Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols E-Book
Akashdeep Bhardwaj
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- Herausgeber: Bentham Science Publishers
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Smart Home and Industrial IoT Devices: Critical Perspectives on Cyber Threats, Frameworks and Protocols provides an in-depth examination of the Internet of Things (IoT) and its profound impact on smart homes and industrial systems. The book begins by exploring the significance of IoT in smart homes, followed by an analysis of emerging cyber threats targeting smart homes and cyber-physical systems. It presents AI and machine learning-based frameworks for monitoring water quality and managing irrigation in agriculture, highlighting their role in IoT ecosystems. The text also discusses a framework to mitigate cyber-attacks on robotic systems and introduces a multinomial naive Bayesian classifier for analyzing smart IoT devices. Dataflow analysis and modeling experiments are detailed, along with a comparison of IoT communication protocols using anomaly detection and security assessment. The book concludes with a discussion on efficient, lightweight intrusion detection systems and a unique taxonomy for IoT frameworks. This book is ideal for students, researchers, and professionals seeking to understand and secure IoT environments.
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PREFACE
In the ever-evolving landscape of technology, the proliferation of Smart Home and Industrial IoT (Internet of Things) devices has become a defining hallmark of our era. These interconnected gadgets promise unparalleled convenience, efficiency, and automation in our daily lives and industrial processes. From smart thermostats that regulate our home temperatures to industrial sensors that optimize manufacturing processes, these devices have transformed the way we interact with our environment. They hold the power to enhance our lives and redefine our industries, but they also bring with them a complex set of challenges that demand our immediate attention.
The title of this book, "Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks, and Protocols", encapsulates the essence of our exploration into this technological realm. This book is an endeavor to shed light on the multifaceted landscape of Smart Home and Industrial IoT devices, focusing particularly on the critical perspectives that have emerged because of their proliferation.
At the heart of this book lies a crucial examination of the potential cyberthreats that have surfaced with the rapid integration of these devices into our lives and industries. The interconnected nature of IoT devices, while offering unparalleled convenience and data-driven decision-making, also creates a myriad of security vulnerabilities. Cyberattacks on these devices can have far-reaching consequences, both at the individual level in our homes and at the industrial scale in our factories. Understanding these threats and the strategies to mitigate them is paramount in safeguarding our privacy, security, and economic stability.
Moreover, this book delves into the frameworks and protocols that underpin the functioning of Smart Home and Industrial IoT ecosystems. The selection and implementation of these frameworks play a pivotal role in determining the effectiveness of these devices. We explore the standards, communication protocols, and architectural paradigms that drive IoT systems, offering insights into the advantages and limitations of each approach.
The critical perspectives presented within these pages are not intended to dissuade the adoption of Smart Home and Industrial IoT devices but rather to inform and empower individuals, organizations, and policymakers. By delving into the complexities of these technologies, we can make more informed decisions, build more resilient systems, and harness the true potential of the IoT revolution.
In this book, we bring together a diverse array of voices, from cybersecurity experts to industry practitioners, to provide a comprehensive and holistic view of the Smart Home and Industrial IoT landscape. Our aim is to provide readers with the knowledge and insights required to navigate the challenges and opportunities presented by these technologies.
As we embark on this journey through the world of Smart Home and Industrial IoT devices, we invite readers to approach this exploration with a critical eye and an open mind. The future of IoT is rich with possibilities, but it is also fraught with challenges. By gaining a deeper understanding of the cyberthreats, frameworks, and protocols, we can collectively shape a safer, more efficient, and connected world for generations to come.
We hope this book serves as a valuable resource for all those intrigued by the ever-expanding world of Smart Home and Industrial IoT devices and the critical perspectives that surround them.
