Securing high-value electronic equipment: an internet of things driven approach for camera security

A greenhouse is a covered structured area which protect the plants from extreme weather condition, providing a controlled environment for their growth and cultivation. The innovative Internet of Things (IoT) technology uses a series of sensors connected to a central computer to control the greenhouse environment. Greenhouse sensor systems include elements that monitor and control temperature, humidity, soil moisture, lighting, and external weather conditions. The research aims to design a greenhouse monitoring and control system based on the Internet of Things (IoT). In smart greenhouses, IoT involves sensors, devices, and information and communication infrastructure for real-time monitoring, data collection, and processing to control the environment inside the greenhouse unit. Controlled greenhouses, supported by computer technology, can enhance quality and increase crop yields. This research involves three greenhouses that are independently monitored and controlled through an Internet of Things (IoT) cloud platform. Cloud monitoring facilitates better integration of devices across different geographical locations.

Trust is an invisible behavior of any entity. An entity could be a living being or a cyber-physical system. The Internet of Things (IoT) is a connected network of smart objects or things where trusted relationships are crucial. Trust in an entity can increase or decrease based on different parameters and properties of the specific entity. Trusted relationships can dynamically reach based on contextual data collected over time. The heterogeneous behavior of IoT devices makes trust measurement more difficult. The massive deployment of IoT devices and related innovative IoT applications leads to exploring new trust management frameworks for the IoT paradigm. Emerging IoT applications need to trust entities deployed by third-party providers. Innovative external IoT applications need to be dynamically trusted by the IoT devices and IoT gateways. Dynamic trust achievement is a complex process when an entity is new within the network. In this article, we have defined the trust management for IoT and discussed the need for trusted architecture for dynamic IoT infrastructure, and elaborated the requirements of trust management policies. We have also heightened the need for decentralized architecture for trust management for the Internet of Things (IoT). A new edge-centric multi-agent-based dynamic and decentralized trust management model is proposed and simulated to solve the aforementioned issues. The results of this work are useful for further research in the field of trust management for IoT.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user’s personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users’ privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.

The deployment and application of large-scale Internet of Things require a large number of 13.56-MHz radio-frequency identification (RFID) reader chips with lower cost. The existing RFID reader module needs to integrate multiple chips, such as microcontroller and high-frequency (HF) RFID reader IC on a single printed circuit board (PCB), which has the bottleneck of cost, power consumption, stability, and reliability. In order to adapt to the development trend of high integration, miniaturization, and low-power consumption of RFID reader, this article proposes a fully integrated low-cost RFID reader System-on-a-Chip (SoC), which integrates RF transceiver and analog circuit, baseband protocol processing, microcontroller, memory, and interface circuit into a single chip. The proposed reader IC supports communication protocols, such as ISO/IEC 14443 Type A and Type B, ISO/IEC 15693, and ISO/IEC 7816. The reader can communicate with contact and contactless IC cards. The modulation depth of the transmitter circuit ranges from 1% to 100%, and the maximum transmitting current is 126 mA. The receiver circuit is composed of I/Q clock generation, switched capacitor sampling circuit, variable gain amplifier (VGA) with filter, comparator, and pulse shaping circuit, which has good anti-noise performance. This 13.56-MHz RFID reader SoC is fabricated in a 180-nm CMOS process and is housed in a 64-pin QFP package. The total area is 8.75 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , including 1.47 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for analog and RF circuits.

Deltas are vulnerable to river and seawater flooding due to their low-lying topography and proximity to bodies of water. In the port city of Busan in Korea, the oceanic climate’s heavy rain increases the risks and concerns of subsequent rainwater flooding. The master plan of Eco-Delta City acknowledges these flood risks and includes a number of mitigation strategies. It adopts different types of structural and non-structural mechanisms, including a ground elevation level increase, which takes into account the 200-year flood plain, a wide riparian buffer zone, and low-impact development strategies. The plan’s Intelligent Flood Monitoring System uses innovative IoT (Internet of Things) and digital twin technologies to provide enhanced flood management.

This paper proposes an innovative internet of things (IoT) based communication framework for monitoring microgrid under the condition of packet dropouts in measurements. First of all, the microgrid incorporating the renewable distributed energy resources is represented by a state-space model. The IoT embedded wireless sensor network is adopted to sense the system states. Afterwards, the information is transmitted to the energy management system using the communication network. Finally, the least mean square fourth algorithm is explored for estimating the system states. The effectiveness of the developed approach is verified through numerical simulations.

