Mist Computing Research Articles

Edge Computing and Cloud Computing for Internet of Things: A Review

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management.

DICOMIST: An methodology for Performing Distributed Computing in Heterogeneous ad hoc Networks

The Internet of Things (IoT) has emerged as a cornerstone technology, transforming how we interact with our surroundings. Despite their widespread adoption, IoT devices encounter challenges related to processing capabilities and connectivity, frequently necessitating the delegation of tasks to remote cloud servers. This offloading, essential for enhancing user experience, poses challenges, particularly for latency-sensitive applications. Edge-centric paradigms like fog and mist computing have emerged to address these challenges, bringing computational resources closer to end-users. However, efficiently managing task offloading in dynamic IoT environments remains a complex issue. This paper introduces DIstributed COmputing for MIST (dicomist), a methodology designed to facilitate task offloading in IoT settings. Dicomist utilizes wireless mesh networks to organize mobile nodes, employing clustering and classification techniques. Tasks are treated as consensus problems, enabling distributed computation among selected nodes. Real-world experiments demonstrate dicomist’s effectiveness, underscoring its potential to enhance task offloading in IoT environments.

Statistical pathways to low-carbon cities: Analyzing renewable integration, energy-efficient design, and job creation

To address the existing gap in understanding the intricate dynamics of resource management in Cloud Fog Computing (CFC)-driven Smart Grids (SG), this study introduces an innovative adaptive framework integrating Genetic Optimization Algorithm (GOA) and Cuckoo Search Optimization Algorithm (CSOA), aiming to optimize response times and enhance overall efficiency. In the domain of contemporary energy grids, the implementation of intelligent grids (IG) has ushered in a new era of dependable, streamlined, eco-friendly, and economically viable electric services. These grids constitute a fundamental facet of present-day energy resources. Moreover, the advent of cloud and mist computing (CMC) provides on-demand access to computational resources. Overcoming associated challenges, CMC not only delivers advantages like cost-effectiveness, energy conservation, scalability, flexibility, and adaptability but also presents an innovative model for resource management in IG. This study introduces a CMC-driven strategy for managing resources in IG by digitally replicating key grid components. The proposed framework aims to furnish diverse computational services for resource management within IG through a hierarchical arrangement of CMCs. Additionally, this investigation scrutinizes and simulates two optimization approaches: the cuckoo search and grasshopper optimization algorithms. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy. These algorithms operate on an adaptive machine learning mechanism designed to balance the load, and the study meticulously compares their efficacy in the context of Renewable Integration, Energy-Efficient Design, and Job Creation.

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

Analysis of Security Access Control Systems in Fog Computing Environment

Fog computing is a computing environment that can respond to user operational needs in real time. Aiming at the shortcomings of user privacy protection performance and structural performance, a method of completely hiding access structures is proposed under the framework of cloud and mist computing. The cuckoo filter is applied to the fog computing environment, and users are detected through fog nodes. If an attribute is detected to exist in the fully hidden access structure, the mapping function between the attribute and the access structure line number is returned. The research results show that with the increase of the number of attributes, the advantage of attribute confirmation time for fog servers is gradually obvious; The overall delay of fog computing is shorter, the Time To Live (TTL) is longer, the average delay is only 3 ms, and the delay is lower; The completely hidden access structure constructed by the cuckoo algorithm occupies only 1% of the total system steps, which can more effectively achieve user privacy protection without increasing overhead. The proposed scheme greatly reduces the amount of computation while fully protecting user privacy, and meets the needs of users for fast and secure access.

Integrating cloud and mist computing to lower latency in IoT topologies

AbstractTraditional IoT topologies involve access and core networks that share a common edge. On this edge, border routers and gateways are responsible for converting protocols at different layers of the stack. Devices like sensors and actuators sit on the access network while applications are located on the core network. The application performs predictions that trigger actuation based on received sensor readouts. Prediction, in turn, is the result of machine learning (ML) algorithms that are typically executed on the cloud. An alternative to this approach consists of performing the prediction on constrained devices on the IoT access network. This leads to Tiny ML (TinyML) and mist computing. In this context, there is a trade‐off between latency and computational power that becomes a deciding factor when choosing the application to carry on predictions. This paper introduces an algorithm that can be used to dynamically select the right application based on network layer parameters.

