IoT Objects Research Articles

A Low-Cost Indoor IoT Object Localization Method Based on Monocular Cameras at the Edge Device

A Low-Cost Indoor IoT Object Localization Method Based on Monocular Cameras at the Edge Device

Enhancing IoT data confidentiality and energy efficiency through decision tree-based self-management

Given the widespread deployment of IoT devices characterized by heterogeneity and energy limits, a greater emphasis on IoT data security within IoT environments has become required. This critical situation encouraged the deployment of lightweight encryption solutions to avoid additional attacks. These solutions, which are based on traditional encryption algorithms, are intended to fulfill the demands of resource-constrained devices while maintaining a high security level. In this research, we provide a novel IoT framework that uses lightweight cryptographic ciphers and the Autonomic Computing paradigm to self-manage the security of IoT devices. Our framework is based on a decision tree technique to guarantee the IoT data confidentiality by selecting and changing in an autonomic manner the most appropriate lightweight encryption algorithm to be used in order to improve energy efficiency by extending the lifespan of IoT devices and to provide an adapted security level according to IoT application type and IoT object state.

Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers’ energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings.

Cyber-Security of Embedded IoTs in Smart Homes: Challenges, Requirements, Countermeasures, and Trends

Connected computers and sensors transmit data across the Internet to solve problems and generate new services (IoT). Smart homes use IoT, for example. Smart home technology can monitor temperature, detect smoke, regulate lighting automatically, and install smart locks. It also poses additional security and privacy problems, such as accessing user data through surveillance equipment or false fire alarms. Smart homes are vulnerable to numerous sorts of assaults. This survey emphasizes IoT. We discuss IoT's design, objects, and standards. We also address the tiered Internet of Things framework and smart home security concerns. In this article, researchers examine IoT-based smart home difficulties and offer solutions.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Dynamic security of IoT network traffic using SDN

Nowadays, smart environment has increasingly become a target for emerging cyberattacks that will impact the user's privacy and potentially, security. A remote attacker can illegitimately access connected objects in our IoT environment to obtain information about personal data or access control and availability of IoT objects, which can contribute to a DDoS attack. SDN (Software Defined Network) is a new paradigm that describes a network architecture whose control plane is completely decoupled from the data plane. The SDN concept offers the possibility of testing network functionalities before integrating them, without affecting the operation of the network. This minimizes the risk of network disruption. Our contribution in this paper is the design and implementation of a new type of architecture for securing exchanges in an IoT network using the SDN concept. The main objective of this work is to improve the quality and performance of this technology to ensure data reliability and avoid any type of internal or external attack by detecting the undesirable flow by Snort. This technology will also be used to organize and unify the management processes of all models based on SDN technology.

Мобільна mesh-мережа для управління роєм об'єктів

The purpose of the article is to develop an adaptive wireless mesh-network architecture for managing technological processes, robotic devices and other objects. The article describes the architecture of a wireless mesh-network. The concept of a wireless mesh-network is described. The mesh-network is self-organizing, adaptive, and to be work autonomously able. The presented wireless mesh node controller architecture is the basis for small control systems for many objects and a swarm of objects. The interaction of network nodes is carried out using several transceivers. Multiple transceivers allow for the separation of control traffic and data traffic on different channels. The protocol stack is optimized for the functioning of the mesh network. The topology of a mesh network can change dynamically. That is, network nodes can move in space without losing communication with each other. The current state of the network is updated with a period that depends on the rate of change of the network node in the space. The mesh network configuration strategy and algorithms allow you to effectively build a network topology and establish interaction between network nodes. There can be many objects in the network that can act as a relay. A repeater can be a network node and a special dedicated repeater node. The low cost of network nodes makes it possible to create a low-cost network for performing various functions of object control and data transmission. The network can be used to manage a swarm of objects. Proprietary software is used to increase the efficiency of forming routing tables taking into account cluster structures in a mesh network. The mesh network can be used to control IoT objects, robotic objects, as well as a variety of objects and control systems for various technological processes.

An investigation on teletraffic attributes for channel selection of IoT objects in cognitive radio internet of things networks towards 5G

An investigation on teletraffic attributes for channel selection of IoT objects in cognitive radio internet of things networks towards 5G

Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Light detection and ranging (LiDAR) sensors are increasing in popularity due to the advantages they provide over 2-D sensors in IoT object detection and classification applications, because of their ability to provide very precise distances to objects. Deep learning algorithms need a huge amount of data during training to obtain high accuracy results. When using 2-D images, a vast quantity of data sets are publicly available, but this is not the case for LiDAR point clouds. Each LiDAR model generates a point cloud with unique properties, which causes the data sets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs, it is necessary to generate the data sets manually. For this purpose, the data must be captured and then labeled one by one, which is a very time and cost-consuming process. To overcome this issue and to reduce the development time when using LiDAR sensors with deep learning algorithms, a methodology is proposed in this article to automatically generate point cloud data sets using a 3-D simulator for autonomous cars. In this regard, a data set can be generated for any LiDAR model by adding the specific LiDAR parameters to the simulator. Besides, custom scenarios can be designed and generated, based on the final deployment location, to provide a simulated solution very close to the final implementation. With the proposed methodology, a simulation can be performed to select the LiDAR that best fits certain application requirements, in contrast to the traditional approach where the LiDAR must first be purchased.

