Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Replica allocation for correlated data items in ad hoc sensor networks
ACM SIGMOD Record
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Optimizing multiple in-network aggregate queries in wireless sensor networks
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Modern Applied Statistics with S
Modern Applied Statistics with S
The design space of wireless sensor networks
IEEE Wireless Communications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
The challenges of building mobile underwater wireless networks for aquatic applications
IEEE Network: The Magazine of Global Internetworking
Node clustering in wireless sensor networks: recent developments and deployment challenges
IEEE Network: The Magazine of Global Internetworking
A reliable and data aggregation aware routing protocol for wireless sensor networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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We present an analogy between the operation of a Wireless Sensor Network and the sampling and reconstruction of a signal. We measure the impact of three factors on the quality of the reconstructed data, namely, the granularity of the process under study, the spatial distribution of sensors, and the protocol for clustering and data aggregation. In order to quantify this influence, a Monte Carlo study is performed for estimating the error introduced by the observation process. The phenomenon being observed is described by a Gaussian random field with varying scale, the distribution of sensors is modeled by a new point process and two protocols are assessed: Leach and Skater. We show that Skater performs better than Leach, at the expense of using the sampled data on the clustering stage.