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

The prevalence of heart disease has remained a major cause of mortalities across the world and has been challenging for healthcare providers to detect early symptoms of cardiac patients. To this end, several conventional machine learning models have gained popularity in providing precise prediction of heart diseases by taking into account the underlying conditions of patients. The drawbacks associated with these methods are a lack of generalization and the convergence rate of these methods being much slower. As the healthcare data associated with these systems scale up leading to healthcare big data issues, a Cloud-Fog computing-based paradigm is necessary to facilitate low-latency and energy-efficient computation of the healthcare data. In this paper, a DeepMist framework is suggested which exploits Deep Learning models operating over Mist Computing infrastructure to leverage fast predictive convergence, low-latency, and energy efficiency for smart healthcare systems. We exploit the Deep Q Network (DQN) algorithm for building the prediction model for identifying heart diseases over the Mist computing layer. Different performance evaluation metrics, like precision, recall, f-measure, accuracy, energy consumption, and delay, are used to assess the proposed DeepMist framework. It provided an overall prediction accuracy of 97.6714 % and loss value of 0.3841, along with energy consumption and delay of 32.1002 mJ and 2.8002 ms respectively. To validate the efficacy of DeepMist, we compare its outcomes over the heart disease dataset in convergence with other benchmark models like Q-Reinforcement Learning (QRL) and Deep Reinforcement Learning (DRL) algorithms and observe that the proposed scheme outperforms all others.

In-depth analysis and open challenges of Mist Computing

The advent and consolidation of the Massive Internet of Things (MIoT) comes with a need for new architectures to process the massive amount of generated information. A new approach, Mist Computing, entails a series of changes compared to previous computing paradigms, such as Cloud and Fog Computing, with regard to extremely low latency, local smart processing, high mobility, and massive deployment of heterogeneous devices. Hence, context awareness use cases will be enabled, which will vigorously promote the implementation of advantageous Internet of Things applications. Mist Computing is expected to reach existing fields, such as Industry 4.0, future 6G networks and Big Data problems, and it may be the answer for advanced applications where interaction with the environment is essential and lots of data are managed. Despite the low degree of maturity, it shows plenty of potential for IoT together with Cloud, Fog, and Edge Computing, but it is required to reach a general agreement about its foundations, scope, and fields of action according to the existing early works. In this paper, (i) an extensive review of proposals focused on Mist Computing is done to determine the application fields and network elements that must be developed for certain objectives, besides, (ii) a comparative assessment between Cloud, Fog, Edge, and Mist is completed and (iii) several research challenges are listed for future work. In addition, Mist Computing is the last piece to benefit from the resources of complete network infrastructures in the Fluid Computing paradigm.

Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment

Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment

Fog Computing

Managing the knowledge generated by the Internet of Things (IoT) sensors and actuators is one of the most important. Challenges in introducing IoT systems. Traditional cloud-based IoT systems We challenged the large scale, non- uniformity, and high latency observed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics within the network itself. Use of distributed and integrated computing models. This approach is called the nebula Calculate. This document introduces a conceptual model of fog and mist computing. They refer to the cloud-based computing model of the IoT. This document shows further features Key characteristics and aspects of fog computing, including service models and deployments It provides a baseline of what strategy and fog computing are, and how to use them.

Machine Learning-based Mist Computing Enabled Internet of Battlefield Things

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.

