Pub Date : 2019-06-10DOI: 10.1109/FMEC.2019.8795317
Michael Opoku Agyeman, Ali Al-Mahmood
Stroke survivors often suffer from a permanent or partial disability that restricts the movement of the hands, arms and/or legs. To help patients recover, rehabilitation should be at an earlier stage of the injury. Without motivation, it would be challenging for patients to successfully engage in the recovery process which can sometimes be painful of inconvenient. The application of wearable devices, games and Internet-of-Things (IoT) can create a motivating atmosphere to facilitate the rehabilitation process of patients while enabling remote monitoring of their health and progress. This paper presents the design and implementation of a rehabilitation system for aimed at helping stroke patients suffering from upper limb disability that exploits IoT by integrating gaming and wearable technology.
{"title":"Design and Implementation of a Wearable Device for Motivating Patients With Upper and/or Lower Limb Disability Via Gaming and Home Rehabilitation","authors":"Michael Opoku Agyeman, Ali Al-Mahmood","doi":"10.1109/FMEC.2019.8795317","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795317","url":null,"abstract":"Stroke survivors often suffer from a permanent or partial disability that restricts the movement of the hands, arms and/or legs. To help patients recover, rehabilitation should be at an earlier stage of the injury. Without motivation, it would be challenging for patients to successfully engage in the recovery process which can sometimes be painful of inconvenient. The application of wearable devices, games and Internet-of-Things (IoT) can create a motivating atmosphere to facilitate the rehabilitation process of patients while enabling remote monitoring of their health and progress. This paper presents the design and implementation of a rehabilitation system for aimed at helping stroke patients suffering from upper limb disability that exploits IoT by integrating gaming and wearable technology.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"37 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":"116820368","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.2019.8795313
V. Sarker, J. P. Queralta, Tuan Anh Nguyen Gia, H. Tenhunen, Tomi Westerlund
Increased automation and intelligence in computer systems have revealed limitations of Cloud-based computing such as unpredicted latency in safety-critical and performance-sensitive applications. The amount of data generated from ubiquitous sensors has reached a degree where it becomes impractical to always store and process in the Cloud. Edge computing brings computation and storage to the Edge of the network near to where the data originates yielding reduced network load and better performance of services. In parallel, new wireless communication technologies have appeared to facilitate the expansion of Internet of Things (IoT). Instead of seeking higher data rates, low-power wide-area network aims at battery-powered sensor nodes and devices which require reliable communication for a prolonged period of time. Recently, Long Range (LoRa) has become a popular choice for IoT-based solutions. In this paper, we explore and analyze different application fields and related works which use LoRa and investigate potential improvement opportunities and considerations. Furthermore, we propose a generic architecture to integrate Edge computation capability in IoT-based applications for enhanced performance.
{"title":"A Survey on LoRa for IoT: Integrating Edge Computing","authors":"V. Sarker, J. P. Queralta, Tuan Anh Nguyen Gia, H. Tenhunen, Tomi Westerlund","doi":"10.1109/FMEC.2019.8795313","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795313","url":null,"abstract":"Increased automation and intelligence in computer systems have revealed limitations of Cloud-based computing such as unpredicted latency in safety-critical and performance-sensitive applications. The amount of data generated from ubiquitous sensors has reached a degree where it becomes impractical to always store and process in the Cloud. Edge computing brings computation and storage to the Edge of the network near to where the data originates yielding reduced network load and better performance of services. In parallel, new wireless communication technologies have appeared to facilitate the expansion of Internet of Things (IoT). Instead of seeking higher data rates, low-power wide-area network aims at battery-powered sensor nodes and devices which require reliable communication for a prolonged period of time. Recently, Long Range (LoRa) has become a popular choice for IoT-based solutions. In this paper, we explore and analyze different application fields and related works which use LoRa and investigate potential improvement opportunities and considerations. Furthermore, we propose a generic architecture to integrate Edge computation capability in IoT-based applications for enhanced performance.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"28 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":"129223792","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.2019.8795347
Philipp Kisters, Dirk Bade, Julius Wulk
Smart mobility is one of the cornerstones of smart cities and in particular, smarter routing decisions are desired and required in order to optimize urban, intermodal transports for individuals as well as businesses. Smart routing comes as a multi-criterion optimization problem, not only dealing solely with efficiency anymore but also with ecologic and economic aspects, as well as comfort, safety, fun etc. In this paper, we propose two new routing algorithms that dynamically re-evaluate rainless routes using up-to-date, high-resolution precipitation information, that additionally take user preferences into account and also consider optimal temporal offsets for departure. Thereby, we not only focus on, e.g., cyclist and pedestrians, who would obviously benefit from such routing, but also on more visionary use cases including aerial or water-borne vehicles. We implemented a middle-tier and a mobile application in order to proof the feasibility of our precipitation-based routing algorithms and demonstrate the advantages by presenting preliminary results of an evaluation using real historical precipitation data.
