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Abstract In the field of technology management, methods have been developed to detect technologies that are important in industry by analyzing massive numbers of documents. Such methods have been applied to the field of the Internet of Things as well to investigate technological trends. The Internet of Things consists of several conventional concepts, such as radio frequency identification, near field communication, and sensor networks. However, no research compares these technologies by analyzing massive quantities of papers and patents. Thus, in this study, we explored the research areas of technologies related to the Internet of Things, for which there are opportunities for commercialization in the near future. We also discuss potential applications of these technologies in diverse systems. Highlights • Extracting commercialization opportunities of the Internet of Things • Measuring text similarity between papers and patents • Results were good agreement with public government document contains IoT policy. • Extracted important technological options even experts did not discuss. [ABSTRACT FROM AUTHOR]

Abstract Wireless chemical sensors are increasingly finding use as analytical devices in healthcare diagnostics, wearable sensing (sweat analysis) and in the rapidly emerging area of the Sensor Internet of Things (SIoT). In such wireless scenarios, the application of fluorescence-based sensors is lagging behind other types of transduction mechanisms. In this work a new low-cost and highly portable wireless fluorimeter for optical chemical fluorescence intensity measurements is presented (bill-of-materials approximately €20). The fluorimeter is programmed and communicates wirelessly with mobile devices or personal computers by radio-frequency identification (RFID) or near-field communication (NFC). This novel mobile analytical system is highly adaptable for use with different fluorescent sensor chemistries and analytes. The fluorimeter has been evaluated in the laboratory in two sets of proof-of-concept experiments: firstly fluorimetry in solution, where the fluorescence intensities of fluorescein were recorded, and secondly chloride sensing via a paper-immobilised quinine sulphate fluorescent indicator. The latter represents the first demonstration of wireless (radio-frequency) fluorescence-based chloride sensing. Satisfactory analytical performance was achieved in the entire range of sweat chloride concentrations. The wireless fluorimeter system presented here could make a significant contribution to several emerging areas of mobile chemical sensor research, including wearable sensors for healthcare and sport, mobile point-of-sample diagnostics as well as more generally for the Sensor Internet of Things. [ABSTRACT FROM AUTHOR]

Highlights: [•] The impact of digital technologies on writing and reading is investigated in Finland. [•] The positive affordances of writing on paper are similar to those found in Italy. [•] Students consider important to write a lot, quickly and instantly without losing time. [•] A keyboard is a point of reference for all comparisons in students’ responses. [ABSTRACT FROM AUTHOR]

Self-powered devices are the need of the hour for future technologies and next-generation electronics that require both smartness and sustainability. Here, we have presented an ultra-thin (~ 0.18 mm) self-powered paper-based prototype as a touch-interactive electronic tag for next-generation Internet of Things (IoT) enabling smart security applications. A touch-interactive power paper (TiPP) was developed using in-situ polymerization followed by painting an electrode layer of graphite/silver onto it. Thus, a simple piece of paper was used for energy harvesting without having any physical separation from the electrode of the system. It instantaneously generated an electrical signal of 0.91 W m−2 due to a mechano-responsive charge transfer mechanism. Apart from using conventional electrode materials, graphite pencils were also utilized towards a more simple, environmentally friendly and cost-effective approach. Further, different arrays of TiPP have been designed to create a unique coding system (high/low signal) that can simultaneously enable self-powered sensing and an identification system. This is exhibited by a rapid but simple signal processing method used in several applications like R-G-B color codes, personal ID cards and product identification tags. A straightforward signal processing circuit that includes an effective simulation, is demonstrated to validate the working principle of such self-powered security identification tags. [Display omitted] • Polypyrrole functionalized paper used as active mechanical energy harvester. • Working electrode without physical separation gives a thin device structure. • Prototyped Security tags are interfaced with wireless communication system. • Working principle of electronic logics in the system detailed with simulation. • Prototypes are designed with green and low-cost materials. [ABSTRACT FROM AUTHOR]

Highlights • More than 220 papers are reviewed to provide insights about waste management in smart cities. • The current status of IoT-enabled waste management practices is reviewed. • A conceptual framework is offered for monitoring of product lifecycle data. • The need to connect waste management to the whole product lifecycle is discussed. • The importance of new business models on improving waste recovery is highlighted. Abstract The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product life-cycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed. [ABSTRACT FROM AUTHOR]

The Internet of Things is evolving in terms of technology and applications. IoT networks are made up of a lot of smart devices having networking capabilities, also known as endpoints. The battery life of these endpoints is often limited, which has an effect on how long IoT applications can run in the real world. The Internet's current state was developed using a variety of technologies. IoT technology relies heavily on WSNs, which enable the fusion of diverse systems, data, and applications. The findings demonstrate that the suggested structural design can successfully lower energy usage and increase the WSN's course of life. We will outline the causes of energy waste in WSNs in this paper, along with various methods for reducing it. [ABSTRACT FROM AUTHOR]

