Conference Proceeding

MPM: Map Based Predictive Monitoring for Wireless Sensor Networks.

Autonomic Computing and Communications Systems 01/2009; DOI:10.1007/978-3-642-11482-3_6 In proceeding of: Autonomic Computing and Communications Systems, Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers
Source: DBLP

ABSTRACT We present the design of a Wireless Sensor Networks (WSN) level event
prediction framework to monitor the network and its operational
environment to support proactive self* actions. For example, by
monitoring and subsequently predicting trends on network load or sensor
nodes energy levels, the WSN can proactively initiate
self-reconfiguration. We propose a Map based Predictive Monitoring (MPM)
approach where a selected WSN attribute is first profiled as WSN maps,
and based on the maps history, predicts future maps using time series
modeling. The "attribute" maps are created using a gridding technique
and predicted maps are used to detect events using our regioning
algorithm. The proposed approach is also a general framework to cover
multiple application domains. For proof of concept, we show MPM's
enhanced ability to also accurately "predict" the network partitioning,
accommodating parameters such as shape and location of the partition
with a very high accuracy and efficiency.

0 0
 · 
0 Bookmarks
 · 
48 Views
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: In this paper, we propose a wireless sensor network paradigm for real-time forest fire detection. The wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach. This paper mainly describes the data collecting and processing in wireless sensor networks for real-time forest fire detection. A neural network method is applied to in-network data processing. We evaluate the performance of our approach by simulations.
    Wireless Communications, Networking and Mobile Computing, 2005. Proceedings. 2005 International Conference on; 10/2005
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.
    Sensors. 01/2007;
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: In this paper, we present a method for approximating the values of sensors in a wireless sensor network based on time series fore- casting. More specifically, our approach relies on autoregressive models built at each sensor to predict local readings. Nodes transmit these local models to a sink node, which uses them to predict sensor values without directly communicating with sensors. When needed, nodes send infor- mation about outlier readings and model updates to the sink. We show that this approach can dramatically reduce the amount of communication required to monitor the readings of all sensors in a network, and demon- strate that our approach provides provably-correct, user-controllable er- ror bounds on the predicted values of each sensor.
    Wireless Sensor Networks, Third European Workshop, EWSN 2006, Zurich, Switzerland, February 13-15, 2006, Proceedings; 01/2006

Full-text (2 Sources)

View
9 Downloads
Available from
Jun 14, 2013