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Due to the limited lifetime of the nodes in ad hoc and sensor networks, energy efficiency needs to be an important design consideration in any routing algorithm. It is known that by employing a virtual backbone in a wireless network, the efficiency of any routing scheme for the network can be improved. One common design for routing protocols in mobile ad hoc networks is to use positioning information; we use the node-s geometric locations to introduce an algorithm that can construct the virtual backbone structure locally in 3D environment. The algorithm construction has a constant time., {"references":["A.E. Abdallah, T. Fevens, and J. Opatrny. 3d local algorithm for\ndominating sets of unit disk graphs. In Proc. of the Canadian Conference\non Computational Geometry (CCCG2010), pages 35-38, Winnipeg -\nCanada, 2010.","K. Alzoubi, X. Li, Y. Wang, P. Wan, and O. Frieder. Geometric spanners\nfor wireless ad hoc networks. IEEE Transactions on Parallel and\nDistributed Systems, 14(4):408-421, 2003.","K. Alzoubi, P. Wan, and O. Frieder. Distributed heuristics for connected\ndominating sets in wireless ad hoc networks. Journal of Communications\nNetworks, 4(1):141-149, 2002.","K. Alzoubi, P. Wan, and O. Frieder. Message-optimal connecteddominating-\nset construction for routing in mobile ad hoc networks. In\nProc. of the Third ACM international Symp. Mobile Ad Hoc Networking\nand Computing (MobiHoc 02), pages 157-164, Lausanne, June 2002.","P. Bose, P. Morin, I. Stojmenovic, and J. Urrutia. Routing with\nguaranteed delivery in ad hoc wireless networks. Wireless Networks\njournal, 7(6):609-616, 2001.","V. Chvatal. A greedy heuristic for the set covering problem. Math.\nOper. Res. 4, pages 233-235, 1979.","B. Clark, C. Colbourn, and D. Johnson. Unit disk graphs. Discrete\nMath, 86:165-177, 1990.","J. Czyzowicz, S. Dobrev, T. Fevens, H. Gonzalez-Aguilar, E. Kranakis,\nJ. Opatrny, and J. Urrutia. Local algorithms for dominating and\nconnected dominating sets of unit disk graphs. In Proc. of the 8th Latin\nAmerican Theoretical Informatics Symposium LATIN08, April 2008.","B. Das and V. Bharghavan. Routing in ad-hoc networks using minimum\nconnected dominating sets. In Proc. of the IEEE International Conference\non Communications (ICC 97), Vol. 1, pages 376-380, Monral, June\n1997.\n[10] B. Gao, Y. Yang, and H. Ma. An efficient approximation scheme for\nminimum connected dominating set in wireless ad hoc networks. In\nProc. of the IEEE Vehicular Technology Conference, pages 2744-2748,\nLos Angeles, September 2004.\n[11] M. Garey and D. Johnson. Computers and Intractability-A Guide to the\nTheory of NP-Completeness. CA:Freeman, San Francisco, 1979.\n[12] D. Hochbaum. Approximation Algorithms for NP-Hard Problems. PWA\nPublishing Company, Boston, MA, 1995.\n[13] D. Johnson. Approximation algorithms for combinatorial problems.\nJournal of Computer and System Sciences, pages 256-278, 1974.\n[14] R. Karp. Reducibility among combinatorial problems. In Proceedings\nof the Symposium on Complexity of Computer Computations, pages 85-\n103, 1972.\n[15] M. Mauve, J. Widmer, and H. Hartenstein. A survey of position-based\nrouting in mobile ad-hoc networks. IEEE Network Magazine, 15(6):30-\n39, 2001.\n[16] P. Slavik. A tight analysis of the greedy algorithm for set cover. In\nProc. of the 28th ACM Symposium on Theory of Computing (STOC),\npages 435-441, 1996.\n[17] J. Wu, F. Dai, M. Gao, and I. Stojmenovic. On calculating poweraware\nconnected dominating sets for efficient routing in ad hoc wireless\nnetworks. IEEE/KICS Journal of Communications and Networks,\n4(1):59-70, 2002.\n[18] J. Wu and H. Li. A dominating-set-based routing scheme in ad hoc\nwireless networks. Telecommunication Systems, 18(3):13-36, 2001."]}

As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Naïve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features., {"references":["T. McElroy and J. J. Seta, \"Framing the frame: How task goals\ndetermine the likelihood and direction of framing effects,\" Judgment and\nDecision Making, Vol. 2 (4), Aug 2007, pp. 251-256.","F. Iqbal, R. Hadjidj, B.C.M. Fung, M. Debbabi, \"A novel approach of\nmining write-prints for authorship attribution in email forensics,\" Digital\nInvestigation, Vol. 5 (1), 2008, pp. 42-51.","O. De Vel, A. Anderson, M. Corney, and G. Mohay, \"Mining Email\nContent for Author Identification Forensics\", SIGMOD Record, Vol.\n30(4), 2001, pp. 55-64.","A. Gray, P. Sallis, and S. MacDonell, \"Software Forensics: Extending\nAuthorship Analysis Techniques to Computer Programs,\" in the 3rd\nBiannual Conference International Association of Forensic Linguists,\n1997.","M. Koppel, S. Argamon, and A.R. Shimoni, \"Automatically categorizing\nwritten texts by author gender,\" Literary and Linguistic Computing, Vol.\n17(4), 2002, pp. 401-412.","A. Abbasi, and H. Chen, \"Writeprints: A stylometric approach to\nidentity-level identification and similarity detection in cyberspace,\"\nACM Transactions on Information Systems, Vol. 26(2), March 2008,\npp. 1-29.","M. Koppel, J. Schler, and S. Argamon, \"Computational methods in\nauthorship attribution,\" Journal of the American Society for Information\nScience and Technology, Vol. 60(1), 2009, pp. 9-26.","R. Zheng, J. Li, H. Chen, and Z. Huang, \"A framework for authorship\nidentification of online messages: Writing-style features and\nclassification techniques,\" Journal of the American Society for\nInformation Science and Technology, Vol. 57(3), February 2006, pp.\n378-393,.","F. Iqbal, H. Binsalleeh, B.C.M. Fung, and M. Debbabi, \"Mining\nwriteprints from anonymous emails for forensic investigation,\" Digital\nInvestigation, 2010, pp. 1-9.\n[10] L. Breiman, \"Random forests,\" Machine Learning, 2001, pp. 5-32.\n[11] P. Domingos and M. Pazzani, \"On the optimality of the simple Bayesian\nclassifier under zero-one loss,\" Machine Learning, 2001, pp. 103-137.\n[12] DJ. Hand and K. Yu, \"Idiot's Bayes - not so stupid after all?,\"\nInternational Statistical Review, Vol. 69(3), 2001, pp. 385-399.\n[13] L. Kaelbling, \"Enron email dataset,\" CALO Project,\nhttp://www.cs.cmu.edu/enron/, August 21 2009.\n[14] I. Witten and E. Frank, \"Data Mining: Practical Machine Learning Tools\nand Techniques,\" Margan Kaufmann, San Francisco, 2nd edition, 2005."]}