Alexandre Guitton

Correlation-based data dissemination in traffic monitoring sensor networks

In this work, we investigate the nature of spatio-temporal correlations in an urban traffic scenario, and how they can be exploited to reduce the cost of sensor data propagation to the gateway nodes, where users are connected. We conduct experimental analysis of our proposed algorithms using a real dataset of road traffic data generated in the city of Cambridge.

Utilizing Correlations to Compress Time-Series in Traffic Monitoring Sensor Networks

Wireless sensor networks present significant opportunities for fine-grained and continuous monitoring of road traffic, enabling careful city planning, automated road maintenance and accident detection. Users are typically willing to tolerate a small error in car-flow data, in order to reduce the cost of data propagation from the sensor nodes to the gateway nodes, to which users are connected. In this paper, we first examine the relative performance of Fourier- and wavelet-based algorithms for compressing traffic data locally at the sensor nodes. Using real traffic information from the city of Cambridge (UK), we then demonstrate that car-flow data collected across geographically dispersed sensor nodes exhibit strong spatial and temporal correlations. We then combine lossy Fourier-compression with correlation-based compression to achieve further communication savings within a user-specified error threshold. For a tolerated error of 5-15 cars per 5 min, it is shown that exploitation of temporal correlations yields 14-30% savings relative to Fourier compression alone, whilst use of spatial correlations results in 10-35% savings.

A Study of Approximate Data Management Techniques for Sensor Networks

Recent developments in sensor network technology have enabled the instrumentation of the physical world with smart devices for monitoring purposes. A large variety of applications could benefit from the pervasive deployment of inexpensive wireless sensor nodes, ranging from environmental monitoring to emergency detection and response. A significant challenge is to prolong the monitoring operation of sensor nodes by efficiently using their limited energy, bandwidth and computation resources. In this paper, we survey approximate data management techniques for sensor networks that exploit the tolerance of most applications to small inaccuracies in the reported data in order to extend the network lifetime

Multicast tree aggregation in large domains

Tree aggregation is an efficient proposition that can solve the problem of multicast forwarding state scalability. The main idea of tree aggregation is to force several groups to share the same delivery tree: in this way, the number of multicast forwarding states per router is reduced. Unfortunately, when achieving tree aggregation in large domains, few groups share the same tree and the aggregation ratio is small. In this paper, we propose a new algorithm called TALD (Tree Aggregation in Large Domains) that achieves tree aggregation in domains with a large number of nodes. The principle of TALD is to divide the domain into several sub-domains and to achieve the aggregation in each of the sub-domain separately. In this way, there is possible aggregation in each of the sub-domain and the number of forwarding states is significantly reduced. We show the performance of our algorithm by simulations on a Rocketfuel network of 200 routers.

Routing and processing multiple aggregate queries in sensor networks

We present a novel approach to processing continuous aggregate queries in sensor networks, which lifts the assumption of tree-based routing. Given a query workload and a special-purpose gateway node where results are expected, the query optimizer exploits query correlations in order to generate an energy-efficient distributed evaluation plan. The proposed algorithms, named STG and STS, identify common query sub-aggregates, and propose common routing structures to share the sub-aggregates at an early stage. Moreover, they avoid routing sub-aggregates of the same query through long-disjoint paths, thus further reducing the communication cost of result propagation. In this poster, we provide examples to illustrate the functionality and the communication savings of STG and STS compared to the existing tree-based approach.

Interplay of processing and routing in aggregate query optimization for sensor networks

This paper presents a novel approach to processing continuous aggregate queries in sensor networks, which lifts the assumption of tree-based routing. Given a query workload and a special-purpose gateway node where results are expected, the query optimizer exploits query correlations in order to generate an energy-efficient distributed evaluation plan. The proposed optimization algorithms identify common query sub-aggregates, and propose common routing structures to share the sub-aggregates at an early stage. Moreover, they avoid routing sub-aggregates of the same query through long-disjoint paths, thus further reducing the communication cost of result propagation. The proposed algorithms are fully-distributed, and are shown to offer significant communication savings compared to existing tree-based approaches. A thorough experimental evaluation shows the benefits of the proposed techniques for a variety of query workloads and network topologies.

Hierarchical Aggregation of Multicast Trees in Large Domains

Multicast tree aggregation is a technique that reduces the control overhead and the number of statesinduced by multicast. The main idea of this protocol is to route several groups to the same distribution tree in order to reduce the total number of multicast forwarding states. In this article, we show that this technique cannot be applied to large domains. Indeed, when the number of border routers is large, actual tree aggregation protocols are unable to find similar groups to aggregate to the same tree. However, by dividing the domain into several smaller sub-domains, we prove that it is possible to achieve important savings. A hierarchical protocol is designed to interconnect the trees of the sub-domains together. While previous protocols cannot cope with more than 25 border routers, our protocol still shows significant benefits for domains with 200 border routers.