Pub Date : 2019-06-01DOI: 10.1109/FMEC.2019.8795338
Nour Takiddeen, I. Zualkernan
Smartwatches have finally come of age and represent a unique platform for building IoT applications involving people. Today, smartwatches are used in various IoT scenarios including healthcare and fitness. Since the current smartwatches are equipped with a variety of sensors and heterogenous wireless protocols, they can be used to enact a variety of people-based Social Internet of Things (SIoT). Such applications involve sending sensor data from millions of watches through the IoT cloud. Processors on current watches are powerful enough to run even deep learning algorithms and may support peak download data rates of more than 50 Mbits/second. However, battery life remains a limiting factor. Most smartwatch applications capture and process context. This paper provides a survey and framework based on context computation, edge analytics, and computation off-loading as applied to IoT applications using smartwatches. This framework can be a basis of meaningful discussion of various solutions to address various technical problems like short battery life of smartwatches when used in IoT applications.
{"title":"Smartwatches as IoT Edge Devices: A Framework and Survey","authors":"Nour Takiddeen, I. Zualkernan","doi":"10.1109/FMEC.2019.8795338","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795338","url":null,"abstract":"Smartwatches have finally come of age and represent a unique platform for building IoT applications involving people. Today, smartwatches are used in various IoT scenarios including healthcare and fitness. Since the current smartwatches are equipped with a variety of sensors and heterogenous wireless protocols, they can be used to enact a variety of people-based Social Internet of Things (SIoT). Such applications involve sending sensor data from millions of watches through the IoT cloud. Processors on current watches are powerful enough to run even deep learning algorithms and may support peak download data rates of more than 50 Mbits/second. However, battery life remains a limiting factor. Most smartwatch applications capture and process context. This paper provides a survey and framework based on context computation, edge analytics, and computation off-loading as applied to IoT applications using smartwatches. This framework can be a basis of meaningful discussion of various solutions to address various technical problems like short battery life of smartwatches when used in IoT applications.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129385176","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.2019.8795336
M. Rathore, Y. Jararweh, Hojae Son, Anand Paul
Auto management and controlling road traffic while identifying abnormal driving behavior is one of the key challenges faced by the traffic authorities. In most of the cities, the traffic violations are detected manually by placing sergeants at various regions on the road. Placing sergeants is not economical and does not cover all the metropolitan area. Only in modern countries, traffic authorities have developed systems that use static road cameras to monitor real-time city traffic for identification of major traffic violations. However, these cameras just cover limited areas of the cities, such as, intersections, signals, roundabouts, and main streets. Therefore, in this paper, we have proposed a real-time traffic violation detection model by using vehicular camera along with the edge device in order to control and manage the road traffic. The edge device is equipped with the graphics processing unit (GPU), deployed inside the vehicle, and directly attached to the vehicle camera. The camera monitors every vehicle ahead, whereas, the edge device identifies the suspected driving violation. As a use case, we have tested our model by considering a wrong U-turn as a traffic violation. We designed a wrong U-turn detection algorithm and deployed it on the GPU-enabled edge device. In order to evaluate the feasibility of the system, we considered the efficiency measurements corresponding to the video generation rate and data size. The results show that the system is able to identify violations far faster than the video generation time.
