The survey of related work in the very specialized field of information security (IS) ensurance for the Internet of Things (IoT) allowed us to work out a taxonomy of typical attacks against the IoT elements (with special attention to the IoT device protection). The key directions of countering these attacks were defined on this basis. According to the modern demand for the IoT big IS-related data processing, the application of Security Intelligence approach is proposed. The main direction of the future research, namely the IoT operational resilience, is indicated.
{"title":"Ensuring Information Security for Internet of Things","authors":"N. Miloslavskaya, A. Tolstoy","doi":"10.1109/FiCloud.2017.17","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.17","url":null,"abstract":"The survey of related work in the very specialized field of information security (IS) ensurance for the Internet of Things (IoT) allowed us to work out a taxonomy of typical attacks against the IoT elements (with special attention to the IoT device protection). The key directions of countering these attacks were defined on this basis. According to the modern demand for the IoT big IS-related data processing, the application of Security Intelligence approach is proposed. The main direction of the future research, namely the IoT operational resilience, is indicated.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176912","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}
Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.
{"title":"Cloud Task Scheduling Based on Swarm Intelligence and Machine Learning","authors":"Gaith Rjoub, J. Bentahar","doi":"10.1109/FiCloud.2017.52","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.52","url":null,"abstract":"Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122607207","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}
Cloud users may decide to live migrate their virtual machines from a public cloud provider to another due to a lower cost or ceasing operations. Currently, it is not possible to install a second virtualization platform on public cloud infrastructure (IaaS) because nested virtualization and hardwareassisted virtualization are disabled by default. As a result, cloud users' VMs are tightly coupled to providers IaaS hindering live migration of VMs to different providers. This paper introduces LivCloud, a solution to live cloud migration. LivCloud is designed based on well-established criteria to live migrate VMs across various cloud IaaS with minimal interruption to the services hosted on these VMs. The paper discusses the basic design of LivCloud which consists of a Virtual Machine manager and IPsec VPN tunnel introduced for the first time within this environment. It is also the first time that the migrated VM architecture (64-bit & 32-bit) is taken into consideration. In this study, we evaluate the implementation of the basic design of LivCloud on Amazon EC2 C4 instance. This instance has a compute optimized instance and has high performance processors. In particular we explore three developed options. Theses options are being tested for the first time on EC2 to change the value of the EC2 instance's control registers. Changing the values of the registers will significantly help enable nested virtualization on Amazon EC2.
{"title":"Exploring Live Cloud Migration on Amazon EC2","authors":"I. Mansour, A. Bouchachia, K. Cooper","doi":"10.1109/FiCloud.2017.20","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.20","url":null,"abstract":"Cloud users may decide to live migrate their virtual machines from a public cloud provider to another due to a lower cost or ceasing operations. Currently, it is not possible to install a second virtualization platform on public cloud infrastructure (IaaS) because nested virtualization and hardwareassisted virtualization are disabled by default. As a result, cloud users' VMs are tightly coupled to providers IaaS hindering live migration of VMs to different providers. This paper introduces LivCloud, a solution to live cloud migration. LivCloud is designed based on well-established criteria to live migrate VMs across various cloud IaaS with minimal interruption to the services hosted on these VMs. The paper discusses the basic design of LivCloud which consists of a Virtual Machine manager and IPsec VPN tunnel introduced for the first time within this environment. It is also the first time that the migrated VM architecture (64-bit & 32-bit) is taken into consideration. In this study, we evaluate the implementation of the basic design of LivCloud on Amazon EC2 C4 instance. This instance has a compute optimized instance and has high performance processors. In particular we explore three developed options. Theses options are being tested for the first time on EC2 to change the value of the EC2 instance's control registers. Changing the values of the registers will significantly help enable nested virtualization on Amazon EC2.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006302","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}
G. Pulkkis, Jonny Karlsson, M. Westerlund, Jonas Tana
Proposals and some implementations of Internet of Things (IoT) systems for healthcare are described. Implications of current European Union legislation, the new General Data Protection Regulation, for the security and reliability of healthcare IoT systems and for the privacy of users of these systems are presented. Analytics of healthcare IoT data for the requirements of evidence based healthcare is outlined. Threats to the security and reliability of healthcare IoT systems and to the privacy of the users of these systems, security and reliability requirements, and solutions for security and enhanced reliability are described. Visions for future healthcare IoT are presented and some future research directions are proposed.
