Matteo Sammarco

EPICS: Fair Opportunistic Multi-Content Dissemination

Achieving efficient content dissemination in mobile opportunistic networks becomes a big challenge when content sizes are large and require more capacity than what contact opportunities between nodes may offer. Content fragmentation solves only part of the problem, as nodes still need to decide which fragment to send when a contact happens. To address this problem, we propose EPICS, a protocol designed to quickly exchange large contents in opportunistic networks. Using grey relational analysis, EPICS is able to balance the distribution of contents that have different sizes and creation times, providing fairer delay distribution and faster dissemination. We implemented and evaluated EPICS through real experimentation using Android devices. Results show that EPICS significantly reduces content dissemination delays when compared to classic approaches.

Using geosocial search for urban air pollution monitoring

Abstract While Twitter and other Online Social Networks (OSNs) or microblogs are considered as a source of information for breaking news or uproarious and unexpected events, they could also be exploited as a dense worldwide sensors network for physical measurements. The corpus of geotagged posts from OSNs includes people’s feedbacks about a wide range of topics, with precise temporal and geographical metadata, that can be used as a support or an improvement to hardware sensors. For instance, if collocated people, independently and at the same time, write posts complaining about high temperatures, it could effectively denote a raise of heat in that place. In this paper, we explore the feasibility to use a geographical search on social networks, that is, a geosocial search, about air pollution related posts, as effective air impureness measurements. We evaluate our assumption in large cities over three continents of the planet, where a minimum increment about the number of air pollution related posts in an area, indeed corresponds to a raise of minimum pollution values in such area. Such a correlation can be exploited to integrate and extend existing air pollution monitoring networks. At the end of the manuscript we propose to further employ the time series of posts returned by the geosocial search to predict next pollution values.

PePiT: opportunistic dissemination of large contents on android mobile devices

We demonstrate the design and implementation of PePit, an Android application to disseminate large media contents among mobile users. \pepit is an instantiation of the Prevalence-Aware Content Spreading (PACS) protocol, which enables a more efficient use of the communication opportunities between devices by slicing large contents into small chunks that better fit into shorter contacts.

Poster: ParkMaster: Leveraging Edge Computing in Visual Analytics

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMasters architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.

Trace selection for improved WLAN monitoring

Existing measurement techniques for IEEE~802.11-based networks assume that the higher the density of monitors in the target area, the higher the quality of the measure. This assumption is, however, too strict if we consider the cost involved in monitor installation and the necessary time to collect and merge all traces. In this paper, we investigate the balance between number of traces and completeness of collected data. We propose a method based on similarity to rank collected traces according to their contribution to the monitoring system. With this method, we are able to select only a subset of traces and still keep the quality of the measure, while improving system scalability. In addition, based on the same rank, we identify monitors that can be relocated to enlarge the monitored area and increase the overall efficiency of the system. Finally, our experimental results show that the proposed solution leads to a better tradeoff in terms of unique captured frames over the number of merge operations.

Demo: opportunistic communications to alleviate cellular infrastructures: the FP7-moto approach

Over the latest few years, we have witnessed the widespread diffusion of smartphones, tablets, and other mobile devices with diverse networking and multimedia capabilities. Major operators in the US and Europe are experiencing severe problems in coping with the mobile data traffic generated by their users. The main reason is that the trend of traffic demand is exponentially increasing, while the improvements at the physical layer are bounded by the famous Shannon theorem and by the fact that the licensed spectrum is a limited and scarce resource. The FP7 MOTO project proposes to design, implement, and evaluate an architecture that takes full advantage of the latest advances in opportunistic networking to achieve effi- cient traffic offloading.

Crashzam: Sound-based Car Crash Detection.

Connected vehicles, combined with embedded smart computation capabilities, will certainly lead to a new generation of services and opportunities for drivers, car manufacturers, insurance and service companies. One of the main challenges remaining in this field is how to detect key triggering events. One of these crucial moments is a car accident, for which not only smart connected vehicles can improve drivers’ safety as car accidents are still one of the main causes of fatalities worldwide, but also help them during minor, but very stressful moments. In this paper, we present Crashzam which is an innovative way to detect any type car accidents based on sound produced by car impact, while, so far, crash detection is only a prerogative of accelerometer sensor time series analysis, or its proxy: activation of the airbag. We describe the system design, the sound detection model, and the results based on a dataset with in-car cabin sounds of real crashes. We have beforehand built such dataset with real car accident sounds. Classification is built upon features extracted from the time and frequency domain of the audio signal and from its spectrogram image. Results show that the proposed model is able to easily identify crash sounds from other sounds reproduced in-car cabins. Moreover, considering that Crashzam can run on smartphones, it is a low cost and energy solution, contributing to the spreading of such a car safety feature and reducing delays in providing assistance when an

Scalable Wireless Traffic Capture Through Community Detection and Trace Similarity

Best-performing WLAN monitoring systems must capture as much wireless traffic as possible. To achieve this aim, several monitors are employed to capture wireless exchanges in a target area. Monitors potentially generate large traces that are all merged together to have a more complete, global view of the network behavior. Traces are often more equal than complementary, leading to the underutilization of monitors and to a higher system complexity. In this paper, we propose a methodology to make an efficient use of monitors in order to increase scalability. Such a methodology, based on trace similarity and community detection in graphs, ranks traces to reveal how many and which ones must be merged. Traces at the bottom of the rank, which belong to under-used monitors, are candidates to be relocated somewhere else to extend the target area. We evaluate the proposed methodology in two real-case scenarios. Results show that we can remove up to half of the monitors in our scenarios and still keep the same level of coverage.

Demo: D2D Rescue of Overloaded Cellular Channels

Mobile data traffic is set to triple in three years from now according to Cisco. This trend is a real challenge for operators since wireless capacity is bounded. To boost network capacity further, operators think about paradigm-shifting solutions to relieve their infrastructures. Recently, data offloading received increasing attention from the research community. Operators may leverage unused bandwidth across different technologies to shift part of the traffic onto less critical infrastructure (e.g., through Wi-Fi access points). In a further evolution, they can also benefit from the widespread diffusion of smart mobile devices with multiple communication interfaces. Users become the heart of the offloading strategy by employing multi-hop opportunistic communications to improve content dissemination, while reducing the load on the wireless infrastructure. We propose an architecture that takes advantage of the latest advances in opportunistic networking to achieve efficient traffic offloading. In this approach, terminals are under continuous control of the operator - the cellular infrastructure serves as a control channel to track data dissemination. The demonstration builds on DROiD [2] as injection strategy, and Epics [1] to distribute data opportunistically.

Burst size matters: when node degree helps accelerate opportunistic dissemination

Disseminating large files in opportunistic networks requires splitting the content into smaller pieces in order to leverage short contacts between nodes on the move. A negative consequence of content chopping is that it may generate significant overhead, as nodes have to exchange more signaling information to determine which pieces the neighbor misses. In this paper, we investigate the convenience of exchanging a burst of pieces at once at the risk of sending redundant pieces. Although achieving a good tradeoff between signaling reduction and redundant transmissions is challenging, we found out that node degree is a good indicator to determine burst size. We propose a distributed multi-content dissemination protocol with an adaptive burst dimensioning based on the device neighborhood density.We score its performance using both synthetic mobility traces and a testbed composed of real mobile devices and finely monitor the behavior of the protocol by deploying passive monitors in the target area. Our experiments show that our proposal achieves much faster dissemination than related alternatives that employ a fixed burst size. As a matter of fact, our work provides insights into the necessity of adopting adaptive strategies in practical situations involving device-to-device content dissemination.