Elements of information theory
Elements of information theory
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Calibration as parameter estimation in sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
An evaluation of multi-resolution storage for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Scattered data selection for dense sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Modeling correlations in web traces and implications for designing replacement policies
Computer Networks: The International Journal of Computer and Telecommunications Networking
RUGGED: RoUting on finGerprint Gradients in sEnsor Networks
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
Using More Realistic Data Models to Evaluate Sensor Network Data Processing Algorithms
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Data Mining: Next Generation Challenges and Future Directions
Data Mining: Next Generation Challenges and Future Directions
On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
A wireless sensor network for structural health monitoring: performance and experience
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
On optimal communication cost for gathering correlated data through wireless sensor networks
Proceedings of the 12th annual international conference on Mobile computing and networking
MIST: distributed indexing and querying in sensor networks using statistical models
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Data quality and query cost in pervasive sensing systems
Pervasive and Mobile Computing
Engineering of Software-Intensive Systems: State of the Art and Research Challenges
Software-Intensive Systems and New Computing Paradigms
Spatial statistics and models of spectrum use
Computer Communications
Enabling ε-approximate querying in sensor networks
Proceedings of the VLDB Endowment
Impact of correlation in node locations on the performance of distributed compression
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Energy-efficient data gathering in wireless sensor networks with asynchronous sampling
ACM Transactions on Sensor Networks (TOSN)
Spatial correlation-based mobile agent routing algorithm in wireless sensor networks
APCC'09 Proceedings of the 15th Asia-Pacific conference on Communications
Spatial statistics of spectrum usage: from measurements to spectrum models
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
ACM Transactions on Sensor Networks (TOSN)
A decentralized approach for nonlinear prediction of time series data in sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks
Journal of Signal Processing Systems
StrawMAN: making sudden traffic surges graceful in low-power wireless networks
Proceedings of the 6th Workshop on Hot Topics in Embedded Networked Sensors
International Journal of Sensor Networks
An adaptive and composite spatio-temporal data compression approach for wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Efficiency analysis and derivation of enhanced deployment models for sensor networks
International Journal of Ad Hoc and Ubiquitous Computing
Spatially correlated multi-modal wireless sensor networks: a coalitional game theoretic approach
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
ACM Transactions on Sensor Networks (TOSN)
Balancing lifetime and classification accuracy of wireless sensor networks
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
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The physical phenomena monitored by sensor networks, for example, forest temperature or water contamination, usually yield sensed data that are strongly correlated in space. With this in mind, researchers have designed a large number of sensor network protocols and algorithms that attempt to exploit such correlations.There is an increasing need to synthetically generate large traces of spatially correlated data representing a wide range of conditions to carefully study the performance of these algorithms. Further, a mathematical model for generating synthetic traces would provide guidelines for designing more efficient algorithms. These reasons motivate us to obtain a simple and accurate model of spatially correlated sensor network data.The proposed model is Markovian in nature and can capture correlation in data irrespective of the node density, the number of source nodes, or the topology. We describe a rigorous mathematical procedure and a simple practical method to extract the model parameters from real traces. We also show how to efficiently generate synthetic traces on a given topology using these parameters. The correctness of the model is verified by statistically comparing synthetic and real data. Further, the model is validated by comparing the performance of algorithms whose behavior depends on the degree of spatial correlation in data, under real and synthetic traces. The real traces are obtained from remote sensing data, publicly available sensor data, and sensor networks that we deploy. We show that the proposed model is more general and accurate than the commonly used jointly Gaussian model. Finally, we create tools that can be easily used by researchers to synthetically generate traces of any size and degree of correlation.