The present work deals with a frugal paper-based microbial fuel cell (PMFC) that generates an average power of 25 μW per device using Shewanella putrefaciens. Bacteria colonized on the paper matrix can be activated by readily available nutrient sources leading to instant power generation. Current study translates this technology to energize a wireless Internet of Things (IoT) sensor. A well-designed stacking of multiple cells is adopted to boost the power output. The power profile characteristics of three 10-unit PMFC configurations namely series (C1), parallel (C2), and combination of both (C3) are investigated. Attempts to directly charge a supercapacitor using serially-connected PMFC stack results in voltage reversal (VR). Hence, to harvest this microbial energy prudently, a customized power management system consisting of integrated Maximum Power Point Tracking (MPPT) feature is deployed. This helps in interfacing the PMFCs to capture charge, boost the harvested voltage and to prevent voltage reversal (VR). Power harvested from PMFCs is stored into a supercapacitor in order to drive the wireless IoT sensor module, which in turn measures temperature and communicates the data to a smart phone. The current endeavours can pave the way for "bacteria powered IoT devices" by unlocking the potential of microbes through PMFCs. Image 1 • PMFCs serve as instant power generation sources. • Voltage reversal was observed in serially connected PMFC stacks. • A tailored energy harvesting scheme was proposed for PMFCs. • An IoT temperature sensor node was powered by PMFC stack. [ABSTRACT FROM AUTHOR]

This paper explores the use of Internet of Things (IoT) and smart bin technologies to automate the recycling waste collection process and ensure the reliability of the entire system through the incorporation of security measures and blockchain technology. With the increasing global concern for waste management and sustainable practices, the integration of IoT and smart bins brings forth a multitude of benefits. These include heightened efficiencies, reduced costs, and improved accuracy in waste collection. By integrating IoT sensors into traditional waste bins, these smart bins can gather data regarding the fill levels of waste. This enables waste collectors to optimize their routes and collection schedules, resulting in a more streamlined process. The collected data is securely transmitted to the relevant stakeholders and it is recorded on a blockchain-based platform to enhance transparency and accountability. The incorporation of security measures ensures the privacy and integrity of the collected data throughout the entire data transmission process. This paper emphasizes the immense potential of utilizing IoT and smart bin technologies to revolutionize waste collection practices, thereby contributing to a more efficient and sustainable recycling ecosystem. [ABSTRACT FROM AUTHOR]

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments. [ABSTRACT FROM AUTHOR]

The progression in quantum computing and the rapid development of quantum computation hardware has raised expectations for its application to commercially relevant use cases in the future. However, the need for high-level quantum programming abstractions and targeted use cases paired with vertical applications, which can directly benefit from quantum computing, remains an open challenge. This paper presents our vision for a decentralized architecture for swarm based IoT systems that leverages a high-level continuous development and integration quantum programming suite to support edge processing capabilities for different use cases across the edge-fog-cloud continuum. The planned Quantum DevKit provides the necessary abstractions and low-level backend interfaces for quantum computing infrastructure, enabling edge computation using quantum processing, with extensions to efficient management of Service Level Agreements (SLAs). The paper focuses on the presentation of the development kit and its coupling swarm-based architecture that automates the orchestration of the cloud-to-edge continuum, showcasing the potential of quantum technology in edge processing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an innovative technology used in manufacturing, environmental monitoring, home automation, human health applications, and other fields. IoT devices, however, are susceptible to various security risks. Cyber-attacks on IoT systems can halt the entire system, resulting in data loss. Intrusion Detection Systems (IDS) can quickly identify malicious activity that could have compromised the network's performance. Effective IDS and Intrusion Prevention System (IPS) are necessary to protect IoT systems. Blockchain technology presents another innovative method to strengthen security systems against the latest attacks. This paper focuses on conducting a Systematic Literature Review (SLR) on Blockchain-based Intrusion Detection/Prevention Systems in IoT Networks. We reviewed several relevant blockchain-based IDS and IPS proposed for IoT networks and their mechanisms. The most recent research articles, published between 2017 and 2022, were selected from several databases. Consequently, a comprehensive review of these selected papers sheds light on their techniques, advantages, and drawbacks. [ABSTRACT FROM AUTHOR]

By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented. [ABSTRACT FROM AUTHOR]

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future. [ABSTRACT FROM AUTHOR]

· Infection prevention and control (IPC) strategies innovation is key to improve pandemic preparedness and response. · Current hospital IPC practices do not rely on automation and robotic technology. · High need to raise the awareness of IPC practitioners on such technologies. · High need to implement contextualized alternatives for lower income countries. Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The search strategy returned 1520 citations and 17 papers were included. This review identified 3 main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (ie, air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of healthcare workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries. [ABSTRACT FROM AUTHOR]

