Hardware Software Co-Simulation of an AES-128 based Data Encryption in Image Processing Systems for the Internet of Things Environment

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.

With technological advancements, the dependency on the Internet of things (IoT) has increased. The spectrum of IoT application domain becomes larger day by day. It converts the existing services into smart services. The evolution brings lot of security challenges hand in hand with IoT paradigms. This is due to the limitation of IoT devices like, low computational capabilities, small storage medium, less power back up and low processing speed, etc. To overcome these limitations, a proper authentication technique must be required to do the trust management in a heterogeneous IoT environment and secure all kinds of traffic. In this research document, a dynamic identity-based authentication approach is introduced for IoT environment, which utilizes very lightweight physical unclonable function, one-way hashing function and exclusive-OR(XOR) function to make the scheme lighter. Broadly accepted the Real or Random (RoR) model and the Burrows Abadi Needham logic (BAN logic) are used here, to check the security and the correctness of the devised protocol. In addition to that, the software simulation tool, Automated validation of Internet security protocols and applications (AVISPA) has proved that, the recommended protocol is safe and secure from various attacks. Furthermore, the comparative performance analysis exhibits the supremacy of the recommended scheme over the analogous schemes w.r.t computational power, memory usage, and communication overhead.

The Internet of Things (IoT) is an essential component of the digital age, particularly in ensuring reliable and efficient operations as well as the timely and precise recognition of anomalies within IoT systems. However, anomaly detection in time-series data—especially data collected by edge devices—poses several challenges, including concerns over data privacy and communication overhead. To address these issues, this research proposes a novel deep learning (DL)-based anomaly detection model capable of being trained in real-time on edge devices within an IoT environment. The framework ensures user privacy by enabling distributed edge devices to cooperatively train an anomaly recognition system. This study introduces a new, Intelligent Shark Smell Tuned Deep Isolation Forest (ISS-DIF), which effectively detects anomalies and identifies outliers in industrial IoT sensor data with high accuracy. The hybrid model isolates anomalies rather than profiling normal data, making it particularly suitable for identifying rare anomalies within large datasets. Industrial data are collected from real-world manufacturing environments using IoT edge devices. Following data collection, median filtering is applied to reduce noise, and min-max scaling is employed for data normalization. The ISS component allows for fine-tuning the hyperparameters of the Deep Isolation Forest (DIF) to optimize detection performance. The DIF model is tailored to enhance anomaly detection capabilities in sensor data from industrial IoT applications. For validation, 80% of the dataset was used for training, while the remaining 20% served as the test set. The results demonstrate that the proposed ISS-DIF framework achieved superior training accuracy of 99.30%, with precision of 97.89%, recall of 98.76%, and an F1-score of 97.21%, outperforming the testing metrics. This approach integrates real-time anomaly detection with the processing capabilities of edge devices, thereby improving IoT data analytics and providing a scalable, efficient solution that preserves privacy in irregularity recognition within IoT environments.

<abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report data wirelessly and powered perpetually using clean energy. The developed edge device utilizes solar energy harvesting mechanism through photovoltaic (PV) module, this approach will avoid periodical battery replacement and hence, the energy supplied to the sensor mode is not limited anymore. The implemented testbed is based on open-source hardware and software platforms while the simulation environment is based on MATLAB/SIMULINK 2019a. The effects of temperature and solar irradiance on the performance of the developed approach are examined in order to confirm the leverage of the proposed methodology scheme. The lifetime of the developed green IoT device is predicted based on the device’s activities, current consumption, and energy storage capacity. The obtained results showed that the battery lifetime is extended by 38-49% when the edge device runs on an independent power source.

CICIDS2017 is a well-known benchmark dataset for intrusion detection system (IDS) model creation. However, several studies revealed that the CICIDS2017 dataset still has several weaknesses. This research aims to improve R-CICIDS2017 by removing unrealistic features. After removing unrealistic features, we use a deep neural network (DNN) to predict the R-CICIDS-2017 dataset. We focus our predictions on attacks labeled denial of service (DoS) and distributed DoS (DDoS) in the dataset, as these attacks are the most frequent in the Internet of Things (IoT) world. Because implementation in IoT must be lightweight, we only use 0.1 of the entire dataset. We develop quantization for several data types to determine which model is best suited for running on IoT edge devices. The test results show that the Shannon entropy of the sampled and original datasets for each label is identical. This answer indicates that the DNN model for IDS on IoT with R-CICIDS2017 uses a low-complexity algorithm. The DNN test on CICIDS2017 and R-CICIDS2017 reveals that DNNs using unrealistic datasets exhibit better performance; however, this is not feasible for implementation. DNN with R-CICIDS2017 is optimal because its performance surpasses that of GNB and uses features that can be implemented in the IoT environment. Finally, the test shows that dynamic quantization has better accuracy loss than static int 8 quantization, and its model size is better than static float 16 quantization. We obtain a DNN model that achieves optimum efficiency for IDS deployment on edge devices within IoT networks, utilizing R-CICIDS2017.

