Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795359
G. Saldamli, L. Ertaul, Asharani Shankaralingappa
In recent years, Internet of Things (IoT) have spread everywhere from smart home to wireless sensor networks. The physical devices connected to the internet in IoT networks are resource constrained, so implementing security using conventional cryptographic algorithms are not suitable for these resource constrained devices. Recently, National Institute of Standards and Technology (NIST) has published the techniques of lightweight cryptography, which provides a way to implement security in a limited resource environment (IoT environment). Lightweight Message Authentication Code (MAC) is one of the technique in lightweight cryptography. This paper provides an overview of performance analysis of the lightweight cryptography algorithms, which are hash-based Lightweight MAC - LightMAC, CHASKEY on NUCLEO-F401RE IoT board, which has STM32F401RE microcontroller. Performance of these algorithms is analyzed based on the power consumption, memory consumption, execution time and throughput with various supported key sizes and message lengths.
{"title":"Analysis of Lightweight Message Authentication Codes for IoT Environments","authors":"G. Saldamli, L. Ertaul, Asharani Shankaralingappa","doi":"10.1109/FMEC.2019.8795359","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795359","url":null,"abstract":"In recent years, Internet of Things (IoT) have spread everywhere from smart home to wireless sensor networks. The physical devices connected to the internet in IoT networks are resource constrained, so implementing security using conventional cryptographic algorithms are not suitable for these resource constrained devices. Recently, National Institute of Standards and Technology (NIST) has published the techniques of lightweight cryptography, which provides a way to implement security in a limited resource environment (IoT environment). Lightweight Message Authentication Code (MAC) is one of the technique in lightweight cryptography. This paper provides an overview of performance analysis of the lightweight cryptography algorithms, which are hash-based Lightweight MAC - LightMAC, CHASKEY on NUCLEO-F401RE IoT board, which has STM32F401RE microcontroller. Performance of these algorithms is analyzed based on the power consumption, memory consumption, execution time and throughput with various supported key sizes and message lengths.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126763864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795362
Xiangfeng Dai, Irena Spasic, B. Meyer, Samuel Chapman, F. Andrès
Mobile health (mHealth) is considered one of the most transformative drivers for health informatics delivery of ubiquitous medical applications. Machine learning has proven to be a powerful tool in classifying medical images for detecting various diseases. However, supervised machine learning requires a large amount of data to train the model, whose storage and processing pose considerable system requirements challenges for mobile applications. Therefore, many studies focus on deploying cloud-based machine learning, which takes advantage of the Internet connection to outsource data intensive computing. However, this approach comes with certain drawbacks such as those related to latency and privacy, which need to be considered in the context of sensitive data. To tackle these challenges of mHealth applications, we present an on-device inference App and use a dataset of skin cancer images to demonstrate a proof of concept. We pre-trained a Convolutional Neural Network model using 10,015 skin cancer images. The model is then deployed on a mobile device, where the inference process takes place, i.e. when presented with new test image all computations are executed locally where the test data remains. This approach reduces latency, saves bandwidth and improves privacy.
{"title":"Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection","authors":"Xiangfeng Dai, Irena Spasic, B. Meyer, Samuel Chapman, F. Andrès","doi":"10.1109/FMEC.2019.8795362","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795362","url":null,"abstract":"Mobile health (mHealth) is considered one of the most transformative drivers for health informatics delivery of ubiquitous medical applications. Machine learning has proven to be a powerful tool in classifying medical images for detecting various diseases. However, supervised machine learning requires a large amount of data to train the model, whose storage and processing pose considerable system requirements challenges for mobile applications. Therefore, many studies focus on deploying cloud-based machine learning, which takes advantage of the Internet connection to outsource data intensive computing. However, this approach comes with certain drawbacks such as those related to latency and privacy, which need to be considered in the context of sensitive data. To tackle these challenges of mHealth applications, we present an on-device inference App and use a dataset of skin cancer images to demonstrate a proof of concept. We pre-trained a Convolutional Neural Network model using 10,015 skin cancer images. The model is then deployed on a mobile device, where the inference process takes place, i.e. when presented with new test image all computations are executed locally where the test data remains. This approach reduces latency, saves bandwidth and improves privacy.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133796264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795318
Nesrine Ammar, L. Noirie, S. Tixeuil
To empower end-users in the management of their IoT devices and related services, a natural solution is to design and implement a digital assistant whose role is to facilitate use of IoT devices, e.g. by recommending available services for the given set of existing IoT devices. This digital assistant must be able to identify the core capabilities of the IoT devices that are connected to home networks. In turn, this requires to identify the nature of the IoT devices connected to these home networks (e.g., category of the device, but also manufacturer and exact model of the device). In this article, we address this issue of IoT device identification. We propose a solution based on several existing network protocols. The key idea of our solution is to analyze the packets sent by the device to extract relevant information for device identification purpose. We show that our solution is effective by uniquely identifying 31 devices among 33 of the tested devices: each of these devices is identified by a unique feature vector using the Bag Of Words representation.
