Collective acoustic localization in a network of dual channel low power devices

Astapov, Sergei; Ehala, Johannes; Preden, Jürgo-Sören

doi:10.1109/mixdes.2014.6872235

Cited by 5 publications

(4 citation statements)

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“…2. Digital or analog sensors catch the pertinent signals at fixed intervals ranging from a few seconds in ecological monitoring [13] down to a few milliseconds in vibration-based secondary health checking [14] or even beneath in acoustic restriction applications [15]. Before processing or transmission sampled data should be stored in the memory module.…”

Section: Related Workmentioning

confidence: 99%

High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

Venkatesan¹,

Ramadass²

2023

Computer Systems Science and Engineering

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Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years. Nodes must advance their energy use for expanding network lifetime. The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results. Due to different network resource constraints and malicious attacks, security assurance in wireless sensor networks has been a difficult task. The implementation of these features requires larger space due to distributed module. This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission. The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment. The proposed system supports the real-time application in the range of 33 nanoseconds. The obtained results have been compared with the existing Microcontroller-based system. The power consumption of the proposed system consumes only 3.9 mW, and it is only 24% percentage of AT mega-based node architecture.

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Section: Related Workmentioning

confidence: 99%

High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

Venkatesan¹,

Ramadass²

2023

Computer Systems Science and Engineering

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“…Applications that benefit from those methods include target tracking and shooter localisation for instance. 30 Classification of objects based on assessing characteristics of emitted sound signals is another well-studied signal processing area and its application in WSNs for low computing power devices is an emerging trend. [31][32][33] Until a short time ago, wireless sensor nodes were not powerful enough to perform feature extraction from measured signals and to run conventional classification algorithms.…”

Section: Signal Processing In Wsn Sensor Nodesmentioning

confidence: 99%

Situation awareness via Internet of things and in-network data processing

Ehala

Kaugerand

Pahtma

et al. 2017

International Journal of Distributed Sensor Networks

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Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

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“…Also the requirements and capabilities of the motes vary strongly between different applications and even within a certain network, the architecture of a typical WSN mote consists of the five generic units shown in Figure 3. Analog or digital sensors ( Figure 3a) capture the relevant signals at sampling periods ranging from several seconds in environmental monitoring [Lazarescu2013] down to several milliseconds in vibrationbased structural health monitoring [Battista2013] or even below in acoustic localization applications [Astapov2014].…”

Section: Wireless Sensor Network and Wireless Sensor Node Architecturesmentioning

confidence: 99%

“…First, data sampled from spatially distributed sensors cannot be properly interpreted without knowledge of the exact sampling time. The acceptable uncertainty is typically a small percentage of the application's sampling period, which may range from several seconds in environmental monitoring [Lazarescu2013] down to several milliseconds in vibrationbased structural health monitoring (Section 7.3), or even shorter in acoustic localization applications [Astapov2014]. Thus, synchronization protocols with an accuracy of a few microseconds are required for the more demanding applications.…”

Section: Precise Wireless Time Synchronizationmentioning

confidence: 99%

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

Engel

Koch

Siebel

2015

2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops)

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Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0 Erklärung zur DissertationHiermit versichere ich, die vorliegende Dissertation ohne Hilfe Dritter nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus Quellen entnommen wurden, sind als solche kenntlich gemacht. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen. Darmstadt, den 22.11.2016 (Andreas Engel) AbstractWireless Sensor Networks (WSNs) combine embedded sensing and processing capabilities with a wireless communication infrastructure, thus supporting distributed monitoring applications. WSNs have been investigated for more than three decades, and recent social and industrial developments such as home automation, or the Internet of Things, have increased the commercial relevance of this key technology. The communication bandwidth of the sensor nodes is limited by the transportation media and the restricted energy budget of the nodes. To still keep up with the ever increasing sensor count and sampling rates, the basic data acquisition and collection capabilities of WSNs have been extended with decentralized smart feature extraction and data aggregation algorithms. Energy-efficient processing elements are thus required to meet the ever-growing compute demands of the WSN motes within the available energy budget.The Hardware-Accelerated Low Power Mote (HaLoMote) is proposed and evaluated in this thesis to address the requirements of compute-intensive WSN applications. It is a heterogeneous system architecture, that combines a Field Programmable Gate Array (FPGA) for hardware-accelerated data aggregation with an IEEE 802.15.4 based Radio Frequency System-on-Chip for the network management and the top-level control of the applications. To properly support Dynamic Power Management (DPM) on the HaLoMote, a Microsemi IGLOO FPGA with a non-volatile configuration storage was chosen for a prototype implementation, called Hardware-Accelerated Low Energy Wireless Embedded Sensor Node (HaLOEWEn). As for every multi-processor architecture, the inter-processor communication and coordination strongly influences the efficiency of the HaLoMote. Therefore, a generic communication framework is proposed in this thesis. It is tightly coupled with the DPM strategy of the HaLoMote, that supports fast transitions between active and idle modes. Low-power sleep periods can thus be scheduled within every sampling cycle, even for sampling rates of hundreds of hertz.In addition to the development of the heterogeneous system architecture, this thesis focuses on the energy consumption trade-off between wireless data transmission and insensor data aggregation. The HaLOEWEn is compared with typical software processors in terms of runtime and energy efficiency in the context of three monitoring applications. The building blocks of these applications comprise hardware-acce...

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Collective acoustic localization in a network of dual channel low power devices

Cited by 5 publications

References 5 publications

High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

Situation awareness via Internet of things and in-network data processing

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

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