{"title":"Secure and Reliable Internet of Things Systems for Healthcare","authors":"G. Pulkkis, Jonny Karlsson, M. Westerlund, Jonas Tana","doi":"10.1109/FiCloud.2017.50","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.50","url":null,"abstract":"Proposals and some implementations of Internet of Things (IoT) systems for healthcare are described. Implications of current European Union legislation, the new General Data Protection Regulation, for the security and reliability of healthcare IoT systems and for the privacy of users of these systems are presented. Analytics of healthcare IoT data for the requirements of evidence based healthcare is outlined. Threats to the security and reliability of healthcare IoT systems and to the privacy of the users of these systems, security and reliability requirements, and solutions for security and enhanced reliability are described. Visions for future healthcare IoT are presented and some future research directions are proposed.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129718782","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}
Cloud computing is underpinned by huge datacenters which are considered as significant consumers of energy. Under the umbrella term GreenCloud the scientific community developed different architectures, algorithms and methods to improve energy efficiency of these datacenters. However, approaches which try to modify existing or applying new economical concepts to improve energy efficiency of datacenters are rare. In this paper we propose the GreenCloudTax model which is a flexible IaaS tax system for calculating taxes of virtual machines by using the energy efficiency of the underlying server infrastructure. Thereby, providers relying on energy efficient servers can sell their virtual machines with lower taxes than those with energy inefficient servers. This results in a competitive advantage and consequently leads to reduced energy consumption in total too. We analyzed the effects of our GreenCloudTax model on Cloud markets by a simulation environment which is based on CloudSim's Bazaar-Extension.
{"title":"GreenCloudTax: A Flexible IaaS Tax Approach as Stimulus for Green Cloud Computing","authors":"Benedikt Pittl, W. Mach, E. Schikuta","doi":"10.1109/FiCloud.2017.45","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.45","url":null,"abstract":"Cloud computing is underpinned by huge datacenters which are considered as significant consumers of energy. Under the umbrella term GreenCloud the scientific community developed different architectures, algorithms and methods to improve energy efficiency of these datacenters. However, approaches which try to modify existing or applying new economical concepts to improve energy efficiency of datacenters are rare. In this paper we propose the GreenCloudTax model which is a flexible IaaS tax system for calculating taxes of virtual machines by using the energy efficiency of the underlying server infrastructure. Thereby, providers relying on energy efficient servers can sell their virtual machines with lower taxes than those with energy inefficient servers. This results in a competitive advantage and consequently leads to reduced energy consumption in total too. We analyzed the effects of our GreenCloudTax model on Cloud markets by a simulation environment which is based on CloudSim's Bazaar-Extension.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106653","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}
Vehicular Ad-Hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from Mobile Ad-Hoc NETworks (MANETs), such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network efficiency. This paper proposes a novel Intersection Dynamic VANET Routing (IDVR) protocol, which aims to increase the route stability, average throughput, and reduce end-to-end delay in a grid topology. We used a centralized Software Defined Network (SDN) to gather a real-time traffic information and provide the Intersection Cluster Head (ICH) a Set of Candidate Shortest Routes (SCSR). At the Intersection, An ICH algorithm is proposed based on the maximum Life Time (LT), the LT is the time that each vehicle requires till it leaves the cluster. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput among the SCSR. We used SUMO traffic generator simulator and MATLAB to evaluate the performance of our proposed protocol. Our proposed protocol outperforms many protocols mentioned in the literature, such as IRTIV, VDLA, and GPCR, in terms of end-to-end delay and throughput.
