An Analysis of a Real Mobility Trace Based on Standard Mobility Metrics

Better understanding mobility, being it from pedestrians or any other moving object, is practical and insightful. Practical due to its applications to the fundamentals of communication, with special attention to wireless communication. Insightful because it might pinpoint the pros and cons of how we are moving, or being moved, around. There are plenty of studies focused on mobility in mobile wireless networks, including the proposals of several synthetic mobility models. Getting real mobility traces is not an easy task, but there has been some efforts to provide traces to the public through repositories. Synthetic mobility models are usually analyzed through mobility metrics, which are designed to capture mobility subtleties. This work research on the applicability of some representative mobility metrics for real traces analysis. To achieve that goal, a case study is accomplished with a dataset of mobility traces of taxi cabs in the city of Rome/Italy. The results suggest that the mobility metrics under consideration are capable of capturing mobility properties which would otherwise require more sophisticated analytical approaches

Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft

RECAST: Telling Apart Social and Random Relationships in Dynamic Networks

International audienceIn this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to net- work applications that rely on human routines, such as, e.g., op- portunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from differ- ent kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant dif- ferences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing

Assessing the effectiveness of DTN techniques under realistic urban environments

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Intelligent Transportation Systems (ITS) require collecting and distributing as much relevant information as possible to provide their services. Such information could also offer new possibilities to various service providers in the wider Smart City context. The distribution of this intelligence is carried out through various vehicular networking strategies, the most flexible of all being Delay Tolerant Networking (DTN). DTN protocols can cope with the problems derived from high mobility and the possibility of high node sparsity. Nevertheless, achieving a fair comparison of DTN solutions in an urban environment is a hard task. In this paper we present a generic DTN model that we use to compare various representative DTN solutions in a metropolitan scenario. We highlight the weak and strong points of each evaluated proposal by also taking into consideration different sending strategies adopted to improve the performance of DTN protocols.This work was partially supported by the Ministerio de Econom´ıa y Competitividad, Spain, under Grants TIN2011-27543-C03-01 and BES-2012-052673 and by the Universitat Politecnica de Val ` encia through the ABATIS project (PAID- `05-12).Tornell, SM.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2013). Assessing the effectiveness of DTN techniques under realistic urban environments. En Local Computer Networks (LCN), 2013 IEEE 38th Conference on. IEEE. 573-580. https://doi.org/10.1109/LCN.2013.6761293S57358

Dataset for anomaly detection in a production wireless mesh community network

Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for the administration of their own nodes makes the network very dynamic. There are frequent reboots of the networking devices, and users that join and leave the network. In addition, the unplanned deployment of the network makes it very heterogeneous, with both high and low capacity links. Therefore, anomaly detection in such dynamic scenario is challenging. In this paper we provide a dataset gathered from a production WCN. The data was obtained from a central server that collects data from the mesh nodes that build the network. In total, 63 different nodes were encountered during the data collection. The WCN is used daily to access the Internet from 17 subscribers of the local ISP available on the mesh. We have produced a dataset gathering a large set of features related not only to traffic, but other parameters such as CPU and memory. Furthermore, we provide the network topology of each sample in terms of the adjacency matrix, routing table and routing metrics. In the data we provide there is a known unprovoked gateway failure. Therefore, the dataset can be used to investigate the performance of unsupervised machine learning algorithms for fault detection in WCN. To our knowledge, this is the first dataset that allows fault detection to be investigated from a production WCN.This work has received funding through the DiPET CHIST-ERA under grant agreement PCI2019-111850-2; Spanish grant PID2019- 106774RB-C21; Romanian DIPET (62652/15.11.2019) project funded via PN 124/2020; and has been partially supported by the EU research project SERRANO (101017168) and hardware resources courtesy of the Romanian Ministry of Research and Innovation UEFISCDI COCO research project PN III-P4-ID-PCE-2020-0407.Peer ReviewedPostprint (published version

Performance evaluation of a distributed storage service in community network clouds

Community networks are self-organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user-provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end-user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least-authority file system (Tahoe-LAFS), a decentralized storage system with provider-independent security that guarantees privacy to the users. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe-LAFS. We observed that the write operation of Tahoe-LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe-LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe-LAFS can run on community network clouds with suitable performance for the needed end-user experience.Peer ReviewedPreprin

Fast and reliable robot deployment for substitution networks

International audienceIn this paper, we propose an algorithm to efficiently (re)-deploy the wireless mobile routers of a substitution network by considering the energy consumption, a fast deployment scheme and a mix of the network metric. We consider a scenario where we have two routers in a fixed network and where connectivity must be restored between those two routers with a wireless mobile router. The main objective of the wireless mobile router is to increase the communication performance such as the throughput by acting as relay node between the two routers of the fixed network. We present a fast, adaptive and localized approach which takes into account different network metrics such as Received Signal Strength (RSS), Round-Trip Time (RTT) and the Transmission Rate, between the wireless mobile router and the two routers of the fixed network. Our method ameliorates the performance of our previous approach from the literature by shortening the deployment time, increasing the throughput, and consuming less energy in some specific cases