为了使最终用户能够管理其物联网设备和相关服务，一个自然的解决方案是设计和实现一个数字助理，其作用是促进物联网设备的使用，例如，通过为给定的现有物联网设备集推荐可用的服务。这个数字助理必须能够识别连接到家庭网络的物联网设备的核心功能。反过来，这需要识别连接到这些家庭网络的物联网设备的性质(例如，设备的类别，以及设备的制造商和确切型号)。在本文中，我们将讨论物联网设备识别的这个问题。我们提出了一个基于几种现有网络协议的解决方案。我们的解决方案的关键思想是对设备发送的数据包进行分析，提取相关信息，用于设备识别。我们通过在33个测试设备中唯一识别31个设备来证明我们的解决方案是有效的:每个设备都由使用Bag of Words表示的唯一特征向量识别。
{"title":"Network-Protocol-Based IoT Device Identification","authors":"Nesrine Ammar, L. Noirie, S. Tixeuil","doi":"10.1109/FMEC.2019.8795318","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795318","url":null,"abstract":"To empower end-users in the management of their IoT devices and related services, a natural solution is to design and implement a digital assistant whose role is to facilitate use of IoT devices, e.g. by recommending available services for the given set of existing IoT devices. This digital assistant must be able to identify the core capabilities of the IoT devices that are connected to home networks. In turn, this requires to identify the nature of the IoT devices connected to these home networks (e.g., category of the device, but also manufacturer and exact model of the device). In this article, we address this issue of IoT device identification. We propose a solution based on several existing network protocols. The key idea of our solution is to analyze the packets sent by the device to extract relevant information for device identification purpose. We show that our solution is effective by uniquely identifying 31 devices among 33 of the tested devices: each of these devices is identified by a unique feature vector using the Bag Of Words representation.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123230578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795349
T. Melissaris, K. Shaw, M. Martonosi
Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home’s physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios—asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms’ consistency and concurrency intricacies.
{"title":"OKAPI: In Support of Application Correctness in Smart Home Environments","authors":"T. Melissaris, K. Shaw, M. Martonosi","doi":"10.1109/FMEC.2019.8795349","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795349","url":null,"abstract":"Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home’s physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios—asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms’ consistency and concurrency intricacies.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131899769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795304
C. Anglano, M. Canonico, Marco Guazzone
In Fog Computing, FemtoClouds are emerging computing systems consisting of a set of heterogeneous mobile devices whose users allow to run tasks offloaded by other users. FemtoClouds are well suited to run Bag-of-Tasks (BoTs) applications, but they need effective scheduling algorithms that are able to deal with collections of independently-owned, heterogeneous devices that can suddenly leave the system. In this paper, we present UDFS, an online scheduling algorithm that, by combining knowledge-free task and device selection policies with suitable heterogeneity and volatility tolerance mechanisms, can effectively schedule a stream of BoT applications on FemtoClouds. We evaluate the ability of UDFS to achieve its design goals and to perform better than existing scheduling alternatives, by carrying out a thorough simulation study for a large set of realistic scenarios. Our results indeed show that UDFS can effectively schedule a stream of BoT applications on FemtoClouds, and it can do so more effectively than existing scheduling alternatives.
{"title":"Online User-driven Task Scheduling for FemtoClouds","authors":"C. Anglano, M. Canonico, Marco Guazzone","doi":"10.1109/FMEC.2019.8795304","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795304","url":null,"abstract":"In Fog Computing, FemtoClouds are emerging computing systems consisting of a set of heterogeneous mobile devices whose users allow to run tasks offloaded by other users. FemtoClouds are well suited to run Bag-of-Tasks (BoTs) applications, but they need effective scheduling algorithms that are able to deal with collections of independently-owned, heterogeneous devices that can suddenly leave the system. In this paper, we present UDFS, an online scheduling algorithm that, by combining knowledge-free task and device selection policies with suitable heterogeneity and volatility tolerance mechanisms, can effectively schedule a stream of BoT applications on FemtoClouds. We evaluate the ability of UDFS to achieve its design goals and to perform better than existing scheduling alternatives, by carrying out a thorough simulation study for a large set of realistic scenarios. Our results indeed show that UDFS can effectively schedule a stream of BoT applications on FemtoClouds, and it can do so more effectively than existing scheduling alternatives.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121162761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795325
Nour Mostafa
Fog Computing has been introduced as an emergence technology in Internet of Things (IoT). Fog Computing is introduced as a solution for many enterprises, which include computation, storage, and data exchange capabilities of edge, devises. Users, resources, and data are steadily increasing in number, making scalability and extensibility important issues. With the emergence of the Cloud/Fog it is expected that run time estimates will also be used for resource selection, workflow management, load balancing, and job monitoring. User preferences is the core elements of the fog/cloud service provider, as the idea is to learn, predict, optimize etc. Predictions are a very important step towards automatic resource management. This paper proposes a cooperative fogs system, which consider the user preferences e.g. delay, cost, and privacy and find the optimal choice that meet the user preferences. In addition to ensuring efficient utilization of resources to balance the load in fog computing.