What Liability with the Internet of Things? Insights from the European Case-Law of the PIP Affair

Abstract The purpose of this article is to explain why it is relevant to connect the European case-law on the Medical Devices Directive (MDD), by focussing on the defective breasts prostheses saga, to the future regulation of liability for healthcare IoT objects. I believe that by examining the recent case-law dealing with shortcomings in the regulation of medical devices, it will be possible to build a future liability scheme for defective IoT objects with medical functions. The article discusses how the new Medical Devices Regulation (MDR) is different from the previous MDD and whether it is likely to influence liability schemes for healthcare IoT objects. In conclusion, I argue that, however imperfect, the MDR could support the application of national liability systems in order to provide more effective and more protective liability schemes for IoT objects with medical functions.

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

SummaryInternet of Things (IoT) is the rapidly growing domain that facilitates seamless connectivity to physical objects that makes them suitable and ideal entity of smart environment. The enforcement of trust between the physical objects is essential for completely utilizing the significant potentiality of these connected IoT objects. The traditional security strategies are not potent enough in provisioning comprehensive protection to the smart world. In this paper, Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model (BFPSMTM) is proposed for determining the outranking class that aids in superior trust estimation of interconnected IoT objects to guarantee reliable data dissemination. This BFPSMTM adopted different preference functions depending on the nature and type of criteria or attributes considered for evaluating the trust of the IoT objects during data dissemination. It utilized bipolar fuzzy number for assessing trust that represents an asymmetrical associated between the different judgmental factors considered for decision making. It represented the bipolar linguistic terms into trapezoidal bipolar fuzzy numbers to handle the issue of imprecise and vague knowledge during trust computation. It utilized a trapezoidal bipolar fuzzy number‐based ranking function and identified the crisp real preferences of trusted IoT objects during data dissemination. It incorporated an entropy weighting information and computed the attribute weights based on the normality condition to prevent bias during trust estimation. The results of BFPSMTM confirmed improved throughput of 22.18%, reduced delay of 20.86%, minimized energy consumption of 19.32%, and better packet forwarding rate of 19.84%, with different number of IoT objects in the environment.

Things-Net

The Internet of things aims to create a continuity between real and digital worlds, by integrating physical objects into the digital universe. This will increase the number of connected machines and generate new applications. When combined according to an appropriate logic, IoT objects can produce value-added services that a single object cannot provide. In order to avoid costly errors, this execution logic has to be modeled and validated, using a formal model, before its effective deployment. To address the lack of special-purpose modeling languages for IoT applications, the authors propose the Things-Net model, designed specifically to meet the needs of IoT-based systems. It allows an easy modeling and verification of IoT time-dependent platforms, to ensure that the designed IoT solution fulfills the application's requirements. In order to illustrate the approach, authors apply the modeling and verification techniques to a prototype of an airport management system based on IoT.

Integrating Gen AI and Adaptive AI for Enterprise Solutions

Artificial Intelligence has revolutionized so much that it is influenced into our lives through all day-to-day objects such as phones, IoT objects and other technologies. In the evolution of AI two ground breaking technologies are making a huge impact, Generative AI and Adaptive AI. These technologies can groom the current AI by giving future scope in various domains of digital transformation. When Gen AI is like oxygen in AI as can learn pattern and relationship in a data set well enough to create unique images, text, audio, and video that still adhere to the underlying pattern. Adaptive AI is another emerging field of AI that seek to create intelligent systems to adapt changing circumstances and learning new circumstances. It's so flexible that it is capable of modifying the behavior in response to new information as changes are required in the environment. This paper focus on benefits and scope of integrating both technologies Gen AI and Adaptive AI for the enterprise solutions.

Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project

The integration of Ambient Assisted Living (AAL) frameworks with Socially Assistive Robots (SARs) has proven useful for monitoring and assisting older adults in their own home. However, the difficulties associated with long-term deployments in real-world complex environments are still highly under-explored. In this work, we first present the MoveCare system, an unobtrusive platform that, through the integration of a SAR into an AAL framework, aimed to monitor, assist and provide social, cognitive, and physical stimulation in the own houses of elders living alone and at risk of falling into frailty. We then focus on the evaluation and analysis of a long-term pilot campaign of more than 300 weeks of usages. We evaluated the system’s acceptability and feasibility through various questionnaires and empirically assessed the impact of the presence of an assistive robot by deploying the system with and without it. Our results provide strong empirical evidence that Socially Assistive Robots integrated with monitoring and stimulation platforms can be successfully used for long-term support to older adults. We describe how the robot’s presence significantly incentivised the use of the system, but slightly lowered the system’s overall acceptability. Finally, we emphasise that real-world long-term deployment of SARs introduces a significant technical, organisational, and logistical overhead that should not be neglected nor underestimated in the pursuit of long-term robust systems. We hope that the findings and lessons learned from our work can bring value towards future long-term real-world and widespread use of SARs.

