Faculty Publications
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Item Data Aggregation Using Distributed Compressive Sensing in WSNs(Springer, 2020) Puneeth, D.; Kulkarni, M.The theory of Distributed Compressive Sensing (DCS) is best suited for Wireless Sensor Network (WSN) applications, as sensors are randomly distributed in the area of interest. The inter-signal and intra-signal correlations are explored in DCS through the concept of joint sparse models. In this paper, we have analysed joint sparse models and reconstruction of the signal has been done using joint recovery techniques. The reconstruction performance is achieved using joint recovery (S-OMP) and separate recovery (OMP) mechanisms utilising synthetic signals that possess the inherent qualities of natural signals. Further, we employ DCS on real data to evaluate the data reduction. DCS proves to be a better data aggregation technique depending upon the amount of intra and inter correlation between data signals. Simulation results shows that, even in less sparse environments, DCS performs better than separate recovery, which is well suited for real signals. Simulations also prove that with DCS, we can further reduce the number of measurements (compressed vector length), required for data reconstruction as compared to separate recovery. It has been shown that nearly 50% reduction in the data required for reconstruction in case of synthetic signals and 27% in case of real signals. © Springer Nature Singapore Pte Ltd. 2020.Item Data Aggregation using Compressive Sensing for Energy Efficient Routing Strategy(Elsevier B.V., 2020) Puneeth, D.; Kulkarni, M.Data gathering in an energy efficient way, is one of the crucial concerns in Wireless Sensor Networks (WSNs). With incorporation of Compressive Sensing (CS), as a data aggregation scheme the information contained in the signal, is safely maintained through its projections, which can be reconstructed later. Inclusion of CS, for an energy efficient routing technique further enhances the lifetime of the network. To improve network lifetime, CS has been employed at level 1 (at the leaf nodes). The dimensionality reduction at the transmitter is done, using a measurement matrix. At the receiver, data is recovered using greedy based methods. Greedy based method offers low complexity, and low implementation cost. The success rate of greedy method, depends on the sparsity of the data. Performance evaluation of greedy based method is analyzed, by considering varying sparsity and plotted against number of measurements, required to reconstruct the signal. At the same time, reconstruction error of every method has been evaluated by varying sparsity of the signal. The same is analyzed by considering real temperature, and humidity data sets. © 2020 The Authors. Published by Elsevier B.V.Item Data aggregation using compressive sensing for improved network lifetime in large scale wireless sensor networks(Serials Publications serialspublications@vsnl.net, 2016) Puneeth, D.; Ruthwik, R.; Kulkarni, M.In large scale Wireless Sensor Networks(WSN's) the amount of data generated is enormous. The data has to be processed efficiently before it reaches the Base Station (BS) by using an efficient routing algorithm as well as data aggregation methods. The nodes in WSN's are randomly deployed, the data emerging from these nodes are highly correlated either spatially or temporally. The data aggregation scheme should employ simple encoding since the sensor nodes are battery operated. The proposed method discusses about a data aggregation scheme using Compressive Sensing(CS) technique which makes use of correlation among the sensor nodes. Our primary focus is to increase the lifetime of the overall network. The underlying protocols used are Low-energy adaptive clustering hierarchy (LEACH) and Multi-threshold adaptive range clustering (M-TRAC). We have computed several network parameters for different network configuration. The reconstruction algorithm is sufficiently robust against noise. The reconstruction of the data is done using greedy method and L1 norm regularization. The implementation of the algorithm is done using the real data-set from Intel Lab. Simulation results validate the data aggregation scheme guarantees data accuracy and doubles the network lifetime. © 2016 International Science Press.Item Energy-efficient and reliable data collection in wireless sensor networks(Turkiye Klinikleri Journal of Medical Sciences Talapapa Bulvary no. 102 Hamammonu 1 06230, 2018) Puneeth, D.; Joshi, N.; Atrey, P.K.; Kulkarni, M.Ensuring energy efficiency, data reliability, and security is important in wireless sensor networks (WSNs). A combination of variants from the cryptographic secret sharing technique and the disjoint multipath routing scheme is an effective strategy to address these requirements. Although Shamir's secret sharing (SSS) provides the desired reliability and information-theoretic security, it is not energy efficient. Alternatively, Shamir's ramp secret sharing (SRSS) provides energy efficiency and data reliability, but is only computationally secure. We argue that both these approaches may suffer from a compromised node (CN) attack when a minimum number of nodes is compromised. Hence, we propose a new scheme that is energy efficient, provides data reliability, and is secure against CN attacks. The core idea of our scheme is to combine SRSS and a round-reduced AES cipher, which we call "split hop AES (SHAES)". Both the simulation results and the theoretical analysis are employed to validate the near-sink CN attack, and a secure reliable scheme using SHAES is proposed. © 2018 TÜBITAK.