Sensor network has given rise to a comprehensive view of various environments through their harnessed data. Usage of the data is, however, limited since the current sensor network paradigm promotes isolated networks that are statically tasked. In recent years, users have become mobile entities that require constant access to data for efficient processing. Under the current limitations of sensor networks, users would be restricted to using only a subset of the vast amount of data being collected, depending on the networks they are able to access. Through reliance on isolated networks, proliferation of sensor nodes can easily occur in any area that has high appeals to users. Furthermore, support for dynamic tasking of nodes and efficient processing of data is contrary to the general view of sensor networks as subject to severe resource constraints. Addressing the aforementioned challenges requires the deployment of a system that allows users to take full advantage of data collected in the area of interest to their tasks. In light of these observations, we introduce a hardware-overlay system designed to allow users to efficiently collect and utilize data from various heterogeneous sensor networks. The hardware-overlay takes advantage of FPGA devices and the mobile agent paradigm in order to efficiently collect and process data from cooperating networks. The computational and power efficiency of the system are herein demonstrated.
[Show abstract][Hide abstract] ABSTRACT: Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.
IEEE Internet Computing 08/2008; 12(4):78-83. DOI:10.1109/MIC.2008.87 · 1.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences
[Show abstract][Hide abstract] ABSTRACT: WSNs typically comprise of sensing nodes with limited computational capability and onboard power. The sensed data in a WSN is transmitted from an individual node to a network sink in a multi-hop fashion. Since transmission costs are often several orders of magnitude larger than computational costs, the efficiency of the WSN can be improved by in-network data aggregation techniques. This is, however, problematic because the aggregation to be performed depends on the requirements of the end user/application, and is either unknown at the time of deployment or changes over time. Implementation of fixed aggregation algorithms limits the utility of the network. Software-based implementation of dynamic aggregation techniques offers the required flexibility but has significant processing overhead, especially when the size of the network increases. In this paper, we reduce the processing overhead by implementing dynamic data aggregation using reconfigurable cluster heads (RCHs) based on Field Programmable Gate Arrays (FPGAs). Such an implementation provides the necessary flexibility in data aggregation techniques demanded by real-time applications, while resulting in significant reduction in the query processing time and the overall power consumption in the network. The objective of the paper is to address the performance improvement in Wireless Sensor Networks (WSNs) through the use of reconfigurable cluster heads. Our results demonstrate that different data aggregation algorithms can be dynamically and efficiently implemented on the RCHs in run-time. The proposed approach is a crucial first-step towards the implementation of programmable WSNS.
International Journal of Distributed Sensor Networks 01/2008; 4:194-212. DOI:10.1080/15501320802001234 · 0.67 Impact Factor
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