An Investigation on Teletraffic Attributes for Channel Selection of IoT Objects in Cognitive Radio Internet of Things Networks towards 5G

An Investigation on Teletraffic Attributes for Channel Selection of IoT Objects in Cognitive Radio Internet of Things Networks towards 5G

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review.

The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.

ESOTP: ECC‐based secure object tracking protocol for IoT communication

SummaryObjects/tags are generally passes through multiple readers before achieving the final destination within the IoT network. Hence, object identification is a significant issue as some bogus objects may take part for malicious access. Considering this situation, RFID can be used for unique identification and validation of IoT objects. However, it recognizes many objects at the same time and faces limitations like non‐repudiations, injection of fake objects, and unclonability issues. Alternatively, security limitations between tags and readers are analyzed and found that whole communication depends on limited computational resource and simple operations such as hash function, XOR operation, and so forth. Moreover, the communication channel between reader and backend server is secure due to symmetric or asymmetric key encryption algorithms. As the information is exchanged by the tags with readers, a secure path must be maintained among them for safe communication with cloud server. For the development of secure path and verification of objects, ECC‐based Cramer‐Shoup encryption, cryptographic authentication protocol (CAP), and device fingerprinting can be used. To overcome the existing limitations, we are motivated to design an ECC‐based secure object tracking and key exchange protocol that allows the owner device to be offline during communication. This suggested scheme is referred as ESOTP. Several recognized cryptographic attacks are evaluated, the security strength is validated and found that ESOTP is resilient against those attacks. Finally, a comparative study is performed regarding the security features and performance efficiency of ESOTP with other existing schemes and enhanced results are found.

A reputation‐based RPL protocol to detect selective forwarding attack in Internet of Things

SummaryInternet of Things (IoT) is an internet of smart objects where smart objects communicate with each other. IoT objects are deployed in open medium with dynamic topology. Due lack of infrastructure and centralized management, IoT present serious vulnerabilities to security attacks. Therefore, security is an essential prerequisite for the real‐world deployment of IoT. In this work, we propose reputation‐based RPL protocol where reputation‐based mechanism is embedded into RPL protocol to enhance its capabilities against selective forwarding attack. Reputation is calculated by evaluating data forwarding behavior of IoT node. Data forwarding behavior of IoT node is evaluated by the difference between monitored actual packet loss and estimated normal loss. Calculated reputation value is considered in parent selection. Simulation results show that the proposed approach can accurately detect and isolate selective forwarding attack with improving data delivery ratio of the IoT network.

Standard Setting Organisations for the IoT: How To Ensure a Better Degree of Liability?

This early stage research article aims to outline an issue that, though not strictly connected to the cyber-sphere, is most likely going to affect it. Standard Setting/Developing Organisations (SSOs/SDOs) are getting more and more important in the electronics manufacturing field and also in the IP field by setting de facto mandatory rules for products to be safer but, most importantly, more efficient. Standards create trust in complex objects such as IoT devices, that are increasingly more available, especially the ones for the house. It is worth mentioning that the standards these organisations envision are not comparable to legislation but carry a significant ‘moral’ weight (soft law). However, these organisations are private in character and work on a voluntary basis. The problem lies in the creation of the standard when the essentiality of a patented innovation has to be assessed. These processes rely on the self-certification of businesses that their invention is truly essential to the development of a certain standard, which has led to a proliferation of new Standard Essential Patents (S.E.P.s). But in this case, there are no means to ensure some form of liability of these organisations when defects and shortcomings arise. It is argued that unless some form of liability is created for these organisations, IoT objects will never gain the trust of final users.

Dual Security based on Crypto Steganography Using Two Level Learning In Assist with Dual Offbeat Shielding Design

A sensor node and other electronic devices attached to any IoT object can be involved in the communication over wireless network in IoT environments, which makes it necessary to preprocess a large amount of sensed data before storing it. Therefore, sensing data in the form of images is to be sent to the cloud storage system via wireless medium, but this suffers from image hijacking where data is manipulated, which leads to insecure transmission. To mitigate this problem, two levels of security are employed. Memory retaining is the primary level of enhancing learning which uses past experiences to extract optimal features from sensed images and then subjected to offbeat shielding activities, which include cryptographic steganography. The proposed system creates cloud storage that is protected by the optimal features learned from a neural network, thus ensuring that clouds are secure in the Internet of Things.