Conference Paper

MPM: Map Based Predictive Monitoring for Wireless Sensor Networks.

DOI: 10.1007/978-3-642-11482-3_6 Conference: 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.

Download full-text

Full-text

Available from: Neeraj Suri, Aug 11, 2015
0 Followers
 · 
84 Views
  • Source
    • "K-connectivity is a preventive measure which requires the existence of k alternative paths between each pair of sensor nodes [13]– [15]. Preventive techniques also try to avoid disconnections by predicting energy dissipation patterns and performing re/deployment and energy provisioning [16]–[19] or balancing communication [20]. The deployment of dedicated relay nodes also may extend the network lifetime [21], [22]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The physical number of sensor nodes constitutes a major cost factor for Wireless Sensor Networks (WSN) deployments. Hence, a natural goal is to minimize the number of sensor nodes to be deployed, while still maintaining the desired properties of the WSN. However, sparse networks even while connected, usually suffer from topology irregularities that negatively impact the network lifetime and responsiveness, i.e., sensor data delivery reliability and latency. In addition, sensor node failures easily complicate/enforce/aggravate these irregularities. Valuable efforts have been conducted to discover topology specific anomalies such as coverage holes or critical/bottleneck nodes. Unfortunately, these efforts suffer from at least one of the following drawbacks: (a) They are centralized and consequently inefficient in large-scale networks, (b) they are tailored to one class of anomalies, or (c) do not propose how to remedy the identified anomaly. In this paper, we focus on sparse WSN which usually show varied topology irregularities and propose an in-network and localized strategy that efficiently (i) discovers generic topology irregularities, and (ii) identifies locations for minimal number of new augmented sensor deployments to remedy topology irregularities and sustain the desired operational requirements. We show the effectiveness and efficiency of the solution through a set of extensive simulations.
    Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2011 8th Annual IEEE Communications Society Conference on; 01/2011
  • Source
    • "K-connectivity is a preventive measure which requires the existence of k alternative paths between each pair of sensor nodes [13]– [15]. Preventive techniques also try to avoid disconnections by predicting energy dissipation patterns and performing re/deployment and energy provisioning [16]–[19] or balancing communication [20]. The deployment of dedicated relay nodes also may extend the network lifetime [21], [22]. "
    In Proc. of The Eighth Annual IEEE Communications Society Conference on Sensor, Mesh, and Ad Hoc Communications and Networks (SECON); 01/2011
  • Source
    • ": Refers to the information that can be forecasted before its occurrence. Examples are predicted important events such as, user events and forecasted network partitioning [54]. "
    In Proc. of The International Conference on Information Quality (ICIQ); 01/2010
Show more