Balancing computational and transmission power consumption in wireless image sensor networks
18 Jul 2005-pp 6
TL;DR: This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required and tries to find the best compression ratio able to give a significant reduction of transmission time.
Abstract: The paper presents a methodology to reduce the energy consumption of visual sensor node in wireless sensor networks. This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required. Considering that in a sensor node, the communication task is the most energy consumption, the proposed method tries to find the best compression ratio able to give a significant reduction of transmission time, returning adequate and objective image quality and compression time. Some results are presented applying the proposed methodology to the particular case study: the use of wireless visual sensors to the remote metering of water counters.
Citations
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07 Jul 2009
TL;DR: An overview of the current state-of-the-art in the field of visual sensor networks is provided, by exploring several relevant research directions to provide a better understanding of current research problems in the different research fields ofVisual sensor networks.
Abstract: Visual sensor networks have emerged as an important
class of sensor-based distributed intelligent systems,
with unique performance, complexity, and quality of service
challenges. Consisting of a large number of low-power camera nodes,
visual sensor networks support a great number of novel
vision-based applications. The camera nodes provide information
from a monitored site, performing distributed and collaborative
processing of their collected data. Using multiple cameras in the
network provides different views of the scene, which enhances
the reliability of the captured events. However, the large amount
of image data produced by the cameras combined with the
network's resource constraints require exploring new means
for data processing, communication, and sensor management.
Meeting these challenges of visual sensor networks requires
interdisciplinary approaches, utilizing vision processing, communications
and networking, and embedded processing. In this
paper, we provide an overview of the current state-of-the-art in
the field of visual sensor networks, by exploring several relevant
research directions. Our goal is to provide a better understanding
of current research problems in the different research fields of
visual sensor networks, and to show how these different research
fields should interact to solve the many challenges of visual sensor
networks.
477 citations
TL;DR: An ultra-low power 128 times 64 pixels vision sensor is here presented, featuring pixel-level spatial contrast extraction and binarization, and the pixel-embedded binary frame buffer allows the sensor to directly process visual information, such as motion and background subtraction, which are the most useful filters in machine vision applications.
Abstract: An ultra-low power 128 times 64 pixels vision sensor is here presented, featuring pixel-level spatial contrast extraction and binarization. The asynchronous readout only dispatches the addresses of the asserted pixels in bursts of 80 MB/s, significantly reducing the amount of data at the output. The pixel-embedded binary frame buffer allows the sensor to directly process visual information, such as motion and background subtraction, which are the most useful filters in machine vision applications. The presented sensor consumes less than 100 muW at 50 fps with 25% of pixel activity. Power consumption can be further reduced down to about 30 muW by operating the sensor in Idle-Mode, thus minimizing the sensor activity at the ouput.
81 citations
Cites background from "Balancing computational and transmi..."
...2017000 the node life-time is a critical figure of merit of the system [10]–[14]....
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TL;DR: DCT and DWT compression techniques are analyzed and implemented using TinyOS on a hardware platform TelosB and experimental results show that the overall performance of DWT is better than DCT, and DCT provides better compression ratio than DWT.
74 citations
Cites background from "Balancing computational and transmi..."
...The work presented in [13], is an attempt to propose an algorithm that is energy efficient for the image compression....
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TL;DR: This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery.
Abstract: Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks.
61 citations
Cites background from "Balancing computational and transmi..."
...The work in [37] presents some execution and compression characteristics and discuss energy consumption in visual sensors....
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TL;DR: This article introduces a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm that increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity.
Abstract: Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.
60 citations
References
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TL;DR: The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections.
Abstract: The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.
14,048 citations
TL;DR: The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security, and opportunities depend on development of a scalable, low-cost, sensor-network architecture.
Abstract: W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system
3,415 citations
"Balancing computational and transmi..." refers background in this paper
...In the most of them is demonstrated that the goal of minimizing the total energy consumption can be reached through a reduction of the communication burden, since it requires more power (energy/time) than the computational one [ 5 ]....
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TL;DR: This article presents a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network.
Abstract: This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.
1,820 citations
"Balancing computational and transmi..." refers background in this paper
...Many contributions can be found in the scientific and technical literature dealing with the energy saving problem in the wireless sensor networks, and either hardware or software solutions are proposed [2-4]....
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TL;DR: Although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques, and a graphical measure called Hosaka plots can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.
Abstract: A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.
1,660 citations
Book•
29 Oct 1993
TL;DR: The invention relates to a spark plug tightener with a cylindrical housing having on one end a multi-faceted opening for engaging the spark plug and on the other end an annular shaped profile in front view through which passes a turning shaft.
Abstract: The invention relates to a spark plug tightener with a cylindrical housing having on one end a multi-faceted opening for engaging the spark plug and on the other end an annular shaped profile in front view through which passes a turning shaft with a corresponding profile in front view whereby the turning shaft is held under the tension of cup springs by means of a cover cap screwed over the housing.
630 citations