{"title":"An Intersection Dynamic VANET Routing Protocol for a Grid Scenario","authors":"Ahmad Abuashour, M. Kadoch","doi":"10.1109/FiCloud.2017.19","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.19","url":null,"abstract":"Vehicular Ad-Hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from Mobile Ad-Hoc NETworks (MANETs), such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network efficiency. This paper proposes a novel Intersection Dynamic VANET Routing (IDVR) protocol, which aims to increase the route stability, average throughput, and reduce end-to-end delay in a grid topology. We used a centralized Software Defined Network (SDN) to gather a real-time traffic information and provide the Intersection Cluster Head (ICH) a Set of Candidate Shortest Routes (SCSR). At the Intersection, An ICH algorithm is proposed based on the maximum Life Time (LT), the LT is the time that each vehicle requires till it leaves the cluster. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput among the SCSR. We used SUMO traffic generator simulator and MATLAB to evaluate the performance of our proposed protocol. Our proposed protocol outperforms many protocols mentioned in the literature, such as IRTIV, VDLA, and GPCR, in terms of end-to-end delay and throughput.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116487619","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}
Kabiru M. Maiyama, D. Kouvatsos, Bashir Mohammed, M. Kiran, M. Kamala
Performance is one of the main aspects that should be taken into consideration during the design, development, tuning and optimisation of computer networks supported by cloud computing platforms (CCPs). Queueing network models (QNMs) of CCPs constitute essential quantitative tools of investigation towards identifying acceptable levels of quality-of-service (QoS), whether for upgrading an existing CCP or designing a new one. In this paper, a new stable open QNM with either single or multiple server queueing stations, first-come-first-served (FCFS) scheduling and random routing is proposed for the performance modelling and analysis of an OpenStack Infrastructure as a Service (IaaS) CCP. In this context, it is assumed that the external arrival process is Poisson and the queueing stations provide exponentially distributed service times. Based on Jackson's Theorem, the open QNM is decomposed into individual M/M/c queues with c server(s) (c ≥ 1) and exponential inter-arrival and service times, each of which can be analysed in isolation. Consequently, closed form expressions for key performance metrics of the QNM are determined, such as those for the mean response time, throughput, server (resource) utilisation and the probability of the number of requests by clients at each queueing station during waiting for and/or receiving resource provisioning. The evaluation of these metrics identifies the bottlenecks of the CCP that are causing the worst network delays and associated performance degradation and thus, provides insights into the capacity planning of networks with OpenStack IaaS solutions for CSPs.
{"title":"Performance Modelling and Analysis of an OpenStack IaaS Cloud Computing Platform","authors":"Kabiru M. Maiyama, D. Kouvatsos, Bashir Mohammed, M. Kiran, M. Kamala","doi":"10.1109/FiCloud.2017.54","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.54","url":null,"abstract":"Performance is one of the main aspects that should be taken into consideration during the design, development, tuning and optimisation of computer networks supported by cloud computing platforms (CCPs). Queueing network models (QNMs) of CCPs constitute essential quantitative tools of investigation towards identifying acceptable levels of quality-of-service (QoS), whether for upgrading an existing CCP or designing a new one. In this paper, a new stable open QNM with either single or multiple server queueing stations, first-come-first-served (FCFS) scheduling and random routing is proposed for the performance modelling and analysis of an OpenStack Infrastructure as a Service (IaaS) CCP. In this context, it is assumed that the external arrival process is Poisson and the queueing stations provide exponentially distributed service times. Based on Jackson's Theorem, the open QNM is decomposed into individual M/M/c queues with c server(s) (c ≥ 1) and exponential inter-arrival and service times, each of which can be analysed in isolation. Consequently, closed form expressions for key performance metrics of the QNM are determined, such as those for the mean response time, throughput, server (resource) utilisation and the probability of the number of requests by clients at each queueing station during waiting for and/or receiving resource provisioning. The evaluation of these metrics identifies the bottlenecks of the CCP that are causing the worst network delays and associated performance degradation and thus, provides insights into the capacity planning of networks with OpenStack IaaS solutions for CSPs.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"23 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124134608","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}
N. Miloslavskaya, V. Morozov, A. Tolstoy, Dennis Khassan
The paper presents the work-in-progress in developing since 2016 and using the "Network Security Intelligence" educational and research center (NSIC) in the framework of the NRNU MEPhI's Institute of Cyber Intelligence Systems (ICIS). The NSIC currently consists of two bearing laboratories with Next-Generation Firewall (NGFW) and Data Loss Prevention (DLP) system as their cores respectively. The DLP laboratory can be regarded as an integral NSIC's part, which expands students' knowledge and skills in protection against internal (insider) information security (IS) threats through creative research and discovery. For our NSIC the Russian SearchInform's Information Security Perimeter DLP system has been chosen. Five labs for students were developed on its basis. The main areas of further work in expanding NSIC's usage for training and research conclude the paper.