PureEdgeSim: A simulation framework for performance evaluation of cloud, edge and mist computing environments

Edge and Mist Computing are two emerging paradigms that aim to reduce latency and the Cloud workload by bringing its applications close to the Internet of Things (IoT) devices. In such complex environments, simulation makes it possible to evaluate the adopted strategies before their deployment on a real distributed system. However, despite the research advancement in this area, simulation tools are lacking, especially in the case of Mist Computing [11], where heterogeneous and constrained devices cooperate and share their resources. Motivated by this, in this paper, we present PureEdgeSim, a simulation toolkit that enables the simulation of Cloud, Edge, and Mist Computing environments and the evaluation of the adopted resources management strategies, in terms of delays, energy consumption, resources utilization, and tasks success rate. To show its capabilities, we introduce a case study, in which we evaluate the different architectures, orchestration algorithms, and the impact of offloading criteria. The simulation results show the effectiveness of PureEdgeSim in modeling such complex and dynamic environments.

Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor–critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

Mobile Mist Computing for the Internet of Vehicles†

This paper introduces the innovative Mobile Mist Computing (M2C) concept by addressing a reference architectural model for vehicular networks capable of supporting complex mobile services with stringent time‐space constraints. Cellular technology, thank to its coverage, data rates and latencies, is generally considered the key enabler to support real‐time vehicular applications. Moreover, their integration with the emerging Fog Computing (FC) paradigm can reduce the of the core network, thus minimising the overall service delay. In this scenario, vehicular mobility represents the most relevant challenge to guarantee service continuity, while providing Quality of Service (QoS), especially when Fog Servers (FSs) have limited resources. To this purpose, FC has been firstly integrated with the Software Defined Networking (SDN) and Network Functions Virtualization (NFV) concepts and, subsequently the dynamic replacement of vehicular services (ie, VNF migration) among FSs has been investigated to properly accommodate users mobility. Finally, an optimised strategy, called swapping migration, that can optimise both resources utilisation and outage rate, has been introduced. The proposed scheme has been validated by means of numerical simulations and compared with several benchmarks over realistic scenarios by pointing out latency and reliability as a function of the services request rate and the transmission capacity.

Cloud-based Enabling Mechanisms for Container Deployment and Migration at the Network Edge

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at pushing computation from the Cloud to be much close as possible to data sources, raising in the evolution of new paradigms namely Fog and Mist computing. Specifically, the Fog computing paradigm exploits powerful nodes such as servers, routers, and cloudlets that are coupled with the end devices or their access networks accordingly; they are ”relatively” close by the data sources. Whereas Mist computing, which is a lightweight form of Fog computing, pushes the resources even closer. Precisely, Mist computing uses particular nodes that could reside within the same network (e.g., Local Area Network (LAN)) as the end-devices. Considering the advancement that the hardware is knowing nowadays, Fog and Mist nodes are seen suitable to provide resources such as processing, storage, and networking in the proximity of data sources; thereby, the requirements of the new services could be met. Together with the Cloud, the Fog and Mist paradigms introduce a stacked architecture for data processing where a data pre-processing could be performed at the Mist level, then offloaded vertically to the upper layers (i.e., Fog nodes or the Cloud). In these circumstances, it is fundamental to build a management system able to provision efficiently the Fog/Mist-based applications. For this purpose, the Operating System (OS)-level virtualization using containerization technologies, considering its light footprint, fits as a suitable solution to provide Fog/Mist services. The industrial-grade Cloud middlewares, such as OpenStack, which is a reference architecture for Infrastructure-as-a-Service solutions, are still far away from incorporating this new trend. This article proposes an OpenStack-based middleware platform through which containers can be deployed/managed at the Fog/Mist levels.

A Survey and Tutorial on “Connection Exploding Meets Efficient Communication” in the Internet of Things

Internet-of-Things (IoT)-enabled sensors and services have increased exponentially recently. Transmitting the massive generated data and control messages becomes an overhead on the communication system infrastructure. Many architectures and paradigms have been introduced to address the connection exploding, such as cloudlets, fog, and mist computing. Besides, software-related solutions, such as mobile Internet technologies and software-defined network also take part in mitigating the communication overhead. All of those new techniques have the same purposes summarized in achieving low latency, high throughput, and less storage and computing at the cloud level in addition to other objectives discussed through this survey. We list the proposed solutions, show their advantages and schemes, highlight some of the newest IoT-enabled applications, and show how they benefit from applying the new paradigms.