This paper investigates three substitution box (S-box) designs for lightweight ciphers. The proposed designs involve functional decomposition, which is preferable as compared to the look-up tables or logic gates-based S-box designs due to their advantages, as outlined in this paper. The aim is to reduce the number of literals at the input of the S-box to achieve resource reduction in the overall circuit design of the encryption algorithms. The proposed S-box architectures provide optimized resource mapping for specific target hardware, and high throughput along with lesser energy consumption compared to state-of-the-art designs. The proposed S-box designs are applied to the substitution layers of symmetric lightweight block ciphers with a standard security level. In order to be fair in comparison, the overall cipher designs are targeted for similar FPGA-based implementation as used in existing works, while the architectures of the individual 4-bit S-boxes are evaluated using SAED90 nm standard cell libraries. The performance of the proposed S-box designs is observed to be superior at RFID and ISM frequency ranges which can be deployed in applications constrained to low resources as well as demanding high performance and medium security. • Three S-box architectures are proposed using functional decomposition techniques. • The proposed designs are applied to the substitution layer of each cipher i.e., GIFT, Midori, PRESENT and RECTANGLE. • The throughput and energy of the architectures are evaluated for RFID and ISM band frequencies to manifest the utility of lightweight ciphers in IoT applications. [ABSTRACT FROM AUTHOR]

The main concept of a smart city is to join manual items with electronics, software, sensors, and network connectivity for data contact via Internet of Things (IoT) gadgets. IoT improves production and efficiency by cleverly utilizing remote management, but security as well as privacy risk, rises. Cyber threats are progressing every day, affecting inadequate actions of safety and privacy. In a smart city, the exposed action of an individual or business can keep the whole city in danger. Owing to the trust of numerous modules of smart cities on data and communication techniques, cybersecurity tasks like information leakage and mischievous cyber-attacks. Malicious Activity Detection in the area of cybersecurity for smart cities is dominant in protecting the consistent organization of urban atmospheres. Leveraging innovative machine learning (ML) and deep learning (DL) approaches, mainly anomaly detection methods permits the identification of abnormal patterns and probable threats within the massive streams of data produced by smart city methods. So, this paper designs a Fusion of Optimization Algorithms and DL for Enhanced Malicious Activity Recognition (FOADL-EMAR) technique for cybersecurity in smart cities. The purpose of the FOADL-EMAR technique is to recognize the presence of malicious activities using optimal DL techniques in the environment of a smart city. To accomplish this, the FOADL-EMAR technique uses a Z-score data normalization model to measure the input dataset into a useful format. In addition, the FOADL-EMAR technique uses an improved sand cat swarm optimization (ISCSO) algorithm that can be applied to select an optimal subset of features. An ensemble of three DL techniques is used namely long short-term memory (LSTM), deep neural networks (DNNs), and extreme learning machine (ELM) for malicious activity detection. The Harris Hawks Optimization (HHO) approach has been applied to improve the detection results of the ensemble models. The simulation analysis of the FOADL-EMAR methodology occurs utilizing a benchmark database. The experimentation values demonstrate the improved detection outcomes of the FOADL-EMAR algorithm over other existing methods in terms of different measures. [ABSTRACT FROM AUTHOR]

Global water use has grown steadily in the past decade, and climate change will further worsen the levels of water stress. Another global issue lies in the ageing of water infrastructures, registering conspicuous water losses (40% in the Italian scenario). A great opportunity is represented by the exploitation of new technologies relating to IoT, namely smart meters, which can enable the prediction of water consumption, detect leaks, and customise the service. This paper assesses the economic and environmental impact of smart meters adoption for water consumption, by taking the Italian scenario as a reference context, through the development of an analytical model which considers the benefits and costs associated with their adoption, with the ultimate objective of evaluating the convenience of the investment. Data to feed the model was collected through secondary sources, literature reviews, and interviews with utility companies' employees. Results provide corroborating evidence of the positive impact of smart water meter adoption, both in economic and environmental terms, in particular by increasing the roll-out number, given the higher amount of data available and economies of scale to be exploited. The present study contributes to the academic literature by providing a comprehensive model that considers economic and environmental aspects of smart water adoption, which allows practitioners to have an insightful understanding of the involved variables in such investments. [ABSTRACT FROM AUTHOR]

Enhancing business processes with IoT capabilities enables the collection of data, which can be leveraged to optimize and automate business processes. As a prerequisite for exploiting these benefits in the context of BPM, IoT aspects needs to be explicitly captured in business models. Modeling IoT-driven business processes with BPMN 2.0, however, might be hindered by the ambiguous use of the modeling elements. To tackle this challenge, we have proposed a BPMN 2.0 extension for IoT (BPMNE4IoT) that incorporates specific IoT modeling elements. BPMNE4IoT enables the modeling, execution, monitoring, and recording of IoT-driven business processes. In general, the effectiveness of any BPMN 2.0 extensions depend on its comprehensibility by BPM practitioners. Consequently, user studies are required, which investigate the effects of IoT-specific elements on process model readers. This paper presents the results of such a user study (N=249), which aims to determine whether IoT involvement becomes visually better apparent in our language extension BPMNE4IoT compared to standard BPMN 2.0. In particular, the user study confirms that the involvement of IoT aspects in BPMNE4IoT is more discernible for both experts and non-experts compared to the BPMN 2.0 standard. [ABSTRACT FROM AUTHOR]

