Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

Standard

Recent opportunistic routing protocols address the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty cycled to save energy and
the topology changes due to link dynamics and node mobility.
Unlike traditional approaches that maintain a routing structure (e.g., a tree), opportunistic schemes forward packets to the first neighbor that wakes up, thereby reducing latency and energy consumption and increasing the robustness to network dynamics.
Notwithstanding the many advantages, this paper argues that existing opportunistic protocols do not fully exploit the synergy between duty cycling and routing in that all nodes wake up with the same fixed frequency. Instead, we present Staffetta, a duty-cycle scheduling mechanism that dynamically adjusts each node’s wake-up frequency in a distributed fashion, such that nodes closer to the sink are more active than nodes at the edge of the network. The resulting activity gradient effectively biases the forwarding choices of opportunistic mechanisms towards the sink, as the time to find an awake neighbor is shorter in the direction of the sink. We implement Staffetta in Contiki, and evaluate it in combination with three different opportunistic routing mechanisms
on two testbeds. Our results show that, across the board, Staffetta reduces packet latency by an order of magnitude while halving the energy consumption at minimal overhead.
In some cases the impact is even more profound. Compared to using ORW alone, adding Staffetta can lead to network lifetimes that are six times longer and end-to-end packet deliveries that are 450 times faster. In other words, by smart duty cycling, Staffetta improves opportunistic data collection basically for free.

Acm Digital Library | Paper | Code on GitHub | Presentation

Authors: Marco Cattani and Andreas Loukas and Marco Zimmerling and Marco A. Zuniga and Koen G. Langendoen
Research groups:  Embedded Software group, Delft University of Technology and  Networked Embedded Systems Group, TU Dresden
Conference: 14th ACM Conf. on Embedded Networked Sensor Systems (SenSys)
Year: 2016

Leveraging Proximity Sensing to Mine the Behavior of Museum Visitors

Standard

Face-to-face proximity has been successfully lever- aged 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. 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.

IEEE XplorePaper

Authors: Claudio Martella and Armando Miraglia and Marco Cattani and Maarten van Steen
Research group:  Large-scale Distributed Systems group, VU University Amsterdam and Embedded Software group, Delft University of Technology
Conference: IEEE International Conference on Pervasive Computing and Communications (PerCom)
Year: 2016

Graph Scale-Space Theory for Distributed Peak and Pit Identification

Standard

Graph filters are a recent and powerful tool to process in- formation 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 signal’s peaks and pits. Choosing the correct filter necessitates a-priori information about the sig- nal 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 signal’s 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.

ACM Digital Library | Paper

AuthorsAndreas Loukas and Marco Cattani and Marco A. Zuniga and Jie Gao
Research group: Embedded Software group, Delft University of Technology, Delft, Netherlands and Stony Brook University, Stony Brook, New York
Conference14th Int. Conf. on Information Processing in Sensor Networks (IPSN)
Year: 2015

Lightweight neighborhood cardinality estimation in dynamic wireless networks

Standard

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 net- works consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.

ACM Digital LibraryPaper | Presentation | Video

AuthorsMarco Cattani and Marco A. Zuniga and Andreas Loukas and Koen G. Langendoen
Research group: Embedded Software group, Delft University of Technology, Delft, Netherlands
Conference13th Int. Conf. on Information Processing in Sensor Networks (IPSN)
Year: 2014

SOFA: Communication in Extreme Wireless Sensor Networks

Standard

Sensor networks can nowadays deliver 99.9% of their data with duty cycles below 1%. This remarkable performance is, however, dependent on some important underlying assumptions: low traffic rates, medium size densities and static nodes. In this paper, we investigate the performance of these same resource-constrained devices, but under scenarios that present extreme conditions: high traffic rates, high densities and mobility. To cope with these stringent requirements, we propose a novel communication protocol named SOFA (Stop On First Ack). SOFA utilizes opportunistic anycast to drastically reduce the rendezvous times of asynchronous duty cycled nodes -long rendezvous times are the key limitation of protocols operating under high densities and high traffic conditions. SOFA is also stateless, which makes it resilient to mobility. We implemented SOFA in the Contiki OS and tested it both in simulation and on a 100-node testbed. Our results show that SOFA reliably communicates in mobile networks with extreme densities (hundreds of nodes) and higher traffic rates (packets per second) while maintaining a low duty cycle (~2%). Under these extreme conditions, current duty cycled protocols collapse.

In the first video, we use SOFA to run a push-pull, gossip-based protocol that computes the average of the nodes’ values. Nodes’ values are shown in red, while the averages are shown in black. In the second video, we run SLICE, a push-pull, gossip-based protocol to automatically partition the nodes’ values into “slices”. In this case the nodes’ values (in red) are partitioned in percentiles (in black). The experiment was recorded in real-time speed (1x) in the TU Delft testbed.

Download pdf | Springer Library | SlidesSource code on GitHub

AuthorsMarco Cattani and Marco A. Zuniga and Matthias Woehrle and Koen G. Langendoen
Research group: Embedded Software group, Delft University of Technology, Delft, Netherlands
Conference11th European Conference on Wireless Sensor Network (EWSN)
Year: 2014