Victor Seoane, Florina Almenáres, Celeste Campo, C. García-Rubio
Internet of Things (IoT) can be defined as the interconnection through Internet of an unprecedented number of devices with the purpose of exchanging data. It stands as one of the most popular technologies for the following years and it is requiring substantial changes in the Internet protocols to meet its requirements. As the application layer is decisive for the quality of the connection, this paper analyzes the performance offered by one of the most popular protocols for the application layer in IoT: the Constrained Application Protocol (CoAP). This analysis aims to examine the features and capabilities of this protocol and to determine its feasibility to operate under constrained devices using security support. For this, a realistic network scenario is deployed to run the simulations and to measure bandwidth, consumption of resources (i.e., CPU cycles and bandwidth usage) and communication latency. Additionally, the trade-off between security and performance is discussed measuring the bandwidth overhead and the consumption increase associated to secure the communications. Different ciphering and authentication algorithms are tested, following the recommendations made by the Internet Engineering Task Force (IETF).
{"title":"Performance Evaluation of the CoAP Protocol with Security Support for IoT Environments","authors":"Victor Seoane, Florina Almenáres, Celeste Campo, C. García-Rubio","doi":"10.1145/3416011.3424754","DOIUrl":"https://doi.org/10.1145/3416011.3424754","url":null,"abstract":"Internet of Things (IoT) can be defined as the interconnection through Internet of an unprecedented number of devices with the purpose of exchanging data. It stands as one of the most popular technologies for the following years and it is requiring substantial changes in the Internet protocols to meet its requirements. As the application layer is decisive for the quality of the connection, this paper analyzes the performance offered by one of the most popular protocols for the application layer in IoT: the Constrained Application Protocol (CoAP). This analysis aims to examine the features and capabilities of this protocol and to determine its feasibility to operate under constrained devices using security support. For this, a realistic network scenario is deployed to run the simulations and to measure bandwidth, consumption of resources (i.e., CPU cycles and bandwidth usage) and communication latency. Additionally, the trade-off between security and performance is discussed measuring the bandwidth overhead and the consumption increase associated to secure the communications. Different ciphering and authentication algorithms are tested, following the recommendations made by the Internet Engineering Task Force (IETF).","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3416011.3424754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72464222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Davide Salvo, G. Piñero, P. Arce, Alberto González
We present in this paper a wireless acoustic sensor network (WASN) that recognizes a set of sound events or classes from urban environments. The nodes of the WASN are Raspberry Pi devices that not only record the ambient sound, but they also process and recognize a sound event by means of a deep convolutional neural network (CNN). To our knowledge, this is the first WASN running a CNN classifier over low-cost devices. Moreover, the network has been designed according to the open standard FIWARE, so the whole system can be replicated without the need of proprietary software or specific hardware. Although our low-cost WASN achieves similar accuracy compared to other WASNs that perform the classification through cloud or edge computing, our problem is the high computation load required by deep learning algorithms, even in testing mode. Moreover, the WASNs are designed to be constantly monitoring the ambient, which in our case means constantly classifying the "background sound''. We propose here to introduce a pre-detection stage prior to the CNN classification in order to save power consumption. In our case, the WASN is placed in a big avenue where the "background sound'' event is the usual traffic noise, and we want to detect other sound events as horns, sirens or very loud sounds. We have designed a pre-detection stage that activates the classifier only when an event different from traffic is likely occurring. For this purpose, two parameters based on the sound pressure level are computed and compared with two corresponding thresholds. Experimental results have been carried out with the proposed WASN in the city of Valencia, achieving a six-times reduction of the Raspberry Pi CPU's usage due to the pre-detection stage.
