Internet Of Things Solutions Research Articles

Ultra-Low Power IoT Solution based on WSSN for Monitoring Infrastructures

In recent years, one of the emerging technologies that have had big impact on the field of research is Wireless Smart Sensors Networks (WSSN) given its diversity of features and applications. The accelerating growth in IoT for data communication, for sensing and monitoring applications (ranging from biomedical to environmental targets) has originated a high demand not only for low-power autonomous devices but also for ultra- low cost combining single chip radio with baseband signal and digital data processing [1]. Target applications, such as Smart Grids, Cyber-Physical Systems, Body Area Networks, Internet of Things (IoT) and high-density environmental sensing networks, will be massively available if it is removed the requirement of explicit human action for energy recharge. A third concern for the deployment of large numbers wireless sensor networks is the cost of each individual remote device.

Support Vector Regression for Bluetooth Ranging in Multipath Environments

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth Low Energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multi-Carrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas Multiple Signal Classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments including indoor, outdoor, and non-line-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.

PENGARUH IOT TERHADAP PENINGKATAN PENJUALAN BERBASIS SYARIAH TERHADAP PRODUK KERUK IKAN GURIH CAP IKAN TENGGIRI

The Internet of Things (IoT) has become a rapidly growing technology trend in recent years. IoT enables connectivity between physical devices and digital systems, which can be used for various purposes, including increasing sales. In this study, we investigated the impact of IoT on increasing sharia-based sales of the Cap Ikan Tenggiri savory fish cracker product. This study used a quantitative method with a survey approach. Data were collected through questionnaires distributed to customers of the Cap Ikan Tenggiri savory fish cracker product. The results of the study showed that IoT has a positive impact on increasing sharia-based sales of the Cap Ikan Tenggiri savory fish cracker product. IoT can increase sharia-based sales through several ways, namely: (1) Increase customer transparency and trust: IoT can provide customers with more transparent information about the product, production process, and delivery. This can increase customer trust and encourage them to buy the product. (2) Increase marketing efficiency and effectiveness: IoT can be used to target customers more accurately and effectively. This can increase marketing efficiency and effectiveness, which can lead to increased sales. (3) Increase customer satisfaction: IoT can be used to collect customer feedback in real time. This feedback can be used to improve product and service quality, which can increase customer satisfaction. Based on the results of this study, it is recommended that the Cap Ikan Tenggiri savory fish cracker company can utilize IoT to increase sharia-based sales. The company can implement IoT solutions to improve customer transparency and trust, marketing efficiency and effectiveness, and customer satisfaction.

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.

A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation

The energy consumption of major equipment in residential and industrial facilities can be minimized through a variety of cost-effective energy-saving measures. Most saving strategies are economically viable where several algorithms can be employed to reduce energy consumption to reduce costs to a considerable extent. Machine learning (ML) is one of these techniques. A review of recent research efforts concerning the application of ML strategies to energy conservation and management problems is presented in this study. In addition, ML approaches and strategies for energy-saving problems, management, technologies, and control methods have been discussed. A comprehensive review of all available publications is also used to make observations about past considerations. As a result, it has been concluded that ML is capable of solving a wide range of decision and management problems within a short period of time with minimal energy consumption. In addition, ML perspectives have been viewed from the perspective of emerging communication technologies, instruments, and cyber-physical systems (CPSs), along with the advancement of ultra-durable and energy-efficient Internet-of-Things (IoT) based communication sensors technology. Moreover, a comprehensive review of recent developments in ML algorithms is also included, including safe reinforcement learning (RL), Deep RL, path integral control for RL, and others not previously. Lastly, critical ML considerations such as emergency and remedial measures, integrity protection, fusion with existing robust controls, and combining preventive and emergent measures have been discussed. The implementation of recently applied ML, RL, and IoT strategies for energy management, conservation, and resilient operation is clarified in this paper. The proposed review highlights the advantages and drawbacks of the recent energy conservation strategies. Finally, the perspective solutions have been clarified to cope with the world direction for zero energy buildings.