Cloud, Fog, and Mist Computing: Advanced Robot Applications

As we are witnessing the dawn of the artificial intelligence revolution, it is a good time to discuss the system-architecture challenges of the new age of robotics. Most of the disputes are related to the wide variety of cloud-computing arrangements along the questions of why, with what, and how to exploit existing information technologies to cope effectively with the increasing demands. Traditional industrial robot makers are very conservative, relying exclusively on onboard execution. However, the rising user demand for advanced robot skills, such as dexterous manipulation and free navigation in an unstructured environment, has inspired newcomers to break with tradition and move robot software architecture to the next level. Inspired by the talks of Prof. Imre Rudas on cloud robotics, this article highlights some substantial factors of system design and discusses a real-world industrial example from my RaD practice.

DLBS: Decentralize Load-Balance Scheduling Algorithm for Real-Time IoT Services in Mist Computing

Internet of Things (IoT) has been industrially investigated as Platforms as a Services (PaaS). The naive design of these types of services is to join the classic centralized Cloud computing infrastructure with IoT services. This joining is also called CoT (Cloud of Things). In spite of the increasing resource utilization of cloud computing, but it faces different challenges such as high latency, network failure, resource limitations, fault tolerance and security etc. In order to address these challenges, fog computing is used. Fog computing is an extension of the cloud system, which provides closer resources to IoT devices. It is worth mentioning that the scheduling mechanisms of IoT services work as a pivotal function in resource allocation for the cloud, or fog computing. The scheduling methods guarantee the high availability and maximize utilization of the system resources. Most of the previous scheduling methods are based on centralized scheduling node, which represents a bottleneck for the system. In this paper, we propose a new scheduling model for manage real time and soft service requests in Fog systems, which is called Decentralize Load-Balance Scheduling (DLBS). The proposed model provides decentralized load balancing control algorithm. This model distributes the load based on the type of the service requests and the load status of each fog node. Moreover, this model spreads the load between system nodes like wind flow, it migrates the tasks from the high load node to the closest low load node. Hence the load is expanded overall the system dynamically. Finally, The DLBS is simulated and evaluated on truthful fog environment.

A Practical Evaluation on RSA and ECC-Based Cipher Suites for IoT High-Security Energy-Efficient Fog and Mist Computing Devices.

The latest Internet of Things (IoT) edge-centric architectures allow for unburdening higher layers from part of their computational and data processing requirements. In the specific case of fog computing systems, they reduce greatly the requirements of cloud-centric systems by processing in fog gateways part of the data generated by end devices, thus providing services that were previously offered by a remote cloud. Thanks to recent advances in System-on-Chip (SoC) energy efficiency, it is currently possible to create IoT end devices with enough computational power to process the data generated by their sensors and actuators while providing complex services, which in recent years derived into the development of the mist computing paradigm. To allow mist computing nodes to provide the previously mentioned benefits and guarantee the same level of security as in other architectures, end-to-end standard security mechanisms need to be implemented. In this paper, a high-security energy-efficient fog and mist computing architecture and a testbed are presented and evaluated. The testbed makes use of Transport Layer Security (TLS) 1.2 Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA) cipher suites (that comply with the yet to come TLS 1.3 standard requirements), which are evaluated and compared in terms of energy consumption and data throughput for a fog gateway and two mist end devices. The obtained results allow a conclusion that ECC outperforms RSA in both energy consumption and data throughput for all the tested security levels. Moreover, the importance of selecting a proper ECC curve is demonstrated, showing that, for the tested devices, some curves present worse energy consumption and data throughput than other curves that provide a higher security level. As a result, this article not only presents a novel mist computing testbed, but also provides guidelines for future researchers to find out efficient and secure implementations for advanced IoT devices.

Adaptive mobile Web server framework for Mist computing in the Internet of Things

PurposeThe distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks.Design/methodology/approachThis paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node.FindingsThe prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS.Originality/valueThe proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.