Smart farming is a new concept that can be described as applying different kinds of modern technology to make farming more efficient. One such technology is the Internet of Things, an extension of the Internet and other connections into mundane devices. This paper uses dedicated sensors and cloud computing technologies to model a smart farming system that helps farmers keep track of state of the crops and livestock. It also covers implementing and testing an Android tracker app that is supposed to become the first component of the mentioned system, a client application that communicates with the cloud computing services yet to be implemented. The end product provides its users with an easy way of keeping track of the state of their farms, simplifying the farmers' work and enhancing productivity. [ABSTRACT FROM AUTHOR]

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]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications. [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]

The Internet of Things (IoT) devices are distributed randomly in the environment, antennas with multiple switched beams are needed to unlock the full potential of communication systems. This paper presents an intelligent metasurface (IMS) based pattern and beam reconfigurable antenna for indoor as well as outdoor IoT applications including V2X and 5 G Small Cell. The IMS based antenna system consists of a wideband monopole and a programable IMS acting as a reflector. The single–layered metasurface is comprised of 4×4 square ring-shaped unit, where each unit cell can be controlled by a pair of four diodes. Due to the separate control of each unit cell, the shape and size of the reflector can be controlled, resulting in different radiation patterns. The system offers various radiation patterns, starting from omni–directional to broadside, with single or dual beams, along with beam–steering both in elevation and azimuth plane. The antenna system offers a peak gain up to 8.89 dBi with an operational bandwidth of 4.95–7.6 GHz. To validate the design concept, the system is fabricated and tested. A microcontroller is utilized for the switching IMS configuration. The high agreement between simulated and measured results, combined with the low profile, high gain, pattern–beam reconfigurability make the proposed antenna a suitable candidate for the indoor IoT devices along with outdoor application not limited to V2X communication systems as well as for small cell in smart cities. [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]

Cognitive radio network (CRN) is getting growing interest of the researchers due to its wide applicability for spectrum sharing with massive number of active devices in Internet of Things (IoT). In existing schemes, a large part of the spectrum may remain underutilized which was assigned to service providers as primary users (PU). Secondary user (SU) may be the alternative to use the spectrum. The main challenge is that SU keeps on sending the packets in an incremental manner until a free channel is found in real conditions. It results in excessive communication and packet loss. This paper presents an efficient tree-centric approach where a centralized base-station maintains a tree of available channels and intelligently allocate to the SUs as per the real-time availability of channels. It analyzes for the effective usage of these channels by the SUs. It also manages the authenticated SUs to avoid any interference due to the hidden node problem. A number of extensive simulations are performed using network simulator. The existing instantaneous approaches require 10 to 12 attempts to send request for acquiring free channel with an average delay of 315 ms, the Hybrid-P1 and Hybrid-P2 approaches consumed 4 requests each with a delay of 128 ms to 135 ms. Our proposed CDC mechanism achieves a channel access mostly in 1 to 2 requests with an average delay of about 72 ms. Results are extracted from the trace files that prove the dominance of proposed scheme. [ABSTRACT FROM AUTHOR]

Healthcare monitoring systems have improved with the Internet of Things and machine learning prediction models. Traditional batch machine-learning approaches cannot generate an effective model since most data will be continuous and real-time. Real-time medical data processing is challenging since the entire data is unavailable during prediction. Here, a continuous model adaptation based on incremental learning is demanded. The development of such a system can significantly improve patient outcomes and reduce healthcare costs. This paper proposes an Incremental Naive Bayes Learner that can handle concept drifts in data. The algorithm keeps a sliding window of data constantly updated as new data is added. The adaptive window size determines how much data is used to train the model at any given time. After processing each chunk of data, the algorithm calculates the model's accuracy. The system detects a concept drift and dynamically updates the training set if the accuracy drops below a predefined threshold. We conducted a comparative evaluation of state-of-the-art batch and incremental learning algorithms on different medical datasets, demonstrating the impact of incremental learning. The results demonstrate the effectiveness of our approach: the Agrawal dataset achieved the highest accuracy at 66.6%, followed by Dialysis at 61.6%, while Liver and HCC achieved 50% and 58.3% accuracy, respectively. This approach ensures a sustained high level of accuracy in healthcare monitoring systems over time, even amidst concept drift. [ABSTRACT FROM AUTHOR]

