Mobile Fog Research Articles

Mobile Fog AI for Internet of BattleField Things (IoBT)

New mechanisms provide soldiers automation capabilities that include interactive, mobile, fast, scalable, secured, powerful computational communication in contested environments such as battle fields. Internet of BattleField Things (IoBT) is a technology that develops integrated Internet of Things (IoT) solutions for the war of the future. The IoBT connects soldiers with smart technology in armour, radios, weapons, munitions, other objects such as health sensors, GPS, and various IoT devices. These ``things'', objects, or constrained devices can communicate with each other or across existing network infrastructure such as the Internet or network meshes. They most of the time come with limited capacity in terms of memory storage, computational power and energy. IoT, networking (Device Communication (D2D), Peer to Peer Communication (P2P)), machine intelligence, multimodal sensing and fusion are some areas involved in developing interdisciplinary IoBT networks. Our aim is to equip soldiers with AI powered IoBT devices. Existing D2D/P2P/LPWAN based IoBT technology focuses on edge computing devices which are very limited, being slow, insecure, and lack sufficient scalability. We present a novel concept, i.e., Mobile Fog AI for IoBT to overcome existing IoBT limitations.

Many-to-Many Task Offloading in Vehicular Fog Computing: A Multi-Agent Deep Reinforcement Learning Approach

Vehicular fog computing (VFC) has emerged as a promising solution to mitigate vehicular network computation load. In the hierarchical VFC, vehicles are employed as mobile fog nodes at the edge to provide reliable and low-latency services. Particularly, since privately-owned vehicles are rational nodes, their intentions for both computation provision and service demand should be considered instead of overestimating their willingness. To remunerate the participation intentions of vehicles as well as improve vehicular fog resource utilization in the large-scale VFC, the trading-based mechanism is a potential solution. In this paper, we propose a many-to-many task offloading framework based on the vehicular trading paradigm. This framework enables computational resource trading across different VFC subsystems and decides the multi-tier task offloading results based on the trading consensus. The trading process is viewed as a partially observable Markov decision process (POMDP) and a Multi-Agent Gated actor Attention Critic (MA-GAC) approach is designed to reach an effective and stable offload-and -serve cooperation among vehicles. Theoretical analyses and experiments verify the feasibility and efficiency of the proposed framework, and simulation results demonstrate that the coordinated MA-GAC approach not only benefits vehicles with higher long-term rewards but also optimizes the system social welfare in a distributed manner.

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments.

A Review: E-voting Security in Mobile Fog computing

Conventional voting activities are often replaced by electronic voting (EV) in light of the quick expansion of the Internet. For a variety of reasons, various nations have lately switched to EV rather than conventional voting. Different EV systems were presented up to this point. In both practical and theoretical fields, on the other hand, there is no perfect solution. To meet such objectives, the researchers strive for preserving cryptographic primitives when developing high-efficiency e-voting schemes. The concept of fog computing was developed to improve network infrastructure to satisfy the demands of large amounts of data the same time as also increasing the efficiency of the processing power. Also, it has been created as well to address concerns with Cloud computing, like the distribution environment complexity, real-time response, mobility, and IoT application location awareness. The concentration of this study was on a complete review regarding the systems of EVs through various scholars as a platform to detect flaws or problems in the deployment of extremely secure EV systems. In addition, nations having a history of EV system adoption were examined. A concept for future work on establishing a safe EV system depends on problems discovered in numerous works. Index Terms— fog computing, security, E-voting, cryptographic, cloud computing.