Over the last few decades, Internet of Things (IoT) has become the spotlight area of research within the Industries and Academics. Primarily, IoT devices are characterized by small and nonscalable resources, including low processing capabilities, less internal memory, and short battery life. However, IoT applications demand extensive storage and faster response to ensure seamless and interoperable communication. Hence, Edge Computing and data/task offloading among the edge or cloud servers become promising, while also posing critical research challenges for edge-enabled large-scale IoT ecosystems. Several research activities have addressed the difficulties of determining an efficient and scalable data offloading strategy utilizing edge and cloud computing-supported technologies. This article focuses on the state-of-the-art edge IoT data offloading techniques, and optimization models in the heterogeneous IoT environment. We examine how edge and cloud-supported technologies can handle delay-sensitive IoT applications efficiently. Moreover, we introduce an IoT-based healthcare use case scenario to explain edge data execution and resource provisioning in IoT networks. Finally, we discuss several challenging issues and possible solutions to establish interoperable communication and computation for IoT applications.

Since the deployment of the Internet of Things (IoT), it has transformed everyday life by enabling intelligent environments that improve efficiency and automate services in domains such as agriculture, healthcare, smart cities, and industry. However, the rapid proliferation of IoT devices has introduced significant security challenges, largely driven by the heterogeneity of devices, resource constraints, and the increasing exposure of network communications. This work proposes a lightweight security protection mechanism for IoT networks based on trust modelling. The proposed approach integrates machine learning techniques to evaluate IoT node behavior using network-layer (Layer 3) traffic features under different labeling granularities, including binary, categorical, and subcategorical classifications. By focusing on network-layer observations, the model remains applicable across heterogeneous IoT devices while preserving a low computational footprint. In addition, the Common Vulnerability Scoring System (CVSS) is incorporated as a standardized vulnerability severity metric, enabling the integration of probabilistic security evidence with contextual information about potential impact. This combination allows the estimation of trust to reflect not only the likelihood of anomalous behavior but also its associated severity. Experimental evaluation was conducted using a representative IoT traffic dataset, multiple preprocessing strategies, and several classical machine learning models. The results demonstrate that aggregating traffic-based intrusion detection outputs with vulnerability severity metrics enables a more robust, flexible, and interpretable trust estimation process. This approach supports the early identification of potentially compromised nodes while maintaining scalability and efficiency, making it suitable for deployment in heterogeneous IoT environments.

The Internet of Things makes it possible to exchange information between people, things and things, people and things, and makes information security more and more important. However, there are few standards in the field of information security of the Internet of Things. Therefore, based on the characteristics and application requirements of the Internet of Things environment, this paper optimizes the Advanced Encryption Standard (AES) and builds the data encryption standard DESI(Data Encryption Standard in IoT) in the Internet of Things environment.. The analysis shows that DESI has better security and is therefore suitable for encrypting data in the IoT environment. Keywords: Internet of Things, encryption standard, AES, DESI, information security.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

The Federated Learning (FL) paradigm faces several challenges that limit its\napplication in real-world environments. These challenges include the local\nmodels' architecture heterogeneity and the unavailability of distributed\nInternet of Things (IoT) nodes due to connectivity problems. These factors\nposit the question of "how can the available models fill the training gap of\nthe unavailable models?". This question is referred to as the "Oblique\nFederated Learning" problem. This problem is encountered in the studied\nenvironment that includes distributed IoT nodes responsible for predicting CO2\nconcentrations. This paper proposes the Correlation-based FL (CorrFL) approach\ninfluenced by the representational learning field to address this problem.\nCorrFL projects the various model weights to a common latent space to address\nthe model heterogeneity. Its loss function minimizes the reconstruction loss\nwhen models are absent and maximizes the correlation between the generated\nmodels. The latter factor is critical because of the intersection of the\nfeature spaces of the IoT devices. CorrFL is evaluated on a realistic use case,\ninvolving the unavailability of one IoT device and heightened activity levels\nthat reflect occupancy. The generated CorrFL models for the unavailable IoT\ndevice from the available ones trained on the new environment are compared\nagainst models trained on different use cases, referred to as the benchmark\nmodel. The evaluation criteria combine the mean absolute error (MAE) of\npredictions and the impact of the amount of exchanged data on the prediction\nperformance improvement. Through a comprehensive experimental procedure, the\nCorrFL model outperformed the benchmark model in every criterion.\n

