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Ad-hoc On-demand Distance Vector (AODV)routing protocol is the most popular routing protocol for mobile ad-hoc networks (MANETs). This paper enhances AODV protocol by minimizing its control messages overhead. Enhancements include developing two improved versions of AODV protocol. These two versions use Global Positioning System (GPS) to limit the routing discovery control messages. The first version (AODV-LAR) is a variation of the LocationAided Routing (LAR) protocol. The second version (AODVLine) limits nodes participating in route discovery between source and destination based on their distance from the line connecting source and destination. We evaluate performance of the two proposed protocols using two simulation scenarios. The simulation was done using JIST/SWANS simulator. The results show that the two proposed protocols outperform the original AODV, where the results show a significant reduction of control overhead and delay compared to the original AODV. Results also show that the delivery ratio in the proposed protocols is comparable to the delivery ratio in the original AODV.

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E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. Nowadays, detecting and filtering are still the most feasible ways of fighting spam emails. There are many reasonably successful spam email filters in operation. The identification of spam plays an important role in current anti-spam mechanism.For improving the accuracy of spam detection, an improved Filtering technique is presented which is based on the Improved Digest algorithm and DBSCAN clustering algorithm.Using this technique, mails are represented using improved digest algorithm and then clustered using DBSCAN clustering algorithm. All similar emails which always categorized as spam are identified and clustered together where good mails that don't look similar like other mails are not clustered. This method greatly improves the filtering accuracy against latest proposed algorithms by 30 % and improves the resistance of spam detection against increased obfuscation effort by spammers, while keeping miss-detection of good emails at a similar level of older filtering methods.

Abstract:In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also developed an improved point symmetry-based distance measure and proved its efficiency. We developed a k-means algorithm with a novel distance measure that improves the performance of the classical k-means algorithm. The proposed algorithm does not have the worst-case bound on running time that exists in many similar algorithms in the literature.Experimental results shown in this paper demonstrate the effectiveness of the proposed algorithm. We compared the proposed algorithm with the classical k-means algorithm. We presented the proposed algorithm and their performance results in detail along with avenues of future research.