Abstract-Wireless sensor networks have been widely used for surveillance in harsh environments. In many such applications, the environmental data are continuously sensed, and data collection by a server is only performed occasionally. Hence, the sensor nodes have to temporarily store the data, and provide easy and on-hand access for the most updated data when the server approaches. Given the expensive server-to-sensor communications, the large amount of sensors and the limited storage space at each tiny sensor, continuous data collection becomes a challenging problem.In this paper, we present partial network coding (PNC) as a generic tool for the above applications. PNC generalizes the existing network coding (NC) paradigm, an elegant solution for ubiquitous data distribution and collection. Yet, PNC enables efficient storage replacement for continuous data, which is a major deficiency of the conventional NC. We prove that the performance of PNC is quite close to NC, except for a sublinear overhead on storage and communications. We then address a set of practical concerns toward PNC-based continuous data collection in sensor networks. Its feasibility and superiority are further demonstrated through simulation results.
Abstract-Sensor networks are powerful tools for performing monitoring and surveillance tasks over large areas. A sensor is a cheap, simple device with low power and limited capabilities. In a sensor network a large number of sensors are deployed to span the whole area to be monitored. Due to the simplicity and the large quantity of the sensors involved, collecting data from a sensor network can be time and energy inefficient.In this paper, we investigate making the data gathering task from a sensor network more efficient by using a randomized, layered architecture. The layers in our architecture are constructed in a distributed fashion, with each sensor deciding locally on what layers it will exist. The key property of our technique is that the information is collected from one layer of the architecture containing a small subset of the sensors, resulting in fewer hops and thus smaller data in data aggregation. We provide provably correct results for the delay incurred and the accuracy of the results.In the context of our new techniques, we also explore ways to speed up the data gathering process even further, such as using history information. In addition, we consider how to optimize the structure of our system so that the energy consumption will be evenly distributed among each sensor, thus extending the overall lifetime of the entire network.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.