{"title":"Dynamic Routing Using Precipitation Data","authors":"Philipp Kisters, Dirk Bade, Julius Wulk","doi":"10.1109/FMEC.2019.8795347","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795347","url":null,"abstract":"Smart mobility is one of the cornerstones of smart cities and in particular, smarter routing decisions are desired and required in order to optimize urban, intermodal transports for individuals as well as businesses. Smart routing comes as a multi-criterion optimization problem, not only dealing solely with efficiency anymore but also with ecologic and economic aspects, as well as comfort, safety, fun etc. In this paper, we propose two new routing algorithms that dynamically re-evaluate rainless routes using up-to-date, high-resolution precipitation information, that additionally take user preferences into account and also consider optimal temporal offsets for departure. Thereby, we not only focus on, e.g., cyclist and pedestrians, who would obviously benefit from such routing, but also on more visionary use cases including aerial or water-borne vehicles. We implemented a middle-tier and a mobile application in order to proof the feasibility of our precipitation-based routing algorithms and demonstrate the advantages by presenting preliminary results of an evaluation using real historical precipitation data.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"10 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":"134491086","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.2019.8795311
M. Gergeleit
This paper describes a tree routing mechanism called “Autotree” that automatically establishes an IP-based multi-hop routing on a network of WiFi enabled IoT nodes. It uses NAT and a variation of distance vector routing to dynamically establish an efficient tree without any manual intervention. It automatically adapts to topology changes, thus it can react on node failures and (limited) node mobility. It uses standard IEEE 802.11 links with WPA2 security. Also, the mechanism is completely transparent for other connected WiFi devices. Autotree has been implemented and evaluated on ESP8266 boards.
{"title":"Autotree: Connecting Cheap IoT Nodes with an Auto-Configuring WiFi Tree Network","authors":"M. Gergeleit","doi":"10.1109/FMEC.2019.8795311","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795311","url":null,"abstract":"This paper describes a tree routing mechanism called “Autotree” that automatically establishes an IP-based multi-hop routing on a network of WiFi enabled IoT nodes. It uses NAT and a variation of distance vector routing to dynamically establish an efficient tree without any manual intervention. It automatically adapts to topology changes, thus it can react on node failures and (limited) node mobility. It uses standard IEEE 802.11 links with WPA2 security. Also, the mechanism is completely transparent for other connected WiFi devices. Autotree has been implemented and evaluated on ESP8266 boards.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"126 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":"127972000","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.2019.8795309
Smriti Bhatt, L. Tawalbeh, Pankaj Chhetri, Paras Bhatt
With the advancement of the Internet and technology, the Internet of Things (IoT) has gained significant momentum in recent years. There is rapid growth in the number of smart IoT devices and IoT applications. These devices and applications primarily gather, store, and share user data. However, the IoT devices have limited resources, such as storage, power, and computation. Cloud Computing enables IoT to leverage its unlimited capabilities, such as storage, computation, and analytics. Today, Cloud and IoT have become parallels and move together to form a Cloud-Enabled IoT architecture. This diverse and dynamic Cloud-IoT architecture, where IoT devices connect and disconnect on the fly and share data for analysis and computation with the Cloud raises many security and privacy concerns and broadens the attack surface. For example, in a government organization with high security and data privacy risk, there has to be a secure access control mechanism in place to ensure authorized access to data and resources. In this paper, we discuss the state-of-art of authorization mechanisms in Cloud- Enabled IoT architecture. We present two use cases – A Smart Home use case, and A Smart University Parking use case, to discuss various access control and authorization requirements in Cloud-Based IoT. We then propose an Attribute-Based Access Control (ABAC), a flexible access control approach, to address these access control requirements in the Cloud-Based IoT architecture, mainly in the context of presented use cases.
