Internet Of Things Layers Research Articles

ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

SummaryThe Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system.

Reliability on the Internet of Things with designing approach for exploratory analysis

The Internet of Things (IoT) proposes to transform human civilization so that it is smart, practical, and highly efficient, with enormous potential for commercial as well as social and environmental advantages. Reliability is one of the major problems that must be resolved to enable this revolutionary change. The reliability issues raised with specific supporting technologies for each tier according to the layered IoT reliability are initially described in this research. The research then offers a complete review and assessment of IoT reliability. In this paper, various types of reliability on the IoT have been analyzed with each layer of IoT to solve the issues of failure rates, latency, MTTF, and MTBF. Each parameter has a certain classification and perception as well as enhancement in efficiency, accuracy, precision, timeliness, and completeness. Reliability models provide efficient solutions for different IoT problems, which are mirrored in the proposed study and classified with four types of reliabilities. The field of IoT reliability exploration is still in its initial phases, despite a sizable research record. Furthermore, the recent case study of CHISS is elaborated with discovered behaviors including brand-new aspects such as the multifaceted nature of evolving IoT systems, research opportunities, and difficulties.

Al‐based energy aware parent selection mechanism to enhance security and energy efficiency for smart homes in Internet of Things

AbstractThe growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments.

From protocols to countermeasures: A comprehensive survey into IoT safety

The Internet of Things (IoT) has become a field with enormous potential, effect, and development with the emergence of smart cities, smart houses, and intelligent objects. Computer Information System Company (Cisco Inc). Projects that there will be 51 billion devices which are connected by 2021. However, the majority of those IoT gadgets are simple to alter and attack. These IoT devices are generally more susceptible to assaults than other types of endpoints such as desktops, mobile phones, or tablets because of their limitations in terms of storage, computing, and networking capability. The main IoT security concerns are presented and addressed in this study. Along with the methods utilized for connection, interaction, and administration, we examine and classify common security concerns about the IoT layers of architecture. In addition to the most recent assaults, challenges and innovative approaches, we describe the security necessities of the IoT. In addition, we connect and tabulate IoT safety problems with current literature-based remedies. Above all, we explore that blockchain technology, the software that powers Bitcoin, can play a major role in enabling the resolution of numerous IoT security issues. Difficulties along with unresolved research issues related to IoT security are also listed in this study.

Security Issues on Internet of Things (IoT): A Recent Challenges and Countermeasures

Abstract: This paper contains a surveyas well as an investigation and examination of the present situation statesecurity for the Internet of Things (IoT). The Internet of Things aims to connect anybody with anything, anyplace. In contrast to the fixed Internet, IoT uses multiple wired and wireless networks to connect a large number of machines, limited-resource devices, and sensors. The realization, network, and application layers are the three hypothetical layers that make up an IoT.The security challenges that exist within and across these layers are described in this study. There are also several security ideas that need be implemented at each tier. Previous work on ensuring security for each IoT layer, as well as related countermeasures, is also examined. Finally, the report discusses potential acquisition strategies.

Securing Smart Agriculture: Proposed Hybrid Meta-Model and Certificate-based Cyber Security Approaches

The Internet of Things is a decentralized network of physically connected devices that communicate with other systems and devices over the internet. As the number of IoT-based devices continues to grow at an exponential rate, this technology has the potential to improve nearly every aspect of daily life, from smart networks and transportation to home automation and agriculture. However, the absence of adequate security measures on all levels of the IoT poses a significant security risk, with the potential for cyber-attacks and data theft. While scholars have suggested various security measures, there are still gaps that need to be addressed. In this study, we analyzed previous research and proposed metamodels for security, IoT, and machine learning. We then proposed a new IoT-based smart agriculture model with integrated security measures to mitigate cyber- attacks and increase agricultural output. Our model takes into account the unique features of the smart farming domain and offers a framework for securing IoT devices in this specific application area. Moreover, in order to mitigate a range of cyber security attacks across various layers of IoT, we introduced two certificate-based schemes named CBHA and SCKA for smart agriculture. A comparative analysis of their security with existing literature demonstrates their superior robustness against diverse attacks. Additionally, security testing utilizing scyther affirms the resilience and security of both CBHA and SCKA, establishing them as viable options for ensuring security in smart agriculture

An Enhanced Lightweight Security Gateway Protocol for the Edge Layer

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review.

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

IoT-based data quality and data preprocessing of multinational corporations

Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge.

