Heitor S. Ramos

DRINA: A Lightweight and Reliable Routing Approach for In-Network Aggregation 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 the high density of nodes in these networks, it is likely that redundant data will be detected by nearby nodes when sensing an event. Since energy conservation is a key issue in WSNs, data fusion and aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. In this work, we propose a novel Data Routing for In-Network Aggregation, called DRINA, that has some key aspects such as a reduced number of messages for setting up a routing tree, maximized number of overlapping routes, high aggregation rate, and reliable data aggregation and transmission. The proposed DRINA algorithm was extensively compared to two other known solutions: the Information Fusion-based Role Assignment (InFRA) and Shortest Path Tree (SPT) algorithms. Our results indicate clearly that the routing tree built by DRINA provides the best aggregation quality when compared to these other algorithms. The obtained results show that our proposed solution outperforms these solutions in different scenarios and in different key aspects required by WSNs.

Energy efficient GPS sensing with cloud offloading

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available, consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a Cloud-Offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEO, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

A reactive role assignment for data routing in event-based wireless sensor networks

In this work, we show how we can design a routing protocol for wireless sensor networks (WSNs) to support an information-fusion application. Regarding the application, we consider that WSNs apply information fusion techniques to detect events in the sensor field. Particularly, in event-driven scenarios there might be long intervals of inactivity. However, at a given instant, multiple sensor nodes might detect one or more events, resulting in high traffic. To save energy, the network should be able to remain in a latent state until an event occurs, then the network should organize itself to properly detect and notify the event. Based on the premise that we have an information-fusion application for event detection, we propose a role assignment algorithm, called Information-Fusion-based Role Assignment (InFRA), to organize the network by assigning roles to nodes only when events are detected. The InFRA algorithm is a distributed heuristic to the minimal Steiner tree, and it is suitable for networks with severe resource constraints, such as WSNs. Theoretical analysis shows that, in some cases, our algorithm has a O(1)-approximation ratio. Simulation results show that the InFRA algorithm can use only 70% of the communication resources spent by a reactive version of the Centered-at-Nearest-Source algorithm.

LEAP: a low energy assisted GPS for trajectory-based services

Trajectory-based services require continuous user location sensing. GPS is the most common outdoor location sensor on mobile devices. However, the high energy consumption of GPS sensing prohibits it to be used continuously in many applications. In this paper, we propose a Low Energy Assisted Positioning (LEAP) solution that carefully partitions the GPS signal processing pipeline and shifts delay tolerant position calculations to the cloud. The GPS receiver only needs to be on for less than a second to collect the sub-millisecond level propagation delay for each satellites signal. With a reference to a nearby object, such as a cell tower, the LEAP server can infer the rest of the information necessary to perform GPS position calculation. We analyze the accuracy and energy benefit of LEAP and use real user traces to show that LEAP can save up to 80% GPS energy consumption in typical trajectory-based service scenarios.

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.

A Reactive and Scalable Unicast Solution for Video Streaming over VANETs

Vehicular ad hoc networks (VANETs) are no longer a futuristic promise but rather an attainable technology. The majority of services envisioned for VANETs either require the provisioning of multimedia support or have this support as an extremely beneficial feature. However, the highly dynamic topology of VANETs poses a demanding challenge for the fulfillment of the stringent requirements for video streaming. In this paper, we provide a deep understanding of the issue of unicast video streaming over VANETs and propose a novel protocol, VIRTUS. In video streaming, many packets are transmitted consecutively in a short period of time. VIRTUS takes this into consideration and extend the duration of the decision of nodes to forward packets from a single transmission to a time window. Furthermore, VIRTUS calculates the suitability of a node to relay packets based on a balance between geographic advancement and link stability. We also propose an extension, that separates the process of relay node selection from the transmission of video content and adopts a density-aware mechanism that adapts its behavior according to local density. Consequently, VIRTUS makes use of the reactive aspect of receiver-based solutions while remaining scalable to increases in transmission rates and density. We report through extensive realistic experiments the benefits of using VIRTUS towards delivering video at a higher quality, in a timely fashion, with lower overhead and fewer collisions.

Cooperative target tracking in vehicular sensor networks

Target tracking plays a key role for vehicular ad hoc networks (VANETs) due to the fact that a wide variety of envisioned applications rely on the ability of this technique of detecting, localizing, and tracking objects surrounding a vehicle. This subject has been studied in fields such as airborne traffic, computer vision, and wireless sensor networks. A VANET brings out new challenges that should be addressed. For instance, the cluttered and dense scenarios, communication issues such as short term links, and the variety of objects considered to be targets, are some of the new ingredients to be taken into account. Applications such as collision warning/avoidance systems require strict time constrains, while others impose only mild restrictions. This complex and heterogeneous environment is discussed in this work, where we didactically divide the main problems into four components: the targets¿ motion model, measurement models, data association problem, and filtering. We also discuss the communication issues and how they affect these systems.

Centrality-based routing for Wireless Sensor Networks

The use of topological features, more specifically, the importance of an element related to its structural position is a subject widely studied. For instance, complex networks theory provides some general use of centrality measures that have been applied to a large variety of knowledge fields. This work aims to use centrality information in the design of routing algorithms for Wireless Sensor Networks (WSN). In this context, we propose a new topological measure, the Sink Betweenness, a distributed algorithm to calculate it, and devise a tree-based routing algorithm that takes advantage of it. Simulations comparing our approach to some well-known algorithms show that our tree-based algorithm improves the routing overlap, favoring data fusion.

Data Driven Performance Evaluation of Wireless Sensor Networks

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed.

An intelligent transportation system for detection and control of congested roads in urban centers

Traffic jams frustrate drivers and cost billions per year in time and fuel consumption. In order to avoid such problems, this paper presents an intelligent transportation system that collects real-time traffic information and is able to detect and manage traffic congestion based on this information. Simulation results show that the proposed protocol can reduce the average travel time, CO2 emission and fuel consumption. In particular, the average travel time was reduced in approximately 23%, the average fuel consumption in 9% and average CO2 emission in 10%.