Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.
Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes.
More than ever companies are challenged to rethink their offerings while simultaneously being provided with a unique opportunity for creating or recreating their product-service systems. This paper seeks to address how servitisation can utilise the third wave of Internet development, referred to as the Internet of Things (IoT), which may unlock the potential for innovative product-service systems on an unprecedented scale. By providing an analysis of this technological breakthrough and the literature on servitisation, these concepts are combined to address the question of how organisations offering product-service systems can reap the benefits that the IoT. An analysis of three successful IoT implementation cases in manufacturing companies, representing different industry sectors such as metal processing, power generation and distribution, is provided. The results of the empirical research presented in the paper provide an insight into different ways of creating value in servitisation. The paper also proposes a framework that is aimed at proving a better understanding of how companies can create value, and add it to their servitisation processes with, the data obtained by the IoT based solutions. From the value chain perspective, IoT aided servitisation enables organisations to extend their value chains in order better serve their customers which, in turn, might result in increased profitability. The article proposes further research avenues, and offers valuable insight for practitioners.
Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani
et al.
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
Abstract The internet of things (IoT) enabled a common operating picture (COP) across the various applications of modern day living. The COP is achieved through the advancements seen in wireless sensor network devices that were able to communicate through the network thereby exchanging information and performing various analysis. In IoT, the exchange of information and data authentication is only done through the central server there by leading to the security and privacy concerns. Chances of device spoofing, false authentication, less reliability in data sharing could happen. To address such security and privacy concerns, a central server concept is eliminated and blockchain (BC) technology is introduced as a part of IoT. This paper elaborates the possible security and privacy issues considering the component interaction in IoT and studies how the distributed ledger based blockchain (DL-BC) technology contribute to it. Applications of BC with respect to focused sectors and category were clearly studied here. Various challenges specific to IoT and IoT with BC were also discussed to understand blockchain technology contribution.
The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.
Abstract We are witnessing an unprecedented expansion of Internet of Things (IoT) market, whose nodes are already outnumbering human population several times. Despite the huge popularity of IoT, its further expansion is slowed down by a lack of viable power supply methods capable to replace wires or batteries. Due to IoT demand for alternative supply, energy harvesting (EH) gathers attention from scientific groups all around the world. In particular, thermoelectricity (TE) seems to be a natural and intuitive candidate for IoT owing to magnitude and omnipresence of heat losses and amenability to direct, vibrationless, noiseless and reliable conversion. This review provides up-to-date comparison and evaluation of a recent progress in the field of thermoelectricity, resulting primarily from multidisciplinary optimization of materials, topologies and controlling circuitry. The improvement in materials integrates two trends: nanostructural modulation of pre-existing, conventional thermoelectric materials and synthesis of novel ones. Regarding topology, TE responds better and better to miniaturization trend of semiconductor industry, driven by miniaturization trend, by proposing alternatives to conventional π-type topology. And finally, recently developed controlling circuits consume extremely low power while idle, exhibit above-90% efficiency and start-up with ultra-low input voltages. Combined, these improvements position TE closer to marketization than ever before.
Abstract The digital world is expanding rapidly and advances in networking technologies such as 4G long-term evolution (LTE), wireless broadband (WiBro), low-power wide area networks (LPWAN), 5G, LiFi, and so on, all of which are paving the way for the emergence of sophisticated services. The number of online applications is increasing along with more computation, communication, and intelligent capabilities. Although current devices in use today are also getting more powerful in terms of features and capabilities, but they are still incapable of executing smart, autonomous, and intelligent tasks such as those often required for smart healthcare, ambient assisted living (AAL), virtual reality, augmented reality, intelligent vehicular communication, as well as in many services related to smart cities, Internet of Things (IoT), Tactile Internet, Internet of Vehicles (IoV), and so on. For many of these applications, we need another entity to execute tasks on behalf of the user’s device and return the results - a technique often called offloading, where tasks are outsourced and the involved entities work in tandem to achieve the ultimate goal of the application. Task offloading is attractive for emerging IoT and cloud computing applications. It can occur between IoT nodes, sensors, edge devices, or fog nodes. Offloading can be performed based on different factors that include computational requirements of an application, load balancing, energy management, latency management, and so on. We present a taxonomy of recent offloading schemes that have been proposed for domains such as fog, cloud computing, and IoT. We also discuss the middleware technologies that enable offloading in a cloud-IoT cases and the factors that are important for offloading in a particular scenario. We also present research opportunities concerning offloading in fog and edge computing.