{"title":"Authorizations in Cloud-Based Internet of Things: Current Trends and Use Cases","authors":"Smriti Bhatt, L. Tawalbeh, Pankaj Chhetri, Paras Bhatt","doi":"10.1109/FMEC.2019.8795309","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795309","url":null,"abstract":"With the advancement of the Internet and technology, the Internet of Things (IoT) has gained significant momentum in recent years. There is rapid growth in the number of smart IoT devices and IoT applications. These devices and applications primarily gather, store, and share user data. However, the IoT devices have limited resources, such as storage, power, and computation. Cloud Computing enables IoT to leverage its unlimited capabilities, such as storage, computation, and analytics. Today, Cloud and IoT have become parallels and move together to form a Cloud-Enabled IoT architecture. This diverse and dynamic Cloud-IoT architecture, where IoT devices connect and disconnect on the fly and share data for analysis and computation with the Cloud raises many security and privacy concerns and broadens the attack surface. For example, in a government organization with high security and data privacy risk, there has to be a secure access control mechanism in place to ensure authorized access to data and resources. In this paper, we discuss the state-of-art of authorization mechanisms in Cloud- Enabled IoT architecture. We present two use cases – A Smart Home use case, and A Smart University Parking use case, to discuss various access control and authorization requirements in Cloud-Based IoT. We then propose an Attribute-Based Access Control (ABAC), a flexible access control approach, to address these access control requirements in the Cloud-Based IoT architecture, mainly in the context of presented use cases.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"19 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":"129958875","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.2019.8795345
Michele Berno, J. Alcaraz, M. Rossi
In this study, a model for the allocation of processing tasks in Mobile Edge Computing (MEC) environments is put forward, whereby a certain amount of workload, coming from the base stations at the network edge, has to be optimally distributed across the available servers. At first, this allocation problem is formulated as a centralized (offline) optimization program with delay constraints (deadlines), by keeping into account server qualities such as computation speed and cost, and by optimally distributing the workload across a hierarchy of computation servers. Afterwards, the offline problem is solved devising a distributed algorithm, utilizing the Alternating Direction Method of Multipliers (ADMM). Selected numerical results are presented to discuss the key features of our approach, which provides control over contrasting optimization objectives such as minimizing the energy consumption, balancing the workload, and controlling the number of servers that are involved in the computation.
{"title":"On the Allocation of Computing Tasks under QoS Constraints in Hierarchical MEC Architectures","authors":"Michele Berno, J. Alcaraz, M. Rossi","doi":"10.1109/FMEC.2019.8795345","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795345","url":null,"abstract":"In this study, a model for the allocation of processing tasks in Mobile Edge Computing (MEC) environments is put forward, whereby a certain amount of workload, coming from the base stations at the network edge, has to be optimally distributed across the available servers. At first, this allocation problem is formulated as a centralized (offline) optimization program with delay constraints (deadlines), by keeping into account server qualities such as computation speed and cost, and by optimally distributing the workload across a hierarchy of computation servers. Afterwards, the offline problem is solved devising a distributed algorithm, utilizing the Alternating Direction Method of Multipliers (ADMM). Selected numerical results are presented to discuss the key features of our approach, which provides control over contrasting optimization objectives such as minimizing the energy consumption, balancing the workload, and controlling the number of servers that are involved in the computation.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 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":"127969741","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.2019.8795319
J. Bassey, D. Adesina, Xiangfang Li, Lijun Qian, Alexander J. Aved, Timothy S. Kroecker
Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed extensively. To ensure secure operations of these systems with wireless transmission capabilities, Radio Frequency (RF) surveillance is important to monitor their activities in RF spectrum and detect unauthorized IoT devices. Specifically, in order to prevent an adversary from impersonating legitimate users using identical devices from the same manufacturer, unique “signatures” must be obtained for every individual device in order to uniquely identify each device. In this study, a novel intrusion detection method is proposed to detect unauthorized IoT devices using deep learning. The proposed method is based on RF fingerprinting since physical layer based features are device specific and more difficult to impersonate. RF traces are collected from six “identical” ZigBee devices via a USRP based test bed. The traces span a range of Signal-to-Noise Ratio, to ensure robustness of the model. A convolutional neural network is used to extract features from the RF traces, and dimension reduction and de-correlation are performed on the extracted features. The reduced features are then clustered to identify IoT devices. We show that the proposed method is able to identify devices that are not observed during training. The results not only highlight the benefit of deep learning based feature extraction, but also show promising prospects for being able to distinguish new devices (classes) that are not observed during training.