With the advent of 5G and the promise of 6G, edge computing is becoming increasingly important. Edge computing involves the proximity of computational resources to the devices and sensors responsible for data generation, reducing delay and improving real time data processing capabilities. EdgeX is an openly available framework designed for edge computing. that provides a platform for building IoT services and applications. In this paper, we evaluate the EdgeX open edge server for use in IoT services. We first deploy a web service application into the edge using a specific port, and then test the EdgeX server to see whether it can be used as a reliable and effective open-source edge server. Our results show that the EdgeX server is capable of supporting IoT services and can be used as an open-source edge server for a variety of applications. We conclude that EdgeX is a valuable resource for developing edge computing solutions, and that the open edge server can significantly contribute to the advancement of forthcoming IoT service innovations. [ABSTRACT FROM AUTHOR]

With the exponential growth in connected smart devices that interchange sensitive, crucial, and personal data over the Internet of Things (IoT)-based cyberspace, IoT becomes undefended to cyber assaults. Intrusion Detection System (IDS) is a vital segment of security tool to detect cyber assaults. However, IDS produce high False Positive Rate (FPR) because IDS improves accuracy at the same time as it increases FPR. Therefore, a more effective cyber assaults detection system is needed. Based on the aforementioned issues, this paper proposes an approach to recognizing cyber assaults in the IoT environment. This approach first performs various data pre-processing steps. Next, important features are optimized using modified Binary Pigeon Inspired Optimizer. After that, optimized feature subset is fed separately to the monolithic classifiers such as k-Nearest Neighbours (kNN), Support Vector Machine (SVM), Decision Tree (DT) and bagging-based ensemble model such as Random Forest (RF) for detection of cyber assaults. In this study, UNSW-NB15 is used to test the success of the system, and related existing methods are compared with these results. Based on the results of the experiment, the proposed system with RF is the most effective in terms of accuracy (99.41%), F1-score (99.33%), detection rate (99.09%), and precision (99.92%) with a minimum FPR (0.03%) for a subset of optimized features (4 independent features out of 42). [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) has created a large attack surface for adversaries to launch destructive cyber attacks. Intrusion Detection Systems (IDS) can be used to identify and monitor anomalies and threats in the IoT system. In this paper, we compare the performance of various machine learning algorithms for building IDS, including decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), Ada Boost, and support vector machine (SVM), using the IoTID20 dataset. This dataset has three target classes, including a binary class for normal or abnormal behavior and classes for categories and sub-categories of the binary class. We select the most relevant features to minimize execution time and improve accuracy. Our results show that the decision tree algorithm generally achieved the highest accuracy at 99.80% with the lowest error rate compared to the other algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a revolutionary paradigm that has gained significant prominence in recent years. It represents the interconnection of everyday objects, devices and machines to the internet, allowing them to collect, exchange and analyze data. As the number of connected objects grows exponentially, energy consumption and data security become essential challenges to ensure a sustainable development of green IoT. Data compression theory can be used as an enticing key to downsize the large amount of circulated information, since wireless communication is the prime energy consuming component in IoT devices. In addition, data encryption algorithms are crucial to protect sensitive information and ensure the integrity and confidentiality of IoT systems. Therefore, combining compression and encryption can lead to improved overall IoT system performance in terms of both power cost and data privacy. In this paper, a lightweight and secure compression approach is introduced to efficiently manage the energy expenditure and provide end-to-end security for energy constrained devices. This proposed algorithm combines the performance of LTC compression and AES encryption to securely encode collected data in IoT devices. The energy model analysis has shown that the suggested method is effective in terms of processing energy, reduces the volume of transmitted data and guarantees end-to-end information privacy. [ABSTRACT FROM AUTHOR]

The proliferation of the Internet of Things (IoT) has enriched modern life, but their increasing ubiquity raises concerns about environmental impact. To address this, comprehensive Life Cycle Assessments (LCAs) of IoT products, which have historically been manual, costly, and time-consuming, are vital. Noting the recurring nature of core components in IoT devices, such as CPUs and sensors, we propose to use graphs and machine learning to simplify and scale LCA estimations for IoT products. This paper introduces a novel approach to representing IoT devices as graphs with specific component characteristics and interconnections. Applied to a preliminary dataset of smart home IoT devices, the methodology unveils insights into structural similarities using a composite kernel approach. This initial phase lays the groundwork for the machine learning component. The integration of machine learning planned as part of ongoing research, provides a pathway for efficient and timely ecological assessments, ensuring that the rapid growth of IoT aligns with sustainable practices. [ABSTRACT FROM AUTHOR]