Significance of IoT for Smart Homes and Cities
Akashdeep Bhardwaj1,*Abstract
The integration of the Internet of Things (IoT) has ushered in a transformative era for both residential environments and urban landscapes, re-defining the way people live, work, and interact within them. This chapter delves into the profound significance of IoT in the realm of smart homes and cities, exploring the multifaceted impact it has on enhancing efficiency, sustainability, and quality of life. In the context of smart homes, IoT technology seamlessly intertwines household devices, appliances, and systems, creating a networked ecosystem that enables automation, remote control, and intelligent decision-making. This interconnectivity offers residents unprecedented levels of convenience, energy efficiency, and security while paving the way for innovative services like predictive maintenance and health monitoring. Extending the scope to smart cities, this chapter explains how IoT transforms urban environments into dynamic, data-driven entities. Through an intricate web of sensors, actuators, and data analytics, cities can optimize resource allocation, traffic management, waste disposal, and energy consumption. This leads to reduced congestion, improved air quality, and a more sustainable urban infrastructure. However, the integration of IoT into the fabric of smart homes and cities also raises significant challenges pertaining to data privacy, security, and interoperability. These complexities necessitate robust governance frameworks and technological solutions to ensure the responsible and secure implementation of IoT technologies.
INTRODUCTION
According to forecasts by Thales, there will be 83 billion Internet of Things (IoT) devices worldwide by 2024, up from 35 billion in 2020 [1]. According to Indian Retailer, IoT implementations will account for 20% of all devices by 2024 [2]. With the current cloud strategy, this rapid, amazing, and unparalleled development is not sustainable. Instead, a novel computing paradigm that can handle data quickly and efficiently without compromising delivery or security is needed. Applications based on the Internet of Things are producing unprecedented amounts and types of privacy-sensitive data from the devices of billions of end users. Concerns about low latency speeds, large burst rates, and geographically dispersed sites have resulted in an alarming situation. To satisfy the ever-changing
demands of end users, the next generation of cloud paradigms is anticipated to be more responsive and energy efficient. In addition to IoT, the Internet of Everything (IoE) and Web of Things (WoT) are beginning to link commonplace items and gadgets to cloud-hosted service apps [3, 4].
The sustainability of cloud and smart fog delivery services is impacted by the growth of data centres, which also raises delivery costs and carbon footprints. For edge computing, Cisco came up with the phrase “fog computing”. A developing IoT paradigm is fog computing technology [5]. Centralized data processing would be unable to scale up and meet the requirements of such fog environments, as fog nodes and IoT devices generate data logs, and WoT and IoE bring every object online. The solution suggested by the scientific and commercial communities to deal with the problems is fog computing. Fog leverages the actual end-user device's network sensors to gather information and enable remote monitoring. Numerous industries, including healthcare, manufacturing, retail, finance, consumer products, and communication applications, have seen a sharp increase in the use of this technology. Corporates throughout the world are frantically looking for ways to run effective applications on IoT and fog technologies.
By providing computing, application connectivity, networking, storage, decision-making, data processing, and management close to the IoT device producing the data, smart fog computing closes the business gap between cloud and IoT devices. To solve these concerns, other computing paradigms akin to smart fog computing, such as Cloud of Things, edge computing, mist computing, or cloudlets, have also been proposed. These fog computing requirements cannot be met by traditional cloud systems. Current solutions call for transmitting data for processing from the network edge IoT node to the data centre. As a result, latency increases as several IoT devices' data streams take up available bandwidth and interfere with service delivery. Because cloud computing is extended to the network's edge and reduces latency and congestion, smart fog computing has emerged as the answer to the Internet of Things. Delivery and security threats can be reduced by lowering the amount of data sent over the Internet. A standard for fog computing with an open architecture is being promoted by the OpenFog Consortium [6]. This approach suggests creating multi-layered, hierarchically distributed fog clusters with a swarm of computational clients and edge nodes. Higher-layer fog clusters gather, and process data filtered from lower levels, while each cluster handles data from a single geographic area of the device farm.
These tiers carry out distinct logical tasks like control, storage, monitoring, local operations, and business decision-making. The network, storage, and computing are extended to the network edge via this system-level architecture. To do this, data must be delivered via intelligent edge devices rather than via the Internet to cloud data centres. This expedites decision-making and signifies a departure from conventional design that relies on cloud-based apps and the Internet. The following are necessary components of a successful fog computing architecture.
