Internet Of Things Nodes Research Articles

Routing Overhead Aware Optimal Cluster based Routing Algorithm for IoT Network using Heuristic Technique

Globally, billion of devices in heterogeneous networks are interconnected by the Internet of Things (IoT). IoT applications require a centralized decision-making system due to the failure-prone connectivity and high latency of such a system. Low-latency communications are a primary characteristic of applications. Since IoT applications usually have small payload sizes, reducing communication overhead is crucial to improving energy efficiency. Researchers have proposed several methods to resolve the load balancing issue of IoT networks and reduce communication overhead. Although these techniques are not effective, in terms of high communication costs, end-to-end delay, packet loss ratio, throughput, and node lifetimes negatively impact network performance. In this paper, we propose a communication overhead aware optimal cluster-based (COOC) routing algorithm for IoT networks based on a hybrid heuristic technique. Using three benchmark algorithms, we form load-balanced clusters using k-means clustering, fuzzy logic, and genetic algorithm. In the next step, compute the rank of each node in a cluster using multiple design constraints, which are optimized by using the improved COOT bird optimum search algorithm (I-COOT). After that, we choose the cluster head (CH) according to the rank condition, thereby reducing the communication overhead in IoT networks. Additionally, we design chaotic golden search optimization algorithm (CGSO) for choosing the optimal best path between IoT nodes among multiple paths to ensure optimal data transfer from CHs. To conclude, we validate our proposed COOC routing algorithm against the different simulation scenarios and compare the results with existing state-of-the-art routing algorithms.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.

Real Time Cattle Health Monitoring Using IoT, ThingSpeak, and a Mobile Application

A new cattle health monitoring system called My Herd was developed using Internet of Things (IoT), Thing Speak, and a mobile application. My Herd continuously monitors the health status of cattle by collecting and analyzing physiological parameters such as body temperature, heart rate, and activity level. The data is then transmitted to the ThingSpeak cloud platform through IoT nodes, where it is analyzed using MATLAB algorithms. A mobile application is also developed to provide farmers with real time monitoring and alerts in case of any abnormality in cattle health. The proposed system was tested on a group of cattle, and the results showed that it can accurately detect and diagnose various health conditions. My Herd provides farmers with a cost effective and efficient solution for monitoring the health of their cattle, which can ultimately lead to improvement in productivity and profitability in the livestock industry.

GASE: A Lightweight Group Authentication Scheme With Key Agreement for Edge Computing Applications

Motivated by the fact that mass authentication is one of the desirable security features in the edge computing paradigm, we propose a lightweight group authentication protocol with a session key-agreement. Most of the previously proposed group authentication schemes (GASs) are heavyweight and do not support multiple authentications or key-agreement. On the other hand, our protocol, which is based on secret sharing scheme and aggregated message authentication code, is lightweight and provides multiple asynchronous authentications. Furthermore, we implement a simple key refreshing mechanism in which, in each session, a new session-key between an Internet of Things node and the authenticating server is established without the need for redistributing new shares. Our security analysis includes proving that our protocol provides group authentication, message forward secrecy, and prevents several attacks. Additionally, we present a formal automated verification using Verifpal tool. Furthermore, we show that our scheme has better performance than other relative schemes in terms of communication complexity, secret-share redistribution, and session key derivations.

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde–Buzo–Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

TSO Clustered Protocol to Extend Lifetime of IoT Based Mobile Wireless Sensor Networks

Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.

Security Challenges of Selective Forwarding Attack and Design a Secure ECDH-Based Authentication Protocol to Improve RPL Security

Today, we could describe the Internet of Things (loT) as the pervasive and global network that provides a system for monitoring, controlling, processing, and analyzing the data generated by IoT devices. The huge amount of data generated by IoT devices when transported and routed through the internet presents several challenges. One of the common routing protocols in IoT networks is RPL (Routing Protocol for Low Power and Lossy Networks), but it is prone to security issues and attacks. Due to the presence of sensitive data in IoT and its exchange in the open network, issues of privacy and security in this network should be given special attention. In addition, the nodes in the Internet of Things have limited resources, and the symmetric encryption key is used to encrypt the data of all nodes, which has security weaknesses. Therefore, an efficient and secure authentication scheme is needed so that IoT nodes can authenticate each other and share a secure session key. In this article, we review security aspects of RPL protocols focusing on selective forwarding attacks. Further, we propose a key agreement and authentication mechanism based on ECDH (Elliptic-Curve Diffie–Hellman). We show that our design is very secure, that it meets security requirements, and that it can withstand known attacks while having low costs for computation and communication.

