Efficient path key establishment for wireless sensor networks

Key predistribution schemes have been proposed as means to overcome wireless sensor network constraints such as limited communication and processing power. Two sensor nodes can establish a secure link with some probability based on the information stored in their memories, though it is not always possible that two sensor nodes may set up a secure link. In this paper, we propose a new approach that elects trusted common nodes called “Proxies” which reside on an existing secure path linking two sensor nodes. These sensor nodes are used to send the generated key which will be divided into parts (nuggets) according to the number of elected proxies. Our approach has been assessed against previously developed algorithms, and the results show that our algorithm discovers proxies more quickly which are closer to both end nodes, thus producing shorter path lengths. We have also assessed the impact of our algorithm on the average time to establish a secure link when the transmitter and receiver of the sensor nodes are “ON.” The results show the superiority of our algorithm in this regard. Overall, the proposed algorithm is well suited for wireless sensor networks

Algerian Modern Colloquial Arabic Speech Corpus (AMCASC): regional accents recognition within complex socio-linguistic environments

The Algerian linguistic situation is very intricate due to the ethnic, geographical and colonial occupation influences which have lead to a complex sociolinguistic environment. As a result of the contact between different languages and accents, the Algerian speech community has acquired a distinctive sociolinguistic situation. In addition to the intra- and inter- lingual variations describing day-to-day linguistic behavior of the Algerian speakers, their speech is characterized by the presence of many linguistic phenomena such as bilingualism and code switching. The study of automatic regional accent recognition in such a type of environment is a new idea in the field of automatic languages, dialect and accent recognition especially that previous studies were conducted using monolingual evaluation data. The assessment of the effectiveness of GMM-UBM and i-vectors frameworks for accent recognition approaches through the use of the Algerian Modern Colloquial Arabic Speech Corpus (AMCASC), which is a linguistic resource collected for this purpose, shows that not only the recording conditions mismatch, channels mismatch, recordings length mismatch and the amplitude clipping which have a non-desirable effect on the effectiveness of these acoustic approaches but also language contact phenomena are other perturbation sources which should be taken into consideration especially in real life applications

Regional accents recognition based on i-vectors approach: The case of the Algerian linguistic environment

Regional accents recognition is one of the most important research topics in speech processing. However, the development of such systems is time-consuming and an expensive process since it requires collecting and processing a large amount of training and evaluation data for each target accent. This work presents some preliminary results about the Algerian regional accents recognition using the i-vectors approach. The experiments have been carried out using a dataset collected from the east and the center of Algeria. The obtained results show the benefits of using the i-vectors approach to recognize both regional accents and to assess the impact of evaluation data quality on the accuracy of the proposed recognition method. However, it has been noted that the confusion rates obtained between both accents may be related to the evaluation data quality, the linguistic similarity existing between both accents and some languages contact phenomenon such as the code switching