Dynamic Cipher Puzzle for Efficient Broadcast Authentication in Wireless Sensor Networks.

The use of signature-based broadcast authentication for code and data dissemination in wireless sensor networks (WSNs) cannot be avoided. It increases security but requires high computation. Adversaries can exploit the latter condition as an opportunity to send many false signatures. Filtering methods can overcome this vulnerability. Cipher Puzzle is a filtering method that has low storage overhead along with high security, especially against denial of service (DoS) attacks. However, its number of hash iterations cannot be bounded, which causes sender-side delay. This paper proposes a Dynamic Cipher Puzzle (DCP), which uses a threshold function to limit the number of hash iterations. Hence, time at the sender-side can be used more efficiently. Besides, its dynamic puzzle-strength increases the obscurity of the transmitted packet. Simulation and experimental results were analyzed with Arduino 2560. The theoretical results show that the quadratic function outperformed the compared methods. The scheme decreased sender-side delay by 94.6% with a guarantee of zero solution probability in 1.728 × 10 - 13 . The experimental results show that the consumption of resources at the sensor node increases with an acceptable value. Moreover, DCP increases the complexity for the attacker to implement probability and signature-based DoS attacks.

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction.

OBJECTIVES: In recent years, research on the use of electronic noses (e-nose) has developed rapidly, especially in the medical and food fields. Typically, e-nose is coupled with machine learning algorithms to detect or predict multiple sensory classes in a given sample. In many cases, comprehensive and complete experiments are required to ensure the generalizability of the predictive model. For this reason, homogeneous data sets are important to use. Homogeneous data sets refer to the data sets obtained from different observations in almost similar environmental condition. In this data article, e-nose homogeneous data sets are provided for beef quality classification and microbial population prediction. DATA DESCRIPTION: This data set is originated from 12 type of beef cuts. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors for 2220 min. The formal standards, issued by the Meat Standards Committee, are used as a reference in labeling beef quality. Based on the number of microbial populations, meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled. The data set is formatted in "xlsx" file. Each sheet represents one beef cut. Moreover, data sets are good cases for feature selection algorithm stability test, especially to solve sensor array optimization problems.