M. Elena Renda

A measurement-based study of beaconing performance in IEEE 802.11p vehicular networks

Active safety applications for vehicular networks aims at improving safety conditions on the road by raising the level of “situation awareness” onboard vehicles. Situation awareness is achieved through exchange of beacons reporting positional and kinematic data. Two important performance parameters influence the level of situation awareness available to the active safety application: the beacon (packet) delivery rate (PDR), and the packet inter-reception (PIR) time. While measurement-based evaluations of the former metric recently appeared in the literature, the latter metric has not been studied so far. In this paper, for the first time, we estimate the PIR time and its correlation with PDR and other environmental parameters through an extensive measurement campaign based on IEEE 802.11p technology. Our study discloses several interesting insights on PIR times that can be expected in a real-world scenarios, which should be carefully considered by the active safety application designers. A major insight is that the packet inter reception time distribution is a power-law and that long situation awareness black-outs are likely to occur in batch, implying that situation awareness can be severely impaired even when the average beacon delivery rate is relatively high. Furthermore, our analysis shows that PIR and PDR are only loosely (negatively) correlated, and that the PIR time is almost independent of speed and distance between vehicles. A third major contribution of this paper is promoting the Gilbert-Elliot model, previously proposed to model bit error bursts in packet switched networks, as a very accurate model of beacon reception behavior observed in real-world data.

Georoy: A location-aware enhancement to Viceroy peer-to-peer algorithm

The success of experiences such as Seattle and Houston Wireless has attracted the attention on the so called wireless mesh community networks. These are wireless multihop networks spontaneously deployed by users willing to share communication resources. Due to the community spirit characterizing such networks, it is likely that users will be willing to share other resources besides communication resources, such as data, images, music, movies, disk quotas for distributed backup, and so on. To support resource exchange in these wireless mesh community networks, algorithms for efficient retrieval of information are required. In this paper we introduce Georoy, an algorithm for the efficient retrieval of the information on resource location based on the Viceroy peer-to-peer algorithm. Differently from Viceroy, Georoy exploits the capability of setting and managing a direct mapping between the resource ID and the node which maintains information about its location so as to speed up the search process. Simulation results show that Georoy enables efficient and scalable search of resources and can be successfully used in wireless mesh community networks.

Measuring IEEE 802.11p Performance for Active Safety Applications in Cooperative Vehicular Systems

In this paper, we present a measurement study of application layer performance in IEEE 802.11p vehicular networks. More specifically, our focus is on active safety applications, which are based on the exchange of beacon messages containing status information between close-by vehicles. We consider two performance metrics relevant to active safety applications: the first is application-layer goodput, which can be used to optimize congestion control techniques aimed at limiting the beaconing load on the wireless channel; the second is the beacon reception rate, which is useful to estimate the level of situation awareness achievable onboard vehicles. Our measurements were conducted using a prototypal, 802.11p compliant communication device developed by NEC, in both stationary and mobile V2V scenarios, and disclosed several useful insights on 802.11p application-level performance. To the best of our knowledge, the ones presented in this paper are the first application-level measurements of IEEE 802.11p based vehicular networks reported in the literature.

TRStalker: an efficient heuristic for finding fuzzy tandem repeats

Motivation: Genomes in higher eukaryotic organisms contain a substantial amount of repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage and are characterized by close spatial contiguity. They play an important role in several molecular regulatory mechanisms, and also in several diseases (e.g. in the group of trinucleotide repeat disorders). While for TRs with a low or medium level of divergence the current methods are rather effective, the problem of detecting TRs with higher divergence (fuzzy TRs) is still open. The detection of fuzzy TRs is propaedeutic to enriching our view of their role in regulatory mechanisms and diseases. Fuzzy TRs are also important as tools to shed light on the evolutionary history of the genome, where higher divergence correlates with more remote duplication events. Results: We have developed an algorithm (christened TRStalker) with the aim of detecting efficiently TRs that are hard to detect because of their inherent fuzziness, due to high levels of base substitutions, insertions and deletions. To attain this goal, we developed heuristics to solve a Steiner version of the problem for which the fuzziness is measured with respect to a motif string not necessarily present in the input string. This problem is akin to the ‘generalized median string’ that is known to be an NP-hard problem. Experiments with both synthetic and biological sequences demonstrate that our method performs better than current state of the art for fuzzy TRs and that the fuzzy TRs of the type we detect are indeed present in important biological sequences. Availability: TRStalker will be integrated in the web-based TRs Discovery Service (TReaDS) at bioalgo.iit.cnr.it. Contact: marco.pellegrini@iit.cnr.it Supplementary information: Supplementary data are available at Bioinformatics online.

K-Boost: a scalable algorithm for high-quality clustering of microarray gene expression data.