本文介绍了自2016年以来在NRNU MEPhI网络智能系统研究所(ICIS)框架下开发和使用“网络安全智能”教育研究中心(NSIC)的工作进展。NSIC现有两个轴承实验室，分别以NGFW (generation Firewall)和DLP (Data Loss Prevention)系统为核心。DLP实验室可以被视为NSIC不可分割的一部分，通过创造性的研究和发现，扩展学生在保护内部(内部)信息安全(IS)威胁方面的知识和技能。我们的NSIC选择了俄罗斯搜索表单的信息安全边界DLP系统。在此基础上开发了五个学生实验室。最后提出了进一步扩大NSIC在训练和研究中的应用的主要工作领域。
{"title":"DLP as an Integral Part of Network Security Intelligence Center","authors":"N. Miloslavskaya, V. Morozov, A. Tolstoy, Dennis Khassan","doi":"10.1109/FiCloud.2017.15","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.15","url":null,"abstract":"The paper presents the work-in-progress in developing since 2016 and using the \"Network Security Intelligence\" educational and research center (NSIC) in the framework of the NRNU MEPhI's Institute of Cyber Intelligence Systems (ICIS). The NSIC currently consists of two bearing laboratories with Next-Generation Firewall (NGFW) and Data Loss Prevention (DLP) system as their cores respectively. The DLP laboratory can be regarded as an integral NSIC's part, which expands students' knowledge and skills in protection against internal (insider) information security (IS) threats through creative research and discovery. For our NSIC the Russian SearchInform's Information Security Perimeter DLP system has been chosen. Five labs for students were developed on its basis. The main areas of further work in expanding NSIC's usage for training and research conclude the paper.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620759","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}
The Semantic Data are built from triples, that contain subjects, predicates and objects. On the other hand we can consider the triples as edges. The subject and the object are the nodes and the predicate is the label of the edge. In this view the Semantic Data define a graph. This graph can be very large, because a Semantic Dataset contains millions of triples. To query this dataset we can use the SPARQL query language. Since the Big Data tools appeared the researchers try to evaluate the SPARQL with that tools. In the last few year the distributed graph analytic tools appeared too. So the challenge is: use the graph analytic tools to evaluate the semantic query on the semantic graph. In this paper we present the PSparkql that extends the Sparkql with parallel query plan. The system uses the Spark GraphX distributed graph analytic tool. We show less edges enough for the evaluation than the Sparkql is using. We also collect some statistics (number of predicates, data properties) about the graph to change the evaluation order of the SPARQL query. We compare our results with related works: the Sparkql and the S2X.
{"title":"P-Spar(k)ql: SPARQL Evaluation Method on Spark GraphX with Parallel Query Plan","authors":"G. Gombos, A. Kiss","doi":"10.1109/FiCloud.2017.48","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.48","url":null,"abstract":"The Semantic Data are built from triples, that contain subjects, predicates and objects. On the other hand we can consider the triples as edges. The subject and the object are the nodes and the predicate is the label of the edge. In this view the Semantic Data define a graph. This graph can be very large, because a Semantic Dataset contains millions of triples. To query this dataset we can use the SPARQL query language. Since the Big Data tools appeared the researchers try to evaluate the SPARQL with that tools. In the last few year the distributed graph analytic tools appeared too. So the challenge is: use the graph analytic tools to evaluate the semantic query on the semantic graph. In this paper we present the PSparkql that extends the Sparkql with parallel query plan. The system uses the Spark GraphX distributed graph analytic tool. We show less edges enough for the evaluation than the Sparkql is using. We also collect some statistics (number of predicates, data properties) about the graph to change the evaluation order of the SPARQL query. We compare our results with related works: the Sparkql and the S2X.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"1999 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120970537","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}
Low Power Wide Area Networks (LPWAN) are expected to interconnect a high number of simple and inexpensive devices. The ability to control transmission parameters including modulation scheme and output power, enables reduction of the deployment costs, improvement of the network reliability, in terms of error performance under suboptimal conditions, and could contribute to the reduction of the maintenance complexity with respect to scaling of the network. This paper presents an analysis of the widely accepted adaptive data rate (ADR) algorithm implementation used in current LoRaWAN™-based network infrastructures. The authors propose improved variants of the algorithm concerning the design goals listed above and provide an experimental evaluation of their network performance.
{"title":"Proposal of Adaptive Data Rate Algorithm for LoRaWAN-Based Infrastructure","authors":"Vojtech Hauser, Tomás Hégr","doi":"10.1109/FiCloud.2017.47","DOIUrl":"https://doi.org/10.1109/FiCloud.2017.47","url":null,"abstract":"Low Power Wide Area Networks (LPWAN) are expected to interconnect a high number of simple and inexpensive devices. The ability to control transmission parameters including modulation scheme and output power, enables reduction of the deployment costs, improvement of the network reliability, in terms of error performance under suboptimal conditions, and could contribute to the reduction of the maintenance complexity with respect to scaling of the network. This paper presents an analysis of the widely accepted adaptive data rate (ADR) algorithm implementation used in current LoRaWAN™-based network infrastructures. The authors propose improved variants of the algorithm concerning the design goals listed above and provide an experimental evaluation of their network performance.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114648532","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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