Energy Consumption and Latency Analysis for Wireless Multimedia Sensor Networks

Abstract

Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks.

Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes

“…This approach opens up a new design exploration dimension where image processing may be local, partially local or totally remote. In their work, Pinto et al [18] analyzed three scenarios to find out when it is best to send data and when to process them locally. Khursheed et al [19] investigated the partition between hardware (Field-Programmable Gate Array or FPGA) and software (micro-controller) but also between local and central processing in order to achieve an optimal partition point that guarantees a minimum energy consumption in the camera node.…”

A Survey of Energy-Efficient Communication Protocols with QoS Guarantees in Wireless Multimedia Sensor Networks

“…However, it is shown in [5] that the energy used for computation in WMSNs is intrinsically high. For example, in a simple vehicle tracking application, the energy for frame capture and processing can reach up to 12% of the overall energy consumption in the detection of an event [61]. Consequently, WMSNs must try to utilize the more energy-efficient vision processing algorithms, such as energy-efficient image processing [26,27,28,29,30,31] and energy-efficient video compressing [32,33,34].…”

“…Meanwhile, possible in-network data storage solutions can also be used to limit the necessity of data transmissions. Local Processing: Local processing refers to using onboard image analysis techniques to extract useful imagery components for the description of events of interest [59,60,61]. Depending on the intelligence of algorithms, local processing can be categorized into different levels [60], in which the required bandwidth decreases with the increase of algorithm intelligence and level of inference, as shown in Figure 2.…”

“…Depending on the intelligence of algorithms, local processing can be categorized into different levels [60], in which the required bandwidth decreases with the increase of algorithm intelligence and level of inference, as shown in Figure 2. One example is the large vehicle detection scenario described in [61]. Instead of streaming raw video data, a camera sensor in its basic setting transmits a whole frame to the sink whenever motion is detected.…”