Abstract
Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: (1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; (2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; (3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.
Highlights
Over the past two decades, wireless sensor networks (WSN) have become one of the most promising technologies with integrated microprocessors, low-power analog and digital electronics, and advances in wireless communication
As a result, distributed systems with more powerful sensor nodes are introduced as Wireless Multimedia Sensor Networks (WMSN) [3], [4]
In the context of this introduction, the present study aims at developing a new approach to WMSNs proposing an effective solution to the mentioned problems
Summary
Over the past two decades, wireless sensor networks (WSN) have become one of the most promising technologies with integrated microprocessors, low-power analog and digital electronics, and advances in wireless communication. One of the main contributions of this study is to capture and process visual and audio data in addition to scalar sensor data, and fuse all of them to accurately detect and recognize objects in the monitored area of the WMS nodes. An object extraction method using data fusion at three different layers is developed by collecting pre-fused data from the sensor nodes to the base station, which extends the wireless sensor network lifetime by reducing the amount of data to be transmitted over the network In this context, the data obtained from the PIR, vibratory and acoustic sensors are used at the first layer. In the context of second-layer fusion, image and audio data undergoes a fusion process to increase the accuracy of object classification After these operations on the sensor node, the generated synthesis information is transmitted to the base station via the Zigbee wireless protocol.
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