Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor Networks

Anagnostopoulos, Christos; Hadjiefthymiades, Stathes; Kolomvatsos, Kostas

doi:10.1145/2882966

Cited by 22 publications

(14 citation statements)

References 71 publications

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“…Through this app, the spectral properties of the WGM sensors can be monitored in real time. As the key elements in wireless sensor networks (WSNs) 23 – 25 , sensor nodes should have the capability to collect sensing signals, perform signal analysis, and communicate with other sensor nodes or the gateway sensor node. The architecture of our wireless WGM sensor node is shown in Fig.…”

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confidence: 99%

Wireless whispering-gallery-mode sensor for thermal sensing and aerial mapping

Chen

Zhao

et al. 2018

Light Sci Appl

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The Internet of Things (IoT)1,2 employs a large number of spatially distributed wireless sensors to monitor physical environments, e.g., temperature, humidity, and air pressure, and has many applications, including environmental monitoring3, health care monitoring4, smart cities5, and precision agriculture. A wireless sensor can collect, analyze, and transmit measurements of its environment1,2. Currently, wireless sensors used in the IoT are predominately based on electronic devices that may suffer from electromagnetic interference in many circumstances. Being immune to the electromagnetic interference, optical sensors provide a significant advantage in harsh environments6. Furthermore, by introducing optical resonance to enhance light–matter interactions, optical sensors based on resonators exhibit small footprints, extreme sensitivity, and versatile functionalities7,8, which can significantly enhance the capability and flexibility of wireless sensors. Here we provide the first demonstration of a wireless photonic sensor node based on a whispering-gallery-mode (WGM) optical resonator, in which light propagates along the circular rim of such a structure like a sphere, a disk, or a toroid by continuous total internal reflection. The sensor node is controlled via a customized iOS app. Its performance was studied in two practical scenarios: (1) real-time measurement of the air temperature over 12 h and (2) aerial mapping of the temperature distribution using a sensor node mounted on an unmanned drone. Our work demonstrates the capability of WGM optical sensors in practical applications and may pave the way for the large-scale deployment of WGM sensors in the IoT.

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confidence: 99%

Wireless whispering-gallery-mode sensor for thermal sensing and aerial mapping

Chen

Zhao

et al. 2018

Light Sci Appl

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“…Context-aware applications, crowdsensing applications (Ganti et al 2011;Lane et al 2010), environmental monitoring (Oliveira and Rodrigues 2011), forest monitoring (Awang and Suhaimi 2007;Zervas et al 2011;Kang et al 2013;Anagnostopoulos et al 2016) (through unnamed vehicles), agriculture monitoring (Nittel 2009), road traffic monitoring, surveillance, video analytics (Simoens et al 2013), marine environment monitoring (Xu et al 2014), watershed monitoring systems (Eidson et al 2009;Nguyen et al 2010) over large-scale data streams require efficient, accurate and timely data analysis in order to facilitate (near) real-time decision-making, data stream mining, and situational context awareness (Kolomvatsos et al 2016).…”

Section: Motivationmentioning

confidence: 99%

“…In this class of edge analytics, e.g., Simonetto and Leus (2014), Kejela et al (2014) and Gemulla et al (2011), contextual data and/or model's meta-data are circulated within the edge network, which evidently requires energy for data and meta-data dissemination adding extra communication overhead. -Group-based centralized analytics This methodology refers to a group-based communication and single localized computation/processing scheme e.g., Anagnostopoulos et al (2012Anagnostopoulos et al ( , 2016; McConnell and Skillicorn (2005); Papithasri and Babu (2016); Manjeshwar and Agrawal (2001). Specifically, an EN is responsible for a group of SANs and maintains a set of historical contextual data of each SAN within the group.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive intelligence to the edge: impact on edge analytics

2017

Self Cite

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We rest on the edge computing paradigm where pushing processing and inference to the edge of the Internet of Things (IoT) allows the complexity of predictive analytics to be distributed into smaller pieces physically located at the source of the contextual information. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation and predictive modeling within the edge network. Our idea is based on the capability of the edge nodes to (1) monitor the evolution of the sensed time series contextual data, (2) locally determine (through prediction) whether to disseminate contextual data in the edge network or not, and (3) locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this on-line decision making, we eliminate data transfer at the edge of the network, thus saving network resources by exploiting the evolving nature of the captured contextual data. We provide comprehensive analytical, experimental and comparative evaluation of the proposed mechanism with other mechanisms found in the literature over real contextual datasets and show the benefits stemmed from its adoption in edge computing environments.

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“…More specifically phase I and phase II are identical to that of the RPP. In phase III where the GHs announce that they have detected phenomena (lines [14][15][16][17]. The sent message 'GH info -msg + phenomena flag + Z-value' has the GH information, a flag to inform that they have phenomena and the Zvalue of the GH (line 17).…”

Section: Rzo: Rzo Optimisation Strategymentioning

confidence: 99%

“…An approach for reducing network energy consumption by reducing the amount of transmitted data through compression is proposed by Chen et al [13]. Anagnostopoulos et al [14] deploy mobile sensor nodes close to the phenomena to attain high-quality monitoring. They used the particle swarm optimisation technique to calculate the new locations of the sensors.…”

Section: Introductionmentioning

confidence: 99%

Optimising energy in distributed phenomena detection for mobile WSN

Safia

Kamel

Aghbari

2020

IET wirel. sens. syst.

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We propose an energy-aware distributed scheme, general phenomena detection, to detect phenomena in data gathered from mobile sensors. In the proposed algorithm, mobile sensors self-organise themselves into groups and elect group heads (GHs) based on the location of the phenomena. To better share, the extra battery power overhead, GHs and subsequently group membership are updated periodically (every window). Each GH gathers readings from sensors within its group and processes the data to detect possible phenomena within its geographical boundaries. Then, GHs communicate with each other to discover and report global phenomena. To further reduce the energy cost, the study proposes three optimisation strategies: the first strategy, reporting by partial participation (RPP), limits the number of participating GHs in reporting the phenomena. RPP achieved a saving in the energy cost of around 50%. The second strategy, reporting by Z-order (RZO), provides an overall short communication path between GHs by using Z-order. RZO achieved a saving of more than 35% of the required energy. The study also proposes a lazy window update strategy that is suitable for a wireless sensor network (WSN) with slow sensor speed. The proposed solutions are validated via comprehensive experiments performed on an NS2 network simulator.

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Accurate, Dynamic, and Distributed Localization of Phenomena for Mobile Sensor Networks

Cited by 22 publications

References 71 publications

Wireless whispering-gallery-mode sensor for thermal sensing and aerial mapping

Wireless whispering-gallery-mode sensor for thermal sensing and aerial mapping

Predictive intelligence to the edge: impact on edge analytics

Optimising energy in distributed phenomena detection for mobile WSN

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