Optimal Container Migration for Mobile Edge Computing: Algorithm, System Design and Implementation

Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end- users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, an optimal live migration for containerized edge computing service is studied. This paper presents three zero-downtime migration techniques based on state duplication and state reproduction techniques, and then, proposes an optimal migration technique selection algorithm that jointly minimizes the response time and network traffic during migration. For validation and performance comparison, the proposed migration techniques are implemented on off-the-shelf hardware with Linux operating system. The evaluation results showed that compared with a naive migration, the optimal approach reduced the response time and network load by at least 74.75% and 94.79%, respectively, under considered scenarios

Design and Implementation of a Full-Duplex Pipelined MAC Protocol for Multihop Wireless Networks

In multihop wireless networks, data packets are forwarded from a source node to a destination node through intermediate relay nodes. With half-duplex relay nodes, the end-to-end delay performance of a multihop network degrades as the number of hops increases, because the relay nodes cannot receive and transmit at the same time. Full-duplex relay nodes can reduce their per-hop delay by starting to forward a packet before the whole packet is received. In this paper, we propose a pipelined medium access control (PiMAC) protocol, which enables the relay nodes on a multihop path to simultaneously transmit and receive packets for full-duplex forwarding. For pipelined transmission over a multihop path, it is important to suppress both the self-interference of each relay node with the full-duplex capability and the intra-flow interference from the next relay nodes on the same path. In the PiMAC protocol, each relay node can suppress both the self- and intra-flow interference for full-duplex relaying on the multihop path by estimating the channel coefficients and delays of the interference during a multihop channel acquisition phase. To evaluate the performance of the PiMAC protocol, we carried out extensive simulations and software-defined radio-based experiments

Towards High Data Rate Hybrid RF/Optical Lunar Communication Architecture

Background Motivation Lunar science and exploration is set to explode in the coming decade. NASA\u27s Artemis Project will send first woman and next man to the moon by 2024 [1]. Dozens of additional Lunar missions are planned by 2028 [2]. Lunar missions will include human crews, rovers, smallSats, and more These missions will require a reliable and high data rate network

MAC Protocols for mmWave Communication: A Comparative Survey

With the increase in the number of connected devices, to facilitate more users with high-speed transfer rate and enormous bandwidth, millimeter-wave (mmWave) technology has become one of the promising research sectors in both industry and academia. Owing to the advancements in 5G communication, traditional physical (PHY) layer-based solutions are becoming obsolete. Resource allocation, interference management, anti-blockage, and deafness are crucial problems needing resolution for designing modern mmWave communication network architectures. Consequently, comparatively new approaches such as medium access control (MAC) protocol-based utilization can help meet the advancement requirements. A MAC layer accesses channels and prepares the data frames for transmission to all connected devices, which is even more significant in very high frequency bands, i.e., in the mmWave spectrum. Moreover, different MAC protocols have their unique limitations and characteristics. In this survey, to deal with the above challenges and address the limitations revolving around the MAC layers of mmWave communication systems, we investigated the existing state-of-the-art MAC protocols, related surveys, and solutions available for mmWave frequency. Moreover, we performed a categorized qualitative comparison of the state-of-the-art protocols and finally examined the probable approaches to alleviate the critical challenges in future research

A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research

Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity

Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions

Factor structure and symptom classes of ICD-11 complex posttraumatic stress disorder in a South Korean general population sample with adverse childhood experiences

Background Adverse childhood experiences (ACE) are known as risk factors for poor adulthood mental health, including ICD-11 posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD). While many studies focused on the association of ACE and CPTSD, examining variant symptom patterns related to ACE is lacking. Objective This study aimed to identify the factorial validity of the ICD-11 CPTSD and its distinctive symptom classes in Korean adults with ACE from a representative community sample and examine the risk factors and clinical symptoms that distinguish the CPTSD symptom classes. Methods We conducted a cross-sectional retrospective study with the International Trauma Questionnaire data from 800 adult general population with ACE histories. A confirmatory factor analysis, latent class analysis, analysis of variance and multinomial logistic regression were conducted. Results Results of confirmatory factor analysis supported a six-factor correlation model, while a two-factor higher-order model with PTSD and disturbances in self-organization (DSO) as correlated constructs also showed excellent fit. A latent class analysis identified six classes, including a distinctive ICD-11 CPTSD and PTSD, additionally a DSO with sense of threat, a DSO, an emotion dysregulation, and a low symptom class, showing distinguished features in ACE patterns, lifetime trauma, depression, somatization, panic disorder, and subtypes of dissociation. Conclusions The factorial and discriminant validity of ICD-11 CPTSD for Korean ACE survivors were confirmed. Recognizing the pervasive impact of patterns of ACEs and lifetime trauma would be helpful in access to and delivery of appropriate mental health services. Variation in symptom presentations of CPTSD and the role of dissociation should be of concern, that it may bring complicated life outcomes to people with ACEs