B. Computer Systems Organization
F. Hoseini; A. Shahbahrami; A. Yaghoobi Notash
Abstract
One of the most important and typical application of wireless sensor networks (WSNs) is target tracking. Although target tracking, can provide benefits for large-scale WSNs and organize them into clusters but tracking a moving target in cluster-based WSNs suffers a boundary problem. The main goal of ...
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One of the most important and typical application of wireless sensor networks (WSNs) is target tracking. Although target tracking, can provide benefits for large-scale WSNs and organize them into clusters but tracking a moving target in cluster-based WSNs suffers a boundary problem. The main goal of this paper was to introduce an efficient and novel mobility management protocol namely Target Tracking Based on Virtual Grid (TTBVG), which integrates on-demand dynamic clustering into a cluster- based WSN for target tracking. This protocol converts on-demand dynamic clusters to scalable cluster-based WSNs, by using boundary nodes and facilitates sensors’ collaboration around clusters. In this manner, each sensor node has the probability of becoming a cluster head and apperceives the tradeoff between energy consumption and local sensor collaboration in cluster-based sensor networks. The simulation results of this study demonstrated that the efficiency of the proposed protocol in both one-hop and multi-hop cluster-based sensor networks.
F.1. General
A. Telikani; A. Shahbahrami; R. Tavoli
Abstract
Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that ...
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Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved against association rule mining method. This process strongly rely on the minimizing the impact of data sanitization on the data utility by minimizing the number of lost patterns in the form of non-sensitive patterns which are not mined from sanitized database. This study proposes a data sanitization algorithm to hide sensitive patterns in the form of frequent itemsets from the database while controls the impact of sanitization on the data utility using estimation of impact factor of each modification on non-sensitive itemsets. The proposed algorithm has been compared with Sliding Window size Algorithm (SWA) and Max-Min1 in term of execution time, data utility and data accuracy. The data accuracy is defined as the ratio of deleted items to the total support values of sensitive itemsets in the source dataset. Experimental results demonstrate that proposed algorithm outperforms SWA and Max-Min1 in terms of maximizing the data utility and data accuracy and it provides better execution time over SWA and Max-Min1 in high scalability for sensitive itemsets and transactions.