An Efficient Method for Implementing Applications of Smart Devices Based on Mobile Fog Processing in a Secure Environment

An Efficient Method for Implementing Applications of Smart Devices Based on Mobile Fog Processing in a Secure Environment

Defense scheme against advanced persistent threats in mobile fog computing security

In mobile fog computing (MFC), data processing and applications are concentrated in devices at the network’s edge. This means that data can be processed in local intelligent devices without being sent to the cloud for execution, bringing great convenience to end-users. Fog computing is a semi-virtualised service computing architecture model between cloud computing and personal computing. However, its virtualized environment is vulnerable to advanced persistent threats (APT). In this paper, we propose an APT defense scheme based on MFC’s Double Q-learning (DQL) algorithm. First, we apply prospect theory (PT) to build a static subjective game model between APT attackers and legitimate users. In addition, a dynamic game scheme based on DQL is proposed against APT attacks. Finally, we compare our proposed method with the existing methods, i.e., the Q-learning algorithm, Sarsa algorithm and Greedy algorithm. The experimental results show that the proposed method can effectively suppress the attack motivation of APT attackers, improve the utility of legitimate users, and protect the security of the fog computing environment.

Energy-efficient scheduling based on task prioritization in mobile fog computing

Energy-efficient scheduling based on task prioritization in mobile fog computing

Joint Optimization of Trajectory, Task Offloading, and CPU Control in UAV-Assisted Wireless Powered Fog Computing Networks

This paper studies a rotary-wing unmanned aerial vehicle (UAV)-assisted wireless powered fog computing network, where a UAV serves as both a mobile wireless energy source and a mobile fog server to provide charging and computing services to a group of ground sensors. The UAV first broadcasts radio frequency (RF) signals to charge the sensors, and then the sensors use the harvested energy to complete their tasks by local computing and fog computing, where the partial offloading policy is employed. In order to achieve green communication system design, an optimization problem is formulated to minimize the UAV’s energy consumption by jointly optimizing the UAV’s trajectory, the task offloading allocation and computing resource allocation, where the piecewise nonlinear energy harvesting (EH) model, the charging requirements of sensors and velocity constraints of UAV are taken into account together. To solve the non-convex problem, an iterative method is designed based on the successive convex approximation (SCA) theory. Simulation results show that our presented scheme converges within a few iterations, by using which the UAV’s energy consumption can be greatly reduced compared to benchmark schemes. It is also observed that the harvested energy by the linear EH model have obvious bias compared to that obtained by the nonlinear EH model. And in the case of nonlinear EH model, as circuit saturation threshold increases, the UAV moves closer to the sensors such that their tasks can be completed, which may result in a higher energy consumption of the UAV. Besides, the velocity variation of UAV is relatively small by introducing the velocity constraint, which avoids drastic steering of the UAV in our considered scenario.

Fault tolerant data offloading in opportunistic fog enhanced IoT architecture

Internet of Things (IoT) is characterized by the large volumes of data collection. Since IoT devices are themselves resource-constrained, this data is transferred to cloud-based systems for further processing. This data collected over a period of time possesses high utility as it is useful for multiple analytical, predictive and prescriptive tasks. Therefore, it is crucial that IoT devices transfer the collected data to network gateways before exhausting their storage to prevent loss of data; this issue is referred to as the “data offloading problem”. This paper proposes a technique for fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the cloud with a minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture. The effectiveness of the proposed method is verified through simulation experiments to assess the reduction in data loss by use of proposed data offloading scheme. It is demonstrated that the method outperforms a state-of-art method.

Effective Mobility Identification in Mobile Fog Environment with the Internet of Things

Fog extends the cloud to be closer to the end devices so that it acts on IoT data within a millisecond. Almost 60% of data can be analyzed that is physically near to the IoT data. This proximity has various advantages, including reduced latency, which improves the user experience. However, because the distance to a fog service may vary as a user moves from one location to another, user mobility may restrict such benefits in practice. A fog service migration is based on a mitigation approach that allows the service to always be close enough to a user. Quality of Service is decreased because of the mobility of the user’s location. Predicting the future location in advance improves the efficiency of service provisioning. In this work, a dynamic mobility model is proposed to find the user location in advance. This experiment was carried out by LuST mobility data set collected by Luxembourg Simulation of Urban Mobility (SUMO) Traffic (LuST). This result is give better accuracy of location prediction up to 98.87% when compared with existing methods.

A novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid via mobile fog computing

With the vigorous development of edge computing, edge devices are more and more powerful and widely deployed. Mobile fog computing-based Smart Grid emerges as the times require and attracts much attention, by combining Smart Grid and mobile fog computing. Nonetheless, user privacy is also facing new challenges, which are caused by mobility. Particularly the key management problem when multiple users apply for key frequently. For the purpose of solving this problem, this paper proposes a novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid with mobile fog computing and quantum key pool. Firstly, we develop a multi-level and dynamic privacy-preserving aggregate signcryption method without trusted authority, including space-based data aggregation and time-based data aggregation. To enhance the security of key management, we introduce quantum key pool to fast distribute the keys. Secondly, we implement a data query and share method based on proxy re-encryption. It not only supports users to query historical data and bills, but also realizes secure data sharing with other third parties. Ultimately, we design formal security analysis and digital simulation experiments. The analysis results show that the proposed scheme can achieve the characteristics of secure, high efficiency, lightweight and robustness. In the background of the increasingly wide application of quantum technology, we hope this work is a trigger for other scholars and institutions in the hybrid application of quantum and classical technology.

Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process

Mobile Fog Computing (MFC) paradigm can be integrated as a unit called as Multi-Access Edge Computing (MFC) in a fifth-generation (5G) network. There are extensive researches coercing to the MFC. Task scheduling is an important issue in the area of MFC to solve computing capacities such as limited CPU power, storage capacity, memory constraints, and limited battery life in Mobile Devices (MDs). The multi-criteria decision-making problem in fog nodes has not been widely studied. According to the variety and difficulty of criteria, the scheduling in the fog node has become a challenge. The previous works in the tasks scheduling context considered a few criteria of dynamic scheduling without covering other enough criteria. Besides, in MFC, the tasks come with different priorities. We present a scheduling algorithm based on the Priority Queue, Fuzzy and Analytical Hierarchy Process namely PQFAHP in our paper. We use PQFAHP to combine several priorities and prioritize multi-criteria. In our paper, dynamic scheduling includes the completion time, energy consumption, RAM, and deadline criteria. Our experimental results show that the proposed algorithm can consider multi-criteria for scheduling Our proposed work is one of the multi-criteria algorithms that performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, mean response time, and the number of scheduled tasks on the MFC side. This paper has considerable contributions related to the scheduling of fog computing. For instance, it could decrease 14.2%, 49%, and 26% in average waiting time, delay, and energy consumption respectively, and increase 10.8% in service level.

Device and network driven cellular networks architecture and mobility management technique for fog computing-based mobile communication system

In this paper, we propose a metric and performance-based mode switching procedure for 5G and beyond mobile fog networks. The network interchange mode of communication between network-centric and device-centric modes, the mode switching procedure is subject to the network’s quality of service (QoS) and quality of experience (QoE) parameters. If the quality parameters are less than the pre-set threshold value, the network selects a relay node as intermediate nodes to avail the device-centric mode of communication. During the communication, the network information’s are retrieved from the nearby fog nodes and the process by intelligent base stations. Our next contribution is a relay node assisted mobility management procedure. As the mobile nodes change the position frequently within the network service area, an efficient mobility management procedure is required to improve the network’s performance. We propose a unique mobility management procedure, it follows the movement prediction procedure and helps the mobile nodes to avail the device-centric communication in dynamic fog networks, we classify it for dynamic and static mobile users. To check the effectiveness, efficiency, and parameterized dependency of the proposed work, we formulate the process with respect to several dependent factors and comparing our results with the latest state-of-the-art networks technologies subject to communication delay and latency, energy consumption, without any compromise in node mobility and call to mobility ratio. Finally, we achieve better performance to gain the QoS and QoE.

