Horacio A. B. F. de Oliveira

Localization systems for wireless sensor networks

Monitoring applications define an important class of applications used in wireless sensor networks. In these applications the network perceives the environment and searches for event occurrences (phenomena) by sensing different physical properties, such as temperature, humidity, pressure, ambient light, movement, and presence (for target tracking). In such cases the location information of both phenomena and nodes is usually required for tracking and correlation purposes. In this work we summarize most of the concepts related to localization systems for WSNs as well as how to localize the nodes in these networks (which allows the localization of phenomena). By dividing the localization systems into three distinct components -distance/angle estimation, position computation, and localization algorithm - besides providing a didactic viewpoint, we show that these components can be seen as subareas of the localization problem that need to be analyzed and studied separately.

Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems

A new kind of ad hoc network is hitting the streets: Vehicular Ad Hoc Networks (VANets). In these networks, vehicles communicate with each other and possibly with a roadside infrastructure to provide a long list of applications varying from transit safety to driver assistance and Internet access. In these networks, knowledge of the real-time position of nodes is an assumption made by most protocols, algorithms, and applications. This is a very reasonable assumption, since GPS receivers can be installed easily in vehicles, a number of which already comes with this technology. But as VANets advance into critical areas and become more dependent on localization systems, GPS is starting to show some undesired problems such as not always being available or not being robust enough for some applications. For this reason, a number of other localization techniques such as Dead Reckoning, Cellular Localization, and Image/Video Localization has been used in VANets to overcome GPS limitations. A common procedure in all these cases is to use Data Fusion techniques to compute the accurate position of vehicles, creating a new paradigm for localization in which several known localization techniques are combined into a single solution that is more robust and precise than the individual approaches. In this paper, we further discuss this subject by studying and analyzing the localization requirements of the main VANet applications. We then survey each of the localization techniques that can be used to localize vehicles and, finally, examine how these localization techniques can be combined using Data Fusion techniques to provide the robust localization system required by most critical safety applications in VANets.

Secure localization algorithms for wireless sensor networks

In the military and emergency preparedness class of applications, wireless sensor networks have a number of desirable characteristics, such as being autonomous systems that can be deployed in a remote - possibly hostile - environment and can perform tasks like battlefield surveillance or enemy tracking, as well as monitor the security of military facilities. One of the main challenges in this kind of application is security. Due to their key role in WSNs and also their fragility, localization systems can be the target of an attack that could compromise the entire functioning of a WSN and lead to incorrect military plans and decision making, among other problems. In this article we show how current localization systems are vulnerable to these security attacks, and how existing techniques can be used to prevent or impede these attacks in WSNs.

DV-Loc: a scalable localization protocol using Voronoi diagrams for wireless sensor networks

Localization systems have been identified as a key issue in the development and operation of wireless sensor networks. DV-Hop, a well-known localization algorithm, has recently been proposed for WSNs. Its basic idea relies on transforming the distance to all beacon nodes from hops to meters by using the computed average size of a hop. Despite its advantages, the DV-Hop algorithm has some limitations, mainly due to its high communication cost and energy consumption, which unfortunately limit its applicability to small or medium-sized sensor networks. The scalability issue of DV-Hop is a challenging problem that needs to be addressed. In this article we propose a novel localization-based protocol and show how Voronoi diagrams can be used efficiently to scale a DV-Hop algorithm while maintaining and/or reducing further DV-Hops localization error. In our localization scheme, nodes can also be localized by their Voronoi cells. In order to evaluate the performance of our scheme, we present an extensive set of simulation experiments using ns-2. Our results clearly indicate that our proposed algorithm performs and scales better than DV-Hop.

Error analysis of localization systems for sensor networks

The establishment of a localization system is an important task in wireless sensor networks. Due to the geographic correlation of the sensed data, location information is commonly used to name the gathered data, address nodes and regions, and also improve the performance of many geographic algorithms. Depending on the localization algorithm, different error behaviors (e.g., mean, probability distribution, and correlation) can be exhibited by the sensor network. The process of understanding and analysing this behavior is the first step toward a mathematical model of the localization error. Furthermore, this knowledge can also be used to propose improvements to these systems. In this work, we divide the localization systems into three components: distance estimation, position computation, and the localization algorithm. We show how each component can affect on the final error of the system. In this work, we concentrate on the third component: the localization algorithm. The error behaviors of three known localization algorithms are evaluated together in similar scenarios so the different behaviors of the localization error can be identified and analysed. The influence of these errors in geographic algorithms is also analysed, showing the importance of understanding the error behavior and the importance of geographic algorithms which consider the inaccuracy of position estimations.

