Lightweight authentication for recovery in wireless sensor networks

Wireless sensor networks (WSNs) suffer from a wide range of security attacks due to their limited processing and energy capabilities. Their use in numerous mission critical applications, however, requires that fast recovery from such attacks be achieved. Much research has been completed on detection of security attacks, while very little attention has been paid to recovery from an attack. In this paper, we propose a novel, lightweight authentication protocol that can secure network and node recovery operations such as re-clustering and reprogramming. Our protocol is based on hash functions and we compare the performance of two well-known lightweight hash functions, SHA-1 and Rabin. We demonstrate that our authentication protocol can be implemented efficiently on a sensor network test-bed with TelosB motes. Further, our experimental results show that our protocol is efficient both in terms of computational overhead and execution times which makes it suitable for low resourced sensor devices.

eLearning content authentication using bipartite matching

Content authenticity and correctness is one of the important challenges in eLearning as there can be many solutions for one specific problem in the cyber space. Therefore, we feel the necessity of mapping problem to solutions using graph partition and weighted bipartite matching. This paper presents a novel architecture and methodology for a personal eLearning system called PELS that is developed by us. We also present an efficient algorithm to partition question-answer (QA) space and explore best possible solution to a particular problem. Our approach can be efficiently applied to social eLearning space where there is one-to-many and many-to-many relationship with a level of bonding. The main advantage of our approach is that we use QA ranking by adjusted edge weights provided by subject matter experts (SME) or expert database. Finally, we use statistical methods called confidence interval and hypothesis test on the data to check the reliability and dependability of the quality of results.