Bader Alobaywi, Mohammed G. Almutairi, Frederick T. Sheldon
Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems.
With the rapid development of communication technologies, the Internet of Things (IoT) is getting out of its infancy, into full maturity, and tends to be developed in an explosively rapid way, with more and more data transmitted and processed. As a result, the ability to manage devices deployed worldwide has been given more and advanced requirements in practical application performances. Most existing IoT platforms are highly centralized architectures, which suffer from various technical limitations, such as a cyber-attack and single point of failure. A new solution direction is essential to enhance data accessing, while regulating it with government mandates in privacy and security. In this paper, we propose an integrated IoT platform using blockchain technology to guarantee sensing data integrity. The aim of this platform is to afford the device owner a practical application that provides a comprehensive, immutable log and allows easy access to their devices deployed in different domains. It also provides characteristics of general IoT systems, allows for real-time monitoring, and control between the end user and device. The business logic of the application is defined by the smart contract, which contains rules and conditions. The proposed approach is backed by a proof of concept implementation in realistic IoT scenarios, utilizing Raspberry Pi devices and a permissioned network called Hyperledger Fabric. Lastly, a benchmark study using various performance metrics is made to highlight the significance of the proposed work. The analysis results indicate that the designed platform is suitable for the resource-constrained IoT architecture and is scalable to be extended in various IoT scenarios.
Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital systems have changed the way we live, play and work. Yet existing IoT platforms cannot evaluate whether an IoT app or environment is safe, secure, and operates correctly. In this paper, we present Soteria, a static analysis system for validating whether an IoT app or IoT environment (collection of apps working in concert) adheres to identified safety, security, and functional properties. Soteria operates in three phases; (a) translation of platform-specific IoT source code into an intermediate representation (IR), (b) extracting a state model from the IR, (c) applying model checking to verify desired properties. We evaluate Soteria on 65 SmartThings market apps through 35 properties and find nine (14%) individual apps violate ten (29%) properties. Further, our study of combined app environments uncovered eleven property violations not exhibited in the isolated apps. Lastly, we demonstrate Soteria on MalIoT, a novel open-source test suite containing 17 apps with 20 unique violations.
Samuel Marchal, Markus Miettinen, T. D. Nguyen
et al.
IoT devices are being widely deployed. But the huge variance among them in the level of security and requirements for network resources makes it unfeasible to manage IoT networks using a common generic policy. One solution to this challenge is to define policies for classes of devices based on device type. In this paper, we present AuDI, a system for quickly and effectively identifying the type of a device in an IoT network by analyzing their network communications. AuDI models the periodic communication traffic of IoT devices using an unsupervised learning method to perform identification. In contrast to prior work, AuDI operates autonomously after initial setup, learning, without human intervention nor labeled data, to identify previously unseen device types. AuDI can identify the type of a device in any mode of operation or stage of lifecycle of the device. Via systematic experiments using 33 off-the-shelf IoT devices, we show that AuDI is effective (98.2% accuracy).
Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feed-forward neural networks model for binary and multi-class classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.
Adversarial attacks have been widely studied in the field of computer vision but their impact on network security applications remains an area of open research. As IoT, 5G and AI continue to converge to realize the promise of the fourth industrial revolution (Industry 4.0), security incidents and events on IoT networks have increased. Deep learning techniques are being applied to detect and mitigate many of such security threats against IoT networks. Feed- forward Neural Networks (FNN) have been widely used for classifying intrusion attacks in IoT networks. In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network. Our analysis is performed using the BoT- IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. In our experimental results, the FNN outperforms the SNN for intrusion detection in IoT networks based on multiple performance metrics such as accuracy, precision, and recall as well as multi-classification metrics such as Cohen Cappa’s score. However, when tested for adversarial robustness, the SNN demonstrates better resilience against the adversarial samples from the IoT dataset, presenting a promising future in the quest for safer and more secure deep learning in IoT networks.
Behnam Khayer, Siamak Mirzaei, Hooman Alavizadeh
et al.
Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem.
The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Context Protocol (MCP) emerges as a critical enabler, providing standardized communication between LLMs and physical devices. We propose IoT-MCP, a novel framework that implements MCP through edge-deployed servers to bridge LLMs and IoT ecosystems. To support rigorous evaluation, we introduce IoT-MCP Bench, the first benchmark containing 114 Basic Tasks (e.g., ``What is the current temperature?'') and 1,140 Complex Tasks (e.g., ``I feel so hot, do you have any ideas?'') for IoT-enabled LLMs. Experimental validation across 22 sensor types and 6 microcontroller units demonstrates IoT-MCP's 100% task success rate to generate tool calls that fully meet expectations and obtain completely accurate results, 205ms average response time, and 74KB peak memory footprint. This work delivers both an open-source integration framework (https://github.com/Duke-CEI-Center/IoT-MCP-Servers) and a standardized evaluation methodology for LLM-IoT systems.