A Voronoi Approach for Scalable and Robust DV-Hop Localization System for Sensor Networks

Localization systems have been identified as a key issue to the development and operation of the Wireless Sensor Networks (WSN). A DV-Hop localization system works by transforming the distance to all beacon nodes from hops to units of length measurement (e.g., meters, feet) using the average size of a hop as a correction factor. Despite its advantages, a DV-Hop algorithm has some disadvantages, such as its large communication cost that limits its scalability, and its mapping from hops to distance units that introduces errors that are propagated to the computation of a node location. This last issue has been solved by some recent works, but the scalability problem still is an open problem that limits this technique to small or medium sized networks. In this work, we propose a novel approach that uses Voronoi diagrams in order to scale a DV-Hop localization algorithm while mantaining or even reducing its localization error. Two types of localization can result from the proposed algorithm: the physical location of the node (e.g., latitude, longitude), or a region limited by the nodes Voronoi cell. The algorithm evaluation is performed by comparison with similar algorithms. We show how the proposed algorithm can scale in different aspects such as communication and processing costs when increasing the number of nodes and beacons.

Localization in time and space for wireless sensor networks: A Mobile Beacon approach

Localization in time and space can be defined as the problem of solving both synchronization and positioning problems at the same time. This is a key problem for wireless sensor networks that need to determine the timing and location information of detected phenomena, especially for tracking applications. In this paper, we discuss the relationship between these two problems and propose the Mobilis (Mobile Beacon for Localization and Synchronization) algorithm, a new time-space localization algorithm for wireless sensor networks. The main aspect of the Mobilis algorithm is the use of a mobile beacon for both localization and synchronization. A mobile beacon is a node that is aware of its time and position (e.g. equipped with a GPS receiver) and that has the ability to move around the sensor field. In our algorithm, the synchronization component uses the packets required by the positioning component to improve its performance. Similarly, the positioning component benefits from the communication required by the synchronization component to decrease errors. We also present an extensive set of experiments and simulations to evaluate the performance of our algorithm.

Greedy Routing and Data Aggregation in wireless sensor networks

In this work, we propose a new Greedy Forward algorithm to perform routing and data aggregation in WSNs. Differently from current Greedy Forward algorithms, our approach takes advantage of a sink node capable of long-range communication and uses the RSSI (Received Signal Strength Indicator) of exchanged packets to aggregate and forward data. Based on this, we propose the GRDA (Greedy Routing and Data Aggregation) algorithm with two different variations: GRDA Selection and GRDA Election. In the GRDA Selection, neighbors exchange packets with RSSI information and the next hop of the packet is then selected from a routing table at each step. In the GRDA Election, the next hop is dynamically elected at each step and no extra packets are required for the routing task. Our approach also takes advantage of RSSI values to create a time metric to guide in-network data aggregation. Our results clearly show significant energy savings and efficient data delivery achieved by the proposed algorithms in different scenarios with all the benefits of a Greedy Forward algorithm.

Routing and Data Aggregation toward a High Speed Sink in Wireless Sensor Networks

In this work, we study the impact of data aggregation in WSNs with mobile sink nodes that can move at higher speeds. In these cases, propagated queries cannot be answered by using the sinks position when the query was sent, since the sink node will probably be elsewhere. Therefore, conventional data aggregation schemes may not be appropriate due to this high speed. Thus, we propose and evaluate the performance of three new algorithms for data routing and aggregation in WSNs when the sink is moving at a high speed. These algorithms explore the sink movement to create a routing graph composed of the union paths that intersect the sinks trajectory. At each hop, the sink speed and current network delays are used as metric to guide in-network data aggregation. Our results clearly show significant energy savings and efficient data delivery achieved by the proposed algorithms in different scenarios.

Inserting DVFS Code in Hard Real-Time System Tasks

Applying Dynamic Voltage and Frequency Scaling (DVFS) in real-time systems is not a trivial task. Real-Time tasks are bounded to timing constraints in such a way that a simple performance degradation may cause the system to totally fail. Thus, this work aims at gathering two DVFS approaches (intra and inter-tasks) to define a methodology for optimizing energy consumption in hard real-time systems. The intra-task approach analyzes execution flow of a task and identifies where new instructions can be inserted in order to change supply voltage and frequency when the worst-case path is not followed. On the other hand, the inter-task analyzes how long a task waits due to interferences (e.g. preemption, shared resources), verifies system schedulability, and defines a set of initial optimal frequencies in multi-task environment. The proposed method generates a new code with the same functionality as the original one, but with the advantage of having instructions to change voltage and frequency while taking into account the task interferences, and the new added overheads. Moreover, the experimental results show not only timing constraints were satisfied, but also the energy consumption was reduced around 16% and 18% compared to the total consumption of the highest available frequency in two evaluated paths.