{"title":"Cooperative Fog Communications using A Multi-Level Load Balancing","authors":"Nour Mostafa","doi":"10.1109/FMEC.2019.8795325","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795325","url":null,"abstract":"Fog Computing has been introduced as an emergence technology in Internet of Things (IoT). Fog Computing is introduced as a solution for many enterprises, which include computation, storage, and data exchange capabilities of edge, devises. Users, resources, and data are steadily increasing in number, making scalability and extensibility important issues. With the emergence of the Cloud/Fog it is expected that run time estimates will also be used for resource selection, workflow management, load balancing, and job monitoring. User preferences is the core elements of the fog/cloud service provider, as the idea is to learn, predict, optimize etc. Predictions are a very important step towards automatic resource management. This paper proposes a cooperative fogs system, which consider the user preferences e.g. delay, cost, and privacy and find the optimal choice that meet the user preferences. In addition to ensuring efficient utilization of resources to balance the load in fog computing.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795355
Roberto Casadei, Mirko Viroli
Fog and Mobile Edge Computing are promising paradigms aimed at bringing cloud-like functionality at the edge of the network, close to end users and IoT devices, hence complementing the offer of traditional cloud computing, which is based on powerful but distant data centers. A notable, specific thread of research explores so-called edge-clouds, i.e., small clouds emerging from the combination of resources of proximate edge-devices, whose goal is to provide on-demand storage and computation power to nearby users. In order to support the engineering of such edge computing ecosystems, in this paper we describe an approach for coordinating resources and computations in edge-clouds that assumes connectivity only to nearby devices (abstracting from the concrete communication technology) and tolerates unreliability by self-adaptation to device failure, mobility and withdrawal. Most notably, we delineate a decentralized, self-organizing, spatial approach that works by dynamically partitioning the system into areas, each one governed by an elected manager, and setting up downstream and upstream coordination flows from managers to peripheral nodes (i.e., workers and users) and vice versa. We provide an implementation schema in the ScaFi framework for aggregate programming and evaluate a basic request scheduling scenario through simulation.
{"title":"Coordinating Computation at the Edge: a Decentralized, Self-Organizing, Spatial Approach","authors":"Roberto Casadei, Mirko Viroli","doi":"10.1109/FMEC.2019.8795355","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795355","url":null,"abstract":"Fog and Mobile Edge Computing are promising paradigms aimed at bringing cloud-like functionality at the edge of the network, close to end users and IoT devices, hence complementing the offer of traditional cloud computing, which is based on powerful but distant data centers. A notable, specific thread of research explores so-called edge-clouds, i.e., small clouds emerging from the combination of resources of proximate edge-devices, whose goal is to provide on-demand storage and computation power to nearby users. In order to support the engineering of such edge computing ecosystems, in this paper we describe an approach for coordinating resources and computations in edge-clouds that assumes connectivity only to nearby devices (abstracting from the concrete communication technology) and tolerates unreliability by self-adaptation to device failure, mobility and withdrawal. Most notably, we delineate a decentralized, self-organizing, spatial approach that works by dynamically partitioning the system into areas, each one governed by an elected manager, and setting up downstream and upstream coordination flows from managers to peripheral nodes (i.e., workers and users) and vice versa. We provide an implementation schema in the ScaFi framework for aggregate programming and evaluate a basic request scheduling scenario through simulation.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"22 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795343
Tadeu F. Oliveira, L. Silveira
This paper presents a proposal for energy optimization on Software-Defined Network (SDN) by a proper controller placement. First, the discussion shows the definition of SDN and the role of controllers, then the current state-of-the-art on distributed SDN controllers is briefly discussed showing that current research on this topic focuses on fault-tolerance and load-balancing. Then the energy saving SDN strategies are presented and the problem of saving energy on the control-plane is given further details. A new approach based on parallelism and lower processor frequencies is demonstrated finally, the expected results and key contributions are defined.