More and more sensor nodes which are deployed in smart cities for data collection and analysis form the ubiquitous urban Internet of Things. Energy saving and rapid data collection play a key role in environmental monitoring, military search, medical care, and general city management in smart cities. Therefore, it is a very meaningful and challenging work to design an energy efficient and fast routing protocol to collect data. Although the widely used duty cycle mechanism can effectively save energy, it brings greater delay to routing. Using wake-up radio enable nodes can effectively reduce the delay, but it will bring a large network deployment cost. So how to reduce the deployment cost and quickly route data is a challenge issue for sensor-based system. In this paper, Pre-Awake for Wake-up Radio Enable Sensor based system (PA-WuRES) routing protocol is proposed to reduce delay and deployment cost. The main contribution of our work are as follows: (a) A novel PA-WuRES protocol is proposed which requires much less WuR(WuR,Wake-up Radio) enable nodes, so its deployment cost is lower. (b) Using WuR enable nodes will increase a certain amount of energy consumption. Therefore, this paper proposes a differentiated service data routing mechanism to minimize energy consumption. (c) Full theoretical analysis and experimental results show that the proposed PA-WuRES protocol reduces the deployed WuR enable nodes by 80%, but sensitive-data routing delay is reduced by 48.4%. [ABSTRACT FROM AUTHOR]

The authors of the paper have recently been involved in various projects related to the Internet of Things (IoT) and its practical application in several fields. These areas include Industry 4.0, smart cities, and healthcare - with a focus on behavior change. The projects used up to date technologies such as IoT, Machine Learning, Big Data, System of Systems (SoS) and Cloud Computing among others. The authors have specifically focused on specific IoT and SoS architectures, aiming to support and illustrate a variety of use cases. In this context, SoS expands the use of localized cloud, improving integration and communication technologies. It is essential that these SoS provide reliable quality of service (QoS), which includes management and monitoring of various QoS attributes. The performance of smart applications is highly dependent on the quality of the data they receive, requiring robust communication and sufficient bandwidth. To address the challenge of promoting interconnectivity and interoperability between different smart devices are protocols such as MQTT, CoAP, HTTP and WebSockets. They facilitate network-based device communication, ensuring data sharing and coordination. The paper explores a variety of use cases and these in relation to specific IoT and SoS architectures. The objective is to highlight the transformative impact these technologies have on global performance. [ABSTRACT FROM AUTHOR]

Multi-criteria methods are commonly used in facility location problem to evaluate different facility locations in the presence of a set of criteria in order to select the best facility location. The increasing availability of Internet-of-Things (IoT) data, can be a help in gathering the necessary data for the location evaluation. The paper proposes a New Revised Group DEMATEL (NRG-DEMATEL) multi-criteria method, aiming to analyse cause-and-effect relationships among a set of criteria. This method provides general conditions which imply the convergence to the null matrix of the sequence of powers of the initial relation matrix, hence the total influence matrix can be computed. DEMATEL is infeasible when the sequence of powers of the initial relation matrix does not converge to the null matrix. The NRG-DEMATEL can be applicable to all situations feasible or infeasible. For the cases that are feasible, our method leads to the same result as the original method DEMATEL. The causality diagram and the digraph, that can visualize the causal relationships of a set of criteria, are built with the NRG-DEMATEL method. In order to reduce the complexity of the digraph, a threshold value is set to filter out negligible effects. Two methods for computing the thresholds are considered in this paper: the arithmetic mean method and the arithmetic mean - standard deviation method. An application of the NRG-DEMATEL to hospital facility problem is realized. [ABSTRACT FROM AUTHOR]

Solid waste management is one of the problems that has gained greater prominence in recent years. This topic has been discussed with greater emphasis in conjunction with environmental issues, driven by changes in the most diverse fields provided by the recent post-pandemic scenario. However, due to traditional or inefficient waste management approaches, most trash bins placed in cities can be seen as overflowing. Therefore, a remote monitoring system is needed to alert the level of garbage in bins to the relevant authority for waste clearance. In this scenario, the Internet of Things (IoT) paradigm plays a vital role in improving smart city applications by tracking and managing city processes. This paper implements a solution based on Low-Power Wide-Area Network (LPWAN) and blockchain technologies to provide the required data available for increasing efficiency in solid waste collection. Finally, this paper provides an evaluation of the proposed architecture related to latency and throughput metrics. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) represents an important area that is evolving rapidly in the context of digitalization efforts related to the Construction 4.0 era. This paper deals with the Internet of Things applications within the construction industry in selected phases of the facility's life cycle, namely the construction and operational phases. In particular, relevant examples of its use were collected, discussed and supplemented with several review papers on a given topic. Results of the analysis show that the potential of the Internet of Things usage is wide and varied in connection with the complexity of the construction projects and the long-term service life of the facilities. The concepts of the Internet of Things usage in the above-mentioned life cycle phases are mostly different focusing e.g., on quality, health and safety issues within the case of construction phase or smart concepts and energy management during the operational phase. Furthermore, one common theme was identified for both investigated phases of the life cycle, namely the interconnection of Building Information Modelling and the Internet of Things. It can be expected that the importance of such interconnection will continue to rise as construction projects implemented with the support of the Building Information Modelling become more and more common in the future. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a significant trend in the field of information technology and encourages the implementation of cyber-physical systems, smart connected products, and new business models. Many enterprises struggle to create business value from IoT technology because they have difficulties defining their organizational integration. Model-driven engineering (MDE) is considered an effective technique to address the complexity of IoT application development. Existing approaches focus on requirements from a technical perspective and exhibit a lack of integration with organizational and business model aspects. The paper proposes a modeling approach and a tool to support the development and configuration of IoT solutions in the example of air conditioning and cleanroom technologies (ACT) facilities. The main contributions of this paper are (a) an architecture for the IoT application, (b) a modeling language and tool support for IoT modeling, and (c) the expected practical benefits in the industrial case. [ABSTRACT FROM AUTHOR]