Low Latency: Performance can be significantly impacted by any delays in data processing, data transmission to the cloud data centre, and data return to the application [7].Applications in manufacturing sector that monitor health, respond to emergencies, shut down production floors in real time, or restore electrical service must have a minimal latency of even milliseconds.Bandwidth conservation: Large computing and storage resources are needed for Big Data, predictive analytics, and data mining; these resources are typically found in the cloud. Noise and false positives are minimized in logs produced by IoT devices and real-time systems, such as Boeing airplanes that produce 10 TB of data in just 30 minutes of flight time or offshore oil rigs that can produce 500 GB of data in a week. Sending this much data to the cloud from hundreds of thousands of edge devices and nodes is not feasible [8].Data Security: Both in transit and at rest, created IoT data must be private, secure, and compliant. On the unprotected Internet, cyber security risks like man-in-the-middle assaults, sniffers, and denial-of-service attacks are serious problems. Data privacy is largely governed by law. Industry legislation in some nations prohibits offsite data storage, collecting, or disclosure for commercial use, such as the USA's Federal Information Security Management Act 2002, Canada's Personal Information and Electronic Documents Act, and the UK's General Data Protection Regulation [9].Standardize Communications: While data transfer occurs in IoT nodes and devices via Bluetooth, Wireless, ZWave, or even BigZee, cloud devices interact over TCP/IP Protocol using IP addressing.Location of data processing: Analyzing data obtained near the device node can frequently be the difference between averting catastrophe or cascading failures. Rugged IoT devices are necessary because fog nodes, which gather data from IoT devices, are typically dispersed over a wide geographic area with a variety of extreme weather conditions.Over the Internet, cloud computing providers offer hosted, scalable enterprise applications. IoT is largely responsible for the rapid expansion of smart fog computing technology, which localizes physical computing, networking, and storage together with analytics and machine learning. To manage the fog data demand and delivery, cloud service providers such as Amazon, Google, Amazon, IBM, and Microsoft have enabled cloud-based delivery models for SaaS, PaaS, and IaaS. In the field of computation technology, several paradigms have previously been created while keeping in mind the idea of fog computing.
Mobile cloud computing (MCC)and mobile edge computing (MEC)are two cutting-edge technologies that will be essential to the deployment of 5G mobile [10, 11]. These are thought to be the most direct extensions of edge and cloud computing capabilities. As the number of smartphone users has increased recently, end users are employing their handheld devices to install and run apps at the network edge rather than utilizing traditional Internet and cloud data centers. The mobile devices that generate the data logs frequently have limited computing, energy, storage, and network capacity. Consequently, rather than executing those programs locally, data processing is frequently carried out, and application data is processed outside of mobile devices. By supplying the required processing power for mobile apps on end-user portable devices, MCC facilitates remote execution. As a result, mobile devices, radio access networks, and authorized third-party apps are all included in the MCCC design. IoT applications, video surveillance, geolocation services, augmented reality, local content delivery, and data caching are a few use case examples. The primary goals of MCC's feature set are to increase the capacity for remote processing and multitenancy to offer a wider range of application services, get around restrictions on mobile resources, and increase battery life.
Using cloudlets is another approach to fog computing. These consist of lightweight agents inside a three-tier architecture of middleware that includes mobile devices, cloudlets, and the cloud. Cloudlets are built on top of conventional cloud technology, have low end-to-end latency and sufficient compute power, and are deployed for exclusive self-management. Cloudlets are not the same as fog computing technology since cloudlets do not require application virtualization, which is energy-intensive, resource-intensive, and does not support offline operation. These are compact, fully operational hosting centers with servers and virtual machines that may offer computing and dynamic provisioning services. Because they are close to the data source, have lower latency, can improve availability and service reliability, and are portable with integrated security measures, micro data centers can benefit fog and other technologies. These centers can support new services and applications in multi-tenancy scenarios while also reducing bandwidth consumption through data compression, local processing, and analytics.
For instance, the fog environment found in smart cities consists of dispersed sites, heterogeneous networks, and IoT devices and nodes that are only loosely connected. This includes gathering, processing, and optimizing data from Internet of Things devices. Either Big Streams (information gathered from Internet of Things nodes) or Big Data (enduring information preserved with judgment and saved on cloud storage) comprise the data. To make informed decisions quickly, this also entails seeing patterns in real time and using predictive analysis.
This may make it possible to analyze city infrastructure in real time and may present new opportunities for governance. IoT networks, which are made up of intelligent IoT nodes and devices, are currently the source of data aggregates. To be processed and stored, this data is transferred to cloud servers via the Internet. Cloud data centres with high scalability provide Big Data processing infrastructure and computational applications. However, cloud data processing falls short of meeting IoT delivery requirements when processing huge volumes of data is necessary due to on-demand scalability and distributed across numerous locations with minimal latency.