This paper presents an innovative Internet of Things (IoT)-based smart emergency response system that aims to improve safety and efficiency across vehicles, homes and healthcare settings. The proposed system combines advanced sensors, communication technologies, and intelligent algorithms to provide a comprehensive solution for real-time data monitoring and emergency management. Here, the vehicle device is equipped with temperature, gas, flame, GPS, piezo, and accelerometer sensor and the system can detect accidents, hazardous conditions, precise location automatically notifying emergency services. The home devices incorporate Flame Sensor, humidity sensor, DHT11, MQ-2 gas sensor, MQ-4 Gas Sensor, MQ-135 gas sensor, and intrusion detection to ensure immediate alerts and response to potential security threats. Lastly the health device is equipped with temperature, humidity, stress levels, heart rate, oxygen saturation, and fetal heart rate sensor which can monitor vital signs and detect anomalies, triggering alerts for medical emergencies and facilitating timely intervention. The novelty of the proposed study is it incorporates the three devices, processes the data and automatically notifies the emergency responders for further emergency assistance. Moreover, the system utilizes a cloud-based platform to collect, analyze, and disseminate data, enabling seamless coordination between emergency responders such as ambulance for health and accident emergency, fire brigade for fire emergency and affected individuals. We have also developed a MySQL-PHP-Laravel based live dashboard and emergency response system where both the user and relevant stakeholder will have the access which could mitigate the emergency situation in the quickest amount of time without human intervention. The findings of our study reveals that incorporation of IoT and smart infrastructure offers significant improvement of server response time 3 ms, accuracy > 99% and overall effectiveness of emergency management. This integrated approach not only enhances individual safety but also contributes to broader public safety goals, showcasing the potential of IoT technologies in creating smarter, safer environments.

To tackle the challenge of improving Power Quality (PQ) in modern power grids, we introduce an innovative Internet of Things (IoT)-based Smart Grid (SG) energy surveillance system. Our research is driven by the necessity to enhance power quality and optimize energy management in increasingly complex grids that incorporate renewable energy sources like Solar PV and Wind Generating Systems. Traditional methods for managing power quality often fall short, resulting in inefficiencies and potential disruptions. Our solution features an advanced IoT-based system that utilizes the Adaptive Neuro-Fuzzy Inference System (ANFIS), combining Artificial Neural Networks (ANN) and Fuzzy Logic Systems to enhance power distribution and control. This system uses a Wireless Sensor Network for real-time data collection and analysis, allowing for precise monitoring of electricity usage and improved energy management and cost reduction. Our findings indicate that this innovative approach not only boosts power quality but also significantly enhances the efficiency of renewable energy sources, showing a 20.50% performance increase during the startup phase of Solar PV-Wind Generating Systems. This highlights the system’s potential to advance power quality management and provide substantial benefits in energy regulation and cost efficiency.

The proliferation of the Internet of Things (IoT) has brought unprecedented connectivity, introducing complex trust and reputation management challenges across vast, heterogeneous networks. This paper introduces the DeepTrust framework, a novel approach leveraging deep learning algorithms to assess and manage trust and reputation in IoT environments dynamically. We demonstrate the framework’s superiority in accurately identifying trustworthy and untrustworthy devices through extensive experiments, significantly enhancing IoT security. DeepTrust has demonstrated marked superiority over existing methods, showcasing enhanced accuracy in identifying trustworthy versus untrustworthy devices, thereby significantly bolstering IoT network security. Specifically, our results reveal an improvement in accuracy by 15%, precision by 20%, and recall rates by 18% compared to conventional models, highlighting DeepTrust’s effectiveness in real-time, adaptive trust assessments. There are several avenues for enhancing and expanding the DeepTrust framework. Future research will explore optimization techniques for reducing computational demands, enabling deployment on resource-constrained IoT devices. Additionally, incorporating incremental learning mechanisms could improve the framework’s adaptability to new and changing IoT environments. Enhancing data privacy and security measures within the framework constitutes another critical development area, ensuring the protection of sensitive information used in trust assessments. Lastly, extending the framework’s applicability across various IoT domains and applications presents a promising direction, aiming to establish a universal trust management solution adaptable to the unique requirements of different IoT ecosystems. By outlining these potential future directions, we aim to highlight the current achievements of the DeepTrust framework and chart a course for its continued development and refinement. This comprehensive approach underscores our commitment to advancing the field of IoT trust and reputation management, paving the way for more secure and reliable IoT networks.

Absensi merupakan sebuah sistem yang sangat penting untuk pengelolaan sumber daya manusia. Absensi juga merupakan sebuah informasi kehadiran yang dapat memepengaruhi nilai keaktifan, produktivitas, hingga kemajuan sebuah instansi. Sehingga diperlukannya sebuah sistem yang mudah digunakan serta dapat digunakan dengan cara otomatis untuk mendapatkan efisiensi yang baik, maka diperlukannya sebuah sistem absensi otomatis berbasis Internet of Things (IoT) berupa penerapan absensi menggunakan RFID Reader yang terintegrasi dengan Google Spreadsheet yang dikendalikan oleh mikrokontroler NodeMCU ESP8266. RFID Reader memiliki sebuah pasangan untuk membaca dan memasukan data kedalam sebuah kartu yang dinamakan RFID Card/Tag, RFID Card/Tag ini berisikan sebuah chip yang dapat menerima data sehingga data akan masuk kedalam RFID Card/Tag melalui RFID Reader. Penggunaannya sangat mudah dengan cara menempelkan RFID Card/Tag ke RFID Reader sehingga data akan terupload ke Google Spreadsheet melalui mikrokontroler NodeMCU ESP8266 yang sudah terintegrasi dengan internet. Maka dari itu proses absensi dengan menggunakan sistem tersebut akan memudahkan sebuah pencatatan absesn tanpa harus menggunakan buku absen atau pencatatan manual. Sehingga dapat meningkatkan efisiensi dalam proses pencatatan absen

