Olivier Dousse

Stochastic geometry and random graphs for the analysis and design of wireless networks

Wireless networks are fundamentally limited by the intensity of the received signals and by their interference. Since both of these quantities depend on the spatial location of the nodes, mathematical techniques have been developed in the last decade to provide communication-theoretic results accounting for the networks geometrical configuration. Often, the location of the nodes in the network can be modeled as random, following for example a Poisson point process. In this case, different techniques based on stochastic geometry and the theory of random geometric graphs -including point process theory, percolation theory, and probabilistic combinatorics-have led to results on the connectivity, the capacity, the outage probability, and other fundamental limits of wireless networks. This tutorial article surveys some of these techniques, discusses their application to model wireless networks, and presents some of the main results that have appeared in the literature. It also serves as an introduction to the field for the other papers in this special issue.

Dynamic Coverage of Mobile Sensor Networks

We study the dynamic aspects of the coverage of a mobile sensor network resulting from continuous movement of sensors. As sensors move around, initially uncovered locations may be covered at a later time, and intruders that might never be detected in a stationary sensor network can now be detected by moving sensors. However, this improvement in coverage is achieved at the cost that a location is covered only part of the time, alternating between covered and not covered. We characterize area coverage at specific time instants and during time intervals, as well as the time durations that a location is covered and uncovered. We further consider the time it takes to detect a randomly located intruder and prove that the detection time is exponentially distributed with parameter 2λrv̅s where λ represents the sensor density, r represents the sensors sensing range, and v̅s denotes the average sensor speed. For mobile intruders, we take a game theoretic approach and derive optimal mobility strategies for both sensors and intruders. We prove that the optimal sensor strategy is to choose their directions uniformly at random between (0, 2π). The optimal intruder strategy is to remain stationary. This solution represents a mixed strategy which is a Nash equilibrium of the zero-sum game between mobile sensors and intruders.

Self-Organization Properties of CSMA/CA Systems and Their Consequences on Fairness

Decentralized medium access control schemes for wireless networks based on CSMA/CA, such as the IEEE 802.11 protocol, are known to be unfair. In multihop networks, they can even favor some links to such an extent that the others suffer from virtually complete starvation. This observation has been reported in quite a few works, but the factors causing it are still not well understood. We find that the capture effect and the relative values of the receive and carrier sensing ranges play a crucial role in the performance of these protocols. Using a simple Markovian model, we show that an idealized CSMA/CA protocol suffers from starvation when the receiving and sensing ranges are equal, but quite surprisingly that this unfairness is reduced or even disappears when these two ranges are sufficiently different. We also show that starvation has a positive counterpart, namely organization. When its access intensity is large the protocol organizes the transmissions in space in such a way that it maximizes the number of concurrent successful transmissions. We obtain exact formula for the so-called spatial reuse of the protocol on large line networks.

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

On the fairness of large CSMA networks

We characterize the fairness of decentralized medium access control protocols based on CSMA/CA, in large multi-hop wireless networks. In particular, we show that the widely observed unfairness of these protocols in small network topologies does not always persist in large topologies. In regular networks, this unfairness is essentially due to the unfair advantage of nodes at the border of the network, which have a restricted neighborhood and thus a higher probability to access the communication channel. In large 1D lattice networks these border effects do not propagate inside the network, and nodes sufficiently far away from the border have equal access to the channel; as a result the protocol is long-term fair. In 2D lattice networks, we observe a phase transition. If the access intensity of the protocol is small, the border effects remain local and the protocol behaves similarly as in one-dimensional networks. However, if the access intensity of the protocol is large enough, the border effects persist independently of the size of the network and the protocol is strongly unfair. In irregular networks, the topology is inherently unfair. This unfairness increases with the access intensity of the protocol, but in a much smoother way than in regular two-dimensional networks. Finally, in situations where the protocol is long-term fair, we provide a characterization of its short-term fairness.

A probabilistic kernel method for human mobility prediction with smartphones

Human mobility prediction is an important problem that has a large number of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address modeling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our probabilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location dataset consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours.

Population size estimation using a few individuals as agents

We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetooth probes. We then construct a parametric statistical model to estimate the total number of visible Bluetooth devices in the festival area. By comparing our estimate with ground truth information provided by probes at the entrances of the festival, we show that the total population can be estimated with a surprisingly low error (1.26% in our experiment), given the small number of agents compared to the area of the festival and the fact that they are regular attendees who move randomly. Also, our statistical model can easily be adapted to obtain more detailed estimates, such as the evolution of the population size over time.

Guest editorial: geometry and random graphs for the analysis and design of wireless networks

The one tutorial and 22 papers in this special issue focus on geometry and random graph for the analysis and design of wireless networks. The papers are organized into five groups: Topology; Outage, throughput, capacity, and scaling laws; Connectivity and coverage; Co-existence of disparate wireless networks and cognitive radio; and Distributed algorithms.

Models of 802.11 multi-hop networks: Theoretical insights and experimental validation

Wireless Multi-Hop CSMA/CA Networks are challenging to analyze. On the one hand, their dynamics are complex and rather subtle effects may severely affect their performance. Yet, understanding these effects is critical to operate upper layer protocols, such as TCP/IP. On the other hand, their models tend to be very complex in order to reproduce all the features of the protocol. As a result, they do not convey much insight into the essential features. We review two models of 802.11 protocols, which are simple enough to first explain why a trade-off needs to be found between fairness and spatial reuse (throughput) in saturated wireless networks (where all nodes have packets to transmit to their neighbors); and then to explain why non-saturated networks (where only some nodes, the sources, have packets to transmit to their destinations in a multi-hop fashion) that are more than 3 hops longs suffer from instability.We confront both models either to realistic simulations in ns-2 or to experiments with a testbed deployed at EPFL. We find that the predictions of both models help us understand the performance of the 802.11 protocol, and provide hints about the changes that need to be brought to the protocol.

Opportunistic Sampling for Joint Population Size and Density Estimation

Consider a set of probes, called “agents”, who sample, based on opportunistic contacts, a population moving between a set of discrete locations. An example of such agents are Bluetooth probes that sample the visible Bluetooth devices in a population. Based on the obtained measurements, we construct a parametric statistical model to jointly estimate the total population size (e.g., the number of visible Bluetooth devices) and their spatial density. We evaluate the performance of our estimators by using Bluetooth traces obtained during an open-air event and Wi-Fi traces obtained on a university campus.