Daniel L. Guidoni

On the design of resilient heterogeneous wireless sensor networks based on small world concepts

In this work, we propose on-line models to design heterogeneous sensor network topologies with small world features. The proposed model takes into account the data communication flow in this kind of network to create network shortcuts toward the sink node in such a way that the communication between the sink and the sensor nodes is optimized. The endpoints of these shortcuts are nodes with more powerful hardware, leading to a heterogeneous sensor network. We evaluate the on-line models and show that they present the same small world features observed in the theoretical models. When the shortcuts are created toward the sink node, with a small number of powerful sensors, the network presents better small world features and interesting tradeoffs between energy and latency in the data communication when compared with the Random Additional Model. We evaluate the resilience of the on-line models considering general and specific failures and, in both cases, the proposed model is more robust and presents a graceful degradation of the network latency, which shows the resilience of those models.

An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks

Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to their high density of nodes, it is very likely that information that is both spatially and temporally correlated can be detected by several nodes what can be exploited to save energy, a key aspect on these networks. Furthermore, it is important to take advantage of these correlations to decrease communication and data exchange. However, current proposals usually result in high delays and outdated data arriving at the sink node. In this work, we go further and propose a new algorithm, called Efficient Data Collection Aware of Spatio-Temporal Correlation (EAST), which uses shortest routes for forwarding the gathered data toward the sink node and fully exploit both spatial and temporal correlations to perform near real-time data collection in WSNs. Simulation results clearly indicate that our proposal can sense an event with a high accuracy of more than 99.7% while still saving the residual energy of the nodes in more than 14 times when compared to the accurate data collection strategy reported in the literature.

A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks

Abstract This paper presents ProFlex, a distributed data storage protocol for large-scale Heterogeneous Wireless Sensor Networks (HWSNs) with mobile sinks. ProFlex guarantees robustness in data collection by intelligently managing data replication among selected storage nodes in the network. Contrarily to related protocols in the literature, ProFlex considers the resource constraints of sensor nodes and constructs multiple data replication structures, which are managed by more powerful nodes. Additionally, ProFlex takes advantage of the higher communication range of such powerful nodes and uses the long-range links to improve data distribution by storage nodes. When compared with related protocols, we show through simulation that Proflex has an acceptable performance under message loss scenarios, decreases the overhead of transmitted messages, and decreases the occurrence of the energy hole problem. Moreover, we propose an improvement that allows the protocol to leverage the inherent data correlation and redundancy of wireless sensor networks in order to decrease even further the protocol’s overhead without affecting the quality of the data distribution by storage nodes.

A scalable and dynamic data aggregation aware routing protocol for wireless sensor networks

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.

An efficient and robust data dissemination protocol for vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) have emerged as an exciting research and application area. The envisioned applications, as well as some inherent VANET characteristics such as highly dynamic topology, frequently disconnected network, and different and dynamic network density, make data dissemination a challenging task in these networks. Several approaches for data dissemination in VANETs have been recently proposed in the literature. However, more work needs to be done since most of the proposed solutions do not effectively address some or all of the main challenges in these scenarios such as the broadcast storm, network partition and temporal network fragmentation. In this work we consider the broadcast storm problem. To tackle this challenge we propose a novel GEographical Data Dissemination for Alert Information (GEDDAI), which eliminates the broadcast storm and maximizes the capability of performing data dissemination across zones of relevance with low overhead, short delays and high coverage. Simulation results show that the data dissemination performed by GEDDAI provides the best efficiency compared with other algorithms, outperforming them for different scenarios in all performed evaluations.

3D Localization in Wireless Sensor Networks Using Unmanned Aerial Vehicle

A wireless sensor network (WSN) is designed to perform event detection, data collection, and reporting such data to a monitoring station. In many cases, it is necessary to know the location of sensor nodes to relate the detection of the event at a specific location. However, the geographical location of the sensor nodes in most applications can only be set after their deposition in the area of interest. Therefore, for the sensor nodes to know their location, it is necessary to use specific algorithms to solve the problem of discovering the geographical position of sensor nodes. This work addresses the problem of 3D localization in WSNs using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with GPS and it flies over the monitoring area broadcasting its geographical position. Thus, the sensor nodes are able to estimate their geographical position without being equipped with GPS receiver. Simulation results show that using an UAV leads to a smaller error in the calculation of geographic location when compared to solutions presented in the literature.

Applying the Small World Concepts in the Design of Heterogeneous Wireless Sensor Networks

We propose a model to design heterogeneous sensor network topologies with small world features based on the Kleinberg model. The proposed model considers the data communication flow in this kind of network to create network shortcuts toward the sink node in a way that the communication between the sink and the sensor nodes is optimized. The end-points of these shortcuts are nodes with more powerful hardware, leading to a heterogeneous sensor network. Simulation results show that, when the shortcuts are created toward the sink node, with a few powerful sensors, the network presents better small world features and the latency is reduced in the data communication when compared with the original Kleinberg model.

Creating small-world models in wireless sensor networks

In a wireless sensor network, data communication may have a strong impact on its design since the energy cost related to the transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other ldquotraditionalrdquo data networks such as the Internet. In a WSN, there is a special node called sink node that is either the origin or the destination of a message whereas in the other networks data communication happens between arbitrary communicating entities. In this scenario, the theory of complex networks is being employed in the design of wireless sensor networks, which have certain non-trivial topological features. One of the most well-known examples of complex networks is small-world network. In this work, we will use the small-world concept as a modeling technique to build efficient data dissemination in a wireless sensor network. We propose and evaluate two small-world models that can be used in the design of a WSN. In particular the Sink Node as Source/Destination model (SSD) exhibits the most interesting tradeoff between energy and latency allowing the design of strict applications that demand a small latency and energy consumption.

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions

Dynamic and Scalable Routing to Perform Efficient Data Aggregation in WSNs

Data aggregation is one of the main methods to conserve energy in wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and, consequently, reducing communication costs. Most data aggregation protocols are generally based on a static routing scheme. Although those protocols can save energy by eliminating data redundancy, in dynamic scenarios, they can incur in high overhead to reconstruct the routing tree. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSNs. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which can adapt to different scenarios without incurring the overhead of the other methods. DST maximizes the number of overlapping routes and selects routes with the highest aggregation rate. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate efficiency and quality of the routing tree. Simulation results show that the routing tree built by DST provides the best efficiency compared with other algorithms outperforming them for different scenarios in all evaluations performed.