The Internet of things is a new generation information network which realizes machine-to-machine communication. In view of the fact that people need the mobility of the Internet of things, the integration scheme of TD-SCDMA network and the Internet of things is designed. 3G TD-SCDMA network is used as the basic network of transmitting information of the Internet of things. At the same time, TD-SCDMA mobile terminal is integrated with RFID reader. TD-SCDMA networks can provide high-bandwidth and high-speed information transmission channel for the Internet of things. TD-SCDMA mobile terminal can realize the mobility of the access part for the Internet of things. Additionally, the network element functions of the mobile Internet of things are described. In order to realize the information exchange between TD-SCDMA network and the Internet of things, the communication protocols of the mobile Internet of things are discussed and designed in detail. Finally, the network performance which is gotten by using OPNET software shows the mobile Internet of things based on TDSCDMA network can be realized by adopting the rational network design and the effective communication protocols.

This study aims to enhance Microbial Fuel Cells (MFCs) reliability for remote environmental monitoring, emphasizing unexplored facets of accurate energy prediction and the integration of renewable energy-powered Internet of Things (IoT) devices. Following comprehensive research, design, and component procurement, an innovative and cost-effective IoT system was developed, leveraging renewable energy from MFCs. Using an Arduino UNO-WiFi, data was collected and showcased on a web page while logged in a Google Firebase database, with an Android app created for intuitive smartphone visualization. Over four months, sensor data was accumulated. An Artificial Intelligence (AI) model, employing Autoregressive Integrated Moving Average (ARIMA), precisely forecasted MFC energy production (RMSE: 0.0119 and 0.0113 for trials 1 and 2). Despite the initial energy production surge, a subsequent decline occurred due to organic matter depletion. This prototype represents an affordable and sustainable solution for cloud-based IoT environmental monitoring with AI-driven energy forecasts, embodying innovation in renewable energy applications and sustainable practices.

Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availability and security characteristics. However, modern IoT infrastructures often consist of a complex and heterogeneous architecture and thus taking into account both architecture and operative details of the IoT infrastructure in a monolithic model is a challenge for system practitioners and developers. In that regard, we propose a hierarchical modeling framework for the availability and security quantification of IoT infrastructures in this paper. The modeling methodology is based on a hierarchical model of three levels including (i) reliability block diagram (RBD) at the top level to capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level to elaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuous time Markov chain (CTMC) at the bottom level to capture detailed operative states and transitions of the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smart factory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smart factory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. A complete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability and security measures are computed and analyzed. The investigation of the case-study’s analysis results shows that more frequent failures in cloud cause more severe decreases of overall availability, while faster recovery of edge enhances the availability of the IoT smart factory infrastructure. On the other hand, the analysis results of the case-study also reveal that cloud servers’ virtual machine monitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the most vulnerable components to cyber-security attack intensity. The proposed modeling and analysis framework coupled with further investigation on the analysis results in this study help develop and operate the IoT infrastructure in order to gain the highest values of availability and security measures and to provide development guidelines in decision-making processes in practice.

The Internet of Things as an emerging global, information service architecture facilitating the exchange of goods in global supply chain networks is developing on the technical basis of the present Domain Name System. Technological advancements lead to smart objects being capable of identifying, locating, sensing and connecting and thus leading to new forms of communication between people and things and things themselves. In this paper, we assume that all the communication channels among Tag, Reader, and Server are insecure. We implement a TTP (Trust-Third-Party) based key management protocol to construct a secure session key among the tag, reader and server to construct a security Mobile RFID (Radio Frequency Identification) service based on the IOT(Internet of Things). A smart mobile phone with a RFID reader can provide Mobile RFID (MRFID) services based on RFID tagged objects. While the mobile RFID system has many advantages, the privacy violation problems at reader side, consumer tracking with malicious purpose and the sensitive information transmission problems among Tag, Reader and Server, are deeply concerned by individuals and scholars. The proposed idea is security and efficiency and supply three advantages for a secure IOT architecture.KeywordsThe Internet of ThingsKey Management ProtocolTrust Third PartyRFIDMobile RFIDRFID Security