EXEGESIS: Extreme Edge Resource Harvesting for a Virtualized Fog Environment

Markakis, Evangelos K.; Karras, Kimon; Ζώτος, Νικόλαος; Sideris, Anargyros; Moysiadis, Theoharris; Corsaro, Angelo; Alexiou, George; Skianis, Charalabos; Mastorakis, George; Mavromoustakis, Constandinos X.; Pallis, Evangelos

doi:10.1109/mcom.2017.1600730

Cited by 57 publications

(24 citation statements)

References 5 publications

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“…Existing mobile computing systems are designed for homogeneous network environments or single communication technologies and therefore have limited bandwidth. They also do not support resource‐intensive IoT and 5G applications or consider network characteristics such as link quality and lifetimes.…”

Section: State Of the Artmentioning

confidence: 99%

“…Finally, the use of standard protocols and services in an extreme heterogeneous environment results in poor performance because the characteristics of one technology may be compromised when accommodating another technology. Existing mobile computing systems 17,18,[22][23][24][25][26] are designed for homogeneous network environments or single communication technologies and therefore have limited bandwidth. They also do not support resource-intensive IoT and 5G applications or consider network characteristics such as link quality and lifetimes.…”

Section: State Of the Artmentioning

confidence: 99%

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Private mobile edge cloud for 5G network applications

Shah

2019

Internet Technology Letters

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Resource‐intensive real‐time IoT and 5G network applications require low latency, high data rates, and considerable computing and storage resources. Mobile cloud computing systems cannot fulfill the requirements of these applications due to high communication latency. Consequently, recently proposed edge computing implementations require adding new infrastructure or updating existing infrastructure. In addition, edge computing does not exploit the capabilities of end devices, which have become more powerful than supercomputers in the last decade. To overcome the drawbacks of mobile cloud and edge computing systems, we have proposed a private mobile edge cloud in which multiple mobile and stationary devices such as sensors, robots, home appliances, and smartphones, interconnected through infrastructure‐less and infrastructure‐based wireless local area networks are combined to create a small‐scale cloud data center at a local physical area such as a home. We have also discussed opportunities and research challenges associated with development of proposed system and a high‐level architecture based on a cross layer design for an efficient management of resources.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Private mobile edge cloud for 5G network applications

Shah

2019

Internet Technology Letters

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“…One of the technologies that has contributed most to the progress of home automation is cloud computing, which offloads home devices from computational-intensive tasks. Nonetheless, in certain home automation scenarios where a fast response and low communications overhead are required, other paradigms have been successful by moving the computing capabilities from the cloud towards the edge of the network [ 3 ]. One such paradigm is fog computing, which moves the cloud computational and communication capabilities close to the sensor nodes in order to minimize latency, to distribute computational and storing resources, to enhance mobility and location awareness, and to ease network scalability while providing connectivity among devices in different physical environments [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Design, Implementation and Practical Evaluation of an IoT Home Automation System for Fog Computing Applications Based on MQTT and ZigBee-WiFi Sensor Nodes

Froiz-Míguez

Fernández‐Caramés

Fraga‐Lamas

et al. 2018

Sensors

181

103

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In recent years, the improvement of wireless protocols, the development of cloud services and the lower cost of hardware have started a new era for smart homes. One such enabling technologies is fog computing, which extends cloud computing to the edge of a network allowing for developing novel Internet of Things (IoT) applications and services. Under the IoT fog computing paradigm, IoT gateways are usually utilized to exchange messages with IoT nodes and a cloud. WiFi and ZigBee stand out as preferred communication technologies for smart homes. WiFi has become very popular, but it has a limited application due to its high energy consumption and the lack of standard mesh networking capabilities for low-power devices. For such reasons, ZigBee was selected by many manufacturers for developing wireless home automation devices. As a consequence, these technologies may coexist in the 2.4 GHz band, which leads to collisions, lower speed rates and increased communications latencies. This article presents ZiWi, a distributed fog computing Home Automation System (HAS) that allows for carrying out seamless communications among ZigBee and WiFi devices. This approach diverges from traditional home automation systems, which often rely on expensive central controllers. In addition, to ease the platform’s building process, whenever possible, the system makes use of open-source software (all the code of the nodes is available on GitHub) and Commercial Off-The-Shelf (COTS) hardware. The initial results, which were obtained in a number of representative home scenarios, show that the developed fog services respond several times faster than the evaluated cloud services, and that cross-interference has to be taken seriously to prevent collisions. In addition, the current consumption of ZiWi’s nodes was measured, showing the impact of encryption mechanisms.

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“…However, a cloud computing based architecture may have certain limitations: All the information and decisions are centralized and managed, in general, by a third party; the cloud availability may be compromised by its massive use, or by cyber-attacks; and, due to the physical distance between patient and cloud, decisions require some time to be taken and communicated, which may be too high in some scenarios. Fortunately, for such scenarios where a fast response and low communications overhead are required, other paradigms have proven to be successful, by moving computing capabilities from the cloud towards the edge of the network [2]. One of such paradigms is fog computing, which transfers the cloud's computational and communication capabilities close to the sensor nodes, in order to minimize latency, to distribute computational and storage resources, to enhance mobility and location awareness, and to ease network scalability while providing connectivity among devices in different physical environments [3,4].…”

Section: Introductionmentioning

confidence: 99%

Design of a Fog Computing, Blockchain and IoT-Based Continuous Glucose Monitoring System for Crowdsourcing mHealth

Fernández‐Caramés

Fraga‐Lamas

2018

5th International Electronic Conference on Sensors and Applications

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Diabetes Mellitus, usually called only Diabetes, is a worldwide chronic metabolic disorder that is characterized by abnormal oscillations in blood sugar levels. Such levels should be monitored by diabetes patients, which traditionally have had to take blood samples by finger-pricking, at least between twice and four times a day. Finger-pricking has a number of drawbacks that can be tackled by Continuous Glucose Monitors (CGMs), which are able to determine blood sugar levels throughout the day and not only at specific time instants. In this paper, the design of an IoT CGM-based system is proposed, whose collected blood sugar sample values can be accessed remotely; thus being able to monitor patients, specifically dependent ones (e.g., children, elders, and pregnant women) and warn them in the case where a dangerous situation is detected. In order to create such a system, a fog computing system, based on distributed mobile smart phones, has been devised to collect data from the CGMs. Moreover, the use of a blockchain is proposed, to receive, validate, and store the collected data with the objective of avoiding untrusted sources and, thus, to provide a transparent and trustworthy data source of a population, which can vary in age, ethnicity, psychology, education, self-care, and/or geographic location, in a rapid, flexible, scalable, and low-cost way. These crowdsourced data can enable novel mHealth applications for diagnosis, patient monitoring, or even public health actions, which can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.

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EXEGESIS: Extreme Edge Resource Harvesting for a Virtualized Fog Environment

Cited by 57 publications

References 5 publications

Private mobile edge cloud for 5G network applications

Private mobile edge cloud for 5G network applications

Design, Implementation and Practical Evaluation of an IoT Home Automation System for Fog Computing Applications Based on MQTT and ZigBee-WiFi Sensor Nodes

Design of a Fog Computing, Blockchain and IoT-Based Continuous Glucose Monitoring System for Crowdsourcing mHealth

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