{"title":"A Low-cost Wireless Acoustic Sensor Network for the Classification of Urban Sounds","authors":"Davide Salvo, G. Piñero, P. Arce, Alberto González","doi":"10.1145/3416011.3424759","DOIUrl":"https://doi.org/10.1145/3416011.3424759","url":null,"abstract":"We present in this paper a wireless acoustic sensor network (WASN) that recognizes a set of sound events or classes from urban environments. The nodes of the WASN are Raspberry Pi devices that not only record the ambient sound, but they also process and recognize a sound event by means of a deep convolutional neural network (CNN). To our knowledge, this is the first WASN running a CNN classifier over low-cost devices. Moreover, the network has been designed according to the open standard FIWARE, so the whole system can be replicated without the need of proprietary software or specific hardware. Although our low-cost WASN achieves similar accuracy compared to other WASNs that perform the classification through cloud or edge computing, our problem is the high computation load required by deep learning algorithms, even in testing mode. Moreover, the WASNs are designed to be constantly monitoring the ambient, which in our case means constantly classifying the \"background sound''. We propose here to introduce a pre-detection stage prior to the CNN classification in order to save power consumption. In our case, the WASN is placed in a big avenue where the \"background sound'' event is the usual traffic noise, and we want to detect other sound events as horns, sirens or very loud sounds. We have designed a pre-detection stage that activates the classifier only when an event different from traffic is likely occurring. For this purpose, two parameters based on the sound pressure level are computed and compared with two corresponding thresholds. Experimental results have been carried out with the proposed WASN in the city of Valencia, achieving a six-times reduction of the Raspberry Pi CPU's usage due to the pre-detection stage.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75107699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Montenegro, Cristhian Iza Paredes, M. Aguilar-Igartua
Vehicular ad hoc networks (VANETs) are relatively new networks that focus on intelligent transportation systems (ITS). The interest in this kind of networks lies in the promising challenge to enhance security in vehicular transportation systems trying to alleviate driving problems. However, this technology has many concerns before its implementation, especially in topics related to privacy, network overhead and security. Some approaches have been studied to ensure security inside vehicular networks and protect them from attackers, either external or internal. Among several options, trust models have acquired great importance and good results when detecting misbehaving in the nodes. The present work aims to evaluate parameters used for the computation of trust metrics applying machine learning techniques. Results show the superior discriminative power of the receiver power coherency metric when detecting misbehaving nodes based on fake position attacks. Simulation results show the effectiveness of our proposal in terms of ability to correctly classify well behaved and misbehaved vehicles.
{"title":"Detection of Position Falsification Attacks in VANETs Applying Trust Model and Machine Learning","authors":"J. Montenegro, Cristhian Iza Paredes, M. Aguilar-Igartua","doi":"10.1145/3416011.3424757","DOIUrl":"https://doi.org/10.1145/3416011.3424757","url":null,"abstract":"Vehicular ad hoc networks (VANETs) are relatively new networks that focus on intelligent transportation systems (ITS). The interest in this kind of networks lies in the promising challenge to enhance security in vehicular transportation systems trying to alleviate driving problems. However, this technology has many concerns before its implementation, especially in topics related to privacy, network overhead and security. Some approaches have been studied to ensure security inside vehicular networks and protect them from attackers, either external or internal. Among several options, trust models have acquired great importance and good results when detecting misbehaving in the nodes. The present work aims to evaluate parameters used for the computation of trust metrics applying machine learning techniques. Results show the superior discriminative power of the receiver power coherency metric when detecting misbehaving nodes based on fake position attacks. Simulation results show the effectiveness of our proposal in terms of ability to correctly classify well behaved and misbehaved vehicles.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79868026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software-defined Vehicular Networks (SDVNs) have been vital for various radio access technologies to support the massive data load of many applications and services. That includes traffic and infotainment applications, as well as network management and routing services, among others. SDVNs elevates the constraint of static hardware-based network devices to programmable units and provide a global view of the network's status and the standard interface between diverse technologies. However, collecting and maintaining the network's information requires a robust discovery protocol to handle vehicles' rapid movement and increased network density. In this paper, we propose an efficient topology discovery protocol for software-defined vehicular networks that improve the discovery performance in terms of overhead and time-complexity by utilizing nodes closeness centrality scores. The proposed protocol is compared to benchmark discovery protocols using Ottawa city urban environment and generated vehicles' mobility.