Security Analysis of IoT Devices: From the system level to the logic level

IoT devices have become more and more widespread and are playing an important role in the evolving digital society. At the same time, IoT nodes are embedded devices that are always online, often making them interesting targets for potential attackers. Since many IoT applications are closely linked to humans, e. g., medical implants, smart appliances and speakers, and (autonomous) cars, it is of crucial importance that they are trusted to provide safety. Trust in embedded devices is therefore a challenge that concerns industry and policy makers alike. As engineers know, today’s embedded systems are often very complex, consisting of software and firmware, PCBs with digital and analog components and, of course, very complex ICs, which include CPUs that make a device “smart.”

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works

Secure authentication protocol for IoT applications based on blockchain technology

The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

The absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDN-based, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed FMDADM framework comprises four main modules and five-tier architecture. The first module implements an early detection process based on the average drop rate (ADR) principle using a 32-packet window size. The second module uses a novel double-check mapping function (DCMF), that aids in earlier attack detection at the data plane level. The third module is an ML-based detection application comprising four phases: data preprocessing, feature extraction, training and testing, and classification. This module detects DDoS attacks using only seven features: two selected and five newly computed features. The last module introduces an attack mitigation process. We applied the proposed framework to three test cases: one single-node attack test case and two multi-node attack test cases, all with real IoT traffic generated and deployed in Mininet-IoT. The proposed FMDADM framework efficiently detects DDoS attacks at high and low rates, can discriminate between attack traffic and flash crowds, and protects both local and remote IoT nodes by preventing infection from propagating to the ISP level. The FMDADM outperformed most existing cutting-edge approaches across ten different evaluation criteria. According to the experimental results, FMDADM achieved the following accuracy, precision, F-measure, recall, specificity, negative predictive value, false positive rate, false detection rate, false negative rate, and average detection time benchmarks:- 99.79%, 99.43%, 99.77%, 99.79%, 99.95%, 00.21%, 00.91%, 00.23%, and 2.64 μs, respectively.

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%).

A press-rotary triboelectric-electromagnetic hybrid energy harvesting device for indoor IoT node power supply and smart home control

A press-rotary triboelectric-electromagnetic hybrid energy harvesting device (PR-TEHD) is proposed to harvest micro-motion energy generated during manual press and rotation movements for IoT node powering and smart home control.

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things (IoT) based system, the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.

Exploiting Multihoming Capabilities in 5G-Enabled IoT Nodes

Exploiting Multihoming Capabilities in 5G-Enabled IoT Nodes

Energy-Efficient UAV Trajectory Planning based on Flexible Segment Clustering Algorithm

This paper plans the energy-efficient UAV trajectory when a UAV gathers data from massive IoT devices in a given area. The UAV trajectory design is addressed by two steps, i.e., IoT node clustering and UAV flight path planning for scanning the clusters, which are formulated as Cluster Minimization (CM) problem and Traveling Salesman Problem (TSP) in this work, respectively. The CM aims to contribute fewest clusters with minimal overlap to cover all the IoT devices and the per cluster size approaching the UAV communication coverage. On the other hand, the TSP seeks to design the shortest flight path to cover all the grouped clusters while minimizing energy consumption. Specifically, this work mainly focuses on the CM problem since the TSP issues have been well addressed in the past. In particular, we design a two-stage ILP optimization model to formulate the CM problem and propose two flexible clustering algorithms with low complexity, i.e., segment clustering (SC) and its variant, saying shifted SC (SSC). For the proposed ILP model and algorithms, we conduct extensive simulations under five different topologies and compare the performance results with existing methods. The simulation results indicate that the performance achieved by the proposed SSC algorithm is closest to the optimal results obtained from the ILP model. Moreover, it outperforms the existing methods under most topologies regarding cluster numbers, trajectory path length, and power consumption.

Secured IoT node design through protected ITULES family group-II (ULWC) implementation against physical attacks for ubiquitous computing

Secured IoT node design through protected ITULES family group-II (ULWC) implementation against physical attacks for ubiquitous computing

A privacy protection method for IoT nodes based on convolutional neural network

A privacy protection method for IoT nodes based on convolutional neural network

Localization of IoT nodes in LoRa using RSS measurements

Localization is used more and more often, not just used to navigate people but also for tracking objects for different purposes. The global positioning system is a well-known system but it is not appropriate for use in some situations such as indoor or built-up areas. A solution in these places is positioning using the internet of things. In recent years, it has gained popularity due to increased internet of things nodes. This paper provides an overview of different approaches to localization and provides a survey of data used for localization as well as different localization methods. At the end, we describe the experiment performed using LoRa technology with four anchor nodes. To estimate the position of the node received signal strength was measured and position was estimated by both trilateration and Min-Max algorithms. The experiment was performed in an open space.