Emergent wireless sensor network limitations: a plea for advancement in core technologies

Perkins, M.; Correal, N.S.; O'Dea, B.

doi:10.1109/icsens.2002.1037345

Cited by 31 publications

(17 citation statements)

References 3 publications

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“…Dramatic advances in RF and MEMS IC design have made possible the use of large networks of wireless sensors for a variety of new monitoring and control applications [1]- [5]. For example, smart structures will actively respond to earthquakes and make buildings safer; precision agriculture will reduce costs and environmental impact by watering and fertilizing only where necessary and will improve quality by monitoring storage conditions after harvesting; condition-based maintenance will direct equipment servicing exactly when and where it is needed based on data from wireless sensors; traffic monitoring systems will better control stoplights and inform motorists of alternate routes in the case of traffic jams; and environmental monitoring networks will sense air, water, and soil quality and identify the source of pollutants in real time.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Locating the nodes: cooperative localization in wireless sensor networks

Patwari¹,

Ash

Kyperountas

et al. 2005

IEEE Signal Process. Mag.

2,662

1,789

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ccurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. Low-power wireless sensors may be many hops away from any other sensors with a priori location information.In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then form a map of the network. Various application requirements (such as scalability, energy efficiency, and accuracy) will influence the design of sensor localization systems. In this article, we describe measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, we show how to calculate a Cramér-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. This is a useful tool to help system designers and researchers select measurement technologies and evaluate localization algorithms. We also briefly survey a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research. INTRODUCTIONDramatic advances in RF and MEMS IC design have made possible the use of large networks of wireless sensors for a variety of new monitoring and control applications [1]- [5]. For example, smart structures will actively respond to earthquakes and make buildings safer; precision agriculture will reduce costs and environmental impact by watering and fertilizing only where necessary and will improve quality by monitoring storage conditions after harvesting; condition-basedA maintenance will direct equipment servicing exactly when and where it is needed based on data from wireless sensors; traffic monitoring systems will better control stoplights and inform motorists of alternate routes in the case of traffic jams; and environmental monitoring networks will sense air, water, and soil quality and identify the source of pollutants in real time.Automatic localization of the sensors in these wireless networks is a key enabling technology. The overwhelming reason is that a sensor's location must be known for its data to be meaningful. As an additional motivation, sensor location information (if it is accurate enough) can be extremely useful for scalable, "geographic" routing algorithms. Note also that location itself is often the data that needs to be sensed; localization can be the driving force for wireless sensor networks in applications such as warehousing and manufacturing logistics.To make these applications viable with possibly vast numbers of sensors, device costs will need to be low (from a few dollars to a few cents depending on the application), sensors will need to last for years or even decades ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Locating the nodes: cooperative localization in wireless sensor networks

Patwari¹,

Ash

Kyperountas

et al. 2005

IEEE Signal Process. Mag.

2,662

1,789

View full text Add to dashboard Cite

show abstract

“…These systems enable the reliable monitoring of a variety of environments for applications that include home security, machine failure diagnosis, Chemical biological detection, medical monitoring, security surveillance [2], environmental monitoring [3], and target detection/tracking, [4,5], precision agriculture, where 978-1-4799-8081-9/15/$31.00 © 2015 IEEE sensors monitor the spatial varlatlOn in soil and crop conditions [6,7] to non-invasive habitat monitoring [8] involve a large number of sensors distributed in a vast geographical area. Many other military and commercial applications, such as forest fire monitoring, inventory control, and structural monitoring are given in the survey papers [9,10]. Multimedia applications in Wireless Sensor Networks (WSN) require different approaches with respect to traditional networks due to the limitations in energy consumption and computational capabilities [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive beamforming using neural network and fuzzy logic model for measurement data fusion

Anitha¹,

Kurahatti²

2015

2015 International Conference on Communications and Signal Processing (ICCSP)

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In the information technology age, multisource multi sensor information fusion encompasses the theory , methods and tools conceived and used for exploiting synergy in the information acquired from multiple sources databases, sensors.In Multi-sensor data fusion technology, array processing is an area of signal processing that has powerful tools for extracting information from signals collected using an array of sensors. A sensor array captures spatially propagating signals arriving from a certain direction and processes them to obtain useful information. To this end, we intend to linearly combine the signals from all the sensors with coefficients in a manner, so as to estimate transmitted data radiating from a particular direction.Current methods to calculate the weight coefficients are complex. simple and effective weight coefficient calculation method is considered and which is based on where the best features of neural network and fuzzy logic is combined. This technique is based on the optimum beamformer and makes it robust to an faulty estimate of the Direction Of Arrival (DOA) even when powerful interferences are within the uncertainty range of the desired source or contact. The new modified beamformer algorithm is the product of an effort to provide more efficient procedure for real time implementation and a better estimate of the position and spectrum of the contact which is helpful in measurement data fusion or localization.

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“…This resource constrained nature of wireless sensor network imparts various challenges like maximizing lifetime of nodes, heterogeneity and scalability of the sensor nodes, responsiveness, energy efficiency, robustness, self adaptation and configuration of sensor nodes etc. [4].…”

Section: Introductionmentioning

confidence: 99%

Congestion Controlled WSN using Genetic Algorithm with different Source and Sink Mobility Scenarios

Verma¹,

Mittal²

2014

IJCA

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Wireless Sensor Networks are extremely densely populated and have to handle large bursts of data during high activity periods giving rise to congestion which may disrupt normal operation. It usually occurs when most of the data packets follow one route to reach from source to destination. Thus, there is a need of some new approach which could control congestion to meet increasing traffic demand and improved utilization of existing resources. Chance of congestion increases when both source and sink node are mobile. Due to mobility of source or sink, there is a need of determining optimal path every time when source or sink changes its position. So selection of optimal path is necessary in order to mitigate chance of congestion in the network. This paper employs new genetic algorithm based approach to determine an optimal path from source to destination for different scenarios of source or/and sink node mobility. Concept of connection value and localization region has been employed to determine an optimal path each time the data packet is being sent. An optimal path is the path that has minimum number of connections. In order to send the data packet from source to destination, there is requirement of genetic algorithm that automatically controls congestion. Simulations are performed for different scenarios of source or/and sink mobility. Significant improvements have been observed in terms of congestion value for genetic algorithm. Simulations results determine best route with minimum connection value by incorporating genetic algorithm.

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Emergent wireless sensor network limitations: a plea for advancement in core technologies

Cited by 31 publications

References 3 publications

Locating the nodes: cooperative localization in wireless sensor networks

Locating the nodes: cooperative localization in wireless sensor networks

Adaptive beamforming using neural network and fuzzy logic model for measurement data fusion

Congestion Controlled WSN using Genetic Algorithm with different Source and Sink Mobility Scenarios

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