Contributions of this chapter
Holistic Exploration of IoT Impact: This work provides a comprehensive exploration of the transformative impact of IoT technology on both smart homes and cities. By examining various facets such as efficiency, sustainability, and quality of life, the chapter offers a nuanced understanding of the multifaceted contributions of IoT in residential and urban environments.Integration of Predictive Services: The chapter highlights the innovative services enabled by IoT in smart homes, particularly predictive maintenance and health monitoring. This emphasis on proactive solutions distinguishes the work by showcasing how IoT transcends traditional automation to offer tangible benefits in terms of cost savings, safety, and well-being for residents.Data-Driven Optimization of Urban Environments: In discussing smart cities, the chapter elucidates how IoT technologies facilitate data-driven optimization across domains such as resource allocation, traffic management, waste disposal, and energy consumption. By providing specific examples, the chapter demonstrates the tangible improvements in urban efficiency, sustainability, and livability enabled by IoT integration.Addressing Challenges and Complexities: The chapter acknowledges the challenges inherent in integrating IoT into smart homes and cities, particularly concerning data privacy, security, and interoperability. By offering insights into potential solutions and governance frameworks, the work goes beyond the mere enumeration of benefits to address the complexities and ethical considerations of IoT implementation, thus contributing to a more holistic understanding of the topic.Clear Contributions to Existing Literature: The second last paragraph of the introduction explicitly outlines the contributions of the work to the existing literature. By emphasizing how the chapter fills gaps and extends the current understanding of IoT in smart homes and cities, the work asserts its novelty and significance within the research landscape.Problem Statement
The integration of the Internet of Things (IoT) into smart homes and cities presents a multitude of opportunities for enhancing efficiency, sustainability, and quality of life. However, this integration also brings forth significant challenges, particularly concerning data privacy, security, and interoperability. As IoT technologies continue to proliferate, there is a pressing need to address these challenges and develop robust governance frameworks and technological solutions to ensure the responsible and secure implementation of IoT in residential and urban environments.
Scope
This chapter aims to delve into the significance of IoT in the realms of smart homes and cities, focusing on its transformative impact and the specific contributions it brings to these environments. The scope encompasses both the micro-level perspective of smart homes, where IoT technology seamlessly integrates household devices and systems to enable automation, remote control, and intelligent decision-making, as well as the macro-level perspective of smart cities, where IoT transforms urban environments into dynamic, data-driven entities optimized for resource allocation, traffic management, waste disposal, and energy consumption. The chapter explores innovative services enabled by IoT in smart homes, such as predictive maintenance and health monitoring, demonstrating the proactive nature of IoT-driven solutions in enhancing residents' quality of life. In the context of smart cities, specific examples are provided to illustrate how IoT technologies facilitate improvements in urban efficiency, sustainability, and liveability.
While highlighting the transformative potential of IoT, the chapter also acknowledges the challenges posed by its integration, including issues related to data privacy, security, and interoperability. By addressing these challenges within the scope of the discussion, the chapter aims to provide valuable insights and guidance for stakeholders involved in smart home and city initiatives, thereby contributing to the advancement of IoT technologies in residential and urban environments. These are the primary findings from this research; while deploying fog computing with IoT devices, as opposed to cloud computing, the processing time reduced from the initial 29.45 to under 7 seconds, which is 75% less. The hops traversed also reduced from 59 to 5 hops, which is 91% less, while the bandwidth usage reduced from 244 to 9 kbps, which is around 97% less.
The introduction section sets the stage for a comprehensive exploration of the significance of IoT in smart homes and cities; however, it could benefit from a clearer delineation of the specific challenges and opportunities presented by IoT integration in these environments. Perhaps the authors could provide a more explicit overview of the key problems addressed in the chapter. Additionally, while the second last paragraph highlights the contributions to the existing literature, integrating some of these insights earlier in the introduction could enhance clarity and set clearer expectations for readers regarding the novel aspects of the work.
Literature Survey
For this research, the authors identified 280 research papers published from 2012 on IoT and fog computing using a four-level selection process and shortlisted 135 relevant research, as illustrated in Fig. (1) below:
Fig. (1)) Four-stage selection criteria.Table 1 below describes the overall spread of the research papers and the subcategories that were selected. The latest reviews are presented in the section below.
