Philippe Nain

Bio: Philippe Nain is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Queue & Queueing theory. The author has an hindex of 33, co-authored 135 publications receiving 4706 citations. Previous affiliations of Philippe Nain include École normale supérieure de Lyon & University of Arizona.

Mobility improves coverage of sensor networks

Abstract: Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.

Properties of random direction models

Abstract: A number of mobility models have been proposed for the purpose of either analyzing or simulating the movement of users in a mobile wireless network. Two of the more popular are the random waypoint and the random direction models. The random waypoint model is physically appealing but difficult to understand. Although the random direction model is less appealing physically, it is much easier to understand. User speeds are easily calculated, unlike for the waypoint model, and, as we observe, user positions and directions are uniformly distributed. The contribution of this paper is to establish this last property for a rich class of random direction models that allow future movements to depend on past movements. To this end, we consider finite oneand two-dimensional spaces. We consider two variations, the random direction model with wrap around and with reflection. We establish a simple relationship between these two models and, for both, show that positions and directions are uniformly distributed for a class of Markov movement models regardless of initial position. In addition, we establish a sample path property for both models, namely that any piecewise linear movement applied to a user preserves the uniform distribution of position and direction provided that users were initially uniformly throughout the space with equal likelihood of being pointed in any direction.

A survey of TCP over ad hoc networks

Abstract: The Transmission Control Protocol (TCP) was designed to provide reliable end-to-end delivery of data over unreliable networks. In practice, most TCP deployments have been carefully designed in the context of wired networks. Ignoring the properties of wireless ad hoc networks can lead to TCP implementations with poor performance. In order to adapt TCP to the ad hoc environment, improvements have been proposed in the literature to help TCP to differentiate between the different types of losses. Indeed, in mobile or static ad hoc networks losses are not always due to network congestion, as it is mostly the case in wired networks. In this report, we present an overview of this issue and a detailed discussion of the major factors involved. In particular, we show how TCP can be affected by mobility and lower-layer protocols. In addition, we survey the main proposals that are intended to adapting TCP to mobile and static ad hoc environments.

Dynamic Coverage of Mobile Sensor Networks

Abstract: 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 2λrvs where λ represents the sensor density, r represents the sensor's sensing range, and vs 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.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Constrained Markov Decision Processes

Abstract: This report presents a unified approach for the study of constrained Markov decision processes with a countable state space and unbounded costs. We consider a single controller having several objectives; it is desirable to design a controller that minimize one of cost objective, subject to inequality constraints on other cost objectives. The objectives that we study are both the expected average cost, as well as the expected total cost (of which the discounted cost is a special case). We provide two frameworks: the case were costs are bounded below, as well as the contracting framework. We characterize the set of achievable expected occupation measures as well as performance vectors. This allows us to reduce the original control dynamic problem into an infinite Linear Programming. We present a Lagrangian approach that enables us to obtain sensitivity analysis. In particular, we obtain asymptotical results for the constrained control problem: convergence of both the value and the policies in the time horizon and in the discount factor. Finally, we present and several state truncation algorithms that enable to approximate the solution of the original control problem via finite linear programs.