Carlos Mauricio S. Figueiredo

Bluepass: An indoor Bluetooth-based localization system for mobile applications

Location-Based Services have become quite popular in mobile computing. However, these services are often not effective when devices are in indoor environments. This paper presents an indoor room-level Bluetooth-based localization system. Fixed stations distributed throughout the environment get the RSSI from user devices. With these data, we compute the distance between the device and the stations, based on the signal propagation. In this paper, we propose a room localization method, based on a voting scheme, that eliminates cells close to a base station detecting devices with a weak signal. Another contribution of this paper is the empirical evaluation of the methods within a real environment.

Using Information Fusion to Assist Data Dissemination in Wireless Sensor Networks

Data dissemination is a fundamental task in wireless sensor networks. Because of the radio range limitation and energy consumption constraint, sensor data is commonly disseminated in a multihop fashion (flat networks) through a tree topology. However, to the best of our knowledge none of the current solutions deals with the moment when the dissemination topology needs to be rebuilt. This work addresses such a problem introducing the use of information fusion mechanisms, where the traffic is handled as a signal that is filtered and translated into evidences that indicate the likelihood of critical failures occurrence. These evidences are combined by a Dempster-Shafer engine to detect the need for a topology reconstruction. Our solution, called Topology Rebuilding Algorithm (TRA), is evaluated through a set of simulations. In the experiments, TRA showed to be efficient in avoiding unnecessary topology reconstructions. Compared to the periodic rebuilding, in some cases, TRA could reduce the traffic overhead in nearly 35% of the traffic produced by the periodicu rebuilding.

Data Stream Based Algorithms For Wireless Sensor Network Applications

A wireless sensor network (WSN) is energy constrained, and the extension of its lifetime is one of the most important issues in its design. Usually, a WSN collects a large amount of data from the environment. In contrast to the conventional remote sensing - based on satellites that collect large images, sound files, or specific scientific data - sensor networks tend to generate a large amount of sequential small and tuple- oriented data from several nodes, which constitutes data streams. In this work, we propose and evaluate two algorithms based on data stream, which use sampling and sketch techniques, to reduce data traffic in a WSN and, consequently, decrease the delay and energy consumption. Specifically, the sampling solution, provides a sample of only log n items to represent the original data of n elements. Despite of the reduction, the sampling solution keeps a good data quality. Simulation results reveal the efficiency of the proposed methods by extending the network lifetime and reducing the delay without loosing data representativeness. Such a technique can be very useful to design energy-efficient and time-constrained sensor networks if the application is not so dependent on the data precision or the network operates in an exception situation (e.g., there are few resources remaining or there is an urgent situation).

Diffuse: A topology building engine for wireless sensor networks

In wireless sensor networks, multihop routing is commonly performed through a routing tree. Eventually, the routing tree needs to be rebuilt to accommodate failures, balance the energy consumption, or improve data aggregation. Most of the current solutions do not detect when the routing topology needs to be rebuilt. In this work, we propose an inference engine, called Diffuse, that uses information fusion techniques to detect when the routing topology needs to be rebuilt. Although the Diffuse applicability is ample, as a proof of concept, we use it to provide a fault-tolerant routing tree. Our solution is based on the data traffic that is pre-processed with a tunable moving average filter and translated into evidences that indicate failure probabilities. These evidences are combined using the Dempster-Shafer theory to determine when the topology needs to be rebuilt. We provide theoretical bounds for our proposed solution that is evaluated through simulation. Our experiments show that, in some cases, our solution can reduce the control traffic by a scale factor of 5.

Design and Construction of Wireless Sensor Network Gateway with IPv4/IPv6 Support

6LoWPAN opens a great opportunity for wireless sensor networks (WSNs) to be operated from the Internet. However, 6LoWPAN requires a gateway in order to make the use of IPv6 feasible inside the WSN. In this paper, a gateway is designed and constructed to interconnect 6LoWPAN wireless sensor networks with IPv6 clients, enabling them to receive sensor nodes readings or send commands to the 6LoWPAN WSN at anytime through direct communication with the sensor nodes. Furthermore, this solution allows IPv4 clients to exchange information with the WSN through a bridge architecture implemented at the gateway. The experimental results show that the communication between 6LoWPAN WSN and IPv6/IPv4 clients is successful.

An integrated approach for density control and routing in wireless sensor networks

Wireless sensor networks (WSNs) are characterized by having scarce resources. The usual way of designing network functions is to consider them isolatedly, a strategy which may not guarantee the correct and efficient operation of WSNs. For this reason, in this paper we propose an integrated design of network functions. We take two important WSN functions - density control and routing - as an example and present two approaches to integrate them. In particular, we present two solutions, named RDC-sync and RDC-integrated, which integrate a geographical density control algorithm with tree routing. The simulations experiments performed prove that the integrated design improves the network performance, especially when density control and routing are fully integrated

Multi: A Hybrid Adaptive Dissemination Protocol for Wireless Sensor Networks

Data dissemination (routing) is a basic function in wireless sensor networks. Dissemination algorithms for those networks depend on the characteristics of the applications and, consequently, there is no self-contained algorithm appropriate for every case. However, there are scenarios where the behavior of the network may vary a lot, such as an event-driven application, favoring different algorithms at different instants. Thus, this work proposes a new hybrid and adaptive algorithm for data dissemination, called Multi, that adapts its behavior autonomously in response to the variation of the network conditions. Multi is based on two algorithms for data dissemination that are also presented and evaluated in this work: SID (Source-Initiated Dissemination), a reactive algorithm where dissemination is started by the source nodes, and EF-Tree (Earliest-First Tree), an algorithm that builds and maintains a tree, in a proactive fashion, to disseminate data towards the sink.

Assessing the communication performance of wireless sensor networks in rainforests

Wireless sensor networks (WSNs) have a great potential to serve as a monitoring tool for several applications. In particular, environmental monitoring is a leading flag for WSNs, which can provide significant environmental data for Ecology applications. More specifically, rainforests face several ecological challenges. In this case, WSNs may be used as a supporting tool for preserving and conserving the flora and fauna of such environments. However, we still need practical experiments that help us understanding how data communication is affected by environmental conditions, such as forest density, high humidity, and extreme temperature variations. In this work, we present a set of experiments that assess the communication performance in rainforests. Results show that we still need technological advances before using WSN platforms for large-scale applications in such environments. Our experiments show that the communication range of a WSN, deployed in a dense forest, is reduced by 78%, compared to other environments.

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

Similarity clustering for data fusion in Wireless Sensor Networks using k-means

Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions.