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AbstractThe project aimed to revolutionize home security by developing innovative, user-friendly, and highly secure smart security devices requiring minimal installation and robust data protection. Through meticulous research and critical design phases, the project successfully achieved its objectives, surpassing initial goals and setting new standards for home security.Objectives & Research Findings:The project aimed to address the critical needs of homeowners, tenants/renters, and property managers by focusing on damage-free installation, easy setup, remote monitoring, and encrypted data transmission. Analysis of burglary statistics emphasized the vulnerability of inadequate door-locking mechanisms, while exploration of sensor technologies identified viable options for detecting door status. Patent analysis provided insights into prior art and innovation trends, guiding design decisions and strategies for intellectual property protection.Critical Design Phase & Achievements:Detailed engineering analysis, testing protocols, risk assessment, and compliance considerations were conducted. Hardware components were carefully selected and tested for optimal performance, and housing units were designed for durability and practical installation. Non-invasive sensors facilitated risk-free installation, while battery-powered sensors ensured portability and longevity. Invasive sensor options were developed for permanent home installation, and user-friendly mobile/web applications provided real-time property security status with secure and encrypted data transmission.Future Recommendations & Conclusion:Future recommendations include extending battery life, enhancing data protection measures, implementing a notification system within the application, and ensuring compatibility with other smart home systems. The project demonstrates how innovative security solutions can evolve alongside technology to meet user needs effectively, setting new benchmarks for convenience, flexibility, and data protection in home security systems. Lessons learned will guide future improvements, ensuring continued innovation and user satisfaction.

In the age of high-tech automation and wireless technology, it is possible to make various devices come together in a given environment, enhancing comfort, energy savings, security, and affordability. We can observe environmental indicators remotely and manage relevant equipment with embedded systems' tiny, low-energy consumption. These devices' power consumption and network bandwidth utilization are critical. Thus, a low-power device must transfer data using minimal redundant protocol. Since Wi-Fi is so widely available, linking all devices within the space through one standard gateway makes sense. This study gives an overview of Message Queuing Telemetry Transport (MQTT), an ideal lightweight protocol for such applications because of its low power and bandwidth requirements. The proposed prototype uses MQTT on the ESP8266, a Wi-Fi-based expansion board. In the case of ESP8266, sensors and actuators are connected directly using Cayenne-based MQTT brokers to provide remote control and monitoring. It allows for efficient and effective communication within the network, ensuring stable data transmission and device management while minimizing power consumption and bandwidth usage. The project has shown how possible it is to use MQTT in the ESP8266 to support smart environments, leading to better IoT-integrated solutions. [ABSTRACT FROM AUTHOR]

Covert channel methods are techniques for improving privacy and security in network communications. These methods consist of embedding secret data within normal network channels, making it more difficult for unauthorized parties to detect such data. This paper presents a new approach for creating covert channels using the Message Queuing Telemetry Transport (MQTT) protocol, widely used in the context of the Internet of Things (IoT). The proposed method exploits storage channels by altering the field length of MQTT messages. Our solution leverages well-known one-way mathematical functions to ensure that data remain hidden from third parties observing the MQTT stream. In this way, we ensure that not only the content of the communication is preserved but also that the communication itself takes place. We conducted a security analysis to show that our solution offers the above-mentioned property even against severe threats, such as an adversary being able to observe all the messages exchanged in the network (even in the clear). Finally, we conducted an overhead analysis of our solution both in terms of the time required to perform the required operations and of the bytes to send. Our study shows that our solution adds no significant time overhead, and the additional overhead in terms of transmitted bytes remains within acceptable limits. [ABSTRACT FROM AUTHOR]

In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments. [ABSTRACT FROM AUTHOR]

Networks for the Internet of Things typically use a gateway to provide connectivity between a low bit rate, low capability sensor network and the broader Internet. The gateway can be subject to very high traffic loads, many concurrent processes and needs to be highly reliable. Functional programming languages such as Erlang and Elixir have proven to be an effective programming paradigm for such scenarios, notably in large-scale telecommunications switches. In this paper, we report on our experience of developing a gateway between a LoRa network and an MQTT broker using the functional programming language Elixir and the more conventional language C++. To obtain an understanding of this approach to development, we first developed an initial prototype on a single-board computer using Elixir. We then developed the same system in C++ and ran experiments to compare the two systems' performance. In order to understand the performance of such systems on low-end IoT devices, we then developed the same system on a low-cost ESP32 micro-controller in both C++ and Elixir. We were able to run the Elixir-based system on an ESP32 micro-controller but found that its performance was significantly poorer than the same system written in C++. We conclude that functional programming has great potential for the development of IoT systems, but work needs to be carried out to improve the supporting libraries and underlying virtual machines. We also note that learning to program in a functional programming language has quite a steep learning curve. [ABSTRACT FROM AUTHOR]

This study explores the development and evaluation of an Internet of Things (IoT) system for real-time soil nutrient monitoring, focusing on crucial soil parameters such as nitrogen, phosphorus, potassium (NPK), and pH. By integrating Message Queuing Telemetry Transport (MQTT) as the communication protocol, the system ensures low latency, reliable data transmission, and effective management of soil data. The tested NPK and pH sensors showed high accuracy and reliability, with the pH sensor providing highly consistent readings, while the NPK sensor demonstrated variability but reliably tracked nutrient trends. We highly recommend further calibrating the NPK sensor to significantly improve its accuracy across various soil conditions. The IoT system, "Soil Station 2.0," effectively assists in decision-making for fertilization and soil management, improving crop yields and soil health. GPS testing of the system revealed high positional accuracy, which is suitable for precision agriculture. MQTT data transmission’s testing showed differences in latency, indicating the need for system optimization for large data transmissions. Overall, the system demonstrated good accuracy, reliability, and efficiency in supporting agricultural decision-making and environmental monitoring.

In the evolving digital landscape, interconnected IoT networks are expanding fast. However, essential security measures are often lacking, which makes it vulnerable to cyber threats. Intrusion Detection Systems (IDSs) are crucial in combating these threats, but IDSs in the IoT domain face significant challenges; one of them is the existence of imbalanced data, where attack activities are underrepresented compared to normal activities. This becomes a problem when handling Machine Learning (ML) tasks to improve the system’s intrusion detection ability. This paper presents a novel solution by leveraging Generative Adversarial Networks (GANs) to create balanced datasets. Unlike traditional methods, our approach generates high-quality synthetic samples that maintain the intricate characteristics of the data, making it possible to improve detection accuracy. Additionally, we introduce three specialized IDSs designed to detect attacks on the lightweight Message Queuing Telemetry Transport (MQTT) protocol, utilizing hybrid Deep Learning (DL) algorithms (CNN-RNN, CNN-LSTM, and CNN-GRU). Experimental results, using a combination of both the original MQTT dataset and a newly generated dataset through GANs, demonstrate the better performance of the generated datasets in multi-class classification. This performance highlights the effectiveness of our GAN-based approach in improving detection accuracy at the same time that it reduces false positives. The ultimate objective is to develop an autonomous system tailored to safeguard IoT environments, providing a robust and adaptable solution to emerging cyber threats. [ABSTRACT FROM AUTHOR]

IoT application uses MQTT, an application layer protocol that facilitates machine-to-machine communication using a central entity called broker. The vulnerability lies in the broker being susceptible to intrusion attempts, where a potential attacker might engage in a Distributed Denial of Service attack. Such an attack involves repetitively transmitting large number of malicious messages or counterfeit connect requests. To send large messages, the attackers must breach the authentication process of MQTT. MQTT employs two authentication approaches to safeguard its system: certificate-based and credential-based authentication. Credential-based authentication is popular as it is easy to implement. However, in MQTT, credential-based authentication is vulnerable to various attacks as credentials are transmitted in plain-text form. In literature, authors have explored different cryptography-based solutions to address these challenges. However, implementing these solutions in IoT systems is impractical due to the substantial computational requirements at the broker and the end devices. The primary objective of this work centres around formulating a PUF-based authentication policy and designing an IDS to track the behaviour of incoming traffic. In the proposed authentication scheme, the PUF mechanisms generate credentials to establish authenticity, thus protecting the network from password-based vulnerabilities like dictionary-based attacks. The second security module of this research implements a Machine Learning based IDS system to track and block fake connect requests in real-time. The proposed IDS system comprises Decision Tree and Neural Network algorithms that operate in parallel. In order to maintain the lightweight nature of the ML model, the system incorporates a feature selection technique. The result section shows that the proposed system effectively and efficiently recognizes fake connect requests in real-time and consumes minimal energy. Additionally, the proposed scheme requires less time than existing schemes in the literature. [ABSTRACT FROM AUTHOR]

Our research investigates the application of machine learning and Internet of Things (IoT) technologies in healthcare, focusing on detecting and classifying coughing episodes. Leveraging deep learning architectures and a comprehensive IoT infrastructure, we developed an automated system capable of monitoring audio signals from a microphone array module to detect coughs and classify their types accurately. The study utilized the COUGHVID dataset for model training and evaluation, employing rigorous preprocessing techniques to ensure data integrity. Through comparative analysis, we identified MobileNet as the optimal model for cough detection, achieving promising results in accuracy, area under the ROC curve (AUC), and F1 score. Furthermore, our emphasis on privacy safeguards and remote medical examination facilitation underscores the practical implications of our research in enhancing healthcare delivery. Our study contributes to advancing technology-enabled healthcare solutions, offering valuable insights and solutions for improving patient care and outcomes.

The rapid development of the IoT (Internet of Things) demands speed and security of communication between connected entities via the Internet. A suitable communication protocol for this communication in terms of speed optimization is MQTT (Message Queuing Telemetry Transport). However, it has security limitations that make it vulnerable to third-party attacks. This research proposes an IoT communication system and server using the MQTT protocol and Elliptic Curve Cryptography (ECC) algorithms to secure communications. ECC efficiently uses computing resources and has a short key size compared to Rivest Shamir Adleman (RSA), so it is suitable for mutual authentication. In addition, data encryption uses the 128-bit Advanced Encryption Standard (AES), which has good security and computing efficiency. The study included testing the mutual authentication speed of ECC and RSA across different key sizes, demonstrating that ECC consistently outperformed RSA in execution time. This study also compared the speed of mutual authentication between ECC and RSA with key sizes of 256 and 3072 bits, respectively; ECC achieved an average speed of 117.33 ms, whereas RSA took 241.92 ms. Furthermore, this study was also tested for the level of security using ECC as a cryptographic algorithm. The system is tested for security by performing sniffing attacks, brute force attacks, replay attacks, and fingerprint matching accuracy by measuring the False Rejection Rate (FRR) and False Acceptance Rate (FAR). The most suitable threshold value is between 30 and 40 within an Equal Error Rate (ERR) between 20% and 30%. The overall testing results show that the system is time-efficient and resilient to attacks.

Over the last decade, health information systems (HIS) have undergone significant changes, particularly in embracing flexible frameworks for ongoing development. This evolution underscores the necessity for information technology (IT) infrastructures that rapidly align with clinical processes. The paper investigates the transition of hospital information systems to comprehensive strategies, examining the benefits and challenges involved. It also examines the increasing demands on health data management systems (HDMS) for patient care and biomedical research. The focus is on how the integration of the Internet of Things (IoT) and open-source enterprise resource planning (ERP) systems, such as Odoo, impacts health information management. The study evaluates the effectiveness and implications of combining these technologies. It provides examples of these integrated systems in action, particularly in resource-limited settings, and evaluates their potential to improve care. The document provides a comprehensive review of the current status and evolution of HIS and HDMS, highlighting the importance of integrating IoT and other cutting-edge technologies in healthcare. This is a crucial aspect for developing countries, where these advancements can significantly improve healthcare outcomes. [ABSTRACT FROM AUTHOR]

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The power grid, manufacturing, and industrial automation are just a few Internet of Things (IoT) settings where publish/subscribe (p/s) systems are increasingly prevalent. These systems may handle a wide range of middleware and communication protocols, ensuring compatibility. The most well-liked publish/subscribe protocol is the Message Queue Telemetry Transport Protocol (MQTT), which uses an agent to transfer information between publishers and subscribers on certain subjects. MQTT can be quickly and simply deployed for IoT settings using a popular wireless MAC layer protocol like Long Range Wide Area Network (LoRaWAN), however, this has not been properly validated. MQTT can be readily set up in cloud environments to do research experiments. To provide an MQTT-based publication design that can handle the LoRaWAN proactive steps, the authors design and provide a simulation framework in NS-3 in this study. To do this, the authors make use of the LoRaWAN library from NS-3 and include connecting it with a middleman that links to numerous publications as well as clients. The authors support many topics at the broker while enabling unicast capabilities from the broker to LoRaWAN end devices. In other words, the proposed work activates the unicast capability from the middleman to LoRaWAN peripheral devices while handling multiple topics at the mediator. To illustrate the viability of our IoT architecture and evaluate its performance at scale, the authors performed several scenarios under it. [ABSTRACT FROM AUTHOR]

With the gradual increase in the proportion of new energy sources in the power grid, there is an urgent need for more flexible resources to participate in short-term regulation. The impact of communication network channel quality will continue to magnify, and factors such as communication latency may directly affect the efficiency and effectiveness of resource regulation. In this context of a large number of flexible demand-side resources accessing the grid, this article proposes a bidirectional channel delay measurement method based on MQTT (Message Queuing Telemetry Transport). It can effectively evaluate the real-time performance of communication links, considering that resources mainly access the grid through the public network. Subsequently, focusing on two typical types of resources on the demand side, namely, split air conditioners and central air conditioners, this article proposes an assessment method for correcting the response capabilities of air conditioning resources considering communication latency. Experimental simulations are conducted, and the results demonstrate that under given communication conditions, this method can more accurately estimate the response capability of air conditioners. This provides a basis for formulating more reasonable scheduling strategies, avoiding excessive or insufficient resource regulation caused by communication issues, and aiding the power grid in achieving precise scheduling. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing. [ABSTRACT FROM AUTHOR]

