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The 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.

This 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.

The prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS.

The 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.

Fog computing is a new field of research and has emerged as a complement to the cloud which can mitigate the problems inherent to the cloud computing model such as unreliable latency, bandwidth constraints, security and mobility. This paper aims to provide detailed survey in the field of fog computing covering the current state-of-the-art in fog computing.

Cloud was developed for IT and not for Internet of Things (IoT); as a result, cloud is unable to meet the computing, storage, control and networking demands of the IoT applications. Fog is a companion for the cloud and aims to extend the cloud capabilities to the edge of the network.

Lack of survey papers in the area of fog computing was an important motivational factor for writing this paper. This paper highlights the capabilities of the fog computing and where it fits in between IoT and cloud. This paper has also presented architecture of the fog computing model and its characteristics. Finally, the challenges in the field of fog computing have been discussed in detail which need to be overcome to realize its full potential.

This paper presents the current state-of-the-art in fog computing. Lack of such papers increases the importance of this paper. It also includes challenges and opportunities in the fog computing and various possible solutions to overcome those challenges.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

Connecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data.

The paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones.

The key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle.

As the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research.

The findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment.

The evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability.

To the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.

With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

This paper aims to study the influence of aerodynamics force of trains passing each other on the dynamic response of vehicle bridge coupling system based on numerical simulation and multi-body dynamics and put forward the speed threshold for safe running of train under different crosswind speeds.

The computational fluid dynamics method is adopted to simulate the aerodynamic force in the whole process of train passing each other by using dynamic grid technology. The dynamic model of vehicle-bridge coupling system is established considering the effects of aerodynamic force of train passing each other under crosswind, the dynamic response of train intersection on the bridge under crosswind is computed and the running safety of the train is evaluated.

The aerodynamic force of trains' intersection has little effects on the derailment factor, lateral wheel-rail force and vertical acceleration of train, but it increases the offload factor of train and significantly increases the lateral acceleration of train. The crosswind has a significant effect on increasing the derailment factor, lateral wheel-rail force and offload factor of train. The offload factor of train is the key factor to control the threshold of train speed. The impact of the aerodynamic force of trains' intersection on running safety cannot be ignored. When the extreme values of crosswind wind speed are 15 m·s⁻¹, 20 m·s⁻¹ and 25 m·s⁻¹, respectively, the corresponding speed thresholds for safe running of train are 350 km·h⁻¹, 275 km·h⁻¹ and 200 km·h⁻¹, respectively.

The research can provide a more precise numerical method to study the running safety of high-speed trains under the aerodynamic effect of trains passing each other on bridge in crosswind.

The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms.

The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase.

For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local.

The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints.

A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.

The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).

The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.

The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

Cloud computing becomes mobile when a mobile device tries to access the shared pool of computing resources provided by the cloud, on demand. Mobile applications may enrich their functionality by delegating heavy tasks to the clouds as the remote processing and storage have become possible by adding asynchronous behavior in the communication. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors, which mostly are not interoperable across clouds. Moreover, by adding asynchronicity, mobile applications must rely on push mechanisms which are considered to be moderately reliable, and thus not recommended in scenarios that require high scalability and quality of service (QoS). To counter these problems, and the purpose of this paper, is to design a middleware framework, Mobile Cloud Middleware (MCM), which handles the interoperability issues and eases the use of process‐intensive services from smartphones by extending the concept of mobile host.

MCM is developed as an intermediary between the mobile and the cloud, which hides the complexity of dealing with multiple cloud services from mobiles. Several applications are presented to show the benefits of mobiles going cloud‐aware. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources using well known load balancing mechanisms like HAProxy and Tsung.

From the study it was found that it is possible to handle hybrid cloud services from mobiles by using MCM. The analysis demonstrated that the MCM shows reasonable performance levels of interaction with the user, thus validating the proof of concept. Moreover, MCM decreases the effort in developing mobile cloud applications and helps in keeping soft‐real time responses by using its asynchronous approach.

MCM fosters the utilization of different types of cloud services rather than the traditional mobile cloud services based on data synchronization. By offloading heavy tasks to the clouds, the framework extends the processing power and storage space capabilities of the constrained smart phones. The applications mentioned in the paper bring an added value by being success stories for mobile cloud computing domain in general.

Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.