{"title":"Real-time Traffic Management Model using GPUenabled Edge Devices","authors":"M. Rathore, Y. Jararweh, Hojae Son, Anand Paul","doi":"10.1109/FMEC.2019.8795336","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795336","url":null,"abstract":"Auto management and controlling road traffic while identifying abnormal driving behavior is one of the key challenges faced by the traffic authorities. In most of the cities, the traffic violations are detected manually by placing sergeants at various regions on the road. Placing sergeants is not economical and does not cover all the metropolitan area. Only in modern countries, traffic authorities have developed systems that use static road cameras to monitor real-time city traffic for identification of major traffic violations. However, these cameras just cover limited areas of the cities, such as, intersections, signals, roundabouts, and main streets. Therefore, in this paper, we have proposed a real-time traffic violation detection model by using vehicular camera along with the edge device in order to control and manage the road traffic. The edge device is equipped with the graphics processing unit (GPU), deployed inside the vehicle, and directly attached to the vehicle camera. The camera monitors every vehicle ahead, whereas, the edge device identifies the suspected driving violation. As a use case, we have tested our model by considering a wrong U-turn as a traffic violation. We designed a wrong U-turn detection algorithm and deployed it on the GPU-enabled edge device. In order to evaluate the feasibility of the system, we considered the efficiency measurements corresponding to the video generation rate and data size. The results show that the system is able to identify violations far faster than the video generation time.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133269729","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.2019.8795353
S. Shukla, D. Ghosal, Kesheng Wu, A. Sim, M. Farrens
Fog computing has the potential to be an energy-efficient alternative to cloud computing for guaranteeing latency requirements of Latency-critical (LC) IoT services. However, even in fog computing low energy-efficiency of homogeneous multi-core server processors can be a major contributor to energy wastage. Recent studies have shown that Heterogeneous Multi-core Processors (HMPs) can improve energy efficiency of servers by adapting to dynamic load changes of LC-services. However, proposed approaches optimize energy only at a single server level. In our work, we demonstrate that optimization at the cluster-level across many HMP-servers can offer much greater energy savings through optimal work distribution across the HMP-servers while still guaranteeing the Service Level Objectives (SLO) of LC-services. In this paper, we present Greeniac, a cluster-level task manager that employs Reinforcement Learning to identify optimal configurations at the server- and cluster-levels for different workloads. We develop a server-level service scheduler and a cluster-level load balancing module to assign services and distribute tasks across HMP servers based on the learned configurations. In addition to meeting the required SLO targets, Greeniac achieves up to 28% energy saving compared to best-case cluster scheduling techniques with local HMP-aware scheduling on a 4-server fog cluster, with potentially larger savings in a larger cluster.
{"title":"Co-optimizing Latency and Energy for IoT services using HMP servers in Fog Clusters","authors":"S. Shukla, D. Ghosal, Kesheng Wu, A. Sim, M. Farrens","doi":"10.1109/FMEC.2019.8795353","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795353","url":null,"abstract":"Fog computing has the potential to be an energy-efficient alternative to cloud computing for guaranteeing latency requirements of Latency-critical (LC) IoT services. However, even in fog computing low energy-efficiency of homogeneous multi-core server processors can be a major contributor to energy wastage. Recent studies have shown that Heterogeneous Multi-core Processors (HMPs) can improve energy efficiency of servers by adapting to dynamic load changes of LC-services. However, proposed approaches optimize energy only at a single server level. In our work, we demonstrate that optimization at the cluster-level across many HMP-servers can offer much greater energy savings through optimal work distribution across the HMP-servers while still guaranteeing the Service Level Objectives (SLO) of LC-services. In this paper, we present Greeniac, a cluster-level task manager that employs Reinforcement Learning to identify optimal configurations at the server- and cluster-levels for different workloads. We develop a server-level service scheduler and a cluster-level load balancing module to assign services and distribute tasks across HMP servers based on the learned configurations. In addition to meeting the required SLO targets, Greeniac achieves up to 28% energy saving compared to best-case cluster scheduling techniques with local HMP-aware scheduling on a 4-server fog cluster, with potentially larger savings in a larger cluster.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860199","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.2019.8795352
{"title":"FMEC 2019 Keynote 4","authors":"","doi":"10.1109/fmec.2019.8795352","DOIUrl":"https://doi.org/10.1109/fmec.2019.8795352","url":null,"abstract":"","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"13 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":"123546632","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.2019.8795365
P. Mishra, Ishita Verma, Saurabh Gupta, Varun S. Rana, Kavitha Kadarla
In the modern era of computing, Cloud security is of paramount importance. Most of the research mainly focused on In-Virtual Machine (VM) security techniques for detecting malware affecting virtual domains running in the Cloud. In-VM security techniques are deployed inside the VM and hence they are very much prone to subversion attacks. In this paper, an-VM monitoring approach based on introspection, called vProVal, is proposed. The vProVal is designed to detect the hidden processes and rootkits that disable the security tool, running in the monitored VM in Kernel VM (KVM)-based cloud environment. It performs the malware detection from outside the VM at the KVM-layer and hence more robust to attacks. The introspection technique used is to extract the low-level details of a running VM from hypervisor by viewing its memory, trapping on hardware events, and accessing the vCPU registers. A preliminary analysis has been performed and the approach is found to be promising.