The applications of IoT have increased manyfold owing to the declining prices of electronic devices and the growing internet. This study discusses at length the various characteristics, applications, merits, demerits, and problems associated with IoT. The current research work showcases the design and development of application coined as MIAWM: MQTT based IoT Application for Weather Monitoring. The application describes how the MQTT protocol can easily be used to perform efficient routing in IoT. The Application describes how to create a simulation for the MQTT Publisher Subscriber model which provides desired weather data to a remote subscriber anywhere in the world. The dataset used contains information about the temperature, precipitation, and wind speed in Berlin and Germany. This paper puts the various quality of service to experiment by comparing the time taken to service the request. [ABSTRACT FROM AUTHOR]

The growth of Internet of Things (IoT) devices has created opportunities in multiple sectors, but has also created the threat of growing attacks. Malicious actors are taking advantages of the lightweight nature of IoT devices and targeting them to perform attacks. This paper presents a novel approach to detect malicious traffic at an early stage to block attacks at the entry point of network so devices could be protected from getting infected. An optimized Machine Learning(ML) based algorithm is proposed specifically designed for light weighted IoT devices. The IoT- 23 dataset is used in this study to create a model and test the performance. The proposed model detects diverse categories of IoT malwares, creating security threats and challenges to IoT systems. MQTT (Message Queuing Telemetry Transport) is utilized to simulate behavior of IoT devices. Attack and normal traffic is passed to the IoT network to record latency and throughput in both scenarios. Performance of five different classifiers is compared with the proposed algorithm and had outstanding results with an accuracy 99.98%. The detection model takes around 52.89 seconds and processes 3965.27 samples per second. Utilization of CPU during the entire process is observed around 3.5%. Future research directions and suggestions have also been given to enhance the security of IoT environment. [ABSTRACT FROM AUTHOR]

Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates in discrete temporal rounds, during which the broker constructs a Merkle hash tree (MHT) from the messages received. Then the broker publishes the root on the blockchain and the MHT itself on IPFS. This mechanism guarantees that both publishers and subscribers can verify the integrity of the message exchanged. Furthermore, the interactions with the blockchain made by the publishers and the broker ensure they cannot deny having sent the exchanged messages. We provide a detailed security analysis, showing that under standard assumptions, the proposed solution achieves both data integrity and accountability. Additionally, we provided an experimental campaign to study the scalability and the throughput of the system. Our results show that our solution scales well with the number of clients. Furthermore, from our results, it emerges that the throughput reduction depends on the integrity check operations. However, since the frequency of these checks can be freely chosen, we can set it so that the throughput reduction is negligible. Finally, we provided a detailed analysis of the costs of our solution showing that, overall, the execution costs are relatively low, especially given the critical security and accountability benefits it guarantees. Furthermore, our analysis shows that the higher the number of subscribers in the system, the lower the costs per client in our solution. Again, this confirms that our solution does not present any scalability issues. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

The Message Queuing Telemetry Transport (MQTT) protocol, widely used in various industries for Machine-to-Machine (M2M) communication, plays a central role in the IoT architecture. However, despite its widespread adoption, an analysis conducted on the shodan search engine revealed up to 540531 vulnerabilities worldwide as of April 2024, underscoring the security challenges facing this protocol. How can an innovative cyber range system be designed to assess and improve the security of MQTT protocols, focusing on in-depth vulnerability analysis, authentication strategy development, and targeted countermeasures against specific attacks? The study includes an analysis of relevant articles, followed by classification into five different categories. Furthermore, the presentation of a cyber range that exposes various attacks, such as replay attacks, Man-In-The-Middle (MITM), Denial of Service (DoS)/Distributed DoS(DDoS), and cam-hackers, enhances the understanding of MQTT protocol vulnerabilities, emphasizes the critical importance of strengthening security, and serves as a reference for new researchers in the field. [ABSTRACT FROM AUTHOR]

The implementation of a Location Service for Emergency Medical Services system (LS4EMSs) is the goal of this study. by integration of pgRouting algorithm, Web Map Application, and Geo-IoT devices. The study is divided into 2 parts: (1) design of a security emergency incident location alarm system that can be used to track security emergencies in real time using Geo-IoT and (2) development of Emergency Routing Service (ERS) system based on web map application. NodeMCU ESP8266 and U-blox Neo-6 m GPS module were used for implementing Geo-IoT which can connect to Wi-Fi and give information including the location of the push button triggered by an individual in an emergency. ERS can determine the best route to take from the hospital or closest ambulance to the location where the Geo-IoT device is located. Free and Open-Source Software for Geospatial (FOSS4G) stack was used in the system's development, since it is easily adaptable to cover different purposes, including fire, flood, or other transport movements. The Integration of Geo-IoT and Web Routing Service for LS4EMSs improves utility as it is a near real-time ERS system. [ABSTRACT FROM AUTHOR]