Naha et al. (2018) [12] presented fog and cloud computing trends along with their technical differences. The authors investigated fog computing architectures and components in detail. This involved defining the role of each component. Fog computing taxonomy was also proposed in this paper, and a discussion on existing research papers and their limitations were presented. The authors also reviewed open issues and gaps in fault tolerance, resource scheduling and allocation, simulation of tools and fog-based microservices.
Yeow et al. (2018) [13] proposed a thematic taxonomy of key characteristic features related to the current decentralized consensus systems. The author analyzed the common and variant features using the criteria from their literature survey. Several open issues based on decentralized consensus for edge-centric IoT and centralization risk and deficiencies in blockchains were also proposed.
Martínez et al. (2023) [14] embarked on a pioneering study aimed at the development of an IoT-powered prototype. This innovation stands poised to revolutionize monitoring practices by enabling real-time parameter tracking without any interruption to the incubator's operations. Of paramount concern is the assurance that the infant's well-being remains unaffected. Central to the endeavor is the meticulous design of an IoT architecture, accompanied by rigorous safety considerations for each constituent element integrated into the prototype. The study's methodology involves comprehensive experimentation, encompassing both simulated and real-life scenarios. Comparative analysis is executed against the benchmark set by the certified measurement instrument Fluke INCU II. Employing non-parametric statistical techniques, the data collected by the prototype is scrutinized alongside the established measurements, affording insights that inform potential areas for enhancement.
In the realm of the Internet of Vehicles (IoV), the seamless exchange and dissemination of traffic information among vehicle nodes are imperative. However, prevailing challenges within the current IoV framework hinder the efficient synchronization of traffic data between nodes while also raising concerns about the propagation of false information by malicious nodes. Addressing these intricate issues, Wang et al. (2023) [15] introduced a novel trust mechanism for the Internet of Vehicles, leveraging the Hotstuff consensus algorithm as its foundation. The proposed approach involves a multi-step process. Initially, the evaluations provided by vehicles are aggregated by a roadside unit (RSU) to formulate trust values and construct data blocks. Building upon the Hotstuff consensus algorithm, the authors have innovatively incorporated a reputation mechanism, thereby refining the algorithm's functionality. This enhancement facilitates the probabilistic selection of leader nodes based on their reputation, ultimately bolstering system throughput and diminishing consensus-reaching delays amongst nodes. Empirical evaluation of the proposed scheme underscores its efficacy. Notably, it demonstrates a marked improvement in consensus efficiency within the IoV system. Furthermore, the mechanism exhibits a heightened capability to counteract the influence of malicious nodes, fortifying the system's overall resilience. Most notably, the introduced approach ensures the expeditious and secure exchange of data amongst vehicles, underscoring its potential to enhance the IoV landscape.
The field of human face image identification has advanced quickly due to the rapid advancement of information technology. Using computers and information technology, face recognition has been effectively used recently in several different fields. This kind of application is very important to the process of digital forensics investigation because it can identify patterns of a human face, such as the distance between eyes, the bridging of the nose, and the shape of the lips, ears, and chin, based on partial matching of images that would be in 24-bit color image format. Principal component analysis (PCA) reduces the dimension of the benchmark dataset, while genetic algorithms and neural nets optimize the searching patterns of image matching and produce highly efficient output in a short amount of time. These techniques are the foundation of the image recognition model that Khan et al. (2022) [16] proposed and implemented in this paper. Based on the trial findings using the Georgia Institute of Technology's collection of human faces, the overall match demonstrated that the suggested model could recognize human faces with a 93.7% accuracy rate. Additionally, by partial matching and re-identification during the forensics investigation process, this approach aids in the examination, analysis, and detection of specific persons. In comparison to other cutting-edge techniques, the experimental result demonstrates the resilience of the suggested model in terms of efficiency.
