integer linear programming model whose objective is to minimize strategic costs (setup costs and IoT deployment costs) and operational costs (transportation costs, costs associated with IoT, and processing costs in centers), simultaneously. In addition, we developed a heuristic algorithm based on Lagrangian relaxation to solve the proposed MILP model. The performance of the proposed algorithm is evaluated by comparing its results with GAMS results using eight small-sized simulated problems. Furthermore, eight large-size problems are simulated and solved by the proposed algorithm. Finally, the data of an Iranian knowledge-based company is employed to validate the presented mathematical model and algorithm, and the sensitivity analysis process is used to examine the accuracy of the obtained results. [ABSTRACT FROM AUTHOR]

Hyperconnectivity, real-time processing, and interoperable communication facilities are key to the Next Generation (NG) Internet of Things (IoT). However, existing IoT architectures employ a diverse range of communication technologies to support desired data rates and coverage range, making them more complex to manage. The emerging IEEE 802.11ah standard, marketed as WiFi HaLow™, enables seamless communication among large-scale and energy-efficient sensor/actuator nodes to the Internet. IEEE 802.11ah introduces key features like Restricted Access Window (RAW) for reducing contention, Hierarchical Traffic Indication Map (TIM) and Association Identifier (AID) for efficient scheduling and device identification, and multiple Modulation and Coding Schemes (MCSs) for adaptive data transmission, enhancing IoT connectivity in Next-Generation IoT (NGI) communication. In this paper, we discuss IEEE 802.11ah for NGI concerning different requirements in terms of communication and computation. We survey the suitability of 802.11ah for NGI as compared to the existing state-of-the-art connectivity solutions. Thereafter, an IEEE 802.11ah-based NGI architecture and protocol stack are proposed and discussed, according to the different distinct features of this novel technology. Finally, we conclude by enumerating research and future directions for the proposed network architecture. • Proposed a Next-Generation Internet of Things (NGI) Architecture using IEEE 802.11ah. • The proposed NGI is for diverse communication and computation needs. • Exploration of communication technologies, standards, and NGI architecture prerequisites. • Suitability analysis of 802.11ah for NGI against current connectivity solutions. • Comparative assessment with related architectures, emphasizing programmability and intelligence. • Comparative study of existing communication standards in the context of NGI. • Presentation of IEEE 802.11ah-based NGI architecture and protocol stack with unique features. • Conclusion includes insights on research directions for the proposed NGI architecture. [ABSTRACT FROM AUTHOR]

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising. [ABSTRACT FROM AUTHOR]

This paper presents a relaxation oscillator (RxO) utilizing subthreshold transistor leakage current compensation (SLC) technology to expand the operating range from 70 °C to 85 °C at high temperatures. The compensated RxO outperforms the uncompensated counterpart by 25 times for temperature coefficient (TC). Additionally, forward body bias (FBB) technology optimizes the digital buffer delay with temperature drift at low supply voltage, extending the oscillator's low-temperature range from −20 °C to −40 °C. Fabricated in a 65 nm CMOS process, the oscillator consumes a mere 8.1 nW at a 0.4 V supply voltage. At room temperature, it operates at 4.22 kHz with an energy efficiency of 1.92 nW/kHz. The oscillator demonstrates a temperature coefficient (TC) of 114.01 ppm/°C within the range of −40 °C to 85 °C, rendering it a prospective choice for low-power Internet of Things (IoT) applications. [ABSTRACT FROM AUTHOR]

This paper introduces a discrete-time delta-sigma ADC for the Internet of Things (IoT) applications. It utilizes second-order 4-bit successive approximation register (SAR) quantizer architecture based on the oversampling technique to ensure a sufficiently high SQNR. Additionally, dynamic weighted averaging (DWA) technique is employed to achieve good feedback CDAC linearity. System-level analysis and circuit implementation analysis are introduced in detail. The implemented prototype of this architecture is manufactured using a 180 nm CMOS process. The proposed ADC, operating at a supply voltage of 1.8 V and a sampling frequency of 2.5 MHz, including biasing circuitry, consumes a total power of 1.3 mW. This ADC achieves a DR of 102.6 dB, SNR of 101.5 dB, and SNDR of 98.6 dB within a 10 kHz bandwidth. As a result, the Schreier figure-of-merits (FoM) for SNR, SNDR and DR is 167.46 dB, 170.36 dB, and 171.46 dB. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs. [ABSTRACT FROM AUTHOR]

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency • The IoT is now playing a vital role in the medical field's approach to providing people. • This paper presents DKCNN-AK architecture for disease prediction. • The DKCNN-AK architecture is used for both disease prediction and severity estimation. • The GSO optimized Fuzzy TOPSIS algorithm is used. [ABSTRACT FROM AUTHOR]