Location monitoring is an important issue for real time management of mobile object positions. Significant research efforts have been dedicated to techniques for efficient processing of spatial continuous queries on moving objects in a centralized location monitoring system. Surprisingly, very few have promoted a distributed approach to real-time location monitoring. In this paper we present a distributed and scalable solution to processing continuously moving queries on moving objects and describe the design of MobiEyes, a distributed real-time location monitoring system in a mobile environment. Mobieyes utilizes the computational power at mobile objects, leading to significant savings in terms of server load and messaging cost when compared to solutions relying on central processing of location information at the server. We introduce a set of optimization techniques, such as Lazy Query Propagation, Query Grouping, and Safe Periods, to constrict the amount of computations handled by the moving objects and to enhance the performance and system utilization of Mobieyes. We also provide a simulation model in a mobile setup to study the scalability of the MobiEyes distributed location monitoring approach with regard to server load, messaging cost, and amount of computation required on the mobile objects.

MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system

Information Networking

Colbar: A collaborative location-based regularization framework for QoS prediction

Context-aware collect data with energy efficient in Cyber-physical cloud systems

An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks

The development of large-scale wireless sensor networks engenders many challenging problems. Examples of such problems include how to dynamically organize the sensor nodes into clusters and how to compress and route the sensing information to a remote base station. Sensed data in sensor systems reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. Noteworthy efficient clustering schemes and data compressing techniques proposed recently leverage the spatiotemporal correlation. These include the framework of Liu et al. and schemes introduced by Gedik et al. However, the previous clustering schemes are based on an impractical assumption of a single-hop network architecture and their cluster construction communication cost is relatively expensive. On the other hand, the computational overhead of recent compressing techniques (e.g. the work of Liu et al. and Douglas et al.) is quite significant; therefore, it is hard for sensor nodes with limited processing capability to perform these techniques. With such motivation, we propose a novel and one-round distributed clustering scheme based on spatial correlation between sensor nodes, and propose a novel light-weight compressing algorithm to effectively save the energy at each transmission from sensors to the base station based on temporal correlation of the sensed data. Besides, the aim of the proposed clustering scheme is not only to group the nodes with the highest similarity in observations into the same cluster, but also to construct and maintain a dynamic backbone for efficient data collection in the networks (with the consideration of sink mobility). Computer simulation shows that the proposed schemes significantly reduce the overall number of communications in the cluster construction phase and the energy consumed in each transmission, while maintaining a low variance between the readings of sensor nodes in the same clusters and high reliability of the compressed data. Copyright © 2010 John Wiley & Sons, Ltd.

SCCS: Spatiotemporal clustering and compressing schemes for efficient data collection applications in WSNs

Indexing provides for selective tuning but suffers from the drawback that, in order to conserve battery power, the client has to wait for and tune to the index segment. In location-aware mobile services (LAMSs), it is important to reduce the query response time since a late query response may contain out-of-date information. In this paper, we present a broadcast-based spatial query processing scheme designed to support nearest neighbor (NN) query processing. With the proposed schemes, broadcast data items are sorted sequentially based on their locations and the clients can selectively tune to the desired data item without the need for an index segment. For the purpose of selective tuning, we present the exponential sequence scheme (ESS) and cluster-based Fibonacci sequence scheme (CFS) schemes. The ESS and CFS schemes attempt to conserve battery power. The performance of our schemes is investigated in relation to various environmental variables such as the distributions of data objects, the average speed of the clients, and the size of the service area. The resulting latency and tuning time are close to the optimum values, as our analysis and simulation results indicate

Energy-Efficient Data Dissemination Schemes for Nearest Neighbor Query Processing

A fundamental problem for peer-to-peer (P2P) applications in mobile-pervasive computing environment is to efficiently identify the node that stores particular data items and download them while preserving battery power. In this paper, we propose a P2P Minimum Boundary Rectangle (PMBR, for short) which is a new spatial index specifically designed for mobile P2P environments. A node that contains desirable data item (s) can be easily identified by reading the PMBR index. Then, we propose a selective tuning algorithm, called Distributed exponential Sequence Scheme (DSS, for short), that provides clients with the ability of selective tuning of data items, thus preserving the scarce power resource. The proposed algorithm is simple but efficient in supporting linear transmission of spatial data and processing of location-aware queries. The results from theoretical analysis and experiments show that the proposed algorithm with the PMBR index is scalable and energy efficient in both range queries and nearest neighbor queries.

Energy Efficient Data Access in Mobile P2P Networks

Sensor nodes transmit the sensed information to the sink through wireless sensor networks (WSNs). They have limited power, computational capacities and memory. Portable wireless devices are increasing in popularity. Mechanisms that allow information to be efficiently obtained through mobile WSNs are of significant interest. However, a mobile sink introduces many challenges to data dissemination in large WSNs. For example, it is important to efficiently identify the locations of mobile sinks and disseminate information from multi-source nodes to the multi-mobile sinks. In particular, a stationary dissemination path may no longer be effective in mobile sink applications, due to sink mobility. In this paper, we propose a Sink-oriented Dynamic Location Service (SDLS) approach to handle sink mobility. In SDLS, we propose an Eight-Direction Anchor (EDA) system that acts as a location service server. EDA prevents intensive energy consumption at the border sensor nodes and thus provides energy balancing to all the sensor nodes. Then we propose a Location-based Shortest Relay (LSR) that efficiently forwards (or relays) data from a source node to a sink with minimal delay path. Our results demonstrate that SDLS not only provides an efficient and scalable location service, but also reduces the average data communication overhead in scenarios with multiple and moving sinks and sources.

/pdf/sink-oriented-dynamic-location-service-protocol-for-mobile-j4b2hinlyk.pdf

Kwangjin Park

Papers

SCCS: Spatiotemporal clustering and compressing schemes for efficient data collection applications in WSNs

Energy-Efficient Data Dissemination Schemes for Nearest Neighbor Query Processing

Energy Efficient Data Access in Mobile P2P Networks

Sink-oriented Dynamic Location Service Protocol for Mobile Sinks with an Energy Efficient Grid-Based Approach

On QoS multicast routing algorithms using k-minimum Steiner trees