Jiaxi You

An Algorithm for Distributed Beacon Selection

This paper investigates wireless sensor networks where a small percentage of nodes are assumed to know their location a priori. These reference nodes enable absolute localization of other nodes in direct neighborhood. Having estimated their location, these nodes in turn provide their location to other nodes within transmission range. Therefore, location information spreads throughout the network. Consequently, in later state of the network, unknowns desiring to determine their location, or to improve it, will be able to choose from a large pool of nodes with known or estimated locations, which we refer to as beacons. We investigate a method to select a subset of beacons to minimize the error of localization. Regarding Cramer-Rao-Lower- Bound on localization error, the method proposed constitutes a significant improvement in comparison with the often used nearest-neighbors approach.

Exploiting RF-Scatter: Human Localization with Bistatic Passive UHF RFID-Systems

In ubiquitous computing, localization of users in indoor environments is a challenging issue. On the one hand, localization data needs to have fine granularity to provide reasonable input for intention recognition and task planning. On the other hand, effects like multi-path interference and signal scattering of RF propagation in indoor environments reduces the accuracy of traditional wireless localization techniques. However, we prove that such adversary effects can be characterized and utilized conversely to localize the source of RF-scatter with passive UHF RFID. Since measuring spatial correlation requires many spatially separated transmitter and receiver pairs, cost-effective and unobtrusively attachable passive RFID-tags are especially suitable for this purpose. The passive tags are spatially distributed in a manner such that it is possible to infer the spatial correlation of Received Signal Strength (RSS). The idea is to characterize the influence of user presence on RSS, and use such relationship for localization. Three localization algorithms are investigated which consist of a Maximum Likelihood Estimator (MLE), and two Linear Least Squares variants. Algorithms are applied to measurement data which we obtained in an indoor environment. The results evidences our idea of human localization in such bistatic RFID systems.

Context-Aware Geographic Routing for Sensor Networks with Routing Holes

Modern sensor networks are deployed in various terrains of interest. As the complexity of their deployed areas is growing, existing geographic routing algorithms are facing challenges. Holes in networks often cause failures in message routing. Energy consumption, scalability, and routing efficiency are also key design challenges. In this paper, we propose a novel geographic routing algorithm called HOle-BYpassing routing with Context-AwareNess (HobyCan). Our approach locally sets up multiple detour paths to bypass almost all kinds of holes. Therefore, contours of holes are extended with multiple detour paths. According to various context information of a sensor network, such as the size of holes or the remaining energy of nodes, disjoint detour paths can be used alternatively to achieve optimal routing paths or load balance of the network. Simulation results demonstrate the performance of our algorithm, as well as the significance of context information as routing parameters.

Distributed selection of references for localization in wireless sensor networks

The main purpose of wireless sensor networks is to provide information about an area of interest. In order to fulfill this task, physical parameters have to be measured by as many sensors as possible to improve the knowledge on the sensed area. In contrast, due to the resource-limited nature of sensor networks, the number of actively participating nodes should be kept to a minimum. This paper investigates the trade-off between the two conflicting requirements with special focus on localization of sensor nodes. A distributed algorithm to select subsets of sensor nodes for localization is analyzed regarding the accuracy of localization.

Characterizing the Influence of Human Presence on Bistatic Passive RFID-System

Using simple and cost-effective tags, passive RFID systems offer a promising aid for identifying and localizing objects and users in indoor environments. Although systems based on radio frequencies usually suffer from multi-path interference and signal scattering, we show that the characteristics of such interference and scattering can be analyzed with passive RFID. We present and analyze measurements of received signal strength (RSS) conducted in an indoor environment using a passive bistatic RFID-System. In order to characterize the influence of human presence on RSS, measurements were conducted for different user locations and orientations in an indoor deployment area. Finally, an analytical approximation of the relation between user location and RSS is presented in accordance to our measurement results.

GAF&Co: Connectivity aware topology management for sensor networks

In order to achieve energy conservation in WSNs, most topology management protocols use a subset of sensor nodes for global routing. Using fewer nodes results in a reduced connectivity of the network, which eventually increases the number of routing holes. Holes in networks often cause failures in message routing due to the local minimum problem. Therefore, traditional geographic routing protocols cannot be applied with such topology management protocols. In this paper, we propose a novel topology management protocol derived from the Geographical Adaptive Fidelity (GAF) protocol, called GAF with COnnectivity-awareness (GAF&Co). Instead of using virtual grids in GAF, our approach employs hierarchical hexagonal cells to avoid local minimums in WSNs. The purpose is to schedule redundant nodes into energy-saving mode, while maintaining the connectivity of a network for simple geographic routing protocols. Comparing to GAF, the number of cells as well as the overall energy consumption of a WSN also drops dramatically with the proposed protocol.

Passive Tracking of Transceiver-Free Users with RFID

Providing location information while people move through a indoor environment is fundamental to smart environments. While many works address this problem, which is often referred to as tracking, the complex radio propagation and the need smart and unobtrusive localization system still represent challenges to current approaches.

Free-CLASH — improved localization-free clustering in large wireless sensor networks

This paper investigates topology management of large wireless sensor networks. Due to their random deployment, nodes have to organize themselves as energy efficient as possible to avoid redundant sensing and transceiving tasks while maintaining complete sensor coverage and connectivity as long as possible.

Budget-Based Clustering with Context-awareness for Sensor Networks

As the scale of modern sensor networks continues to grow, energy consumption, scalability and routing efficiency are becoming key design challenges. Network management plays an important role in achieving these goals. By decomposing a sensor network into smaller groups, clustering and its variants have been presented as efficient ways in network management. In this paper, we propose a dynamic, localized clustering approach derived from generic budget-based clustering techniques. The approach generates dynamic cluster sizes for a hierarchy of cluster heads, with respect to network context such as residual energy and activity rates of sensor nodes. We further refine the local estimated cluster sizes by using additional feedback during clustering process. Simulation results of stochastic deployment are used to demonstrate the performance of our algorithm, as well as the impact of context information as clustering parameters.

Assessing the Energy Efficiency of Localization in Wireless Sensor Networks

We propose a measure to characterize the energy efficiency of algorithms for localization in wireless networks. The measure presented differs from previous approaches in that it is bounded and supports objective comparison. Furthermore, it corresponds to the general understanding that a high value should indicate high efficiency. Simulation results for localization using Maximum Likelihood Estimation (MLE), for Least Squares Estimation (LS) and for Multidimensional Scaling (MDS) are briefly discussed.