Разработка архитектурного решения программного обеспечения для устройств интернет-вещей

The article presents the development of an architectural software solution for Internet of Things (IoT) devices that implements the functionality of an automatic medical drug dispenser, based on the ESP32 hardware platform and utilizing the capabilities of the existing real-time operating systems (RTOS). The software architecture for IoT devices was designed with scalability and fault tolerance in mind. All components of the system interact with each other through asynchronous callback functions, which provides flexibility and extensibility to the architecture. Testing for system fault tolerance was conducted. The architecture can be implemented and used as the basis for any IoT device, allowing for support of modern security and functionality stacks, by implementing this functionality once in any of the devices. The process of designing the software architecture is presented, including the selection of suitable technologies and libraries. Particular attention was paid to ensuring the safety and reliability of the device, including protection against an unauthorized access and errors in operation. The experimental results show high efficiency and accuracy of the automatic medical drug dispenser based on the developed software. The practical part provides examples of implementing the proposed architecture in the C and C++ languages, with examples and basic interaction diagrams between the components. The rxcpp library was also used in writing the C++ implementation, which made it easier to write the code base for interacting with the operating system resources and reusing the multithreaded interaction with the system.

Simulation Studies of Link Group in Elastic Optical Networks Used in Internet of Things Solutions

Abstract The elastic optical network (EON) solution is the most flexible in terms of allocation of resources for various traffic streams transported in core part (edge-cloud) of Internet of Things (IoT) networks. This paper presents the results of a study on the determination of the occupancy distribution and the loss probability for particular traffic flows offered in EONs supporting IoT systems. The purpose of the research was to determine the impact of: the number of links making up a group, the numbers of frequency slot units (FSUs) in individual EON link, the traffic intensity of individual traffic flows, the capacity of the EON link (number of FSUs) and the number of FSUs requested by different traffic streams. The developed simulation program of link group of EON allows the calculation of the values of the occupancy distribution and loss probability. The results obtained from the simulation are presented on the graphs in the form of points with confidence intervals.

RIS-Assisted Ambient Backscatter Communication for SAGIN IoT

The space, air and ground integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS) assisted AmBC system. In the single-BD AmBC scenario, we firstly give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem, and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

Unveiling the Internet of Things (IoT) Applications in Aquaculture: A Survey and Prototype Design with ThingSpeak Analytics

The study examines the potential impact of IoT in aquaculture, and its role in enhancing water quality monitoring as well as the disease prevention. It highlights the transformative power of IoT technology in providing real-time data on water parameters and enabling proactive measures against diseases. The study emphasizes the significance of adopting IoT solutions to optimize water conditions, mitigate disease risks, and enhance fish health. It also explores recent advancements, key challenges, and future directions in IoT applications for aquaculture, including water quality monitoring, feed automation systems, environmental control systems, fish tracking and monitoring systems, remote monitoring and control systems, smart harvesting systems, and disease detection and prevention systems. Based on a comprehensive literature survey, this paper introduces a research proposal focusing on water quality monitoring and disease prevention in fish. The progress thus far encompasses the selection of hardware components, sensor testing, and ongoing activities in programming and debugging.

An energy-efficient and reliable data gathering infrastructure using the internet of things and smart grids

Introduction: The Internet of Things (IoT) and Smart Grids (SGs) growth results from the advancement of computer hardware and ubiquitous computation for energy-efficient and reliable data gathering. Background: The limitations of the present cloud computing framework in energy systems remain unresolved, such as fully recognizing the requisites of high data usage with low latency which is discussed for a cloud computing scheme in IoT-based smart power stations. Problem Statement: IoT growth results from the advancement of computer hardware and ubiquitous computation. Among the various IoT solutions, SGs stand out because they combine several embedded smart techniques to increase the security and dependability of electricity grids. Methodology: A Particle Swarm Optimization based energy efficient integration of Smart Grid (PSO-SG) is designed using IoT. Modern technology establishes a novel hardware and software architecture and integrates cloud services into the cloud-based electricity network. Findings: As a result, a sizable amount of data produced by the electricity network will be examined, handled, and saved at the network. IoT-based power systems will enable the interconnection and administration of large endpoint devices, offer real-time evaluation and treatment of vast data, and promote the modernization of the power grid with the help of the cloud computing model.

