Pub Date : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144812
W. Almobaideen, Ola M. Malkawi
Fog Computing is an emergent network paradigm that arises as a response to the prevalence of Internet of Things (IoT). By the use of fog computing, cloud is extended close to end users to reduce latency, traffic load, and needed bandwidth. Efficient data caching presents the core of fog computing. Moreover, low quality caching techniques may represent an additional burden on network resources in case of high miss ratio. As an emergent paradigm, fog computing raises the demand on efficient caching techniques, these techniques must be compatible with IoT and the wide variety of its applications. In this paper, a new caching approach is proposed, referred to as Application Based Caching for Fog computing, abbreviated as ABCFOG. The proposed approach considers the type of application as the main caching prediction criteria. ABCFOG has been tested under various case studies including three types of applications. It is discussed in details before it has been evaluated by simulation using NS-2 Network Simulator. Three evaluation parameters are measured, hit ratio, response time and bandwidth. Results show that ABCFOG has improved caching with at least 30% in response time and hit ratio. However, an additional cost of bandwidth is needed for such improvement.
{"title":"Application Based Caching in Fog Computing to Improve Quality of Service","authors":"W. Almobaideen, Ola M. Malkawi","doi":"10.1109/FMEC49853.2020.9144812","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144812","url":null,"abstract":"Fog Computing is an emergent network paradigm that arises as a response to the prevalence of Internet of Things (IoT). By the use of fog computing, cloud is extended close to end users to reduce latency, traffic load, and needed bandwidth. Efficient data caching presents the core of fog computing. Moreover, low quality caching techniques may represent an additional burden on network resources in case of high miss ratio. As an emergent paradigm, fog computing raises the demand on efficient caching techniques, these techniques must be compatible with IoT and the wide variety of its applications. In this paper, a new caching approach is proposed, referred to as Application Based Caching for Fog computing, abbreviated as ABCFOG. The proposed approach considers the type of application as the main caching prediction criteria. ABCFOG has been tested under various case studies including three types of applications. It is discussed in details before it has been evaluated by simulation using NS-2 Network Simulator. Three evaluation parameters are measured, hit ratio, response time and bandwidth. Results show that ABCFOG has improved caching with at least 30% in response time and hit ratio. However, an additional cost of bandwidth is needed for such improvement.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125371919","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144965
A. Tahat, T. Edwan, Hamza Al-Sawwaf, Jumana Al-Baw, Mohammad Amayreh
Unmanned aerial vehicles (UAVs) are being broadly employed lately in different domains because of their unique features such as ease of mobility and feasibility. A high fidelity communication link is the basis for guaranteeing the robustness of the UAV network between its ends. To offer reliable models for utilization in designing UAV communication systems, in addition to the processes of planning, deploying, and operating these systems, accurate estimation of the prevailing radio channel framework parameters is required. In this work, we suggest and present a strategy for constructing an empirical path loss (PL) model for air-to-ground radio frequency channels relying on machine learning (ML). ML regression algorithms including K-nearest-neighbors (kNN), Regression Trees (RT) and Artificial Neural Networks (ANN) are utilized in our versatile three-dimensional (3D) technique. To that end, we investigate the use of GPS coordinates (i.e., latitude, longitude, and altitude.) of both of the UAV transmitter and ground receiver, in addition to humidity, temperature and atmospheric pressure as features into the ML algorithm to predict the link PL. Hence, all environment parameters, and the corresponding implicit relationships are incorporated in the learning phase, and the subsequent prediction of the PL. The validity of our model and approach is verified through numerical results.
