Abstract
Cryptographic circuits generally are used for applications of wireless sensor networks to ensure security and must be tested in a manufacturing process to guarantee their quality. Therefore, a scan architecture is widely used for testing the circuits in the manufacturing test to improve testability. However, during scan testing, test-power consumption becomes more serious as the number of transistors and the complexity of chips increase. Hence, the scan chain reordering method is widely applied in a low-power architecture because of its ability to achieve high power reduction with a simple architecture. However, achieving a significant power reduction without excessive computational time remains challenging. In this paper, a novel scan correlation-aware scan cluster reordering is proposed to solve this problem. The proposed method uses a new scan correlation-aware clustering in order to place highly correlated scan cells adjacent to each other. The experimental results demonstrate that the proposed method achieves a significant power reduction with a relatively fast computational time compared with previous methods. Therefore, by improving the reliability of cryptography circuits in wireless sensor networks (WSNs) through significant test-power reduction, the proposed method can ensure the security and integrity of information in WSNs.
Highlights
Wireless sensor networks (WSN) are networks in which data obtained by observing the environment by a large number of sensors deployed in a specific area are sent securely to other sensors or centers in the network
The proposed method was compared with the pattern-based scan chain reordering method [26,27] and the logic topology-based scan chain stitching method [28] for shift-power reduction
A novel scan correlation-aware scan cluster reordering method is proposed for low test power in cryptographic circuits for wireless sensor networks
Summary
Wireless sensor networks (WSN) are networks in which data obtained by observing the environment by a large number of sensors deployed in a specific area are sent securely to other sensors or centers in the network These networks have the following capabilities: not connected to a central node, self-managing and healing, not connected to a specific network topology, multiway routing, preserving the integrity and confidentiality of data, and robustness [1]. HAC technology is widely used as a method for classifying objects in unsupervised machine learning and has multiple applications, such as pattern recognition, computational. HAC technology is widely used as a method for classifying objects in unsupervised machine learning and has multiple applications, such as pattern recognition, computational biology, and data mining [32]. The clustering method builds a solution by initially biology, and mining clustering method builds selecting a solutionand by initially assigning eachdata object to its[32]
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