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).

Fog computing based mechanism on traditional sports video content distribution

Aiming at the problem of traditional sports video content distribution and acceleration in mobile fog computing (MFC), considering the influence of limited storage space of MFC server on traditional sports video content cache, taking the delay of mobile users' access to sports video objects as the optimization objective, and according to the difference of users' interest in different video objects, an interest‐based traditional sports video content distribution acceleration strategy is proposed. According to the storage space of the MFC server, the interest of the mobile users in different sports video objects and the file size of the objects, the strategy selectively caches the objects on the MFC server, and updates the cached video objects on the MFC server in time, so as to meet the traditional sports video content needs of the mobile users to the maximum extent. The simulation results show that the proposed strategy has good convergence performance, and its cache hit rate is relatively stable and obviously better than the existing strategy; when the system is stable, compared with the existing strategy, this strategy can reduce the delay of obtaining video object data by 15%.

Edge computing for Vehicle to Everything: a short review

Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on several methods identified in the literature for implementing effective EC. We describe each method individually and compare them according to their applicability. The findings of this work indicate that most methods can simulate the EC positioning under predefined scenarios. These include the use of Mobile Edge Computing, Cloudlet, and Fog Computing. However, since most studies are carried out using simulation tools, there is a potential limitation in that crucial data in the search for EC positioning may be overlooked and ignored for bandwidth reduction. The EC approaches considered in this work are limited to the literature on the successful implementation of V2X communication and services. The outcome of this work could considerably help other researchers better characterise EC applicability for V2X communications and services.

Evaluation of the Employment of UAVs as Fog Nodes

In fog computing, processing, network, and storage resources are placed close to end users to ensure low latency in comparison to the latency experienced when accessing the cloud. One limitation of this solution, however, is that fog nodes are usually fixed, whereas demands are variable over time at all locations, which may result in either under- or overprovisioning. This limitation incurs high CAPEX and OPEX costs to cope with user demands. One approach to address this problem is to employ mobile fog nodes dispatched to various locations to cope with the variability in resource demand. This article aims to evaluate the use of unmanned aerial vehicles (UAVs) equipped with processing elements as an alternative to fixed nodes in a fog infrastructure to cope with the variable workload in a metropolitan area. Although previous approaches considered UAVs as part of the network, they did not deploy multiple fog nodes mounted on battery-constrained UAVs. Specifically, we propose in this article a solution to the fog node location problem considering both fixed and mobile nodes to evaluate potential replacements of fixed servers with UAVs. Experimental evaluation of the problem using data generated by real mobile users shows that UAVs can indeed replace parts of the fixed fog infrastructure.

Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing

Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

Probability and topic‐based data transmission protocol

SummaryIn the fog computing (FC) models, a fog node supports not only routing functions but also application processes. By the application processes, output data are calculated on input data from sensors and other nodes and sent to target fog nodes which can calculate on the output data. In this article, we consider the mobile topic‐based publish/subscribe fog computing (MPSFC) model where mobile fog nodes communicate with one another by publishing and subscribing messages in wireless networks. Subscription topics of a fog node denote input data on which the node can calculate output data. Publication topics of a message show data carried by the message. In the topic‐based data transmission (TBDT) protocol proposed in our previous studies, while a fewer number of messages are transmitted, the delivery ratio of messages is smaller than the epidemic routing protocol. In this article, we newly propose a pair of time‐to‐live‐based data transmission (TTLBDT) and probability and topic‐based data transmission (PTBDT) protocols in order to increase the delivery ratio. If another node is found in the communication range, a fog node forwards messages to the node. Even if the node is not a target node, the node receives the message with some probability in the PTBDT protocol. In the evaluation, we show the delivery ratios in the TTLBDT and PTBDT protocols are larger than the TBDT protocol. In addition, we show the number of messages exchanged in the PTBDT protocol is smaller than the epidemic routing protocol.