{"title":"vProVal: Introspection based Process Validation for Detecting Malware in KVM-based Cloud Environment","authors":"P. Mishra, Ishita Verma, Saurabh Gupta, Varun S. Rana, Kavitha Kadarla","doi":"10.1109/FMEC.2019.8795365","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795365","url":null,"abstract":"In the modern era of computing, Cloud security is of paramount importance. Most of the research mainly focused on In-Virtual Machine (VM) security techniques for detecting malware affecting virtual domains running in the Cloud. In-VM security techniques are deployed inside the VM and hence they are very much prone to subversion attacks. In this paper, an-VM monitoring approach based on introspection, called vProVal, is proposed. The vProVal is designed to detect the hidden processes and rootkits that disable the security tool, running in the monitored VM in Kernel VM (KVM)-based cloud environment. It performs the malware detection from outside the VM at the KVM-layer and hence more robust to attacks. The introspection technique used is to extract the low-level details of a running VM from hypervisor by viewing its memory, trapping on hardware events, and accessing the vCPU registers. A preliminary analysis has been performed and the approach is found to be promising.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"105 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":"124741216","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.2019.8795346
Giuseppe Astuti, Antonio Brogi, Stefano Forti
Managing large, highly distributed IoT applications over heterogeneous Fog infrastructures so to meet all their stringent QoS (as well as hardware and software) requirements is intrinsically difficult. Different simulation and predictive methodologies have been proposed to estimate key performance indicators of eligible application deployments and managements so to identify the best candidates. In this paper, we describe the current business model environment and discuss two possible business models for creating value from a company provisioning predictive Fog application management services.
{"title":"Making a Business Out of (Predictive Application Management in) the Fog*","authors":"Giuseppe Astuti, Antonio Brogi, Stefano Forti","doi":"10.1109/FMEC.2019.8795346","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795346","url":null,"abstract":"Managing large, highly distributed IoT applications over heterogeneous Fog infrastructures so to meet all their stringent QoS (as well as hardware and software) requirements is intrinsically difficult. Different simulation and predictive methodologies have been proposed to estimate key performance indicators of eligible application deployments and managements so to identify the best candidates. In this paper, we describe the current business model environment and discuss two possible business models for creating value from a company provisioning predictive Fog application management services.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"38 3 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":"130607553","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.2019.8795350
{"title":"FMEC 2019 Keynote 2","authors":"","doi":"10.1109/fmec.2019.8795350","DOIUrl":"https://doi.org/10.1109/fmec.2019.8795350","url":null,"abstract":"","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"74 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":"121156591","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.2019.8795341
Javier Rocher, Daniel A. Basterrechea, Miran Taha, Mar Parra, Jaime Lloret
Illegal dumpings in sewerage can cause problems in wastewater treatment plants, so it may become an environmental problem. In this paper, we propose a system for detecting these illegal dumpings. We use conductivity sensors for detecting a change in the conductivity of water because this change may appear due to a dump. The system is based on two coils. One of the coils is powered by a sinus-wave and the other coil is induced. To prevent damage from water in the copper we encapsulate the coils in a PVC tube. These coils are connected to a Flyport in order to send the values and generate alarms. We tested the prototype with different configurations of coils with encapsulation of 3 and 1 mm. When the encapsulation is of 3 mm, we do not observe differences in the induced voltage. The prototype selected has a difference of 4.10 Volts between the samples 0 and 40 g/l of the table salt. In the verification test this prototype has a relative error of 2.54%.
