Connected IoT Devices Research Articles

Use of Blockchain Technology for Management of Guest Room Keys

Purpose of the study: The hospitality industry is undergoing a transformation with the advent of blockchain technology, especially in hotel keys management. This chapter explores the use of blockchain to manage hotel guest room keys, focusing on its applications, benefits, and future ramifications. Methodology: The chapter begins by clarifying the basic concepts of blockchain, outlining its functionality as a decentralized distributed ledger system and then explores the challenges of traditional core system design and introduces blockchain-based key management solutions. This solution uses cryptographic encryption and decentralized access control mechanisms, integrating smart contracts to automate processes. Main Findings: Blockchain-based key management systems generate and distribute secure digital keys to guest smartphones, protecting it from unauthorized access and tampering blockchain’s immutable recordkeeping for a tamper-proof audit trail, enhancing security and accountability. Hotel networks allow guests to use digital keys in multiple ways, and connecting IoT devices allows for remote control and personalized experiences. Applications of the study: Blockchain technology provides a robust alternative to traditional hotel key management systems, improving security, transparency, and operational efficiency. Future trends, including artificial intelligence and machine learning, promise new innovations. A strategic and collaborative approach is recommended to maximize the benefits of blockchain in the hospitality industry. Novelty/Originality of the study: This chapter provides valuable insights for practitioners and researchers aiming to harness the transformative power of blockchain.

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

An analytical approach to network with IoT security using NodeMCU access point-prevention technique

This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem.

A study of performance characteristics of hybrid energy harvesting systems based on photovoltaics and thermoelectrics

The hybrid photovoltaic/thermoelectric generator (PV/TEG) technology is an advanced and efficient technology that combines the power from PV and TEGs to generate sustainable electricity. This hybrid approach optimizes energy output and ensures cleaner power by connecting IoT devices. Comprehensive studies have been conducted in the past to improve the efficiency of TEG modules. Various material parameters of TEG legs, such as the Seebeck coefficient, thermal conductivity, and electrical resistivity, and geometric parameters, including the cross-sectional area, leg size, leg height and the number of leg pairs, influence the TEG characteristic and determine with this the performance of the hybrid system. This work explores the influence of the TEG leg lengths and numbers of TEGs at various weather conditions on the power generation of a hybrid PV/TEG device, using an analytical model verified by experiments. The paper also analyses the performance characteristics of TEGs along with the hybrid PV/TEG system and concludes that the maximum output power from the TEG module in the hybrid PV/TEG model can be achieved by increasing the leg length.

ANALYSIS OF THE MICROSERVICE ARCHITECTURE OF THE SMART CITY

Recently, the concept of “Smart Cities” has been significantly improved along with the growth and development of the Internet of Things as a new form of sustainable development. Smart cities rely on an independent and distributed infrastructure that includes information processing and management systems, a diverse network infrastructure, and a sensitivity that includes millions of information sources. However, due to the ever-increasing amount of data and the number of connected IoT devices, issues such as high latency, bandwidth barriers, security and privacy, and scalability are the result of the ongoing construction of a smart city network. Developing an efficient, secure, and scalable distributed architecture by bringing computer resources and storage closer to the endpoint is necessary to overcome the limitations of today’s smart city network. There are many types of smart services that aim to automate and perform repetitive routine tasks. In addition, they come in different forms depending on the city’s population, culture, including the level of awareness of citizens, geography, and schedule. Not to mention, on a micro-technical level, they tend to create large data sets with fast continuous execution, thus falling into the category of big data, and therefore can be a source of analytical data. Microservice architectures are born as a solution to this problem. By implementing a single small function that is confined to a running process, independent microservices can be deployed separately in a distributed system. In the context of mobility in a smart city, a service meets the need of a citizen who needs to move from one part of the city to another or provides useful information for the same purpose. In this article, we demonstrate how microservice architecture can be used to create IoT services for multimobility in a smart city.

Lightweight Security for IoT

With the ever-increasing demand for IoT Devices which enable all objects to connect and exchange information in applications such as healthcare applications, Industry 4.0, smart cities and smart homes, etc. IoT devices play a crucial role in our day-to-day life like homes, offices, healthcare, wearable, and agriculture. With the development of IoT devices, securing device-to-device communication has attracted more and more attention and we need to ensure the privacy and security of data amongst these IoT devices. User authentication has emerged as a major security concern while connecting IoT devices and the cloud. Many authentication schemes like mutual authentication, group authentication have been proposed to ensure only authenticated users and with very high confidence we can rely on the decision-making process. Symmetric key based as well as Asymmetric key-based solutions have been proposed but due to the resource constraint nature of the IoT devices designing lightweight, robust, provably secure authentication schemes is a big challenge. This paper discusses the various authentication techniques designed for low-powered IoT devices and proposes a lightweight authentication scheme for IoT.