Service quality (QoS) is the word used to assess a service's overall effectiveness. One of the essential needs for processing medical records in healthcare applications using intelligent measurement techniques is the effective computation of Quality of Service (QoS). Demanding information must often be transmitted to provide medical treatments. As a result, there are strict guidelines for intelligent, safe, and high-quality public network service. The study conducted by Khan et al. (2021) [17] made three distinct contributions. Initially, the authors presented a unique metaheuristic strategy for medical cost-efficient task plans, in which items used by users during data processing and computation through the fog node are listed, and tasks, such as the rate of service schedule, are managed by an intelligent scheduler. Second, an efficient computing method for quality of service (QoS) has been created. It can monitor performance based on a parameter or indicator using the quality-of-experience (QoE) analytical mechanism. Third, on a permissionless public peer-to-peer (P2P) network, a framework for distributed blockchain technology-enabled QoS (QoS-ledger) computing in healthcare applications is provided. This framework maintains medically processed information in a distributed ledger. The authors managed overall node-protected interactions, stored logs in a blockchain-distributed ledger, and devised and implemented smart contracts for secure medical data processing and transfer in serverless peering networks. QoS is calculated on the blockchain public network, according to the simulation result, with transmission power of an average of -10 to -17 dBm, jitter of 34 ms, latency of an average of 87 to 95 ms, throughput of 185 bytes, duty cycle of 8%, delivery route, and response back variable. Therefore, the suggested QoS ledger is a viable option for quality-of-service computation that is not restricted to distributed e-healthcare applications.
The capacity to address all machine intelligence challenges has been made possible by advances in the field of artificial intelligence (AI). The direct training of computers with minimal human input is making machine learning (ML) a popular topic these days as the machines will learn automatically, changing the scenario of hand feeding. Techniques for supervised and unsupervised machine learning are employed for different purposes, such as object identification, feature extraction, pattern recognition, and classification. Machine learning (ML) plays a major role in computer vision (CV) to extract important information from pictures. Numerous fields, including robotics, optical character recognition, surveillance systems, suspect detection, and many more, benefit from CV's excellent contributions. Medical imaging (MI) is an emerging technique that is crucial to improving image quality and identifying important elements of binary medical images. It also helps to transform the original picture into grayscale and sets threshold values for segmentation. CV research is moving toward the healthcare sector. The significance of machine learning, the state-of-the-art, and how ML is applied in computer vision and image processing were covered by Khan et al. (2021) [18]. Information on the kinds of datasets, methodologies, tools and applications was supplied by this survey. Future work difficulties and the limitations of past work were also considered. In addition, the writers list and analyze several unresolved problems that need to be fixed to effectively use machine learning in computer vision and image processing.
The rapid enhancement in the design and development of the Internet of Things creates a new research interest in adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of the industrial Internet of Things and the security-related resource constraints of industrial 5.0. This creates a new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection-related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is a growing gap between the poor performance of linked nodes and industrial IoT, and the high resource needs of permissioned private blockchain ledgers have not yet been fully addressed. More processing power was also needed for the implementation of blockchain proof-of-work, hashing tree and allocation, and the introduction of NuCypher Re-Encryption infrastructure. The first step was to review the literature on blockchain-enabled industrial Internet of Things, its significant implementation challenges, and potential solutions by Khan et al. (2022) [19]. Next, a blockchain Hyperledger sawtooth-enabled framework was suggested by the authors. Peer-to-peer network on-chain and off-chain communication of industrial activities, as well as immutable ledger storage security, are all designed with an acknowledgment in this framework, which offers a secure and trustworthy execution environment. To facilitate seamless industrial node transactions and broadcast content, the authors also created consensus protocols and pseudo-chain codes. The suggested multiple proof-of-work was examined and simulated using a docker enabled by Hyperledger sawtooth to evaluate the information sharing across linked industrial Internet of Things devices while adhering to resource use restrictions.