Blockchain-Empowered Security Enhancement IoT Framework in Building Management System

Centralized architectures, like the cloud model, have their advantages, but they also come with drawbacks, such as higher upfront costs, longer deployment times, and a higher probability of catastrophic failure. Building Management Systems (BMS) is an application that can adopt Internet of Things (IoT) designs and services. However, implementing IoT in a highly modular environment with various moving parts and interdependencies between stakeholders can create security issues. Therefore, this paper proposes a system design using Blockchain technology as a means to protect and control the system, which includes the integration of IoT and BMS technologies. This paper has also included broad discussion on current Blockchain based IoT solution and its IoT limitations in Building Management Systems.

The Integration of Triboelectric Nanogenerators and Supercapacitors: The Key Role of Cellular Materials.

The growing demand for sustainable and efficient energy harvesting and storage technologies has spurred interest in the integration of triboelectric nanogenerators (TENGs) with supercapacitors (SCs). This combination offers a promising solution for powering Internet of Things (IoT) devices and other low-power applications by utilizing ambient mechanical energy. Cellular materials, featuring unique structural characteristics such as high surface-to-volume ratios, mechanical compliance, and customizable properties, have emerged as essential components in this integration, enabling the improved performance and efficiency of TENG-SC systems. In this paper, we discuss the key role of cellular materials in enhancing TENG-SC systems' performance through their influence on contact area, mechanical compliance, weight, and energy absorption. We highlight the benefits of cellular materials, including increased charge generation, optimized energy conversion efficiency, and adaptability to various mechanical sources. Furthermore, we explore the potential for lightweight, low-cost, and customizable cellular materials to expand the applicability of TENG-SC systems in wearable and portable devices. Finally, we examine the dual effect of cellular materials' damping and energy absorption properties, emphasizing their potential to protect TENGs from damage and increase overall system efficiency. This comprehensive overview of the role of cellular materials in the integration of TENG-SC aims to provide insights into the development of next-generation sustainable energy harvesting and storage solutions for IoT and other low-power applications.

Ivycide: Smart Intrusion Detection System Against E-IoT Driver Threats

The rise of Internet of Things (IoT) devices has led to the proliferation of smart environments worldwide. Although commodity IoT devices are employed by ordinary end-users, complex environments such as smart buildings, government, or private offices, or conference rooms require customized and highly reliable IoT solutions. Enterprise Internet of Things (E-IoT) connect such environments to the Internet and are professionally managed solutions usually offered by dedicated vendors As E-IoT systems require specialized training, closedsource software, and proprietary equipment to deploy. In effect, E-IoT systems present an unprecedented, under-researched, and unexplored threat vector for an attacker. In this work, we focus on E-IoT drivers, software modules used to integrate devices into E-IoT systems, as an attack mechanism. We first present PoisonIvy, a series of generalized proof-of-concept attacks used to demonstrate that an attacker can use a malicious driver to perform denial-of-service attacks, gain remote control, and abuse E-IoT system resources. To defend against E-IoT driverbased threats, we introduce IVYCIDE, a novel intrusion detection system used to detect unexpected E-IoT network traffic from an E-IoT system. IVYCIDE operates as a passive monitoring system within an E-IoT system using machine learning and signature-based classification to detect POISONIVY attacks. We evaluated the performance of IVYCIDE in a realistic E-IoT deployment. Our detailed evaluation results show that IVYCIDE achieves an average accuracy of 97% in classifying the type of POISONIVY attack and operates without modifications or operational overhead to existing E-IoT systems.