HANNA: Human-friendly provisioning and configuration of smart devices

Today, there are billions of connected IoT devices and their number continues to grow as they contribute to the digitalization of infrastructures. However, the deployment process of these smart wireless devices when delivered to customer premises is slow and error prone as each of them needs to be provisioned with authentication credentials to access the corporate network. In this paper, we propose HANNA, a human-friendly provisioning and configuration framework for smart devices, that extends the zero-touch paradigm to large IoT deployments by introducing voice assisted configuration in combination with large scale ad-hoc communications to overcome the initial installation effort of IoT deployments. The most prominent role in HANNA is played by the assisting device, which includes a voice assistant capable of correctly understanding a minimum number of keywords required for initial provisioning and configuration of the devices. The device’s role is to interact with the user and ensure that all provisioning details are received. These are then converted into appropriate machine instructions for further use by the mass provisioning mechanism. We provide an example prototype implementation of HANNA and evaluate the performance of the assisting device in the human-to-machine communication phase and the performance of the selected communication technique in the machine-to-machine communication phase. Our results show the potential of existing speech-to-text engines for this application area and also reveal shortcomings with respect to the robustness of the engines in office-like working environments as well as with respect to user’s gender and language proficiency level. Additionally we show that the proposed machine-to-machine provisioning approach is always faster compared to manual provisioning for cases with more than ten devices.

An intelligent fuzzy-based system for handover decision in 5G-IoT networks considering network slicing and SDN technologies

By enabling 5G technology, IoT networks can improve the performance of connected IoT devices. However, handover, ping-pong effect and load balancing are critical issues because of massive and unplaned deployment of small cells. The handover operation in 5G wireless networks is complicated because there are many radio access techniques and technologies. Therefore, the mobility management is required to provide QoS for various applications. In this paper, we present a fuzzy-based system for handover decision in 5G wireless networks considering different network slicing parameters. We implement two Fuzzy-based Handover Decision Models (FHDM): FHDM1 and FHDM2. We evaluate the proposed models by simulations. For FHDM1, Slice Delay (SD), Slice Bandwidth (SB), and Slice Load (SL) are three input parameters and Handover Decision (HD) is the output parameter. For FHDM2, Slice Reliability (SR) is a new considered parameter, so it has four input parameters and the output parameter is HD the same as FHDM1. We found from simulation results that the considered parameters have different effects on HD. For both models, the HD value increases as SD and SL values increase while HD value is decreasing when SB and SR values increase. The results of the simulation indicate that although FHDM2 is more complex than FHDM1, it performs better HD since considers four input parameters. From the simulation results of FHDM2, when we changed SD value form 10% to 90%, we found that the HD value is increased by 50% when SR is 50% and SL is 90%. This indicates that when a user is connecting with the present slice that has bad QoS, it should handover to another slice with better QoS.

A Novel Dynamic Software-Defined Networking Approach to Neutralize Traffic Burst

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation.

Low-Power IoT for Monitoring Unconnected Remote Areas.

This paper deals with IoT devices deployed in remote areas without terrestrial Internet connectivity. We consider connecting IoT devices on the ground to the Internet through an aerial system based on an Unmanned Aerial Vehicle (UAV) for smart agriculture and environmental monitoring. The UAV flying over the remote area receives data from distributed IoT devices. The transmissions between the ground sensors and the UAV are carried out via LoRa. We have proposed a synchronization protocol for the opportunistic communication of LoRa IoT devices with a gateway onboard the UAV to save node battery life. Class A LoRa nodes on the ground transmit only when the UAV is expected to pass close to them; otherwise, they stay in the sleeping state most of the time. This paper provides a detailed description of the formulation of the synchronization protocol. The UAV's flying dynamics have been considered for characterizing its speed and the time of visibility of each IoT sensor. Our model has allowed an analytical approach that can help to determine the best settings for LoRa transmissions. Finally, experiments have been carried out to assess the path loss attenuation, and a laboratory setup of the synchronization protocol has been implemented for the preliminary validation of our scheme.

