Yingqi Xu

Prediction-based strategies for energy saving in object tracking sensor networks

In order to fully realize the potential of sensor networks, energy awareness should be incorporated into every stage of the network design and operation. In this paper, we address the energy management issue in a sensor network killer application - object tracking sensor networks (OTSNs). Based on the fact that the movements of the tracked objects are sometimes predictable, we propose a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions. We compare PES against the basic schemes we proposed in the paper to explore the conditions under which PES is most desired. We also test the effect of some parameters related to the system workload, object moving behavior and sensing operations on PES through extensive simulation. Our results show that PES can save significant energy under various conditions.

Dual prediction-based reporting for object tracking sensor networks

As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms.

On localized prediction for power efficient object tracking in sensor networks

Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and by reducing the amount of long-range transmissions. Performance evaluation, based on mathematical analysis, shows that localized prediction can significantly reduce the power consumption in object tracking sensor networks.

PSGR: priority-based stateless geo-routing in wireless sensor networks

Volunteer forwarding, as an emerging routing idea for large scale, location-aware wireless sensor networks, recently has attracted a significant amount of research attention. However, several critical research issues raised by volunteer forwarding, including priority assignment, acknowledgement collisions and communication voids, have not been well addressed by the existing work. In this paper, we propose a priority-based stateless geo-routing (PSGR) protocol to address these issues. Based on PSGR, sensor nodes are able to locally determine their priority to serve as the next relay node using dynamically estimated network density. This effectively suppresses potential communication collisions without prolonging routing delays. PSGR also overcomes the communication void problem using two alternative stateless schemes, rebroadcast and bypass. We analyze energy consumption and delivery rate of PSGR as functions of transmission range. An extensive performance evaluation has been conducted to compare PSGR with competing protocols, including GeRaf, IGF, GPSR and flooding. Simulation results show that PSGR exhibits superior performance in terms of energy consumption, routing latency and delivery rate, and soundly outperforms all of the compared protocols

Exploring spatial correlation for link quality estimation in wireless sensor networks

The irregularity in quality of wireless communication links poses significant research challenges in wireless sensor network design. Dynamic network conditions and environmental factors make an online, self-adapted link quality estimation mechanism within sensor nodes a necessity for making routing decisions and improving network performance. In this paper, we present a weighted regression algorithm for efficient and accurate estimation of link quality in wireless sensor networks. This algorithm captures the spatial correlation in quality of links between a sensor node and its neighbor nodes, such that the quality of a link to a neighbor node can be estimated based on the quality of links to other nodes geographically close. We evaluate the proposed algorithm using a trace-based simulator which takes into account the variances of link quality over time and spatial locations. The experimental results show that the weighted regression algorithm is able to achieve more accurate estimates than WMEWMA, a state-of-the-art link quality estimator, at a much lower communication cost

ProcessingWindow Queries in Wireless Sensor Networks

The existing query processing techniques for sensor networks rely on a network infrastructure for query propagation and data collection. However, such an infrastructure is very susceptible to network topology transients that widely exist in sensor networks. In this paper, we propose an infrastructure-free window query processing technique for sensor networks, called itinerary-based window query execution (IWQE), in which query propagation and data collection are combined into one single stage and executed along a well-designed itinerary inside a query window. We study the parameters for setting up an itinerary (e.g., width and route) and incorporate into IWQE three data collection schemes based on different performance trade-offs. Finally we demonstrate, by extensive simulations, the superior energy-time efficiency, robustness, and accuracy of IWQE over the current state-of-the-art techniques in supporting window queries under various network conditions.

Energy efficient processing of K nearest neighbor queries in location-aware sensor networks

The k nearest neighbor (KNN) query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we examine in-network processing of KNN queries by proposing two alternative algorithms, namely the GeoRouting Tree (GRT) and the KNN Boundary Tree (KBT). The former is based on a distributed spatial index structure and prunes off the irrelevant nodes during query propagation. The latter is based upon ad-hoc geographic routing and first obtains a region within which at least k nearest sensor nodes are enclosed and then decides the k nearest nodes to the query point. We provide an extensive performance evaluation to study the impact of various system factors and protocol parameters. Our results show that GRT yields a good tradeoff between energy consumption and query accuracy in static scenarios. On the other hand, KBT achieves better energy efficiency while being more tolerant to network dynamics.

Processing k nearest neighbor queries in location-aware sensor networks

Efficient search for k nearest neighbors to a given location point (called a KNN query) is an important problem arising in a variety of sensor network applications. In this paper, we investigate in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks. A set of algorithms, namely the geo-routing tree, the KNN boundary tree and the itinerary-based KNN algorithms, are designed in accordance with the global infrastructure-based, local infrastructure-based and infrastructure-free strategies, respectively. They have distinctive performance characteristics and are desirable under different contexts. We evaluate the performance of these algorithms under several sensor network scenarios and application requirements, and identify the conditions under which the various approaches are preferable.

Compressing Moving Object Trajectory in Wireless Sensor Networks

Some object tracking applications can tolerate delays in data collection and processing. Taking advantage of the delay tolerance, we propose an efficient and accurate algorithm for in-network data compression, called delay-tolerant trajectory compression (DTTC). In DTTC, a cluster-based infrastructure is built within the network. Each cluster head compresses an objects movement trajectory detected within its cluster by a compression function. Rather than transmitting all sensor readings to the sink node, the cluster head communicates only the compression parameters, which not only provide the sink node expressive yet traceable models about the object movements, but also significantly reduce the total amount of data communication required for tracking operations. DTTC supports a broad class of movement trajectories using two proposed techniques, DC-compression and SW-compression, and an efficient trajectory segmentation scheme, which are designed for improving the trajectory compression accuracy at less computation cost. Moreover, we analyze the underlying cluster-based infrastructure and mathematically derive the optimum cluster size, aiming at minimizing the total communication cost of the DTTC algorithm. An extensive simulation has been conducted to compare DTTC with competing prediction-based tracking technique, DPR [28]. Simulation results show that DTTC exhibits superior performance in terms of accuracy, communication cost and computation cost and soundly outperforms DPR with all types of movement trajectories.

Analysis of A Loss-Resilient Proactive Data Transmission Protocol in Wireless Sensor Networks

Many of sensor network applications require reliable data communication such that data packets can be delivered to the destination without loss. However, existing reliable transmission techniques either are too costly for resource-constrained sensor networks or have limited capabilities for achieving desirable reliability. In this paper, an effective coding scheme that exploits the tradeoff between redundant data transmission and encoding/decoding complexity is proposed, with an in-depth study on two key design parameters, the degree of repair packets and the number of repair packets. Furthermore, the expected probability of a destination obtaining all data packets under recoverable and permanent failure model for proactive transmission is analyzed, respectively. Simulations have been conducted to verify our theoretical results. The simulation results reveal profound insights in achieving high communication reliability in wireless sensor networks.