scholarly journals Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Haipeng Peng ◽  
Ye Tian ◽  
Jürgen Kurths
Keyword(s):  
Big Data ◽  
Compressive Sensing ◽  
Data Transmission ◽  
Matrix Multiplication ◽  
Wireless Sensor ◽  
Storage Space ◽  
Secret Key ◽  
Encrypt Data ◽  
The Matrix ◽  
Data Volume

Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

scholarly journals Pseudo-Random Encryption for Security Data Transmission in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2452 ◽  
Author(s):  
Liang Liu ◽  
Wen Chen ◽  
Tao Li ◽  
Yuling Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Transmission ◽  
Multiple Scales ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Inverse Mapping ◽  
The Matrix

The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the confidentiality of data transmission between sensors and an ally fusion center (AFC) over insecure links. For the typical security problem of WSN’s binary hypothesis testing of a target’s state, sensors were divided into flipping and non-flipping groups according to the outputs of a pseudo-random function which was held by sensors and the AFC. Then in order to prevent an enemy fusion center (EFC) from eavesdropping, the binary outputs from the flipping group were intentionally flipped to hinder the EFC’s data fusion. Accordingly, the AFC performed inverse flipping to recover the flipped data before data fusion. We extended the scheme to a more common scenario with multiple scales of sensor quantification and candidate states. The underlying idea was that the sensor measurements were randomly mapped to other quantification scales using a mapping matrix, which ensured that as long as the EFC was not aware of the matrix, it could not distract any useful information from the captured data, while the AFC could appropriately perform data fusion based on the inverse mapping of the sensor outputs.

Chinese Remainder Theorem Based Key Management for Secured Data Transmission in Wireless Sensor Networks

2020 ◽  
Vol 17 (5) ◽  
pp. 2163-2171
Author(s):  
G. Manikandan ◽  
U. Sakthi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Key Management ◽  
Secure Communication ◽  
Positive Impact ◽  
Chinese Remainder Theorem ◽  
Wireless Sensor ◽  
Secret Key ◽  
Secured Data

In secure communication key management plays an important role. Ensuring of security and trust worthiness in any transmission taking place via Wireless Sensor Networks (WSN) can be done using thoughtfully designed key management models. Here, the method of clustering is used to increase the system performance. Also, an effective key generation method is proposed for secured data transmission for the clustered wireless sensor networks. This method uses Chinese Remainder Theorem (CRT) for generating secret key for each cluster and merges with cluster head ID to generate a unique 144-bit encryption keys. Since, CRT uses random numbers for generating keys, separate active keys that are dynamic to ensure secured data transmission. The proposed model provides a positive impact by improving the lifetime, reducing the delay time, memory requirements and energy consumption, when compared with other existing state of art methods. Moreover, it protects data from brute-force attack, forward as well as backward secrecy and node compromised attack.

scholarly journals An Accelerator Design Using a MTCA Decomposition Algorithm for CNNs

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5558
Author(s):  
Yunping Zhao ◽  
Jianzhuang Lu ◽  
Xiaowen Chen
Keyword(s):  
Large Scale ◽  
Parallel Implementation ◽  
Matrix Multiplication ◽  
Storage Space ◽  
Specific Calculation ◽  
Computing Algorithm ◽  
Architecture Framework ◽  
The Matrix ◽  
Field Programmable ◽  
Accelerator Design

Due to the high throughput and high computing capability of convolutional neural networks (CNNs), researchers are paying increasing attention to the design of CNNs hardware accelerator architecture. Accordingly, in this paper, we propose a block parallel computing algorithm based on the matrix transformation computing algorithm (MTCA) to realize the convolution expansion and resolve the block problem of the intermediate matrix. It enables high parallel implementation on hardware. Moreover, we also provide a specific calculation method for the optimal partition of matrix multiplication to optimize performance. In our evaluation, our proposed method saves more than 60% of hardware storage space compared with the im2col(image to column) approach. More specifically, in the case of large-scale convolutions, it saves nearly 82% of storage space. Under the accelerator architecture framework designed in this paper, we realize the performance of 26.7GFLOPS-33.4GFLOPS (depending on convolution type) on FPGA(Field Programmable Gate Array) by reducing bandwidth and improving data reusability. It is 1.2×–4.0× faster than memory-efficient convolution (MEC) and im2col, respectively, and represents an effective solution for a large-scale convolution accelerator.

scholarly journals Genomic big data hitting the storage bottleneck

2018 ◽  
Vol 24 ◽  
pp. e910 ◽  
Author(s):  
Louis Papageorgiou ◽  
Picasi Eleni ◽  
Sofia Raftopoulou ◽  
Meropi Mantaiou ◽  
Vasileios Megalooikonomou ◽  
...  
Keyword(s):  
Big Data ◽  
Technological Development ◽  
Daily Basis ◽  
Storage Space ◽  
Permanent Loss ◽  
High Storage Capacity ◽  
Data Volume ◽  
Extensive Information ◽  
Analyse Data ◽  
The One

During the last decades, there is a vast data explosion in bioinformatics. Big data centres are trying to face this data crisis, reaching high storage capacity levels. Although several scientific giants examine how to handle the enormous pile of information in their cupboards, the problem remains unsolved. On a daily basis, there is a massive quantity of permanent loss of extensive information due to infrastructure and storage space problems.  The motivation for sequencing has fallen behind. Sometimes, the time that is spent to solve storage space problems is longer than the one dedicated to collect and analyse data. To bring sequencing to the foreground, scientists have to slide over such obstacles and find alternative ways to approach the issue of data volume. Scientific community experiences the data crisis era, where, out of the box solutions may ease the typical research workflow, until technological development meets the needs of Bioinformatics. 

scholarly journals Cryptanalysis of an Authentication Scheme Using an Identity Based Generalized Signcryption

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 782 ◽  
Author(s):  
Abdul Waheed ◽  
Arif Iqbal Umar ◽  
Nizamud Din ◽  
Noor Ul Amin ◽  
Saleem Abdullah ◽  
...  
Keyword(s):  
Big Data ◽  
Standard Model ◽  
Data Transmission ◽  
Data Communication ◽  
Cost Effective ◽  
Secret Key ◽  
Security Attacks ◽  
Secure Data Transmission ◽  
Identity Based ◽  
Signcryption Scheme

Secure data transmission is a challenging issue in modern data communication. ID based generalized signcryption is a cost effective security primitive which provides authentication or confidentiality, or jointly confidentiality and authentication. Wei’s proposed an ID based generalized signcryption scheme for authentication and confidentiality of big data in a standard model, claiming that their scheme holds the security of indistinguishability against adaptive chosen-ciphertext attacks and existential unforgeability against adaptive chosen message attacks. In this paper, we analyzed Wei’s scheme by launching security attacks on the scheme to check its validity. As a result, it became clear and proved that the master secret key generated in the scheme is compromisable. Similarly, the mentioned scheme does not hold the security of indistinguishability against adaptive chosen-ciphertext attacks and existential unforgeability against adaptive chosen message attacks. Consequently, Wei’s schemes is prone to attacks and is insecure.

Efficient Routing with Compressive Sensing for Wireless Sensor Network

2017 ◽  
Vol 26 (2) ◽  
pp. 399-412
Author(s):  
Rania Ahmed ◽  
EL-Sayed M. El-Rabaie ◽  
Mohammed Abd-Elnaby ◽  
FathiAbd EL-Samie
Keyword(s):  
Wireless Sensor Network ◽  
Compressive Sensing ◽  
Sensor Network ◽  
Wireless Sensor
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