Research on security encryption mechanism of physical Layer based on iterative quantization method

Third-party eavesdropping is a unsolved problem in the process of data transmission in the physical layer of IoT (Internet of Things) in Power Systems. The security encryption effect is affected by channel noise and the half-duplex nature of the wireless channel, which leads to low key consistency and key generation rate. To address this problem, a reliable solution for physical layer communication security is proposed in this paper. First, the solution improved the key consistency by dynamically adjusting the length of the training sequence during feature extraction; Second, using an iterative quantization method to quantify the RSS (Received Signal Strength) measurements to improve generation rate of the key. Finally, based on the short-time energy method for the extraction of wireless frame interval features, by monitoring the change of inter-frame interval features, we can quickly determine whether there is an eavesdropping device into the link. Simulation results show that the reciprocity of legitimate channels R (R will be explained in detail in the following) is improved by 0.1, the key generation rate is increased by about 70%, and the beacon frames are extracted from the wireless link with good results compared to the methods that do not use dynamic adjustment of the pilot signal during the channel probing phase. The result shows that this method can effectively prevents third-party eavesdropping, effectively improves the key consistency and generation rate, and effectively implements beacon frame detection.

Blockchain- Based Secure and Efficient Scheme for Medical Data

Internet of Things (IoT) fog nodes are distributed near end-user devices to mitigate the impacts of low delay, position awareness, and spatial spread, which aren't permitted by numerous IoT apps. Fog computing (FC) also speeds up reaction times by decreasing the quantity of data sent to the cloud. Despite these advantages, FC still has a lot of work to do to fulfill security and privacy standards. The constraints of the FC resources are the cause of these difficulties. In reality, FC could raise fresh concerns about privacy and security. Although the Fog security and privacy problems have been covered in several articles recently, most of these studies just touched the surface of these difficulties. This paper provides a unique solution for the authentication of data by using hyperledger fabric. The fog layer store data transferred by the IoT layer and calculate the hash value. These hash values are now stored in hyperledger fabric for authentication purposes. The proposed model results compared with lewako’s and Fan’s scheme and found that the proposed model has 25.00 % less encryption time, 09.3 % less decryption time, 17.48 % less storage overhead, and 23.38 % less computation cost as compared to Fan’s scheme.

An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system

Fog computing is a modern research trend that brings cloud computing services closer to the network edge. It serves as middleware between cloud and Internet of Things (IoT) devices by bringing application services and computing resources closer to the device generating the data. However, there are several real-time issues that have not been completely resolved, including load balancing, network utilization, energy consumption, traffic overhead, computational cost, latency, and so on. To address these issues, we propose a novel DDQ-CLF algorithm-based fog server classification model. The proposed fog-enabled secure healthcare system comprises three main layers: the IoT layer, the fog layer, and the cloud layer. In the IoT layer, medical healthcare data containing confidential details of patients is collected through sensors connected to smartphones. In the fog layer, the load balancer agent plays a main role in determining the suitable fog server to process the incoming request based on server characteristics and predefined conditions. Finally, the health status results obtained from the fog layer are communicated to the cloud through a proxy server. These procedures make the system more secure for communicating confidential information through the network layers. The performance of the proposed DDQ-CLF algorithm is evaluated using different indicators, such as traffic overhead, energy consumption, computational cost, makespan, latency, network utilization, LBL, and ARU. The simulation results demonstrate that the proposed DDQ-CLF algorithm achieves a superior performance rate compared to other techniques in terms of all metrics, particularly with the best LBL of about 73.21 %.

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud-IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud's processing, storage, and networking services close to end users. This hierarchical edge-fog-cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud-fog-IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model's variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning's exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

A comprehensive study on cybersecurity challenges and opportunities in the IoT world

AbstractIt has become possible to link anything and everything to the Internet in recent decades due to the expanding Internet of Things (IoT). As a result, our usage of technology has changed a lot, causing digital disruption in the real world. IoT allows drones, sensors, digital set‐top boxes, surveillance cameras, wearable technology, and medical equipment to be connected to the internet. Healthcare, manufacturing, utilities, transportation, and housing are among the various sectors that has become intelligent. Recently, we have seen a surge in cybersecurity challenges and opportunities for the improvement of various IoT applications. Although cybersecurity and the IoT are extensively researched, there is a dearth of studies that exclusively focus on the evolution of cybersecurity challenges in the area of AI and machine learning, blockchain and zero trust, lightweight security, integration of IoT with 5G networks, and many more in the IoT world. The availability of environment‐capturing sensors and internet‐connected tracking devices allows for private life surveillance and cloud data transmission. Therefore, a significant problem for researchers and developers is to ensure the CIA (Confidentiality, Integrity, and Availability) security triangle for people. This paper presents a comprehensive study of cybersecurity applications, challenges, and opportunities in the IoT world. The IoT architectural layer, attacks against the IoT layer, and related issues are highlighted. Furthermore, cybersecurity issues and challenges in IoT along with the strength and weaknesses of existing techniques are discussed in detail. Our study will provide insight into various current cybersecurity research trends in the IoT world.

