Edge Computing in IoT Ecosystems for UAV-Enabled Early Fire Detection

Kalatzis, Nikos; Avgeris, Marios; Dechouniotis, Dimitrios; Papadakis-Vlachopapadopoulos, Konstantinos; Roussaki, Ioanna; Papavassiliou, Symeon

doi:10.1109/smartcomp.2018.00080

Cited by 68 publications

(41 citation statements)

References 11 publications

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“…This study extends the work in [13] and the main contributions of it can be summarized to the following three main topics: a vertical scaling mechanism; contrary to a Cloud Computing environment, the computational resources available on the servers located at the edge are limited [14]. Hence, the simultaneous tenancy of more than one applications at each Edge Server may risk the Quality of Service (QoS) satisfaction.…”

Section: Introductionmentioning

confidence: 71%

Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

Avgeris

Spatharakis

Dechouniotis

et al. 2019

Sensors

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A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system’s architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users’ information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes.

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Section: Introductionmentioning

confidence: 71%

Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

Avgeris

Spatharakis

Dechouniotis

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Contrarily to cloud computing, the resources of edge computing are rather limited; thus, static allocation techniques cannot achieve optimal resource utilization. Furthermore, modern time-and mission-critical IoT-enabled applications [31,32] have strict performance requirements that only dynamic modeling and intelligent allocation algorithms can guarantee. Similarly to cloud, in the edge computing context, most of the relative studies proposed static models alongside with the optimization of a single performance criterion, e.g., energy consumption or response time.…”

Section: Performance Modeling and Resource Allocation In Cloud And Edmentioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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“…Instead of exploring the optimization system model of UAV assisted MEC, [94] presents a three-layer architecture for fire detection application, in which UAVs are used to capture image data. The image data can be processed locally or at the edge or in the cloud.…”

Section: ) Offloading To Uavmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

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Edge Computing in IoT Ecosystems for UAV-Enabled Early Fire Detection

Cited by 68 publications

References 11 publications

Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Air-Ground Integrated Mobile Edge Networks: A Survey

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