12137496
Analyzing Object Detection Quality Under Probabilistic Coverage in Sensor Networks
Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Probabilistic coverage is an appropriate approach to balancing the conflicting design requirements of monitoring applications. Under probabilistic coverage, we present an analytical model to analyze object detection quality with respect to different network conditions and sensor scheduling schemes. Our analytical model facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Applying the model to real sensor networks, we design a set of sensing scheduling protocols to achieve targeted object detection quality while minimizing power consumption. The correctness of our model and the effectiveness of the proposed protocols are validated through extensive simulation experiments.
1740828
Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks
Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments
17699208
A study on object tracking quality under probabilistic coverage in sensor networks
Sensor networks are used for a wide range of object tracking applications, such as vehicle tracking in military surveillance and wild animal tracking in habitat monitoring [1]. These applications, by their nature, enforce certain tracking quality and lifetime requirements. These two requirements, however, are two conflicting optimization goals due to the stringent energy constraints of sensor nodes.
1284934
Online power-aware routing in wireless Ad-hoc networks
This paper discusses online power-aware routing in large wireless ad-hoc networks for applications where the message sequence is not known. We seek to optimize the lifetime of the network. We show that online power-aware routing does not have a constant competitive ratio to the off-line optimal algorithm. We develop an approximation algorithm called max-min zPmin that has a good empirical competitive ratio. To ensure scalability, we introduce a second online algorithm for power-aware routing. This hierarchical algorithm is called zone-based routing. Our experiments show that its performance is quite good.
10818971
Efficient Implementation of Public Key Cryptosystems on Mote Sensors (Short Paper)
We report our implementation of the RSA and ECC public-key cryptosystem on Berkeley Motes. We detail the implementation of 1024-bit RSA and 160-bit ECC cryptosystems on MICA mote sensors. We have achieved the performance of 0.79s for RSA public key operation and 21.5s for private operation, and 1.3s for ECC signature generation and 2.8s for verification. For comparison, we also show our new ECC implementation on TelosB motes with a signature time 1.60s and a verification time 3.30s. For the detailed description of the implementation, we refer to our technical report [13].
8816096
Distributed User Access Control in Sensor Networks
User access control in sensor networks defines a process of granting user the access right to the information and resources. It is essential for the future real sensor network deployment in which sensors may provide users with different services in terms of data and resource access. A centralized access control mechanism requires base station to be involved whenever a user requests to get authenticated and access the information stored in the sensor node, which is inefficient, not scalable, and is exposed to many potential attacks along the long communication path. In this paper, we propose a distributed user access control under a realistic adversary model in which sensors can be compromised and user may collude. We split the access control into local authentication conducted by the sensors physically close to the user, and a light remote authentication based on the endorsement of the local sensors. Elliptic Curve Cryptography (ECC), a public key cryptography scheme, is used for local authentication. We implement the access control protocols on a testbed of TelosB motes. Our analysis and experimental results show that our scheme is feasible for real access control requirement.
12908496
Spatial interest pixels (SIPs): useful low-level features of visual media data
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.
3013042
Feature extraction via generalized uncorrelated linear discriminant analysis
Feature extraction is important in many applications, such as text and image retrieval, because of high dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) was recently proposed for feature extraction. The extracted features via ULDA were shown to be statistically uncorrelated, which is desirable for many applications. In this paper, we will first propose the ULDA/QR algorithm to simplify the previous implementation of ULDA. Then we propose the ULDA/GSVD algorithm, based on a novel optimization criterion, to address the singularity problem. It is applicable for undersampled problem, where the data dimension is much larger than the data size, such as text and image retrieval. The novel criterion used in ULDA/GSVD is the perturbed version of the one from ULDA/QR, while surprisingly, the solution to ULDA/GSVD is shown to be independent of the amount of perturbation applied. We did extensive experiments on text and face image data to show the effectiveness of ULDA/GSVD and compare with other popular feature extraction algorithms.
8351252
Using object deputy model to prepare data for data warehousing
Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.
3052468
Performance Modeling and Analysis for Resource Scheduling in Data Grids
Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.
1016063
A comparative study on content-based music genre classification
Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.