Kwangjin Park

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

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

Energy-Efficient Data Dissemination Schemes for Nearest Neighbor Query Processing

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

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

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.

A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems

When the mobile environment consists of light-weight devices, the energy consumption of location-based services (LBSs) and the limited bandwidth of the wireless network become important issues. Motivated by this, we propose new spatial query processing algorithms to support Mobile Continuous Nearest Neighbor Query (MCNNQ) in wireless broadcast environments. Our solution provides a general client–server architecture for answering MCNNQ on objects with unknown, and possibly variable, movement types. Our solution enables the application of spatio-temporal access methods specifically designed for a particular type, to arbitrary movements without any false misses. Our algorithm does not require any conventional spatial index for MCNNQ processing. It can be adapted to static or moving objects, and does not require additional knowledge (e.g., direction of moving objects) beyond the maximum speed and the location of each object. Extensive experiments demonstrate that our location-based data dissemination algorithm significantly outperforms index-based solutions.

Energy Efficient Data Access in Mobile P2P Networks

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.

On QoS multicast routing algorithms using k-minimum Steiner trees

In this paper, we study how to obtain Steiner trees appropriately for efficient multicast routing. We first introduce a scheme for generating a new weighted multicast parameter by efficiently combining two independent measures: cost and delay. We call our proposal the Weighted Parameter for Multicast Trees (WPMT) algorithm. The WPMT can be adjusted by the weight @w@?[0,1]. For instance, if @w approaches 0, then the delay of the multicast tree may be relatively lower than the delay of other trees that are obtained as @w approaches 1. Otherwise, as the weight approaches 1 then the cost of the obtained tree may be relatively lower compared with other trees. A case study shows how to find an appropriate Steiner tree for each @w. The simulation results show that the use of the proposed WPMT produces results similar to the k-minimum Steiner tree algorithm. The WPMT can be applied to several existing multicast problems as we describe. We also propose several multicast algorithms using the WPMT in order to solve well-known multicast problems, and compare the proposed algorithms-based the WPMT with representative algorithms for the well-known problems.

A Hierarchical Grid Index (HGI), spatial queries in wireless data broadcasting

The main requirements for spatial query processing via mobile terminals include rapid and accurate searching and low energy consumption. Most location-based services (LBSs) are provided using an on-demand method, which is suitable for light-loaded systems where contention for wireless channels and server processing is not severe. However, as the number of users of LBSs increases, performance deteriorates rapidly since the servers’ capability to process queries is limited. Furthermore, the response time of a query may significantly increase with the concentration of users’ queries in a server at the same time. That is because the server has to check the locations of users and potential objects for the final result and then individually send answers to clients via a point-to-point channel. At this time, an inefficient structure of spatial index and searching algorithm may incur an extremely large access latency.To address this problem, we propose the Hierarchical Grid Index (HGI), which provides a light-weight sequential location-based index structure for efficient LBSs. We minimize the index size through the use of hierarchical location-based identifications. And we support efficient query processing in broadcasting environments through sequential data transfer and search based on the object locations. We also propose Top-Down Search and Reduction-Counter Search algorithms for efficient searching and query processing. HGI has a simple structure through elimination of replication pointers and is therefore suitable for broadcasting environments with one-dimensional characteristics, thus enabling rapid and accurate spatial search by reducing redundant data. Our performance evaluation shows that our proposed index and algorithms are accurate and fast and support efficient spatial query processing.

A partial index for distributed broadcasting in wireless mobile networks

To increase the scalability of location-based services (LBSs), researchers have begun to propose methods for processing spatial queries in wireless broadcast environments. In this paper, we examine the issue of spatial query processing within wireless broadcast systems and propose a distributed broadcast system (D-Bcast) model that can be applied to spatial query processing in such environments. Instead of receiving data directly from the server, the client in our proposed system model receives, via a subserver (Ssub), only the portion of the spatial data that has been requested by the querier, which reduces the broadcasting cycle and thus the access time. This distributed data broadcast model represents an efficient method of transmitting data to an arbitrary number of clients, including those that are moving across different main server (Smain) ranges. To implement this model, a hierarchical bitmap-based spatial index (HBI) employing a tree-based indexing scheme is proposed that divides the broadcast space into grids according to the distributions of objects. The grids are constructed such that an index tree for each grid can easily be configured. A full index of the broadcast program is constructed by applying the HBI to each Ssub, with the indexes of the Ssubs corresponding to the respective portions of the broadcast program used to broadcast data to the clients. In this manner, the total broadcasting cycle required to be read by the client is reduced and the query processing is decreased. Our proposed method is more effective than existing wireless broadcasting methods in reducing the location error that occurs when an update is induced by object movement. A cost model and experimental results are used to verify the efficacy of the proposed method.

An Efficient Adaptive Bitmap-based Selective Tuning Scheme for Spatial Queries in Broadcast Environments

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B?DSI) technique.

Efficient Data Access for Location-Dependent Spatial Queries

When the mobile environment consists of light-weight devices, the loss of network connectivity and scarce resources, e.g., low battery power and limited memory, become primary issues of concern in order to efficiently support portable wireless devices. In this paper, we propose an index-based peer-to-peer data access method that uses a new Hierarchical Location-Based Sequential (HLBS) index. We then propose a novel distributed Nearest First Broadcast (NFB) algorithm. Both HLBS and NFB are specifically designed for mobile peer-to-peer service in wireless broadcast environments. The system has a lower response time, because the client only contacts a qualified service provider by accessing the HLBS and quickly retrieves the data to answer the query by using NFB. HLBS and NFB design the index for spatial objects according to the positions of individual clients and transfer the index in the order arranged so that the spatial query can be processed even after the user tunes the partial index. Hence, this design can support rapid and energy-efficient service. A performance evaluation is conducted to compare the proposed algorithms with algorithms based on R-tree and Hilbert-curve air indexes. The results show that the proposed data dissemination algorithm with the HLBS index is scalable and energy efficient in both range queries and nearest neighbor queries.