{"title":"Simplistic Machine Learning-Based Air-to-Ground Path Loss Modeling in an Urban Environment","authors":"A. Tahat, T. Edwan, Hamza Al-Sawwaf, Jumana Al-Baw, Mohammad Amayreh","doi":"10.1109/FMEC49853.2020.9144965","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144965","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are being broadly employed lately in different domains because of their unique features such as ease of mobility and feasibility. A high fidelity communication link is the basis for guaranteeing the robustness of the UAV network between its ends. To offer reliable models for utilization in designing UAV communication systems, in addition to the processes of planning, deploying, and operating these systems, accurate estimation of the prevailing radio channel framework parameters is required. In this work, we suggest and present a strategy for constructing an empirical path loss (PL) model for air-to-ground radio frequency channels relying on machine learning (ML). ML regression algorithms including K-nearest-neighbors (kNN), Regression Trees (RT) and Artificial Neural Networks (ANN) are utilized in our versatile three-dimensional (3D) technique. To that end, we investigate the use of GPS coordinates (i.e., latitude, longitude, and altitude.) of both of the UAV transmitter and ground receiver, in addition to humidity, temperature and atmospheric pressure as features into the ML algorithm to predict the link PL. Hence, all environment parameters, and the corresponding implicit relationships are incorporated in the learning phase, and the subsequent prediction of the PL. The validity of our model and approach is verified through numerical results.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115790725","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144950
Joaquim Silva, Eduardo R. B. Marques, Luís M. B. Lopes, Fernando M A Silva
Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present Jay, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. Jay is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of Jay with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.
{"title":"Jay: Adaptive Computation Offloading for Hybrid Cloud Environments","authors":"Joaquim Silva, Eduardo R. B. Marques, Luís M. B. Lopes, Fernando M A Silva","doi":"10.1109/FMEC49853.2020.9144950","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144950","url":null,"abstract":"Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present Jay, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. Jay is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of Jay with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130870851","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144833
Sahar Bo-saeed, Iyad A. Katib, Rashid Mehmood
Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods -- Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.
{"title":"A Fog-Augmented Machine Learning based SMS Spam Detection and Classification System","authors":"Sahar Bo-saeed, Iyad A. Katib, Rashid Mehmood","doi":"10.1109/FMEC49853.2020.9144833","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144833","url":null,"abstract":"Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods -- Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114289568","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144832
R. W. Anwar, A. Zainal, Tariq Abdullah, Saleem Iqbal
The emergence and rapid growth of Internet of Things (IoT) with unlimited benefits, facilities and applications provided such as smart cities, smart home, intelligent transportation (ITS), smart health and smart grids impacts everyone's lives. However, IoT based systems and applications are vulnerable to various security threats and attacks due to their deployment and use of sensing devices. Moreover, the lack of standardization due to heterogeneity of devices and technologies implementing security in IoT is real challenge. The aim of this review paper is to highlight the various security threats, challenges and attacks faced by IoT enable applications.
{"title":"Security Threats and Challenges to IoT and its Applications: A Review","authors":"R. W. Anwar, A. Zainal, Tariq Abdullah, Saleem Iqbal","doi":"10.1109/FMEC49853.2020.9144832","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144832","url":null,"abstract":"The emergence and rapid growth of Internet of Things (IoT) with unlimited benefits, facilities and applications provided such as smart cities, smart home, intelligent transportation (ITS), smart health and smart grids impacts everyone's lives. However, IoT based systems and applications are vulnerable to various security threats and attacks due to their deployment and use of sensing devices. Moreover, the lack of standardization due to heterogeneity of devices and technologies implementing security in IoT is real challenge. The aim of this review paper is to highlight the various security threats, challenges and attacks faced by IoT enable applications.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130687694","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144975
A. Khalifeh, Husam Abid, Khalid A. Darabkh
Wireless Sensor Networks (WSNs) consist of a large number of small size, limited energy nodes distributed over an Area of Interest (AoI), in order to perform sensing and monitoring operations. Due to their limited energy sources, it is of utmost importance to optimize the nodes' energy consumption thus prolonging the sensors' lifetime. In this paper, an optimization problem is formulated and solved to find the optimal location of the Cluster Head (CH) node with respect to the other nodes such that the communication path loss of the nodes with respect to the CH is minimized. Simulation results have shown that the proposed Cluster Head Positioning Optimization (CHPO) mechanism has proven its effectiveness when compared with the literature work, in reducing the nodes' energy consumption, thus increasing the network lifetime.