Data aggregation involves the integration of correlated data generated by various wireless sensors and devices in WSN and IoT networks, in order to arrive at meaningful interpretation of the data sensed. It also serves as an efficient mechanism to optimize the resources like node energy, communication bandwidth and computation time, in WSN and IoT networks. The paper presents a brief overview of basic concepts related to WSN & IoT networks, data aggregation, and various optimization parameters used by the data aggregation protocols. A major contribution of the paper is to provide a comprehensive survey of various data aggregation protocols developed to address issues related to network topology, interference, fault-tolerance, mobility and security in WSN and IoT networks. Due to resource constraints, WSN and IoT networks are besieged with several competing requirements like latency, energy efficiency, data accuracy, data freshness, temporal correctness etc. The paper presents an in-depth discussion on these requirements, and the tradeoff strategies to be followed by data aggregation approaches to optimize these requirements. The paper highlights some of the gap areas in existing data aggregation approaches and suggests research solutions to amicably plug these gaps. [ABSTRACT FROM AUTHOR]

• Examines resources allocation issue for minimizing the solar power usage in fog computing-enabled wireless IoTs using NOMA. • A typical case is considered to support several IoT devices for offloading computations together to the similar FN. • As a way of achieving an optimal efficiency-complexity balance, a whale optimization algorithm (WOA) is introduced. • The suggested layout is more efficient than current methods in terms of throughput, latency, blackout likelihood, and power usage. Fog computing is used to handle applications that require a lot of calculations at the network's edges for wireless Internet of Things (IoT) networks that have source-limited equipment. In addition to reducing computational time and fronthaul traffic information, fog computing still poses challenges in allocating the existing computing and communication sources, while maintaining the strict quality of service (QoS) standards. The current study examines the issue regarding task management and heterogeneous resource allocation among equipment for wireless IoT networks. As a means of transferring the information to the fog computing nodes (FNs), IoT devices collecting large amounts of information require appropriate offloading decisions. This paper investigates how non-orthogonal multiple access (NOMA) can be deployed for IoT networks in order to enable many devices for transmitting information of the load and the market to a similar FN simultaneously, in a similar frequency range, and in the same code domain. Several IoT devices are optimized together based on their resource allocation and transmission power in this paper while meeting the QoS standards. Additionally, the optimization problem can be expressed in the form of a mixed-integer nonlinear programming problem with the goal of minimizing the system's power usage. The problem would be NP-hard so a whale optimization algorithm (WOA) has been proposed for solving. According to simulation outcomes, the suggested architecture performs well in terms of throughput, delays, blackout likelihood, and power usage. [ABSTRACT FROM AUTHOR]

The introduction of IoT technologies in industrial settings is changing the way manufacturing companies perceive their production processes, allowing for more accurate monitoring of production-related parameters. Due to the introduction of IoT technologies and associated enhancements in terms of data acquisition, processing, and availability, it becomes possible for manufacturing companies to tackle complex issues, such as the efficiency of production. In this paper, the development of an Industrial IoT solution targeting the fish canning industrial sector is described, as an approach to identify and monitor raw input material losses as well as water and energy waste, an endemic issue of the sector, promoting an increased efficiency in the production processes. This paper mainly focuses on the description of the implementation of the proposed IoT technologies for monitoring waste and loss, and its deployment in three distinct environments, including at a fish canning manufacturing company. [ABSTRACT FROM AUTHOR]

Due to the vigorous and ever-evolving demands of the IoT field, resource-constrained devices are being deployed in such areas and several new challenges like sequestration and surveillance, have come into the picture. Multiple lightweight designs have been recommended to overcome the security issues for resource-constrained devices. In applications requiring data to be transmitted fast between various devices, loop unrolling and subsequent pipelining architectures are extremely efficient. In this paper, several efficient hardware implementations of Simeck cipher such as loop unrolling architecture and outer round pipelining technique have been proposed in order to achieve high throughput. This paper has also proposed a bit serialized architecture of the Simeck cipher that reduces the area footprint and used in less resource applications. Our Simeck Serial architecture is 29.5 times more efficient than LiCi cipher and occupies only 7% of the area occupied by LiCi cipher. Similarly, our proposed serial architecture occupies only 13% and 25% of total slices occupied by X 192 and XTEA respectively while still performs 184% and 161% more efficiently. All three architectures are evaluated and compared on the basis of throughput, area utilization, and power consumption for their implementations in different FPGA platforms. • In unroll loop architecture of Simeck cipher, all rounds are unrolled which ultimately vastly reduces the latency while providing an optimum trade-off between area footprint and performance. This makes the design fit for applications demanding less latency. • The proposed outer round pipelining architecture is in turn derived from the full loop unrolling architecture where the loop unrolling greatly simplifies the key scheduling and the round function of the Simeck cipher. With the installation of registers in between each round, throughput is significantly improved and the area footprint is also reduced making design relevant for low resource and high-speed applications. • The proposed serial architecture for Simeck cipher utilizes only 17 slices making it extremely area efficient. [ABSTRACT FROM AUTHOR]