Small and medium-sized businesses (SMEs) have greatly increased their production and efficiency during the past several years because of digitization. The number of SMEs' stakeholders connecting, accessing, trading, adding, and altering transactional executions is increasing, making the process of automating SME transaction execution increasingly complex. Partnership exchanges, financial management, manufacturing and productivity stability, privacy and security, and manufacturing stability are all necessary for SMEs to have a balanced lifespan. Another very important and difficult component of creating and overseeing a safe, decentralized, peer-to-peer industrial development environment for SMEs is the interoperability platform issue. However, because of the existing state of centralized server-based infrastructure, it is difficult to guarantee the integrity, transparency, dependability, provenance, availability, and trustworthiness of SMEs' activities across two separate organizations. Using collaborative approaches of blockchain, the Internet of Things, and AI with ML, Khan et al. (2023) [20] addressed these issues and developed an innovative and safe architecture with a defined process hierarchy/lifecycle for dispersed SMEs. “B-SMEs,” a blockchain with an IoT-enabled permissionless network topology, is meant to address cross-chain platforms. B-SMEs also tackle the issues of lightweight stakeholder authentication in this. Three distinct chain codes are used for that reason. Before being stored on the blockchain's immutable storage, it manages the registration of participating SMEs, daily information management and exchange between nodes, and analysis of partnership exchange-related transaction details. The goal is to manage and optimize daily SME transactions with AI-enabled ML-based artificial neural networks. This way, the proposed B-SMEs use fewer resources in terms of processing power, network bandwidth, and preservation-related problems throughout the course of providing SMEs with services. The current simulation findings demonstrate the advantages of B-SMEs. The authors also showed that the rate of ledger management and optimization during information exchange across chains may grow to 17.3% and that the system's computing resource consumption can be decreased to 9.13%. Therefore, in comparison to SMEs' present technique, only 14.11% and 7.9% of B-SME transactions employ network bandwidth and storage capacity, respectively.
Unique Taxonomy and Innovation
Academic literature has already been researched and published in Fog Taxonomy, considering different areas. Some of them are application, energy management, thermal aware scheduling, planning implementation, storage design, renewable energy and waste heat utilization for IoT and fog computing devices. The proposed research and taxonomy are different from existing nomenclatures in unique ways:
First, taxonomy does not consider the relative performance of fog and IoT solutions in the industry.Second, this taxonomy does not consider standard fog features. Common features like node infrastructure and configuration, design and architecture, virtualization design, IoT node-to-node collaboration, integration and management framework, provisioning of resources and services, service agreement and objectives or cyber security issues and challenges faced at different circumstances and node levels have been previously published by Buyya et al. (2018), Dasgupta et al. (2017) and others.Third, this research paper presents a unique idea on the use of smart fog computing devices as well as proposes the use of blockchain as a future option for research.The proposed taxonomy presents fog computing classification based on fog security and design, node, energy and capacity management, as illustrated in Fig. (2) below:
Fig. (2)) Proposed taxonomy for smart fog computing.The proposed fog computing taxonomy and examples are further described in the form of the reference architecture, as displayed in Fig. (3) and discussed in the section below.
Fig. (3)) Proposed reference architecture for smart fog computing.As per the proposed taxonomy, the authors recommend that the definition of Fog Computing should be redefined as “Fog Computing comprising of distributed entities of Fog nodes, which enable the deployment of Fog and IoT services comprising of at least one or more physical node and sensor device residing at the network edge with Computing, network, storage, processing and sensing capabilities”.
Fog Security
Fog and IoT security breaches have become a high-priority concern lately. CIA documents revealed by WikiLeaks mentioned that smart LEDs connected to the Internet could secretly record conversations. Smart intelligent virtual personal assistant devices like Alexa, Google Assistant, and Amazon Echo take user input and location awareness, play music, or provide information about weather conditions or traffic and stock prices from the Internet. Home gadgets like surveillance cameras, washing machines, microwaves, LED TVs, or mobiles are smart devices connected to the Internet. These may well be inadvertently sending information from homes to hackers and cybercriminals. ISP Dyn came under a DDoS attack [16], which disrupted their network operations for accessing popular websites. Cybercriminals managed to take control of many internet-connected cameras and DVRs and compromised the ISP DNS. Fog security classifications are presented below:
