Selective wakeup discipline for continuous object tracking in grid-based wireless sensor networks

Park, Hosung; Oh, Seungmin; Lee, Euisin; Park, Soochang; Kim, Sang-Ha; Lee, Wanseop

doi:10.1109/wcnc.2012.6214154

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…GAS [38] and COTS [39] similarly construct proactive virtual clusters which means a grid structure calculated with only location information. Each sensor node could know its own cluster by calculation with its own location, a reference point, and a cluster size without communication among sensor nodes.…”

Section: Phase Ii: Integrationmentioning

confidence: 99%

“…Therefore, TG-COD, TGM-COT, GAS, and COTS do not suffer from communication overhead for cluster construction and maintenance. Figure 4 and Table 2 TG-COD [30] CODA [34] GAS [38] COTS [39] PRECO [35] EUCOW [27] / PM-COT [32] COBOM [26] MCHD [40] BFA [37] FPOD [36] BTS-COT [33] / TPE-FTED [29] TGM-COT [31] DeGas [7] / GLDS [28] BRTCO [6] [25] COBOM [26], EUCOW [34], PRECO [35], FPOD [36], BTS-COT [33], PM-COT [32], TPE-FTED [29] CODA [34], GAS [38], COTS [39], BRTCO [6] TG-COD [30], MCHD [40], BFA [37], TGM-COT [31], DeGas [7], GLDS [28] # Dual prediction techniques that are proposed for abbreviation in IMO monitoring are not studied yet in LFO monitoring. Dual prediction for LFO monitoring is much more complex than that for IMO monitoring since it has to predict the next boundary shape not the next point.…”

Section: Phase Ii: Integrationmentioning

confidence: 99%

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Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Yim

Lee

2021

Electronics

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Recently, the demand for monitoring a certain object covering large and dynamic scopes such as wildfires, glaciers, and radioactive contaminations, called large-scale fluid objects (LFOs), is coming to the fore due to disasters and catastrophes that lately happened. This article provides an analytic comparison of such LFOs and typical individual mobile objects (IMOs), namely animals, humans, vehicles, etc., to figure out inherent characteristics of LFOs. Since energy-efficient monitoring of IMOs has been intensively researched so far, but such inherent properties of LFOs hinder the direct adaptation of legacy technologies for IMOs, this article surveys technological evolution and advances of LFOs along with ones of IMOs. Based on the communication cost perspective correlated to energy efficiency, three technological phases, namely concentration, integration, and abbreviation, are defined in this article. By reviewing various methods and strategies employed by existing works with the three phases, this article concludes that LFO monitoring should achieve not only decoupling from node density and network structure but also trading off quantitative reduction against qualitative loss as architectural principles of energy-efficient communication to break through inherent properties of LFOs. Future research challenges related to this topic are also discussed.

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Section: Phase Ii: Integrationmentioning

confidence: 99%

Section: Phase Ii: Integrationmentioning

confidence: 99%

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Yim

Lee

2021

Electronics

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“…As mentioned in the Introduction, most in-network processing schemes reported in the literature try to delineate dynamically the boundaries of an evolving continuous object using a dense array of deployed sensor nodes [5]- [10]. These schemes do not attempt to estimate the local front line's evolution characteristics or predict their spatiotemporal evolution.…”

Section: Experiments 2: Diffusive Hazards With Irregular Shapesmentioning

confidence: 99%

“…They use them to implement a "wake up" mechanism to decide which "sleeping" nodes to activate selectively for near term front tracking in order to reduce the network's overall energy consumption. However, PRECO (PREdictive Continuous Object tracking scheme) requires global sensor nodes synchronization [5] rendering it impractical even for medium size WSNs. Nevertheless, for completeness purposes we compared it to our method under the scenarios of Experiment 1, Experiment 2, and Experiment 2 with spatially varying wind parameters.…”

Section: Experiments 2: Diffusive Hazards With Irregular Shapesmentioning

confidence: 99%

“…WSNs have also been used for single and multiple target tracking [1]- [3], and due to their rapidly dropping cost they are also gaining popularity in environmental monitoring applications [4]. Recently, sensor network-based methods have been proposed for detecting the boundaries of diffusive hazardous phenomena [5]- [12], modeled as "continuous objects". However, traditional target tracking algorithms cannot be applied for continuous object tracking, since the two problems are fundamentally different.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Spatiotemporal Evolution Characteristics of Diffusive Hazards Using Wireless Sensor Networks

Manatakis

Μανωλάκος

2015

IEEE Trans. Parallel Distrib. Syst.

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There is a fast growing interest in exploiting Wireless Sensor Networks (WSNs) for tracking the boundaries and predicting the evolution properties of diffusive hazardous phenomena (e.g. wildfires, oil slicks etc.) often modeled as "continuous objects". We present a novel distributed algorithm for estimating and tracking the local evolution characteristics of continuous objects. The hazard's front line is approximated as a set of line segments, and the spatiotemporal evolution of each segment is modeled by a small number of parameters (orientation, direction and speed of motion). As the hazard approaches, these parameters are re-estimated using adhoc clusters (triplets) of collaborating sensor nodes. Parameters updating is based on algebraic closed-form expressions resulting from the analytical solution of a Bayesian estimation problem. Therefore, it can be implemented by microprocessors of the WSN nodes, while respecting their limited processing capabilities and strict energy constraints. Extensive computer simulations demonstrate the ability of the proposed distributed algorithm to estimate accurately the evolution characteristics of complex hazard fronts under different conditions by using reasonably dense WSNs. The proposed in-network processing scheme does not require sensor node clocks synchronization and is shown to be robust to sensor node failures and communication link failures, which are expected in harsh environments.! 2 delineate the area affected by the diffusive hazard, we present in this paper a novel decentralized algorithm which can estimate with accuracy, using dynamically formed clusters (triplets) of cooperating sensor nodes, the local evolution characteristics (orientation, direction and speed) of a continuous object. The updating of the evolution parameters is based on a Bayesian probabilistic modeling approach which relatively to our prior work ( [13], [14]) : (i) Casts the problem in a framework allowing us to account for the sensing mechanism uncertainties expected in harsh environments and also characterize the uncertainty of the estimated parameters, (ii) Improves the accuracy of the obtained local model parameter estimates, (iii) Leads to simpler algebraic expressions for updating these parameters that can be easily implemented by the commonly used processing-and power-constraint embedded microprocessors of WSN nodes, (iv) Takes into account the possibility of imperfect sensor nodes which may fail to communicate since the approaching hazard may impair their functionality.With respect to related work on predictive modeling ( [11], [12]) our approach exhibits the following advantages: It can track accurately the time varying characteristics of a local front line even if, (i) the WSN is not dense, (ii) the sensor node clocks are not synchronized, (iii) the sensing mechanism is imperfect, (iv) nodes and communication links may fail as the hazard approaches. The ability to track the spatiotemporal evolution characteristics of the local front enables making predictions about its future loca...

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Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

Ping

Zhou

Shi

et al. 2018

Pers Ubiquit Comput

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Selective wakeup discipline for continuous object tracking in grid-based wireless sensor networks

Cited by 11 publications

References 9 publications

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Estimating the Spatiotemporal Evolution Characteristics of Diffusive Hazards Using Wireless Sensor Networks

Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

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