Marco Cattani

Lightweight neighborhood cardinality estimation in dynamic wireless networks

We address the problem of estimating the neighborhood cardinality of nodes in dynamic wireless networks. Different from previous studies, we consider networks with high densities (a hundred neighbors per node) and where all nodes estimate cardinality concurrently. Performing concurrent estimations on dense mobile networks is hard; we need estimators that are not only accurate, but also fast, asynchronous (due to mobility) and lightweight (due to concurrency and high density). To cope with these requirements, we propose Estreme, a neighborhood cardinality estimator with extremely low overhead that leverages the rendezvous time of low-power medium access control (MAC) protocols. We implemented Estreme on the Contiki OS and show a significant improvement over the state-of-the-art. With Estreme, 100 nodes can concurrently estimate their neighborhood cardinality with an error of ≈10%. State-of-the-art solutions provide a similar accuracy, but on networks consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.

Think globally, act locally: on the reshaping of information landscapes

In large-scale resource-constrained systems, such as wireless sensor networks, global objectives should be ideally achieved through inexpensive local interactions. A technique satisfying these requirements is information potentials, in which distributed functions disseminate information about the process monitored by the network. Information potentials are usually computed through local aggregation or gossiping. These methods however, do not consider the topological properties of the network, such as node density, which could be exploited to enhance the performance of the system. This paper proposes a novel aggregation method with which a potential becomes sensitive to the network topology. Our method introduces the notion of affinity spaces, which allow us to uncover the deep connections between the aggregation scope (the radius of the extended neighborhood whose information is aggregated) and the networks Laplacian (which captures the topology of the connectivity graph). Our study provides two additional contributions: (i) It characterizes the convergence of information potentials for static and dynamic networks. Our analysis captures the impact of key parameters, such as node density, time-varying information, as well as of the addition (or removal) of links and nodes. (ii) It shows that information potentials are decomposed into wave-like eigenfunctions that depend on the aggregation scope. This result has important implications, for example it prevents greedy routing techniques from getting stuck by eliminating local-maxima. Simulations and experimental evaluation show that our main findings hold under realistic conditions, with unstable links and message loss.

Leveraging proximity sensing to mine the behavior of museum visitors

Face-to-face proximity has been successfully leveraged to study the relationships between individuals in various contexts, from a working place, to a conference, a museum, a fair, and a date. We spend time facing the individuals with whom we chat, discuss, work, and play. However, face-to-face proximity is not the realm of solely person-to-person relationships, but it can be used as a proxy to study person-to-object relationships as well. We face the objects with which we interact on a daily basis, like a television, the kitchen appliances, a book, including more complex objects like a stage where a concert is taking place. In this paper, we focus on the relationship between the visitors of an art exhibition and its exhibits. We design, implement, and deploy a sensing infrastructure based on inexpensive mobile proximity sensors and a filtering pipeline that we use to measure face-to-face proximity between individuals and exhibits. Our pipeline produces an improvement in measurement accuracy of up to 64% relative to raw data. We use this data to mine the behavior of the visitors and show that group behavior can be recognized by means of data clustering and visualization.

Visualizing, clustering, and predicting the behavior of museum visitors ☆

Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.

Graph scale-space theory for distributed peak and pit identification

Graph filters are a recent and powerful tool to process information in graphs. Yet despite their advantages, graph filters are limited. The limitation is exposed in a filtering task that is common, but not fully solved in sensor networks: the identification of a signals peaks and pits. Choosing the correct filter necessitates a-priori information about the signal and the network topology. Furthermore, in sparse and irregular networks graph filters introduce distortion, effectively rendering identification inaccurate, even when signal-specific information is available. Motivated by the need for a multi-scale approach, this paper extends classical results on scale-space analysis to graphs. We derive the family of scale-space kernels (or filters) that are suitable for graphs and show how these can be used to observe a signal at all possible scales: from fine to coarse. The gathered information is then used to distributedly identify the signals peaks and pits. Our graph scale-space approach diminishes the need for a-priori knowledge, and reduces the effects caused by noise, sparse and irregular topologies, exhibiting: (i) superior resilience to noise than the state-of-the-art, and (ii) at least 20% higher precision than the best graph filter, when evaluated on our testbed.

Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

Opportunistic routing protocols tackle the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty-cycled to save energy and the topology changes unpredictably due to node mobility and/or link dynamics. Unlike protocols that maintain a routing structure, in opportunistic protocols nodes forward packets to any neighbor that wakes up first, reducing latency and energy costs and increasing the resilience to network dynamics. We claim the performance of existing opportunistic routing protocols can be improved while retaining their resilience by harnessing the synergy between duty cycling and opportunistic forwarding. To prove this claim, we present Staffetta, the first practical duty-cycle adaptation scheme for opportunistic low-power wireless protocols. Staffetta dynamically adapts each nodes wake-up frequency to its current forwarding cost, so nodes closer to the sink become more active than nodes farther away. In this way, Staffetta biases the forwarding choices toward the sink as the neighbor waking up first is also likely to offer high routing progress. Experiments on two testbeds with four different opportunistic routing mechanisms demonstrate that Staffetta achieves severalfold performance improvements compared with a fixed wake-up frequency. As a case a point, Staffetta enables ORW, the state-of-the-art opportunistic routing protocol, to reduce end-to-end packet latency by 79-452 × and energy consumption by 2.75-9× while increasing packet delivery ratio compared with ORWs default link-layer settings.

Communication in extreme wireless sensor networks

In spite of using unreliable resource-constrained devices, sensor networks can nowadays deliver 99.9% of their data with duty cycles well below 1%. This remarkable performance is, however, dependent on one or more of the following assumptions: low traffic rates, medium size densities and static nodes.

Exploiting Density to Track Human Behavior in Crowded Environments

For the Internet of Things to be people-centered, things need to identify when people and their things are nearby. In this article, we present the design, implementation, and deployment of a positioning system based on mobile and fixed inexpensive proximity sensors that we use to track when individuals are close to an instrumented object or placed at certain points of interest. To overcome loss of data between mobile and fixed sensors due to crowd density, traditional approaches are extended with mobile-to-mobile proximity information. We tested our system in a museum crowded with thousands of visitors, showing that measurement accuracy increases in the presence of more individuals wearing a proximity sensor. Furthermore, we show that density information can be leveraged to study the behavior of the visitors, for example, to track the popularity of points of interest, and the flow and distribution of visitors across floors.