{"title":"Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning","authors":"J. Bassey, D. Adesina, Xiangfang Li, Lijun Qian, Alexander J. Aved, Timothy S. Kroecker","doi":"10.1109/FMEC.2019.8795319","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795319","url":null,"abstract":"Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed extensively. To ensure secure operations of these systems with wireless transmission capabilities, Radio Frequency (RF) surveillance is important to monitor their activities in RF spectrum and detect unauthorized IoT devices. Specifically, in order to prevent an adversary from impersonating legitimate users using identical devices from the same manufacturer, unique “signatures” must be obtained for every individual device in order to uniquely identify each device. In this study, a novel intrusion detection method is proposed to detect unauthorized IoT devices using deep learning. The proposed method is based on RF fingerprinting since physical layer based features are device specific and more difficult to impersonate. RF traces are collected from six “identical” ZigBee devices via a USRP based test bed. The traces span a range of Signal-to-Noise Ratio, to ensure robustness of the model. A convolutional neural network is used to extract features from the RF traces, and dimension reduction and de-correlation are performed on the extracted features. The reduced features are then clustered to identify IoT devices. We show that the proposed method is able to identify devices that are not observed during training. The results not only highlight the benefit of deep learning based feature extraction, but also show promising prospects for being able to distinguish new devices (classes) that are not observed during training.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 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":"130963492","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.2019.8795328
Mustafa Daraghmeh, I. A. Ridhawi, M. Aloqaily, Y. Jararweh, A. Agarwal
With the rapid development of edge computing and its applications, requests to edge servers is expected to grow, resulting in higher edge network energy consumption. This in essence would also result in higher operational costs for running edge applications. Furthermore, service providers try to manage their resources efficiently to provide appropriate quality of services to their customers while reducing service costs. To appropriately manage resources, it is necessary to apply useful models to measure energy consumption in the edge network. The linear relationship between energy consumption and CPU utilization is one powerful modeling method used to compute the energy consumption of edge network servers. The method calculates the power consumption of a server based on its CPU utilization during run-time. In this paper, we propose a linear power model for the EdgeCloudSim simulator to measure the energy consumption of edge network servers. Moreover, we introduce a simple dynamic power management model used to minimize power consumption in the edge network by switching the edge servers on and off based on provisioned application needs. The experimental and simulation results show a notable reduction in the total energy consumption when applying the proposed simple model on two different orchestration policies to manage the edge network servers.
{"title":"A Power Management Approach to Reduce Energy Consumption for Edge Computing Servers","authors":"Mustafa Daraghmeh, I. A. Ridhawi, M. Aloqaily, Y. Jararweh, A. Agarwal","doi":"10.1109/FMEC.2019.8795328","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795328","url":null,"abstract":"With the rapid development of edge computing and its applications, requests to edge servers is expected to grow, resulting in higher edge network energy consumption. This in essence would also result in higher operational costs for running edge applications. Furthermore, service providers try to manage their resources efficiently to provide appropriate quality of services to their customers while reducing service costs. To appropriately manage resources, it is necessary to apply useful models to measure energy consumption in the edge network. The linear relationship between energy consumption and CPU utilization is one powerful modeling method used to compute the energy consumption of edge network servers. The method calculates the power consumption of a server based on its CPU utilization during run-time. In this paper, we propose a linear power model for the EdgeCloudSim simulator to measure the energy consumption of edge network servers. Moreover, we introduce a simple dynamic power management model used to minimize power consumption in the edge network by switching the edge servers on and off based on provisioned application needs. The experimental and simulation results show a notable reduction in the total energy consumption when applying the proposed simple model on two different orchestration policies to manage the edge network servers.","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":"134356569","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.2019.8795320
Amir Karamoozian, A. Hafid, E. Aboulhamid
Fog computing emerged as a new computing paradigm which moves the computing power to the proximity of users, from core to the edge of the network. It is known as the extension of Cloud computing and it offers inordinate opportunities for real-time and latency-sensitive IoT applications. An IoT application consists of a set of dependent Processing Elements (PEs) defined as operations performed on data streams and can be modeled as a Directed Acyclic Graph (DAG). Each PE performs a variety of low-level computation on the incoming data such as aggregation or filtering. A key challenge is to decide how to distribute such PEs over the resources, in order to minimize the overall response time of the entire PE graph. This problem is known as distributed PE scheduling and placement problem. In this work, we try to address the question of how fog computing paradigm can help reducing the IoT application response time by efficiently distributing PE graphs over the Fog-Cloud continuum. We mathematically formulate the fundamental characteristics of IoT application and Fog infrastructure, then model the system as an optimization problem using Gravitational Search Algorithm (GSA) meta-heuristic technique. Our proposed GSA model is evaluated by comparing it with a well-known evolutionary algorithm in the literature via simulation. Also, a comparative analysis with the legacy cloud infrastructure is done in order to show the significant impact of fog presence on the performance of PE processing. Evaluation of our model demonstrates the efficiency of our approach comparing to the current literature.