{"title":"Improving Energy Conservation Level in WSNs by Modifying CH Node Location","authors":"A. Khalifeh, Husam Abid, Khalid A. Darabkh","doi":"10.1109/FMEC49853.2020.9144975","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144975","url":null,"abstract":"Wireless Sensor Networks (WSNs) consist of a large number of small size, limited energy nodes distributed over an Area of Interest (AoI), in order to perform sensing and monitoring operations. Due to their limited energy sources, it is of utmost importance to optimize the nodes' energy consumption thus prolonging the sensors' lifetime. In this paper, an optimization problem is formulated and solved to find the optimal location of the Cluster Head (CH) node with respect to the other nodes such that the communication path loss of the nodes with respect to the CH is minimized. Simulation results have shown that the proposed Cluster Head Positioning Optimization (CHPO) mechanism has proven its effectiveness when compared with the literature work, in reducing the nodes' energy consumption, thus increasing the network lifetime.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125600699","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144927
A. Toumia, Samuel Szoniecky, I. Saleh
User privacy preferences management is a nontrivial task. In the context of the Internet of Things (IoT), where a huge amount of data is generated, transferred and stored via various local and cloud architectures, privacy protection becomes complex and hard to manage. Indeed, privacy management is a time-consuming activity that requires a lot of knowledge which most of IoT system users often lack or are not keen on acquiring due to its complexity. The knowledge dimension has often been neglected, by both researchers and industry. In this article, we focus on the privacy protection knowledge management aspect. We produce a first version of ColPri, an ontology that sets the basis for a collaborative extensible privacy protection knowledge management system that is able to collaboratively produce diagnosis of IoT stakeholders privacy policies. This paper aims to investigate collaborative privacy knowledge management in the IoT and how non-technical users could benefit from it to easily configure their privacy policies. It allows an open exchange of privacy-related knowledge. We propose ColPri, a collaborative privacy ontology after specifying design requirements that guided our choices during the ontology creation process. This ontology lays out the use of a privacy community to create and develop privacy-related information within a user-centric privacy architecture. Then, we show how to use this ontology through a use case scenario. Finally, we describe future research based on this work.
{"title":"ColPri: Towards a Collaborative Privacy Knowledge Management Ontology for the Internet of Things","authors":"A. Toumia, Samuel Szoniecky, I. Saleh","doi":"10.1109/FMEC49853.2020.9144927","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144927","url":null,"abstract":"User privacy preferences management is a nontrivial task. In the context of the Internet of Things (IoT), where a huge amount of data is generated, transferred and stored via various local and cloud architectures, privacy protection becomes complex and hard to manage. Indeed, privacy management is a time-consuming activity that requires a lot of knowledge which most of IoT system users often lack or are not keen on acquiring due to its complexity. The knowledge dimension has often been neglected, by both researchers and industry. In this article, we focus on the privacy protection knowledge management aspect. We produce a first version of ColPri, an ontology that sets the basis for a collaborative extensible privacy protection knowledge management system that is able to collaboratively produce diagnosis of IoT stakeholders privacy policies. This paper aims to investigate collaborative privacy knowledge management in the IoT and how non-technical users could benefit from it to easily configure their privacy policies. It allows an open exchange of privacy-related knowledge. We propose ColPri, a collaborative privacy ontology after specifying design requirements that guided our choices during the ontology creation process. This ontology lays out the use of a privacy community to create and develop privacy-related information within a user-centric privacy architecture. Then, we show how to use this ontology through a use case scenario. Finally, we describe future research based on this work.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129309991","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144955
I. Baig, U. Farooq, N. Hasan, Manaf Zghaibeh, U. Rana, Ahthasham Sajid
Multi-Carrier Waveform (MCW) design has become one of the challenging research issue for 5G Multi-Access Edge Computing (MAEC). A large number of different MCWs have been designed and proposed in the literature as an alternative to the conventional Orthogonal Frequency Division Multiplexing (OFDM) waveform. Although, OFDM based waveforms are broadly employed in many real-time systems, but they cannot support the stringent requirements of 5G MAEC. Therefore, more flexible MCWs need to be investigated and designed. Hence, in this paper a new MCW with minimum Peak-to-Average Ratio (PAPR) has been designed and proposed. The proposed MCW is based on precoding and power control of the transmitted Universal Filtered Multi-Carriers (UFMC) signals. Computer simulations in MATLAB® have been carried out to show the performance of proposed precoding based MCW. It is concluded from the computer simulation results that the proposed precoding based waveform outperforms the standard UFMC waveform.