{"title":"Water Conductivity Sensor based on Coils to Detect Illegal Dumpings in Smart Cities","authors":"Javier Rocher, Daniel A. Basterrechea, Miran Taha, Mar Parra, Jaime Lloret","doi":"10.1109/FMEC.2019.8795341","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795341","url":null,"abstract":"Illegal dumpings in sewerage can cause problems in wastewater treatment plants, so it may become an environmental problem. In this paper, we propose a system for detecting these illegal dumpings. We use conductivity sensors for detecting a change in the conductivity of water because this change may appear due to a dump. The system is based on two coils. One of the coils is powered by a sinus-wave and the other coil is induced. To prevent damage from water in the copper we encapsulate the coils in a PVC tube. These coils are connected to a Flyport in order to send the values and generate alarms. We tested the prototype with different configurations of coils with encapsulation of 3 and 1 mm. When the encapsulation is of 3 mm, we do not observe differences in the induced voltage. The prototype selected has a difference of 4.10 Volts between the samples 0 and 40 g/l of the table salt. In the verification test this prototype has a relative error of 2.54%.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"7 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":"114404526","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.2019.8795323
M. Zanella, G. Massari, W. Fornaciari
Achieving an optimal management of the energy budget of mobile devices, while matching the applications performance requirements is always a challenging task. In our research, we are exploring the possible benefits of driving run-time management strategies, from an application perspective, by integrating the programming model with the run-time system and exploiting suitable API for explicit application requirements specification.
{"title":"Run-Time Managed Mobile Application Execution","authors":"M. Zanella, G. Massari, W. Fornaciari","doi":"10.1109/FMEC.2019.8795323","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795323","url":null,"abstract":"Achieving an optimal management of the energy budget of mobile devices, while matching the applications performance requirements is always a challenging task. In our research, we are exploring the possible benefits of driving run-time management strategies, from an application perspective, by integrating the programming model with the run-time system and exploiting suitable API for explicit application requirements specification.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"26 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":"124527495","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.2019.8795335
Raghubir Singh, S. Armour, Aftab Khan, M. Sooriyabandara, G. Oikonomou
Computation offloading plays a critical role in reducing task completion time for mobile devices. The advantages of computation offloading to cloud resources in Mobile Cloud Computing have been widely considered. In this paper, we have investigated different scenarios for offloading to less distant Multi-Access Edge Computing (MEC) servers for multiple users with a range of mobile devices and computational tasks. We present detailed simulation data for how offloading can be beneficial in a MEC network with varying quantitative mobile user demand, heterogeneity in mobile device on-board and MEC processor speeds, computational task complexity, communication speeds, link access delays and mobile device user numbers. Unlike previous work where simulations considered only limited communication speeds for offloading, we have extended the range of link speeds and included two types of communication delay. We find that more computationally complex applications are offloaded preferentially (especially with the higher server:mobile device processor speed ratios) while low link speeds and any delays caused by network delays or excessive user numbers degrade any advantages in reduced task completion times offered by offloading. Additionally, significant savings in energy usage by mobile devices are guaranteed except at very low link speeds.
{"title":"The Advantage of Computation Offloading in Multi-Access Edge Computing","authors":"Raghubir Singh, S. Armour, Aftab Khan, M. Sooriyabandara, G. Oikonomou","doi":"10.1109/FMEC.2019.8795335","DOIUrl":"https://doi.org/10.1109/FMEC.2019.8795335","url":null,"abstract":"Computation offloading plays a critical role in reducing task completion time for mobile devices. The advantages of computation offloading to cloud resources in Mobile Cloud Computing have been widely considered. In this paper, we have investigated different scenarios for offloading to less distant Multi-Access Edge Computing (MEC) servers for multiple users with a range of mobile devices and computational tasks. We present detailed simulation data for how offloading can be beneficial in a MEC network with varying quantitative mobile user demand, heterogeneity in mobile device on-board and MEC processor speeds, computational task complexity, communication speeds, link access delays and mobile device user numbers. Unlike previous work where simulations considered only limited communication speeds for offloading, we have extended the range of link speeds and included two types of communication delay. We find that more computationally complex applications are offloaded preferentially (especially with the higher server:mobile device processor speed ratios) while low link speeds and any delays caused by network delays or excessive user numbers degrade any advantages in reduced task completion times offered by offloading. Additionally, significant savings in energy usage by mobile devices are guaranteed except at very low link speeds.","PeriodicalId":101825,"journal":{"name":"2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"27 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":"130239749","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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