Physical Security: Node hardware and chip safety, data-at-rest, node authentication.Data Security: Data-in-motion, multi-tenancy, data ownership, data flow and encryption, access control, secure key management.Network Security: insecure wireless protocols, sniffing, man-in-the-middle, active impersonation message replay, message distortion, illegal resource consumption.Platform Security: Insecure APIs, account hijacking, app vulnerability, APTs, malicious insiders, DDoS, brute force, node and app level vulnerabilities.Virtual App Security: Hypervisor and virtual machine-based attacks, no logical segregation, side-channel attacks, privilege escalation, service abuse, inefficient resource configuration and policies.Web App Security: XSS (cross-site scripting), CSRF (cross-site request forgery), session/account hijacking, insecure direct object references, drive-by attacks, SQL injection, malicious re-directions.Malware Protection: Performance reduction, infections from bots, ransomware, rootkits, viruses, worms, trojans, and spyware.Fog Design
Fog and IoT architecture with hierarchical designs, in contrast to cloud computing, are very different. Fog parallelizes data computing at the network edge instead of centralized data center processing. This helps satisfy the location awareness, data transfer and low latency issues. This helps improve the delivery efficiency for fog applications. The below classification details further on the fog design and architecture:
Fog Architecture: Centralized, decentralized, distributed, heterogeneous.Technology: Horizontal-system, heuristic linear, framework, metaheuristic.Quality of Service: CPU MIPS, throughput and round trip, bandwidth consumption, service uptime, data loss, processing speed and resource utilization, local awareness.Category: academic, research, commercial.Model: simulation, prototype, analytical.Domain usage: Personal wearables, home domestic devices, private and public sector spheres like farming, energy, healthcare and wellness, manufacturing, oil and gas, smart city, mining, education, transportation.Fog Node Management
This relates to the end management framework for fog nodes and sensors to enhance processing, interoperability, interaction and sharing of application resources. Fog node management classifications are described below:
Purpose: Reduce processing cost, save energy, minimize bandwidth and network interference, satisfy service agreements.Function: Sensor, app node, base station, cloudlet, server.Grouping: Stand-alone, cluster, client-server, p2p.Virtual Technology: VMware, Zen, Azure, Google, KVM.Virtual Machine: Pre/posy copy for migration, shared/dedicated storage, compression, or write throttling.Provisioning: Interoperability, scalability, configuration, detection, reliability, deployment.Policy Modules: Decision engine, multi-tenancy application administrator, conflict resolver, repository holder, policy enforcer.Lifecycle Assessment: Activity monitoring, update and patching, provisioning, deployment and version control, audit, regulatory compliance, location awareness and secure node comm./de-commissioning.Energy Management
Energy management for smart, sustainable fog computing is a critical component for fog and cloud service providers. By improving energy utilization, service provider reduces electricity and operational costs. This aspect involves optimizing the environment at the hardware component and application software system level, as explained below:
Hardware level: Use of energy-efficient transistors, logical gates, clock frequency, voltage components.Software level: Optimize memory allocation registers, buffers, kernel, reduce cpu intensive cycles.API Calls: Avoid high energy-consuming calls Activity.FindViewByID, Broadcast.Receiver, Location.API.Coding level: Efficient energy code, energy-aware resource provisioning techniques like reducing the clock speed when waiting for data, reducing the processor frequency.Cooling Mechanism: Efficient ventilation along with heating and temperature monitoring for improving energy efficiency.Resource Utilization: Optimum utilization by reserving resources in advance for dynamic allocation.Capacity Management
Fog node and IoT device capacity is calculated based on the following classifications:
Physical components for data processing, storage, networking.Anticipated log workload management: Batch, sequential or FIFO processing.Service level agreements.Software licensing and auto profiling modules to cater for dynamic or additional logs.Data Centre Infrastructure Management Model (DCIM) tools for real-time capacity management and forecasting and trending, including the ‘what if’ scenarios.Experimental Setup
Smart vehicular management is a viable use case for fog and IoT technology. The authors designed and implemented two experimental setups. The first setup involves standard cloud implementation, and the second setup employs fog computing and IoT Sensor nodes to compare the performance of the vehicle management fog application regarding the response time and bandwidth consumed. The architecture and implementation involved deploying 50 sensor nodes across the university areas and routes. Each sensor was a high gain receiver with an antenna having MediaTek 3329 chipset hardware running on 5V DC interfacing with 5V microprocessors and 4GB memory chip with a position accuracy of less than 3.0 meters. These sensors detected the speed of each passing vehicle along the university roads, sending data to the cloud for query processing on the cloud server and executing the query processing engine locally for the fog infrastructure. These sensor devices were initially set up in a catch-and-forward state to send traffic data generated to the university cloud servers connected to the Internet via MPLS and wireless circuits; this simulated the cloud deployment. Then, the nodes were configured to store traffic data captured, perform the queries locally and then send the processed data to the local micro data center server; this simulated the fog and IoT deployment.
Both deployments as illustrated in Fig. (5
