Energy Consumption Models for Smart Camera Networks

SanMiguel, Juan C.; Cavallaro, Andrea

doi:10.1109/tcsvt.2016.2593598

Cited by 23 publications

(17 citation statements)

References 46 publications

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“…The image compression algorithm (SVD) in the literature [8] is used to process the image compression task. The energy consumption model of the data processing in the literature [44], [45] is selected. e c,j = NC T V 2 dd (28) In formula (25), N is the number of clock cycles to complete an image compression subtask.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Mobile Wireless Multimedia Sensor Networks Image Compression Task Collaboration Based on Dynamic Alliance

Liu

et al. 2020

IEEE Access

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In mobile wireless multimedia sensor networks' image compression task collaborations, the existing methods do not consider the dynamic changes in the processing ability and the locations of the cooperative nodes. when processing the image compression tasks, these methods will cause frequent interruptions and result in data re-transmission for those tasks. in this paper, an image compression task collaboration algorithm based on dynamic alliance is proposed to solve this problem in mobile wireless multimedia sensor networks. first, a dynamic task alliance is established by the camera node based on the location, computing capability and resource usage of ordinary nodes. then, the location and average moving velocity of the camera nodes and ordinary nodes are considered to calculate the task stable execution time. the image compression task is divided into an image transfer sub task and an image compression sub task based on the task stable execution time. finally, an image compression task collaboration allocation optimization model is established according to the transmission time, the execution time, the execution cost, and the network energy consumption. the gradient method is used to realize the cooperative allocation of image compression tasks. simulation results show that the proposed algorithm can realize task load balancing, reduce the execution time, and network energy consumption. INDEX TERMS Mobile multimedia sensor networks, image compression, task coordination, task decomposition.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Mobile Wireless Multimedia Sensor Networks Image Compression Task Collaboration Based on Dynamic Alliance

Liu

et al. 2020

IEEE Access

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“…There is a wide range of communication technologies that could be suitable for IoT systems. However, the suitability of a technology mainly depends on the constraints of the specific user case [29][30][31][32]. In the case of battery-operated smart camera systems, these constraints are energy consumption; relatively high data amount compared to traditional IoT systems with low duty cycle; and real-time performance.…”

Section: Communication Technologiesmentioning

confidence: 99%

“…The latter two result in high data rate requirements, if nodes exchange raw image data. Despite the need for high data rates, previous approaches on smart camera systems also considered traditional communication for wireless sensor networks such as Bluetooth Low Energy (BLE) or, 802.15.4 with rather low data rates [29,33]. Therefore, we introduce the following categories for communication technologies.…”

Section: Communication Technologiesmentioning

confidence: 99%

Communication and computation inter-effects in people counting using intelligence partitioning

Shallari

Krug

O’Nils

2020

J Real-Time Image Proc

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The rapid development of the Internet of Things is affecting the requirements towards wireless vision sensor networks (WVSN). Future smart camera architectures require battery-operated devices to facilitate deployment for scenarios such as industrial monitoring, environmental monitoring and smart city, consequently imposing constraints on the node energy consumption. This paper provides an analysis of the inter-effects between computation and communication energy for a smart camera node. Based on a people counting scenario, we evaluate the trade-off for the node energy consumption with different processing configurations of the image processing tasks, and several communication technologies. The results indicate that the optimal partition between the smart camera node and remote processing is with background modelling, segmentation, morphology and binary compression implemented in the smart camera, supported by Bluetooth Low Energy (BLE) version 5 technologies. The comparative assessment of these results with other implementation scenarios underlines the energy efficiency of this approach. This work changes pre-conceptions regarding design space exploration in WVSN, motivating further investigation regarding the inclusion of intermediate processing layers between the node and the cloud to interlace low-power configurations of communication and processing architectures.

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“…The proposed energy consumption model is based on the power and activation times of a three-state model [25]. Cluster nodes, in active state, always have their cameras and radio on to acquire and process information data about the target and transmit or receive data between themselves and their neighbours, which results in most of the energy consumption.…”

Section: Problem Formulation and System Modelsmentioning

confidence: 99%

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

Cheng

Tang

et al. 2017

Sensors

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In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

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Energy Consumption Models for Smart Camera Networks

Cited by 23 publications

References 46 publications

Mobile Wireless Multimedia Sensor Networks Image Compression Task Collaboration Based on Dynamic Alliance

Mobile Wireless Multimedia Sensor Networks Image Compression Task Collaboration Based on Dynamic Alliance

Communication and computation inter-effects in people counting using intelligence partitioning

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

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