{"title":"On the Fog-Cloud Cooperation: How Fog Computing can address latency concerns of IoT applications","authors":"Amir Karamoozian, A. Hafid, E. Aboulhamid","doi":"10.1109/FMEC.2019.8795320","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795320","url":null,"abstract":"Fog computing emerged as a new computing paradigm which moves the computing power to the proximity of users, from core to the edge of the network. It is known as the extension of Cloud computing and it offers inordinate opportunities for real-time and latency-sensitive IoT applications. An IoT application consists of a set of dependent Processing Elements (PEs) defined as operations performed on data streams and can be modeled as a Directed Acyclic Graph (DAG). Each PE performs a variety of low-level computation on the incoming data such as aggregation or filtering. A key challenge is to decide how to distribute such PEs over the resources, in order to minimize the overall response time of the entire PE graph. This problem is known as distributed PE scheduling and placement problem. In this work, we try to address the question of how fog computing paradigm can help reducing the IoT application response time by efficiently distributing PE graphs over the Fog-Cloud continuum. We mathematically formulate the fundamental characteristics of IoT application and Fog infrastructure, then model the system as an optimization problem using Gravitational Search Algorithm (GSA) meta-heuristic technique. Our proposed GSA model is evaluated by comparing it with a well-known evolutionary algorithm in the literature via simulation. Also, a comparative analysis with the legacy cloud infrastructure is done in order to show the significant impact of fog presence on the performance of PE processing. Evaluation of our model demonstrates the efficiency of our approach comparing to the current literature.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"9 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":"125552237","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 : 2019-06-01DOI: 10.1109/FMEC.2019.8795327
Suresh K. Peddoju, Himanshu Upadhyay, S. Bhansali
The healthcare industry is rapidly adopting new technologies such as the Internet of Things (IoT), which are dropping costs and improving healthcare outcomes. Such IoT systems typically include edge devices (glucose monitors, ventilators, pacemakers), gateway devices that aggregate the data from the edge devices and transmit it to the cloud, and cloud-based systems which analyze the device data to draw conclusions, display information, or direct the connected devices to take action. This process can lead to communication lags and delayed responses to patient conditions/treatment. The aim of this proposal is to overcome these delays with IoT technology and allow for prompt urgent treatment to patients. The solution proposed includes a model to monitor and process the data disseminated by wearable devices related to the patients’ health issues and connect the data to IoT cloud platforms. Analysis of the patients’ health data to identify anomalies will be performed at the device level by developing an offline machine learning model using specific algorithms for anomaly detection and deploying them on the IoT devices or IoT gateway. Processing of the real-time health data will be performed at the device level and the prediction of anomalous data will be sent to the third-party cloud for implementing any necessary actions.
{"title":"Health Monitoring with Low Power IoT Devices using Anomaly Detection Algorithm","authors":"Suresh K. Peddoju, Himanshu Upadhyay, S. Bhansali","doi":"10.1109/FMEC.2019.8795327","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795327","url":null,"abstract":"The healthcare industry is rapidly adopting new technologies such as the Internet of Things (IoT), which are dropping costs and improving healthcare outcomes. Such IoT systems typically include edge devices (glucose monitors, ventilators, pacemakers), gateway devices that aggregate the data from the edge devices and transmit it to the cloud, and cloud-based systems which analyze the device data to draw conclusions, display information, or direct the connected devices to take action. This process can lead to communication lags and delayed responses to patient conditions/treatment. The aim of this proposal is to overcome these delays with IoT technology and allow for prompt urgent treatment to patients. The solution proposed includes a model to monitor and process the data disseminated by wearable devices related to the patients’ health issues and connect the data to IoT cloud platforms. Analysis of the patients’ health data to identify anomalies will be performed at the device level by developing an offline machine learning model using specific algorithms for anomaly detection and deploying them on the IoT devices or IoT gateway. Processing of the real-time health data will be performed at the device level and the prediction of anomalous data will be sent to the third-party cloud for implementing any necessary actions.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126613416","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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