Abstract—Wireless sensor networks (WSN) have become a mainstream technology for environmental monitoring and observing various variables of interest over extended periods of time via large-scale networks of sensors. WSNs have a wide range of applications including wildfire detection, healthcare, military, and habitat monitoring. In all such application areas, gathering and then relaying captured data to a central unit is often considered the primary task of the network. Scientific analysis however often requires WSNs to capture and store variables for long periods of time. Storing and managing flows of data tend to be challenging issues because WSNs often consist of nodes with limited processing, memory, and power resources. Therefore the software layer in WSNs needs to implement an efficient data storage allocation mechanism in order to provide sufficient memory space for multiple applications. In this paper we propose a novel statistical approach for estimating applications storage requirements. Our proposed mechanism has been originally developed and implemented in a new WSN middleware called Sensomax, which is an agent-based decentralized middleware with multiple concurrent applications support for dynamic data gathering in WSNs. The mechanism described here proved to be an effective technique for proactively allocating memory to multiple applications with different operational paradigms.
Index Terms—Storage, WSN, probability, distribution, concurrency.
Mo Haghighi is with the Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK and Large-Scale Complex IT Systems (LSCITS) (e-mail: Mo.Haghighi@bristol.ac.uk).
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Cite:Mo Haghighi, "Dynamic Data Storage Estimation for Multiple Concurrent Applications Using Probability Distribution Modeling in WSNs," Journal of Advances in Computer Networks vol. 1, no. 3, pp. 254-259, 2013.
