5184285
Design and Analysis of Spatiotemporal Multicast Protocols for Wireless Sensor Networks
AbstractWe propose a new multicast communication paradigm called “spatiotemporal multicast” for supporting applications which require spatiotemporal coordination in wireless sensor networks. In this paper we focus on a special class of spatiotemporal multicast called “mobicast” featuring a message delivery zone that moves at a constant velocity 
$$\vec v$$
. The key contributions of this work are: (1) the specification of mobicast and its performance metrics, (2) the introduction of four different mobicast protocols along with the analysis of their performance, (3) the introduction of two topological network compactness metrics for facilitating the design and analysis of spatiotemporal protocols, and (4) an experimental evaluation of compactness properties for random sensor networks and their effect on routing protocols.
7611602
Spatiotemporal multicast in sensor networks
Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper.
1071507
On greedy geographic routing algorithms in sensing-covered networks
Greedy geographic routing is attractive in wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may incur long routing paths or even fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks that provide sensing coverage over a geographic area (e.g., surveillance or object tracking systems). Our geometric analysis and simulation results demonstrate that existing greedy geographic routing algorithms can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under greedy geographic routing algorithms. Furthermore, we propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that allows sensing-covered networks to achieve an asymptotic network dilation lower than 4:62 as long as the communication range is at least twice the sensing range. Our results show that simple greedy geographic routing is an effective routing scheme in many sensing-covered networks.
6194551
Algorithms for spatial outlier detection
A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. We propose a suite of spatial outlier detection algorithms to overcome this disadvantage. We formulate the spatial outlier detection problem in a general way and design algorithms which can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approaches can not only avoid detecting false spatial outliers but also find true spatial outliers ignored by existing methods.
1234945
Spatial Databases - Accomplishments and Research Needs
Spatial databases, addressing the growing data management and analysis needs of spatial applications such as geographic information systems, have been an active area of research for more than two decades. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and field data, as well as query processing (e.g., cost models, bulk load). Another important need is to apply spatial data management accomplishments to newer applications, such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and associated research needs of the near term.
2968463
Making Data Mining Models Useful to Model Non-paying Customers of Exchange Carriers
Due to both limitations of technologies and the nature of the problems, data mining may not be able to solve a problem completely in a way as one wishes. When this happens, we need to first understand the actual need of business, characteristic of available partial solution, and then make compromises between the technology solution and business needs. A majority of the papers published in data mining conferences and journals seem to concentrate only on the success side of the story. In this paper, we discuss our experiences and the complete process from near failure to success when applying inductive learning techniques to predict non-paying customers of competitive local exchange carriers (CLEC’s), currently at 20%. Experiments with a number of stateof-the-art methods and algorithms found that most customers were labeled as paying on time. Cost-sensitive learning is not possible since the target company cannot define a cost-model. Finally, after discussing with the billing department, a compromised but still useful solution is to predict the probability that someone will default. The billing team can use the predicted score to prioritize collection efforts as well as to predict cash flow. We have found that two randomized decision tree ensemble methods (Fan’s random decision tree and a probabilistic extension of Breiman’s random forest) are consistently more accurate in posterior probability estimation than single decision tree based probability calibration methods. The software, both Fan’s RDT and probabilistic extension of random forest, as well as a longer version of this paper will be made available by the contacting author.
17196005
Analysis of payment transaction security in mobile commerce
Mobile payment is the process of two parties exchanging financial value using a mobile device in return for goods or services. This paper is an analysis of the security issues in mobile payment for m-commerce. We introduce m-commerce and mobile payment, discuss the public key infrastructure as a basis for secure mobile technologies, and study the features for different security technologies employed in current m-commerce market, including WAP, SIM application toolkit and J2M. In addition, we compare the effectiveness of these security technologies in supporting a secure mobile payment, and discuss research issues to enhance the security of mobile payment for large scale deployment of m-commerce.
6432483
Evaluating GML support for spatial databases
This work presents a study on Geography Markup Language (GML), the issues that arise from using GML for spatial databases and solutions that have been proposed. We concentrate on three aspects of GML, including storage, parsing, and querying. GML is an XML encoding for storing geographic data. It has been developed by the OpenGIS as a medium for uniform geographic data storage and exchange among diverse applications. The underlying concepts of XML therefore can also be applied to GML data. This results in both advantages and disadvantages, which are discussed In the work. GML is a comparatively new language in the field of geographic information systems and still in its developmental stage. Most of the data processing techniques need to be further developed in order for GML to be an efficient medium for geographic data storage and processing.