{"title":"A Distributed Topology Discovery Protocol for Software-Defined Vehicular Networks","authors":"Noura Aljeri, A. Boukerche","doi":"10.1145/3416011.3424758","DOIUrl":"https://doi.org/10.1145/3416011.3424758","url":null,"abstract":"Software-defined Vehicular Networks (SDVNs) have been vital for various radio access technologies to support the massive data load of many applications and services. That includes traffic and infotainment applications, as well as network management and routing services, among others. SDVNs elevates the constraint of static hardware-based network devices to programmable units and provide a global view of the network's status and the standard interface between diverse technologies. However, collecting and maintaining the network's information requires a robust discovery protocol to handle vehicles' rapid movement and increased network density. In this paper, we propose an efficient topology discovery protocol for software-defined vehicular networks that improve the discovery performance in terms of overhead and time-complexity by utilizing nodes closeness centrality scores. The proposed protocol is compared to benchmark discovery protocols using Ottawa city urban environment and generated vehicles' mobility.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78193650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a mathematical modelling, using stochastic analysis, of the effect of an interference buildup caused by a sudden increase in the number of users that access a digital cellular communication system. For such an increase in power, a heavy tail probability distribution is generated, producing an expected increase in the error probability during the adaptation process.
{"title":"Epidemic Interference in a Cellular System","authors":"M. Alencar","doi":"10.1145/3416011.3424748","DOIUrl":"https://doi.org/10.1145/3416011.3424748","url":null,"abstract":"This article presents a mathematical modelling, using stochastic analysis, of the effect of an interference buildup caused by a sudden increase in the number of users that access a digital cellular communication system. For such an increase in power, a heavy tail probability distribution is generated, producing an expected increase in the error probability during the adaptation process.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86067952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robust connectivity and sufficient bandwidth are not natural in most wireless network architectures. The increase of data rates that is mostly caused by multimedia traffic relying on QoS requirements complicates almost lossless delivery. Especially MANETs communications face these challenges, as nodes are moving at runtime. It is well known, that these network architectures have difficulties delivering time-critical data, since no central instance is in place. Bandwidth-demanding multimedia traffic easily causes stressed and overloaded network segments which result in bottlenecks and dropped packets. Introducing a SDN controller is a logical consequence to distribute traffic on nodes based on the knowledge of the entire topology. QoS requirements of all flows can be considered by the controller during routing. However, this brings up the question of how to keep the controller's topology up to date regarding lost and newly arisen connections since nodes are moving continuously. An outdated view of the topology results in deployed routes where no continuous connection between the nodes remains active. We therefore introduce CeTUP, a controller-equipped topology update process designed to provide an overview of the network as accurate as possible, before routing takes place by the controller. We show that this update process achieves QoS conform delivery rates even when nodes are moving at a speed of up to 60~km/h.