Energy price and availability have become an urgent global challenge. Manufacturers need to model and reduce energy consumption and cost. To enable energy smart manufacturing using Industry 4.0, there is a need to develop a framework for energy monitoring using Internet of Things (IoT). In this paper, an energy-monitoring device has been developed using ESP32 as IoT system to monitor energy consumption on the fused deposition modelling machine. The energy consumed is measured and categorised according to its manufacturing process states. This paper proposes an IoT system capable of simultaneously measuring the energy consumed in the machine's components during the printing process into idle state, warm-up state, printing state and cool-down state. This platform technology can be a basis for simultaneous monitoring of multiple machines in a production system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important and a major key component to support human life. Sensors are the sensing devices that play an important role in IoT to detect environmental conditions. The significance of IoT is increasing due to the increase in the things connected to the internet using Internet Protocol version-6 (IPv6). Low-Power and Lossy Network (LLN) Routers used in IoT environment have limited processing, memory, and energy resources and hence it can't use the traditional routing protocols such as Open Shortest Path First (OSPF), Routing Information Protocol (RIP), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing(DSR) etc. However, Routing Protocol for Low power and Lossy Networks (RPL) is one of the prominent protocols which are developed to overcome the above issues but it could be exposed to specific types of attacks such as decreased rank attack, black hole attack, sinkhole attack, selective forwarding attack and version attacks, etc. Hence, it is necessary to develop a mechanism to detect and prevent such attacks. In this paper, efficient machine learning based Intrusion Detection System for Internet of Things is proposed to monitor the network activities against attacks and to detect the intruders more efficiently. This proposed machine learning based detection model performs feature selection and classification based on two new algorithms proposed in this paper called genetic recursive feature selection algorithm and fuzzy k-nearest neighbor classifier and detects the aforementioned attacks with maximum accuracy and minimum false positive rate. This work is also investigated on other performance metrics like precision, recall and F1-score to show the performance comparison between this proposed work with genetic recursive feature selection and fuzzy k-nearest neighbor classification algorithms and the existing works on intrusion detection. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond. [ABSTRACT FROM AUTHOR]

Advanced 5 G and 6 G technologies have accelerated the adoption of the Internet of Things (IoT) and are a priority in providing support for high-speed communication and fast data analysis. One of IoT networks benefits is automated networking, which unfortunately increases the risk of security, integrity, and privacy breaches. Therefore, in this paper, we propose a weighted stacked ensemble model combining deep convolutional generative adversarial and bidirectional long short-term memory networks. The proposed model has been regularized, and hyperparameter tuning has been performed. The tuned model is then evaluated on four publicly available current IoT datasets. The proposed model exhibits significant improvement in standard performance measures for both binary and multiclass classification. Generalization error has been reduced by a rate of 0.005% and to overcome the issue of overfitting, a L2 regularization technique has been deployed. The overall Accuracy of the model on various datasets is 99.99% for BOT-IoT, 99.08% for IoT23, 99.82% for UNSWNB15, and 99.96% for ToN_IoT, respectively, alongside improvements in Precision, Recall, and F1-score. • Identification of research gaps and performed data preprocessing by gradient descent. • The proposed method is an infusion of advanced neural networks. • This ensemble of neural networks has reduced the generalization error and variance. • Hyperparameter tuning and regularization are used for achieving refined results. • The model is compared with the existing literature to establish the novelty. [ABSTRACT FROM AUTHOR]

With the evolution of Industry 4.0, the most advanced technologies have been invented and due to rapid globalisation, supply chains (SC) have become vulnerable to various risks. Risk management performs a key role in operating SC effectively in variety of uncertain circumstances. This paper aims to reveal the adaptability of digital technologies in supply chain networks, identify and analyse the various emerging supply chain risks in the industry 4.0 environment, and develop a framework to mitigate the risk level. A systematic literature review was conducted to identify and analyse the emerging supply chain risks, and Analytical Hierarchy Process (AHP) method was used to develop a conceptual risk mitigation framework. The contribution of this study lies in the taxonomy study, and findings revealed that digitalisation of supply chain design for risk mitigation shed light on future research. Additionally, it focuses on the potential to enhance supply networks' efficiency and responsiveness. [ABSTRACT FROM AUTHOR]