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

Abstract

We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to 'opportunistically' modify the previous placement of nodes to attain high quality phenomena monitoring. The required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the paper (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.

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

“…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.…”

Predictive intelligence to the edge: impact on edge analytics

“…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).…”

“…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.…”

Optimising energy in distributed phenomena detection for mobile WSN

“…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).…”

“…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.…”