{"title":"CeTUP: Controller-equipped Topology Update Process for Tactical Ad-hoc Networks","authors":"Klement Streit, G. Rodosek","doi":"10.1145/3416011.3424752","DOIUrl":"https://doi.org/10.1145/3416011.3424752","url":null,"abstract":"Robust connectivity and sufficient bandwidth are not natural in most wireless network architectures. The increase of data rates that is mostly caused by multimedia traffic relying on QoS requirements complicates almost lossless delivery. Especially MANETs communications face these challenges, as nodes are moving at runtime. It is well known, that these network architectures have difficulties delivering time-critical data, since no central instance is in place. Bandwidth-demanding multimedia traffic easily causes stressed and overloaded network segments which result in bottlenecks and dropped packets. Introducing a SDN controller is a logical consequence to distribute traffic on nodes based on the knowledge of the entire topology. QoS requirements of all flows can be considered by the controller during routing. However, this brings up the question of how to keep the controller's topology up to date regarding lost and newly arisen connections since nodes are moving continuously. An outdated view of the topology results in deployed routes where no continuous connection between the nodes remains active. We therefore introduce CeTUP, a controller-equipped topology update process designed to provide an overview of the network as accurate as possible, before routing takes place by the controller. We show that this update process achieves QoS conform delivery rates even when nodes are moving at a speed of up to 60~km/h.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73434561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. M. Mezher, J. Cardenas-Barrera, Carlos Lester Dueñas Santos, J. Meng, Eduardo Castillo Guerra
RF-Mesh networks have been extensively used for the deployment of smart grid communications and large-scale implementations of them are expected to continue growing. As a RF-Mesh network grows, latency becomes a concern and interconnection devices are inserted to increase coverage, performance and resiliency. The optimal position of the interconnection devices and collectors represents a NP-hard problem whose solution is approximated by heuristic and computationally expensive solutions. This paper presents a recursive partitioning approach to positioning key devices in large-scale wireless mesh networks that significantly reduces the computational demand of an existing positioning algorithm. Theoretical analysis of performance improvement, along with results of extensive simulations using a publicly available dataset, demonstrate that the proposed approach can improve the execution time of the original algorithm up to 20 times without affecting important QoS parameters.
{"title":"ROPS: Recursively Optimized Prepartitioning Strategy to allocate Key Devices Positions in Large-Scale RF Mesh Networks","authors":"A. M. Mezher, J. Cardenas-Barrera, Carlos Lester Dueñas Santos, J. Meng, Eduardo Castillo Guerra","doi":"10.1145/3416011.3424756","DOIUrl":"https://doi.org/10.1145/3416011.3424756","url":null,"abstract":"RF-Mesh networks have been extensively used for the deployment of smart grid communications and large-scale implementations of them are expected to continue growing. As a RF-Mesh network grows, latency becomes a concern and interconnection devices are inserted to increase coverage, performance and resiliency. The optimal position of the interconnection devices and collectors represents a NP-hard problem whose solution is approximated by heuristic and computationally expensive solutions. This paper presents a recursive partitioning approach to positioning key devices in large-scale wireless mesh networks that significantly reduces the computational demand of an existing positioning algorithm. Theoretical analysis of performance improvement, along with results of extensive simulations using a publicly available dataset, demonstrate that the proposed approach can improve the execution time of the original algorithm up to 20 times without affecting important QoS parameters.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76592098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing availability of vehicle trajectory and road network datasets is crucial for the development of novel trajectory data mining-based applications. For instance, we can design more efficient routing protocols by applying vehicle trajectory prediction. In this paper, we propose a new cluster-based framework to predict road-network constrained trajectories. The framework, designed to perform long-term predictions, combines several steps that use historical trajectory datasets to train prediction models. Experimental results show the framework's effectiveness and efficiency to predict trajectories with different characteristics in a new real-world, large-scale scenario. Besides that, the framework outperformed some other solutions found in the literature in terms of prediction accuracy and computational overhead.mmm;
{"title":"A Cluster-based Framework for Predicting Large Scale Road-Network Constrained Trajectories","authors":"R. Sousa, A. Boukerche, A. Loureiro","doi":"10.1145/3416011.3424751","DOIUrl":"https://doi.org/10.1145/3416011.3424751","url":null,"abstract":"The increasing availability of vehicle trajectory and road network datasets is crucial for the development of novel trajectory data mining-based applications. For instance, we can design more efficient routing protocols by applying vehicle trajectory prediction. In this paper, we propose a new cluster-based framework to predict road-network constrained trajectories. The framework, designed to perform long-term predictions, combines several steps that use historical trajectory datasets to train prediction models. Experimental results show the framework's effectiveness and efficiency to predict trajectories with different characteristics in a new real-world, large-scale scenario. Besides that, the framework outperformed some other solutions found in the literature in terms of prediction accuracy and computational overhead.mmm;","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73511743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：