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We proposed an -local spatial clustering algorithm for wireless sensor network

Distributed clustering-based aggregation algorithm for spatial correlated sensor networks

IEEE Sensors Journal, no. 3 (2011): 641-648

Cited by: 100|Views99
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Abstract

In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in-network data aggregation. In this paper, an α -local spatial clustering algorit...More

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Introduction
  • A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions [1], [2].
  • In 1970, Tobler’s first law of geography was formulated to state that “Everything is related to everything else, but near things are more related than distant things” [3]
  • This statistical observation implies that data correlation increases with decreasing spatial separation.
Highlights
  • A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions [1], [2]
  • We propose a new distributed clustering algorithm based on the dominating set theory to choose the CHs and construct clusters by measuring the spatial correlation between sensors
  • We proposed an -local spatial clustering algorithm for wireless sensor network
  • The experimental results show that the aggregated network can provide the environmental information in very high accuracy in comparison with the original network
  • This algorithm is useful for the applications such as the environmental surveillance where the sensors are always distributed in very high density
Methods
  • Output: Weighted -Dominating Set (Step1) ;.
  • Pardo { / Parallel process for (2.1) node is a dominatee; (2.2).
  • 1. Each GD broadcasts an INDICATOR message embedded with its identity to all its -neighbors to indicate its dominator status.
  • 2. Each dominatee chooses a cluster to join: a.
  • If receives only one INDICATOR message from a dominator , it join the cluster of.
  • INDICATOR messages from a set of dominators.
  • Chooses a to join if it satisfies: 3.
  • If dominatee decides to join , it sends a
Results
  • AGGREGATION interval for the population mean is reached and, simulation results are measured by taking the average of all cases.
  • The values of is set to ensure that the number of ID is about 5%, 10%.
  • . Each node randomly picks up a data item from the dataset as its sampled data.
  • These data are used to calculate the Spatial Correlated Weight
Conclusion
  • CONCLUSION AND FUTURE WORK

    In this paper, the authors proposed an -local spatial clustering algorithm for WSNs.
  • The experimental results show that the aggregated network can provide the environmental information in very high accuracy in comparison with the original network.
  • This algorithm is useful for the applications such as the environmental surveillance where the sensors are always distributed in very high density
Summary
  • Introduction:

    A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions [1], [2].
  • In 1970, Tobler’s first law of geography was formulated to state that “Everything is related to everything else, but near things are more related than distant things” [3]
  • This statistical observation implies that data correlation increases with decreasing spatial separation.
  • Objectives:

    The authors' aim is to develop methods that can focus the attention of the network resources only on relevant information for the task at hand.
  • Methods:

    Output: Weighted -Dominating Set (Step1) ;.
  • Pardo { / Parallel process for (2.1) node is a dominatee; (2.2).
  • 1. Each GD broadcasts an INDICATOR message embedded with its identity to all its -neighbors to indicate its dominator status.
  • 2. Each dominatee chooses a cluster to join: a.
  • If receives only one INDICATOR message from a dominator , it join the cluster of.
  • INDICATOR messages from a set of dominators.
  • Chooses a to join if it satisfies: 3.
  • If dominatee decides to join , it sends a
  • Results:

    AGGREGATION interval for the population mean is reached and, simulation results are measured by taking the average of all cases.
  • The values of is set to ensure that the number of ID is about 5%, 10%.
  • . Each node randomly picks up a data item from the dataset as its sampled data.
  • These data are used to calculate the Spatial Correlated Weight
  • Conclusion:

    CONCLUSION AND FUTURE WORK

    In this paper, the authors proposed an -local spatial clustering algorithm for WSNs.
  • The experimental results show that the aggregated network can provide the environmental information in very high accuracy in comparison with the original network.
  • This algorithm is useful for the applications such as the environmental surveillance where the sensors are always distributed in very high density
Tables
  • Table1: RESULTS OF NETWORK AGGREGATION
  • Table2: ACCURACY PERFORMANCE OF PATTERN RECOGNITION
  • Table3: ACCURACY PERFORMANCE COMPARISON
Download tables as Excel
Related work
  • Many clustering algorithms have been proposed for ad-hoc and sensor networks recently. LEACH [4] is the most famous application-specific algorithm that uses clustering to prolong the network lifetime. As clustering is vital for efficient resource utilization and load balancing in large-scale sensor networks, it is not surprising that an increasing amount of research interest has been drawn towards clustering algorithms during the last few years. In general, such research can be classified primarily into two perspectives either finding a smallest set of the CHs based on Graph Theory, or finding an optimal set of the CHs based on residual energy of each node.

    From the graph theory perspective, heuristic algorithms are always used to generate approximate results based on either a centralized or distributed model of operation. For a centralized model, Guha and Khuller propose two CDS (Connected Dominating Set) construction strategies in [27], which contain two greedy heuristic algorithms with bounded performance guarantees. Other algorithms, e.g., [28] and [29], are motivated by either of these two heuristics. However, for large-scale WSNs, a distributed CDS algorithm could be more effective due to the lack of a centralized administration. An example of the distributed implementations of [27] is provided in [30]. Other greedy heuristics including [31]–[33] have also come under investigation recently. Moreover, distributed CDS construction approaches have also been investigated based on Maximum Independent Set [34], multipoint relaying [35], and Spanning Tree [36].
Funding
  • This work was supported in part by the EPSRC Project Mobile Environmental Sensing System Across a Grid Environment (MESSAGE) under Grant EP/E002102/1 and jointly funded by the Engineering and Physical Sciences Research Council and the Department for Transport
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