This paper presents DISQO, a DIStributed Framework for Spatial Queries over Moving Objects. Distinguished from existing work, DISQO aims at achieving high scalability and system performance in support of both snapshot and continuous spatial queries over moving objects. The design of DISQO is based on our observation that exchanging object location information and query information between the location server and moving objects can reduce communication cost and facilitate scalable query processing. Thus, DISQO is built upon the notions of roaming regions and query maps in correspondence with object location information and query information. A comprehensive performance evaluation has been conducted to demonstrate the superiority of DISQO design, compared with existing state-of-the-art frameworks for monitoring moving objects.

Location-based services (LBS) have emerged as one of the killer applications for mobile and pervasive computing environments. Due to limited bandwidth and scarce client resources, client-side data caching plays an important role of enhancing the data availability and improving the response time. In this demonstration, we present CS Cache Engine suitable for LBS. The underlying caching model is Complementary Space Caching (CS caching) scheme that we have recently presented in [citation]. Different from conventional data caching schemes, CS caching preserves a global view of the database by maintaining physical objects and capturing those objects in the server but not in the cache as Complementary Regions (CRs) in the cache. As a result, with the CS Cache Engine implementing CS caching, client assertiveness on their own answered queries is enhanced so that unnecessary requests over the wireless channel can be avoided; various kinds of location-based queries are naturally supported; and the client's ability to prefetch objects is introduced such that the response time can be further improved. In this demonstration paper, we discuss the architecture and the functionality of the CS Caching Engine that adopts CS caching. Specifically, for this demonstration, a tourist information named TravelGuide is prototyped with the support of this cache engine.

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.

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.

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.