B. S. Adiga

Lightweight IBE scheme for Wireless Sensor nodes

Application of cryptography for secure Wireless Sensor Networks poses challenging problems because all the cryptographic algorithms are computationally intensive and wireless nodes have limited resources. In this paper, we describe an Identity Based Elliptic Curve Cryptosystem (IBE-ECC) based on Tate pairing which is lightweight without any Public Key Infrastructure (PKI) and no key exchanges. In addition, we propose a novel lightweight IBE-ECC scheme in which encryption is performed with no Tate pairing (computationally intensive) and is used only in decryption. This scheme is relevant for wireless nodes and in particular for Internet of Things (IoT) framework, wherein the wireless nodes do only message encryption, whereas the decryption is delegated to centralized servers or Gateways. We have also implemented our proposed scheme on a mobile phone with android platform for performance analysis. We observed that our proposed IBE-ECC scheme outperforms the existing IBE scheme in terms of complexity and efficiency of operation. Further, our scheme is based on super singular elliptic curves E/GF(p) where p is a special prime of the form Crandall primes. For such primes modular reduction can be implemented efficiently fast using Mohan-Adiga algorithm.

SpreadStore: A LDPC Erasure Code Scheme for Distributed Storage System

A wide proliferation of distributed storage systems call for more robust and efficient systems than regular replication based storage systems. The Low density parity check (LDPC) codes have become a popular alternative to traditional Reed-Solomon (RS) erasure codes due to their low computational complexity. In this paper we propose, Spread Store- a distributed storage system based on LDPC code. Also, a brief analysis is made by examining different LDPC codes from the perspective of their efficiency and erasure handling capabilities.

Guaranteed error correction based on Fourier Compressive Sensing and Projective Geometry

The sparse error correction is intimately related to Compressive Sensing. Exploiting this connection, the paper proposes an error correction scheme pivoted on partial Fourier matrix constructed using cyclic difference sets. The scheme is elegant in terms of computationally efficient encoding and decoding as well as guaranteed error correction.

Decoding architectures for Projective Geometry based LDPC codes

Projective geometry (PG) based low density parity check (LDPC) codes have many useful properties, including easy encoding and decoding by simple majority logic technique. With these useful features, they can be useful error control codes in future. In this paper, we present three novel architectures comprising of one parallel and two semi-parallel decoder architectures for popular PG-based LDPC codes. These architectures have no memory clash and further are reconfigurable for different lengths (and their corresponding rates). The three architectures can be configured either for the regular belief propagation based decoding or majority logic decoding (MLD).

Reliable data transmission in sensor networks using compressive sensing and real expander codes

The resource-constraints in the sensor networks make reliable data communication a challenging task. Particularly, the limited availability of battery and computing power necessitates designing computationally efficient means for providing data compression and protection against data loss. In this paper, we propose to integrate the emerging framework of compressive sensing (CS) with real expander codes (RECs), coined as CS-REC, for robust data transmission. CS works as a computationally inexpensive data compression scheme, while RECs act as an elegant application layer erasure coding scheme. The benefits provided by RECs are twofold: one, RECs require only few addition-subtraction operations over real numbers for encoding and decoding; two, they provide graceful degradation in recovery performance with increase in the number of erasures. Through elaborate simulations, we show that CS-REC can achieve the recovery performance close to the case where there is no data loss. Further, again via simulations, we demonstrate the usefulness of CS-REC for reliably transmitting image data in multimedia sensor networks.

Effect of Check Node Processing on the Performance of Message Passing Algorithm in the Context of LDPC Decoding for DVB-S2

Low density parity check (LDPC) codes have become very popular in recent times due to their capacity approaching performance and are considered in the next generation digital video broadcasting DVB-S2 standard. This paper presents few results and observations obtained when some of the existing decoding algorithms are studied in the context of the standardized codes. These results with further consolidation will facilitate to arrive at the right algorithm for decoder design

Burst error correction using partial fourier matrices and block sparse representation

There is a strong relation between sparse signal recovery and error control coding. It is known that burst errors are block sparse in nature. So, here we attempt to solve burst error correction problem using block sparse signal recovery methods. We construct partial Fourier based encoding and decoding matrices using results on difference sets. These constructions offer guaranteed and efficient error correction when used in conjunction with reconstruction algorithms which exploit block sparsity.

On the usage of Projective Geometry based LDPC codes for wireless applications

The Projective Geometry (PG) based Low Density Parity Check (LDPC) codes are gaining popularity due to their ease of encoding and decoding by the simple majority logic technique. These codes have a flurry of other useful properties, making them ideal candidates/codes for error correction for future wireless applications. This paper brings out a detailed study on the performance of PG codes for different decoding algorithms, bearing in mind the implementation issues. The work is extended by providing results for the shortening (rate reduction) and Chase Combining, which aids in bringing out their usefulness for wireless applications.

On using complex homotopy for impulse noise cancellation in images

The area of Compressive Sensing (CS) has been witnessing a flurry of research activity, bringing out solid theoretical results and its usefulness in different applications. With the need to address practicalities, fast reconstruction algorithms are being proposed quite recently. Towards studying their effectiveness in different applications, we have chosen one attractive candidate, the complex homotopy, for CS-based impulse-noise cancellation in images. Compared to popular Compressive Sampling Matching Pursuit (CoSaMP), complex homotopy demonstrated its superiority both in terms of speed of execution and reconstructed image quality. The paper, apart from presenting the supporting results, identifies some useful product codes for the selected application. An attempt is also made towards providing pointers for further examination.

On 2-adically extended projective geometry based codes and their efficient decoding architecture

In this paper we consider a new class of codes over rings obtained through 2-adically extending the Projective Geometry (PG) codes by means of lifting. We also propose an elegant and configurable bit-serial Majority Logic (MLG) decoding architecture consisting of simple blocks, making it amenable for Very Large Scale Integration (VLSI). Some preliminary performance results are also provided to demonstrate the utility of the coding scheme in random and burst error scenarios. The proposed code and the associated decoder appears to be a useful candidate for both communications involving power and complexity constrained devices as well as in storage systems, especially nanoscale memories.