Shahran Rahman Alve, Muhammad Zawad Mahmud, Samiha Islam
et al.
The Internet of Things (IoT) is expanding at an accelerated pace, making it critical to have secure networks to mitigate a variety of cyber threats. This study addresses the limitation of multi-class attack detection of IoT devices and presents new machine learning-based lightweight ensemble methods that exploit its strong machine learning framework. We used a dataset entitled CICIoT 2023, which has a total of 34 different attack types categorized into 10 categories, and methodically assessed the performance of a substantial array of current machine learning techniques in our goal to identify the best-performing algorithmic choice for IoT application protection. In this work, we focus on ML classifier-based methods to address the biocharges presented by the difficult and heterogeneous properties of the attack vectors in IoT ecosystems. The best-performing method was the Decision Tree, achieving 99.56% accuracy and 99.62% F1, indicating this model is capable of detecting threats accurately and reliably. The Random Forest model also performed nearly as well, with an accuracy of 98.22% and an F1 score of 98.24%, indicating that ML methods excel in a scenario of high-dimensional data. These findings emphasize the promise of integrating ML classifiers into the protective defenses of IoT devices and provide motivations for pursuing subsequent studies towards scalable, keystroke-based attack detection frameworks. We think that our approach offers a new avenue for constructing complex machine learning algorithms for low-resource IoT devices that strike a balance between accuracy requirements and time efficiency. In summary, these contributions expand and enhance the knowledge of the current IoT security literature, establishing a solid baseline and framework for smart, adaptive security to be used in IoT environments.
The evolution of next-generation Internet-of-Things (IoT) in recent years exhibits a unique segment that wireless communication paradigms are oriented towards not only improved spectral efficiency transmission but also energy efficiency. This paper addresses these critical issues by proposing a novel communication model, namely power beacon-assisted energy-harvesting symbiotic radio. In particular, the limited energy primary IoT source communicates with its destination by first harvesting energy from a dedicated power beacon and then performing information exchange, while the backscatter device communicates by exploiting the available radio frequency emitted by the primary IoT source. The destination uses successive interference cancellation mechanisms to decode both its received signals. To assess the performance quality of the proposed communication model, we theoretically derive the coexistence outage probability (COP) in terms of highly accurate expressions and upper-bound and lower-bound approximations. Subsequently, we carry out a series of numerical results to verify the developed theory frameworks on the one hand, and on the other hand, analyze the COP performance against the variations of system key parameters (transmit signal-to-noise ratio, the time-splitting coefficient, the energy conversion efficiency factor, the reflection coefficient, and the coexistent decoding threshold). Our numerical results demonstrate that the proposed communication model can potentially work well in practices with reliable communication over 90% (COP is less than 0.1). Additionally, it also demonstrates that optimizing the reflection coefficient at the backscatter device can facilitate achieving minimal COP performance.
The swift expansion of Low-Power Internet of Things (LP-IoT) devices has significantly impacted industries such as smart homes, healthcare, agriculture, and industrial automation. In these interconnected environments, reliable wireless communication is essential for efficient data transmission. Channel estimation techniques play a crucial role in assessing the state of the wireless channel before transmitting data. While conventional techniques have been developed, they often struggle in dynamic environments due to substantial computational demands and limited adaptability. Recent advancements in Machine Learning (ML) offer promising improvements in wireless channel estimation for LP-IoT by capturing complex relationships and adapting to changing conditions of wireless channel. Considering the ML models as the potential substitute in the field of wireless channel estimation, this paper builds on our previous work by providing a detailed analysis of two advanced ML-based models, which demonstrates their applicability and reliability in practical indoor environments. While these models have shown potential, a comprehensive analysis of their accuracy in expanded environments has been lacking. To address this gap, we conduct a probability analysis to evaluate the models’ estimation accuracy and confidence levels, alongside a scalability analysis to assess performance as network size and complexity grow. The results confirm the effectiveness of these ML-based models and provide valuable insights into their suitability for large-scale LP-IoT applications. Ultimately, this study contributes to the advancement of intelligent LP-IoT communication systems by bridging the gap between theoretical research and real-world deployment.