The explosion of Internet of things (IoT) devices has led to pretty much saturated network infrastructures, thus congestion often becomes severe, especially in applications where users require low latency, high throughput and real-time responses. Hence, traditional congestion control mechanisms like static routing protocols, Active Queue Management (AQM) and TCP variants are not suitable for this dynamic and heterogeneous IoT environment as they are reactive, rigid and cannot adapt to dynamic changes. The contribution is to investigate the potential of AI paradigms—such as ML, DL, RL and hybrid models- to provide Intelligent and proactive congestion Control in IoT systems. The paper compares the strengths and weaknesses of each method, such as RL’s generalizability and DL’s ability to capture patterns, as well as limitations in terms of scalability, interpretability, and computational resource requirements. Moreover, it emphasizes important research gaps in model generalization process, evaluation criteria and cross-platform fusion. Next steps in research discussions on the future of research take into account lightweight AI architectures, Explainable AI (XAI) frameworks, scalability of FL, and standard benchmarking data sets. The hybrid-AI congestion prediction model built in this work is validated across simulation tools and real-data sets and is observed to result in a significant decrease in latency, packet loss and energy consumption. This paper paves the way for scalable, intelligent and secure AI-based congestion management solutions for various IoT networks.

The Internet of Things environment requires the detection of events and communication of these detected events in real-time to process the reported events timely. It is a possibility of simultaneous occurrence of multiple events in the network. There is a need for cooperation among the devices to report the events from any place and at any time in the network. This requires efficient connectivity, reachability, and coverage in the network. An effective deployment strategy helps in achieving better network coverage. Moreover, to support the real-time reporting of the events in a heterogeneous Internet of Things environment, there is a need to dynamically determine the computation scheme to be used by the devices in the network. In this context, the paper proposes a hybrid computing scheme for an efficient quasi-based deployment technique in the Internet of Things environment, which focuses on dynamically choosing the type of computation scheme in real-time. The proposed work is implemented and evaluated on an IoT-based platform to show its efficacy.KeywordsInternet of ThingsQuasi-based deploymentCollaborative computing

Consumer electronics is a device designed to meet the everyday needs of individuals, including home appliances, smartphones, and laptops. The Internet of Things (IoT) environment is witnessing tremendous growth, driven by the simultaneous increase and continuous cyber-attack innovation. The imperative to fortify IoT devices against emerging threats became more prevalent as these threats became increasingly complex. In this context, threats include malware attacks, unauthorized access, and data breaches. Securing these devices requires robust encryption, regular software updates, and vigilant monitoring to protect sensitive information and ensure user safety. Threat detection using deep learning (DL) includes leveraging cutting-edge neural network models to detect and alleviate security threats within digital environments. DL, a branch of machine learning (ML), excels in processing abundant data to detect anomalies and patterns that might indicate possible threats. This technique improves classical threat detection techniques by reducing false positives and enhancing accuracy. By continuously analyzing data from various sources, such as network traffic and user behavior, DL techniques can quickly recognize and respond to emerging vulnerabilities, providing a proactive and adaptive security solution essential for protecting consumer electronics and IoT devices. This study develops a new Hybrid DL-based Threat Detection with Blockchain Technology (HDLTD-BCT) technique for secure IoT-based consumer electronics. The presented HDLTD-BCT technique can improve security among consumer electronics in the IoT environment. For this purpose, the HDLTD-BCT technique initially applies BC technology to monitor data flow in the IoT platform. The HDLTD-BCT technique scales the input data for threat detection using a min–max scalar. Besides, the detection process is performed by HDL, comprising a stacked LSTM-based denoising autoencoder (LSDAE) model. To enhance the HDL method’s performance, the Maritime Cyber Warfare Officers (MCWO) performs the hyperparameter selection process. The experimental results of the HDLTD-BCT technique can be validated utilizing the NSLKDD dataset. The simulation outcomes of the HDLTD-BCT technique exhibited a superior accuracy value of 99.68% compared to other DL models.

여러 사물 인터넷 기기들을 조합, 활용하여 다양한 서비스를 제공할 수 있게 된 환경에서 추천시스템은 사용자가 원하는 서비스를 선택하는 데 있어 도움을 줄 수 있다. 기존의 추천 시스템에 대한 많은 연구는 주로 개인 사용자 대상의 추천에 집중되어 있으나 사물 인터넷 환경에서는 개인과 그룹 모두 사용자가 될 수 있으므로 그룹에 대한 추천 방법이 필요로 하다. 본 연구는 사물 인터넷 환경에서 그룹사용자들의 서비스 선호도를 분석하고, 이를 바탕으로 특정 장소에서 서비스를 이용한 적이 없는 새로운 그룹 사용자에게 서비스를 추천할 수 있는 방법을 개발하였다. 본 연구에서는 그룹의 구성 정보를 기반으로 그룹들 간의 유사도를 측정하여 사용자 기반 협업 필터링을 적용하였다. 실험에서는 실제 사물 인터넷 테스트 베드 환경에서 수집된 데이터를 사용하였으며 실험 결과를 통해 제안한 서비스 추천 방법이 효과적임을 확인할 수 있었다.