{"title":"Distributed SDN controllers optimization for energy saving","authors":"Tadeu F. Oliveira, L. Silveira","doi":"10.1109/FMEC.2019.8795343","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795343","url":null,"abstract":"This paper presents a proposal for energy optimization on Software-Defined Network (SDN) by a proper controller placement. First, the discussion shows the definition of SDN and the role of controllers, then the current state-of-the-art on distributed SDN controllers is briefly discussed showing that current research on this topic focuses on fault-tolerance and load-balancing. Then the energy saving SDN strategies are presented and the problem of saving energy on the control-plane is given further details. A new approach based on parallelism and lower processor frequencies is demonstrated finally, the expected results and key contributions are defined.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131377566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795360
Kamil Macheta, Krzysztof Mateusz Malarski, M.N. Petersen, S. Ruepp
The Internet of Things technology is vastly growing, and more and more applications are being linked to IoT. IoT is moving from a massive deployment and gadget-like applications to also include critical applications for industry, telemedicine or utilities. In this work, we present our plans for a simulation framework that can evaluate and provide end-to-end latency bound connectivity through network slicing.
{"title":"Network Slicing for End-to-End Latency Provisioning in Internet of Things","authors":"Kamil Macheta, Krzysztof Mateusz Malarski, M.N. Petersen, S. Ruepp","doi":"10.1109/FMEC.2019.8795360","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795360","url":null,"abstract":"The Internet of Things technology is vastly growing, and more and more applications are being linked to IoT. IoT is moving from a massive deployment and gadget-like applications to also include critical applications for industry, telemedicine or utilities. In this work, we present our plans for a simulation framework that can evaluate and provide end-to-end latency bound connectivity through network slicing.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795316
G. Saldamli, S. Deshpande, Kaustubh Jawalekar, Pritam Gholap, L. Tawalbeh, L. Ertaul
Forests are one of the prime protectors of earths ecological balance. Unfortunately, the forest fire is typically only discovered when it has already spread over a massive area, making its manage and stoppage onerous and even impossible at times. The end result is devastating loss and irreparable damage to the environment and atmosphere. Among other terrible penalties of forest fires are long-term disastrous outcomes such as impacts on local weather patterns, global warming, and extinction of rare species of the flora and fauna. Millions of hectares of forest are destroyed by means of fire each and every year. Areas destroyed through these fires are massive and trigger greater carbon monoxide than the average automobile traffic. Monitoring of the plausible threat areas and an early detection of fire can substantially shorten the reaction time and also minimize the potential damage as well as the cost of firefighting. Economic fire alarm structures are quintessential in imparting an early warning in the event of fire. They help to save lives and protect property whilst additionally satisfying the needs of insurance groups and government departments. The proposed system is capable of detecting smoke, different flammable gases and fire and notifying any hazardous situation using an Iot Dashboard. This wildfire smoke detection system with IoT framework provides a smart and cost efficient solution that saves lot of flora, fauna and human lives.
{"title":"Wildfire Detection using Wireless Mesh Network","authors":"G. Saldamli, S. Deshpande, Kaustubh Jawalekar, Pritam Gholap, L. Tawalbeh, L. Ertaul","doi":"10.1109/FMEC.2019.8795316","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795316","url":null,"abstract":"Forests are one of the prime protectors of earths ecological balance. Unfortunately, the forest fire is typically only discovered when it has already spread over a massive area, making its manage and stoppage onerous and even impossible at times. The end result is devastating loss and irreparable damage to the environment and atmosphere. Among other terrible penalties of forest fires are long-term disastrous outcomes such as impacts on local weather patterns, global warming, and extinction of rare species of the flora and fauna. Millions of hectares of forest are destroyed by means of fire each and every year. Areas destroyed through these fires are massive and trigger greater carbon monoxide than the average automobile traffic. Monitoring of the plausible threat areas and an early detection of fire can substantially shorten the reaction time and also minimize the potential damage as well as the cost of firefighting. Economic fire alarm structures are quintessential in imparting an early warning in the event of fire. They help to save lives and protect property whilst additionally satisfying the needs of insurance groups and government departments. The proposed system is capable of detecting smoke, different flammable gases and fire and notifying any hazardous situation using an Iot Dashboard. This wildfire smoke detection system with IoT framework provides a smart and cost efficient solution that saves lot of flora, fauna and human lives.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128471445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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