Microarray technology for profiling gene expression levels is a popular tool in modern biological research. Applications range from tissue classification to the detection of metabolic networks, from drug discovery to time-critical personalized medicine. Given the increase in size and complexity of the data sets produced, their analysis is becoming problematic in terms of time/quality trade-offs. Clustering genes with similar expression profiles is a key initial step for subsequent manipulations and the increasing volumes of data to be analyzed requires methods that are at the same time efficient (completing an analysis in minutes rather than hours) and effective (identifying significant clusters with high biological correlations). In this paper, we propose K-Boost, a clustering algorithm based on a combination of the furthest-point-first (FPF) heuristic for solving the metric k-center problem, a stability-based method for determining the number of clusters, and a k-means-like cluster refinement. K-Boost runs in O (|N| x k) time, where N is the input matrix and k is the number of proposed clusters. Experiments show that this low complexity is usually coupled with a very good quality of the computed clusterings, which we measure using both internal and external criteria. Supporting data can be found as online Supplementary Material at www.liebertonline.com.

FPF-SB: a scalable algorithm for microarray gene expression data clustering

Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose FPF-SB, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the k- center problem and a stability-based method for determining the number of clusters k. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality.

AMIC@: All MIcroarray Clusterings @ once

The AMIC@ Web Server offers a light-weight multi-method clustering engine for microarray gene-expression data. AMIC@ is a highly interactive tool that stresses user-friendliness and robustness by adopting AJAX technology, thus allowing an effective interleaved execution of different clustering algorithms and inspection of results. Among the salient features AMIC@ offers, there are: (i) automatic file format detection, (ii) suggestions on the number of clusters using a variant of the stability-based method of Tibshirani et al. (iii) intuitive visual inspection of the data via heatmaps and (iv) measurements of the clustering quality using cluster homogeneity. Large data sets can be processed efficiently by selecting algorithms (such as FPF-SB and k-Boost), specifically designed for this purpose. In case of very large data sets, the user can opt for a batch-mode use of the system by means of the Clustering wizard that runs all algorithms at once and delivers the results via email. AMIC@ is freely available and open to all users with no login requirement at the following URL http://bioalgo.iit.cnr.it/amica.

Multihop Beaconing Forwarding Strategies in Congested IEEE 802.11p Vehicular Networks

Multihop propagation of situational information is a promising technique for improving beaconing performance and increasing the degree of situation awareness onboard vehicles. A possible way of achieving this is by piggybacking information on the beacon packets that are periodically sent by each vehicle in the network, as prescribed by the Dedicated Short-Range Communications standards and the European Telecommunications Standards Institute. However, prescribed limitations on beacon size imply that only information on a very small number of surrounding vehicles can be piggybacked in a beacon packet. In most traffic situations, this number is well below the typical number of vehicles within the transmission range, implying that multihop forwarding strategies must be devised to select which neighboring vehicles information to include in a transmitted beacon. In this paper, we designed different multihop forwarding strategies and assessed their effectiveness in delivering fresh situational information to surrounding vehicles. Effectiveness is estimated in terms of both information age and the probability of experiencing a potentially dangerous situation-awareness blackout. Both metrics are estimated as a function of the hop distance from the transmitting vehicle and in the presence of different levels of radio channel congestion. The investigation is based on extensive simulations whose multihop communication performance is corroborated by real-world measurements. The results show that network-coding-based strategies substantially improve forwarding performance, as compared with a randomized strategy, reducing the average information age by up to 60% and the blackout probability by up to two orders of magnitude. We also consider the effect of multihop propagation of situational information on the reliability of a forward collision warning application and show that network-coding-based propagation yields a factor-3 improvement of reliability with respect to a randomized forwarding strategy and even higher improvements with respect to the case of no propagation.

Evaluating multi-hop beaconing forwarding strategies for IEEE 802.11p vehicular networks

Multi-hop propagation of situational information is a promising technique for improving beaconing performance and increasing the degree of situational awareness onboard vehicles. However, limitation on beacon size prescribed by standardization bodies implies that only information about 3-4 surrounding vehicles can be piggybacked in a beacon packet. In most traffic situations, the number of vehicles within transmission range is much larger than 3-4, implying that multi-hop forwarding strategies must be devised to select which neighboring vehicles information to include in a transmitted beacon. In this paper, we investigate the effectiveness of different multi-hop forwarding strategies in delivering fresh situational information to surrounding vehicles. Effectiveness is estimated in terms of both average information age and probability of experiencing a situational-awareness blackout of at least 1 sec. Both metrics are estimated as a function of the hop distance from the transmitting vehicle. The investigation is based on extensive simulations whose multi-hop communication performance is corroborated by real-world measurements. The results show that network-coding based strategies substantially improve forwarding performance as compared to a randomized strategy, reducing the average information age of up to 60%, the blackout probability of two orders of magnitude, and providing a performance similar to that of an idealized strategy in which complete situational information is included in the beacon.

Measurement-based modeling of packet inter-reception time in presence of radio channel congestion

In this paper, we study the performance of the beaconing mechanism underlying active safety vehicular applications in presence of different levels of channel congestion. The importance of this study lies in the fact that channel congestion is considered a major factor influencing communication performance in vehicular networks, and that ours is the first investigation of the effects of congestion based on extensive, real-world measurements. We present a set of measurement-based models of the packet inter-reception time, and we show how these models can be used to estimate the application-level benefits, expressed in terms of increased reliability, of congestion control and of multi-hop propagation of situational information.