Collaborative Sensor Network algorithm for predicting the spatiotemporal evolution of hazardous phenomena

IEEE Trans. Parallel Distrib. Syst.

Μανωλάκος

2015

Self Cite

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...

Section: Model Updatingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

IEEE Trans. Parallel Distrib. Syst.

Μανωλάκος

2015

Self Cite

“…To address these severe limitations we have introduced in [12,13] a distributed approach which is based on probabilistic modeling and can continuously estimate, with high accuracy, the space-and time-varying evolution characteristics (direction and speed) of a progressing hazard using low density WSNs. As for all WSN schemes, computer simulations can be used to assess the expected estimation accuracy as a function of the network's density.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge our proposed emulation scheme is the first attempt to use a small number of sensor nodes to realistically estimate the energy consumption of a collaborative algorithm in a large-scale WSN implementation. We demonstrate its capabilities using the distributed algorithm we introduced in [12,13] for estimating the spatiotemporal evolution parameters of diffusing environmental hazards. WSN emulation provides convincing evidence that the collaborative algorithm is suitable for large-scale WSN deployment since it respects the memory, processing and energy constraints of commodity sensor nodes used in WSN implementations.…”

Section: Introductionmentioning

confidence: 99%

“…We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network's size when implementing a collaborative algorithm we developed in [12] for tracking the spatiotemporal evolution of a progressing environmental hazard.…”

mentioning

confidence: 99%

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Simulation-driven emulation of collaborative algorithms to assess their requirements for a large-scale WSN implementation

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Nennes

Bakas

et al. 2014

Self Cite

Assessing how the performance of a decentralized wireless sensor network (WSN) algorithm's implementation scales, in terms of communication and energy costs, as the network size increases is an essential requirement before its field deployment. Simulations are commonly used for this purpose, especially for large-scale environmental monitoring applications. However, it is difficult to evaluate energy consumption, processing and memory requirements before the algorithm is really ported to a real WSN platform. We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network’s size when implementing a collaborative algorithm we developed in for tracking the spatiotemporal evolution of a progressing environmental hazard

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

Μανωλάκος

2020

IEEE Access

Self Cite

Environmental hazards (wildfires, floods, oil spills) are often modeled as ''continuous objects'' which evolve in space and time taking irregular shapes. Tracking their boundaries and accurately predicting their spatiotemporal spreading patterns is of paramount importance to combat their often catastrophic consequences. Wireless Sensor Networks (WSN) have been very instrumental for this purpose. However, current WSN-based methods require a prohibitively large density of deployed sensors to achieve a reasonable boundary reconstruction accuracy because they are based on the explicit identification of nodes close to the boundary. We present a novel approach that can track and predict the global boundary using only a relatively small number of distributed local front estimates. Our approach first filters and fuses the available sparse set of local front estimates (e.g. vectors of local orientation, direction and speed) and then uses the resulting information to reconstruct a smooth curve prediction of the evolving object's boundary at a future time. Moreover, since the uncertainty of the local front parameters is modeled, it can provide a heatmap representation of the evolving object, indicating the probability for each point in the area of interest to be reached at a future time by the spreading hazard. These predictive modeling capabilities when combined enable effective decision support for crisis management. We demonstrate that different types of continuous objects can be tracked with accuracy, even when only a relatively small number of noisy local front estimates is available. Our approach is practical since it can be applied in many situations where global boundary prediction updates are important to obtain by new sparsely distributed noisy local front estimates as soon as they reach a control center while the hazard is still progressing.INDEX TERMS Continuous object tracking, predictive modeling, hazards tracking, boundary reconstruction algorithm, environmental monitoring.