More than two decades of research on IoT in agriculture: a systematic literature review

PurposeAgriculture is one sector where the Internet of things (IoT) is expected to make a major impact. Yet, its adoption in the sector falls behind expectations. The purpose of this paper is to present the state-of-the-art of IoT in agriculture and investigate its slow adoption in the sector.Design/methodology/approachThe authors have undertaken a systematic review and a synthesis of 1355 relevant publications over the last decade.FindingsThis literature review reveals that the “big three” barriers for the overall sector are cost, skills and standardization. The lack of connectivity and data governance are two key reasons why most of the proposed IoT solutions are standalone systems of limited scope, while the majority of commercial IoT efforts focus on practices in the protected indoor environment. Lastly, the analysis of past research along the five layers of the IoT system architecture reveals limited attention to barriers and solutions at the business layer, which represents a research opportunity for information systems scholars.Research limitations/implicationsIt is possible that some of relevant publications were missed in the literature search, although the search queries were kept as broad as possible to avoid the exclusion of any relevant work. Any publication written in any other language other than English was excluded from the review. Given the geographical distribution of the reviewed English publications (see section 4.1), it is highly likely that important works written by Chinese and European scholars in their native language were overlooked.Practical implicationsThis study provides practical insights into the technical and organisational challenges on the ground. It is the hope that this literature review lays the groundwork for IS researchers who are well positioned to investigate technology adoption challenges in the relatively understudied agriculture sector.Originality/valueTo the best of the authors’ knowledge, this is the first comprehensive review of adoption barriers and solutions across all five layers of the IoT system architecture.

Bee colony remote monitoring based on IoT using ESP-NOW protocol.

Information and communication technologies, specifically the Internet of Things (IoT), have been widely used in many agricultural practices, including beekeeping, where the adoption of advanced technologies has an increasing trend. Implementation of precision apiculture methods into beekeeping practice depends on availability and cost-effectiveness of honey bee colony monitoring systems. This study presents a developed bee colony monitoring system based on the IoT concept and using ESP8266 and ESP32 microchips. The monitoring system uses the ESP-NOW protocol for data exchange within the apiary and a GSM (Global System for Mobile communication)/GPRS (General packet radio service) external interface for packet-based communication with a remote server on the Internet. The local sensor network was constructed in a star type logical topology with one central node. The use of ESP-NOW protocol as a communication technology added an advantage of longer communication distance between measurement nodes in comparison to a previously used Wi-Fi based approach and faster data exchange. Within the study, five monitoring devices were used for real-time bee colony monitoring in Latvia. The bee colony monitoring took place from 01.06.2022 till 31.08.2022. Within this study, the distance between ESP-NOW enabled devices and power consumption of the monitoring and main nodes were evaluated as well. As a result, it was concluded that the ESP-NOW protocol is well suited for the IoT solution development for honeybee colony monitoring. It reduces the time needed to transmit data between nodes (over a large enough distance), therefore ensuring that the measurement nodes operate in an even lower power consumption mode.

Determining Real-Time Communication Feasibility in IoT Systems Supported by LoRaWAN.

LoRaWAN is a long range and low power protocol devised for connecting devices under the Internet of Things (IoT) paradigm. This protocol was not conceived to support real-time message delivery; therefore, it is not always feasible using it to support IoT solutions involving large wireless sensors networks and time constraint messaging, e.g., in early warning systems for natural hazards, remote monitoring of industrial machinery or autonomous control of transportation systems. This paper presents a model that provides certainty, at the design time of IoT systems, about the real-time communication capability of their supporting network. It allows solution designers: (1) to decide if developing or not a real-time IoT solution based on the feasibility of its communication infrastructure, and (2) to improve the communication infrastructure to try making real-time communication feasible using LoRaWAN.

DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

Multihop Task Routing in UAV-Assisted Mobile-Edge Computing IoT Networks With Intelligent Reflective Surfaces

The cooperation between Unmanned Aerial Vehicles (UAVs) and ground Mobile Edge Computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this paper, we propose a multi-hop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing Intelligent Reflective Surfaces (IRSs) to further improve tasks offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no UAV-UAV relaying scheme, especially when IRSs are deployed. Furthermore, a Convolutional Neural Network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.

Backscatter‐assisted Non‐orthogonal multiple access network for next generation communication

AbstractNon‐orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy‐limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed‐form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future.

Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique.

In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.