Performance evaluation of AMQP over QUIC in the internet-of-thing networks

In today’s world, the use of IoT devices is growing every day. For connecting IoT devices from various vendors and supporting a variety of IoT use cases, an interoperable protocol like AMQP is necessary. Researchers are striving to minimize delay because many IoT applications are sensitive to it. The transport layer protocol that is used underneath, such as TCP or UDP, is one of the main causes of the delay. Although TCP is slower than UDP because to the three-way handshake and the usage of TLS for security, it is more reliable than UDP. The Internet Engineering Task Force has introduced a new transport layer protocol called QUIC that combines the best features of UDP and TCP to offer quick and reliable communication. In this study, we integrated QUIC and AMQP1.0 using the Go programming language. The Docker tool was used to containerize the AMQP1.0 Broker, Sender, and Receiver implementations. The performance of AMQP1.0 over TCP and AMQP1.0 over QUIC was benchmarked in the NS3 simulator over various wireless networks including WiFi, 4G/LTE, and satellite. QUIC showed considerable improvement over lossy networks. The results showed that switching from TCP to QUIC at the transport level lowered Communication Time by 8.57% over Satellite network. Although Round Trip Time was almost same yet Start up Latency showed improvement of 52%, 38% and 34% in case of WiFi, 4G/LTE and Satellite respectively. In addition, the performance of AMQP1.0 over TCP and AMQP1.0 over QUIC has been evaluated over different Packet Loss values, the results show that AMQP1.0 over QUIC outperforms AMQP1.0 over TCP in all the cases. The testing results revealed that TCP performance was degraded by 20%, 16%, and 36% over WiFi, 4G/LTE, and satellite, respectively at Packet loss of 15%, while QUIC performance was only degraded by 4%, 8%, and 9% in each of these cases.

Integrated publish/subscribe and push-pull method for cloud based IoT framework for real time data processing

Cloud-based IoT is a platform that connects smart devices to the cloud for real-time data analysis. Any device on the IoT platform can connect to the cloud through messaging. The integrated publish/subscribe and push/pull methods for a cloud-based IoT framework that is scalable for connecting IoT devices and processing the real-time data are proposed. The proposed framework uses a publish/subscribe messaging broker and a push-pull method for transmitting the data from the device to the cloud. The IoT devices publish the data via the broker, to which the cloud service providers subscribe. This publishing and transferring of data from the broker to the cloud is implemented with the help of a push-pull mechanism. In this mechanism, the broker makes the computations required to select the cloud service provider. Hence, the overhead of the device is reduced. All computations go in parallel, which reduces the latency of the system. The system is flexible for any number of devices, brokers, and cloud service providers, which shows that the system is scalable. The results demonstrated the effectiveness of the model, which was developed using a cloud-based IoT framework with a focus on scalability and latency.

A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT

5G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective low-latency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system and introduces new powerful attack vectors leading to severe security and data privacy risks. Malware attacks are starting targeting weak but highly connected IoT devices showing the importance of security and privacy in this scenario. This article designs a 5G-enabled system, consisted in a deep learning based architecture aimed to classify malware attacks on the IIoT. Our methodology is based on an image representation of the malware and a convolutional neural networks that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy.

A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

This paper examines the up-to-dateness of installed firmware versions of Internet of Things devices accessible via public Internet. It takes a novel approach to identify versions based on the source code of their web interfaces. It analyzes data sets of 1.06m devices collected using the IoT search engine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Censys</i> and then maps the results against the latest version each manufacturer offers. A fully scalable and adaptive approach is developed by applying the SEMMA data mining process. This approach relies on three data artifacts: raw data from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Censys</i> , a mapping table with firmware versions, and a keyword search list. The results confirm the heterogeneity of connected IoT devices and show that only 2.45 percent of the IoT devices “in the wild” run the latest available firmware. Installed versions are 19.2 months old on average. This real-world evidence suggests that the updating processes and methods used by engineers so far are not sufficient to keep IoT devices up-to-date. This paper identifies and quantifies influencing factors and captures the global and diverse distribution of IoT devices. It finds manufacturer and device type influence the up-to-dateness of firmware, whereas the country in which the device is deployed is less significant.