{"title":"A Precoding Based Power Domain UFMC Waveform for 5G Multi-Access Edge Computing","authors":"I. Baig, U. Farooq, N. Hasan, Manaf Zghaibeh, U. Rana, Ahthasham Sajid","doi":"10.1109/FMEC49853.2020.9144955","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144955","url":null,"abstract":"Multi-Carrier Waveform (MCW) design has become one of the challenging research issue for 5G Multi-Access Edge Computing (MAEC). A large number of different MCWs have been designed and proposed in the literature as an alternative to the conventional Orthogonal Frequency Division Multiplexing (OFDM) waveform. Although, OFDM based waveforms are broadly employed in many real-time systems, but they cannot support the stringent requirements of 5G MAEC. Therefore, more flexible MCWs need to be investigated and designed. Hence, in this paper a new MCW with minimum Peak-to-Average Ratio (PAPR) has been designed and proposed. The proposed MCW is based on precoding and power control of the transmitted Universal Filtered Multi-Carriers (UFMC) signals. Computer simulations in MATLAB® have been carried out to show the performance of proposed precoding based MCW. It is concluded from the computer simulation results that the proposed precoding based waveform outperforms the standard UFMC waveform.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129516562","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144809
J. P. Queralta, Qingqing Li, Zhuo Zou, Tomi Westerlund
This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.
{"title":"Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed Robotic Systems","authors":"J. P. Queralta, Qingqing Li, Zhuo Zou, Tomi Westerlund","doi":"10.1109/FMEC49853.2020.9144809","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144809","url":null,"abstract":"This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383969","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 : 2020-04-01DOI: 10.1109/FMEC49853.2020.9144775
Ioanna-Vasiliki Stypsanelli, Samir Medjiah, B. Prabhu
In order to ensure service continuity of connected cars moving inside a Fog Computing infrastructure under a Service Level Agreement, a service needs to migrate from a fog node to another. An approach is to always keep migrating the service towards the fog node that is the closest to the current position. However, frequent service migrations have a migration and network cost. Intuitively, the more migrations are triggered, the bigger this cost is. In this work we look into ways to reduce this cost by studying how to minimize the number of VM migrations triggered. We introduce a general case in which we minimize a linear combination of the infrastructure cost and the number of service migrations given statistics on the routes taken by the vehicles. This problem can be represented as a bipartite graph where the minimization problem is an instance of the Weighted Set Cover problem. For a special case of pair-wise mobility model in which the origin and destination of vehicles are in the coverage range of adjacent base stations, we first present a static offline ILP formulation of the migration minimization problem. For this simple case, we then propose two heuristics inspired by the greedy algorithm for the weighted set cover problem as polynomial approximations.
{"title":"Reducing Service Migrations in Fog Infrastructures by Optimizing Node Location","authors":"Ioanna-Vasiliki Stypsanelli, Samir Medjiah, B. Prabhu","doi":"10.1109/FMEC49853.2020.9144775","DOIUrl":"https://doi.org/10.1109/FMEC49853.2020.9144775","url":null,"abstract":"In order to ensure service continuity of connected cars moving inside a Fog Computing infrastructure under a Service Level Agreement, a service needs to migrate from a fog node to another. An approach is to always keep migrating the service towards the fog node that is the closest to the current position. However, frequent service migrations have a migration and network cost. Intuitively, the more migrations are triggered, the bigger this cost is. In this work we look into ways to reduce this cost by studying how to minimize the number of VM migrations triggered. We introduce a general case in which we minimize a linear combination of the infrastructure cost and the number of service migrations given statistics on the routes taken by the vehicles. This problem can be represented as a bipartite graph where the minimization problem is an instance of the Weighted Set Cover problem. For a special case of pair-wise mobility model in which the origin and destination of vehicles are in the coverage range of adjacent base stations, we first present a static offline ILP formulation of the migration minimization problem. For this simple case, we then propose two heuristics inspired by the greedy algorithm for the weighted set cover problem as polynomial approximations.","PeriodicalId":110283,"journal":{"name":"2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115158546","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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