Ensuring construction material provenance using Internet of Things and blockchain: Learning from the food industry

Ensuring material provenance is widely considered a promising solution to the persistent issues related to material fraudulence in the construction industry. However, current strategies of managing construction logistics and supply chain perplex provenance tracing and tracking by adding too many intermediaries and using low technologies. By learning from the food industry which shares similar complexity, prolonged supply chain, and numerous stakeholders, this research aims to develop a framework deployable for material provenance tracing and tracking in the construction industry. It does so by mixing the uses of (a) cross-sectoral learning; (b) design science research; and (c) internet of things (IoT) and blockchain technology. The developed framework has four interconnected layers, namely the business layer with different stakeholders and activities, the IoT layer to collect the provenance footprints, the blockchain layer with a mainchain to store open provenance data and sidechains to store organizational private data, and the application layer to facilitate the management of quality, safety, payment, logistic and supply chain, and sustainability. The underpinning philosophy of the framework is to capture the IoT-driven provenance footprints and put them in custody in blockchain. The framework is further illustrated and refined by using a pilot construction project in Hong Kong, which was endeavored to track steel provenance from its adjacent Pearl River Delta, the so-called “World's Factory”. The framework shows enormous prospects, e.g., adopting digital twins, lifecycle traceability, improved efficiency, and transparent operations, meanwhile facing challenges, e.g., under-developed regulations, scalability issues, and information leakage risks, which all call for future research.

A Survey on Zero-Knowledge Authentication for Internet of Things

The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legitimacy of nodes and protect data security. Naturally, the authentication factors always include various sensitive users’ information, such as passwords, ID cards, even biological information, etc. How to prevent privacy leakage has always been a problem faced by the IoT. Zero-knowledge authentication is a crucial cryptographic technology that uses authenticates nodes on the networks without revealing identity or any other data entered by users. However, zero-knowledge proof (ZKP) requires more complex data exchange protocols and more data transmission compared to traditional cryptography technologies. To understand how zero-knowledge authentication works in IoT, we produce a survey on zero-knowledge authentication in privacy-preserving IoT in the paper. First, we overview the IoT architecture and privacy, including security challenges and open question in different IoT layers. Next, we overview zero-knowledge authentication and provide a comprehensive analysis of designing zero-knowledge authentication protocols in various IoT networks. We summarize the advantages of ZKP-based authentication in IoT. Finally, it summarizes the potential problems and future directions of ZKP in IoT.

Framework and Capability of Industrial IoT Infrastructure for Smart Manufacturing

The Internet of Things (IoT) and smart manufacturing (SM) are mutually reinforcing. The establishment of IoT-based common facilities for SM is the premise of building SM system. Industrial IoT (IIoT) infrastructure for SM refers to common facilities based on IoT that support SM in industries or sectors, and plays a dominant role and faces severe challenges in the intelligence of SM. The infrastructure is independent of the products and production process in a specific factory. This paper develops conceptual and capability frameworks of IIoT infrastructure from a unified perspective of IIoT-related SM industries. These frameworks reflect relationships between IIoT and SM with in-depth relationships among basic facilities of IIoT infrastructure and lay the foundation of SM. In this paper the common characteristics and high-level requirements with respect to the different IoT layers of IIoT infrastructure are analyzed, and the capability framework and relevant capabilities of IIoT infrastructure are summarized according to the characteristics and requirements. In order to help service providers implement their systems to meet the needs of SM, the existing and newly developed IIoT infrastructure are integrated partially or in whole according to the intelligence level, so as to provide technical guidance for stakeholders to apply emerging ICTs to SM.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance. The employment of machine learning-empowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework. The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency. For economic evaluation of the proposed framework with minimal efforts, EdgeCloudSim and FogNetSim++ simulation environments are deployed in this study. The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach, improved response rate, and prediction mechanism. Moreover, the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

A Semantic Gateway for Internet of Things Interoperability at the Application Layer

Abstract Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.