Design and Implementation of IOT Connection With Websocket Using PHP

IOT design and connecting between devices has been developed in recent years. Connection between device has been done from using Bluetooth, internet thru api, restfull api, socket, websocket, mttq and many others method. Api and socket is the most used method in connecting IOT devices. This reasearch tends to implement the websocket in connecting and controlling IOT devices. From the application of this research, the devices can be connected and implement with high speed of average 700ms with loaded netword. According to the results of the test, number of loads and data size can increase the time duration for each communication between IOT devices and websocket server. The larger the data, the more possible the anomaly may accured where the number of connections will experience lagging in command execution between the IOT devices and the controller The bidirectional connection in websocket make the data exchange posibbly fast.&#x0D;

Yōkobo

As part of a multidisciplinary research and a PhD project to strengthen the connection between retired couples living at home, we imagined and designed Yōkobo. It is a robot at the crossroads of a sensitive approach and a robotic trend that bridges the gap between humans (Human-Robot-Human Interactions field). As a theoretical contribution, Yōkobo is at the intersection of various concepts: behavioral objects, robjects, weak robotics, and slow technology. Yōkobo is a trinket bowl placed in the entrance of homes. Its discreet presence expresses hospitality and celebrates small moments of everyday life, welcoming visitors and inhabitants of the house. The name comes from the contraction of “yōkoso” (welcome in Japanese) and “robot” (with French pronunciation). In addition to these functions, Yōkobo expresses the state of the home using data from connected IoT devices, combining various house parameters (such as temperature, air quality, etc.) to express the home's “mood” through its motions. Finally, Yōkobo used in tandem with house keys, can convey a trace, a message based on motion. And a trace is a memory of the partner's passage. Yōkobo is resolutely innovative and disruptive. It does not sit within the lineage of the general vision of what robots are and what they can do: it is an object intended to be unobtrusive, stemming from ambient computing, while having an ongoing subtle presence. It does not make sounds, unlike voice assistants and the trend for using voice modality interaction. It expresses its environment only through motion and light. to move away from home's companion robots and the biases they can generate through facial representation, Yōkobo has neither an anthropomorphic shape nor can talk. Yōkobo is intended to be made of natural materials such as ceramic, wood, or wool to break with the idea of plastic, disposable, and toy robots, and to improve its integration in everyday home life. as a slow technology product, understanding and integrating Yōkobo into one's life takes time and requires accepting not having a clear, repetitive, and instantaneous response to an action. Its contribution is not measured in terms of efficiency and utility; it is the sum of different experiences with the product over time that creates the object's meaning and value. Getting to know Yōkobo's expressive motions is continuous and progressive. Yōkobo is an object that is understood through perception and touches the poetic sensibility of its users. Yōkobo is a concept that puts people's relationships at the center. It does not impose itself to propose an exclusive Human-Object relationship. It reveals the presence of the other by expressing the last impermanent trace of the other's passage. It is an object of sensitive presence. This work is the result of interdisciplinary research between roboticists, designers, and ergonomists. The navigation (directions and overlay) of this pan.able demonstrates the design and engineering processes, as well as the interaction modalities.

A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics

A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics

Scalable Edge Computing Environment Based on the Containerized Microservices and Minikube

The growing number of connected IoT devices and their continuous data collection will generate huge amounts of data in the near future. Edge computing has emerged as a new paradigm in recent years for reducing network congestion and offering real-time IoT applications. Processing the large amount of data generated by such IoT devices requires the development of a scalable edge computing environment. Accordingly, applications deployed in an edge computing environment need to be scalable enough to handle the enormous amount of data generated by IoT devices. The performance of MSA and monolithic architecture is analyzed and compared to develop a scalable edge computing environment. An auto-scaling approach is described to handle multiple concurrent requests at runtime. Minikube is used to perform auto-scaling operation of containerized microservices on resource constraint edge node. Considering performance of both the architecture and according to the results and discussions, MSA is a better choice for building scalable edge computing environment.

Efficient Approach for Anomaly Detection in Internet of Things Traffic Using Deep Learning

The network intrusion detection system (NIDs) is a significant research milestone in information security. NIDs can scan and analyze the network to detect an attack or anomaly, which may be a continuing intrusion or perhaps an intrusion that has just occurred. During the pandemic, cybercriminals realized that home networks lurked with vulnerabilities due to a lack of security and computational limitations. A fundamental difficulty in NIDs is providing an effective, robust, lightweight, and rapid framework to perform real-time intrusion detection. This research proposes an efficient, functional cybersecurity approach based on machine/deep learning algorithms to detect anomalies using lightweight network-based IDs. A lightweight, real-time, network-based anomaly detection system can be used to secure connected IoT devices. The UNSW-NB15 dataset is used to evaluate the proposed approach DeepNet and compare results alongside other state-of-the-art existing techniques. For the classification of network-based anomalies, the proposed model achieves 99.16% accuracy by using all features and 99.14% accuracy after feature reduction. The experimental results show that the network anomalies depend exceptionally on features selected after selection.

A field test of a federated learning/federated analytic blockchain network implementation in an HPC environment

The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.