Paper Title
Optimization of Wireless Sensor Networks Design for Energy Saving using Swarm Intelligence

Energy saving in wireless sensor networks (WSNs) is a critical problem for a diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered a practical solution to this problem. Each cluster should have a controller denoted as a Cluster Head (CH), and a number of nodes located within its supervision area. Clustering demonstrated a valid result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSN, to make efficient use of the available energy sources, and reducing traffic transmission can be achieved. In solving this problem, we need to find the optimal distribution of sensors and CHs; thus we can increase the network lifetime while minimizing the energy consumption. In this talk, we are proposing three hybrid clustering algorithms based K-Means clustering and Particle Swarm Optimization (PSO) to achieve efficient energy management of the WSNs. Our proposed algorithms are compared to the traditional clustering techniques such as the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and K-means clustering. Biography Alaa F. Shetais a Professorat the Department of Computing Sciences, Texas A&M University-Corpus Christi, TX, USA. He received his B.E., M.Sc. degrees in Electronics and Communication Engineering from the Faculty of Engineering, Cairo University in 1988 and 1994, respectively. He received his Ph.D. from the Computer Science Department, School of Information Technology and Engineering, George Mason University, Fairfax, VA, USA in 1997. Prof. Sheta published more than 120 journal and conference papers. He successfully graduated about two dozen M.Sc. graduate students together with some Ph.D. students in USA, UK,and Syria. He also published two books in the area of Landmine Detection and Classification and Image Reconstruction of a Manufacturing Process by LAP LAMBERT Academic Publishing. He is also the co-editor of the book entitled,” Business Intelligence and Performance Management - Theory, Systems,and Industrial Applications” by Springer Verlag, United Kingdom, published in March 2013. He received the Best Poster Award from the SGAI International Conference on Artificial Intelligence, Cambridge, UKin December 2011 for his publication on Quality Management of Manufacturing Processes. He received funding from a number of national and international agency in Egypt, Saudi Arabia, Jordan,andUSA. He was nominated as the Program Chair of the Science and Information Conference 2013 sponsored by the SAI Organization and Co-Sponsor by the IEEE Computational Intelligence Society, London, theUK on October 2013. He has been an invited speaker in number of national and international conferences. He was a consultant for the Ministry of Communication and Information Technology, Egypt on the years 2002-2004. During then, he was the Vision Group Leader for the Information Infrastructure Program and project manager of the Economic Activities Project developed in collaboration with the Egyptian Ministry of Agriculture and both the Cancer Registry Network and the Telemedicine Projects with the Egyptian Ministry of Health. He was also hired as a part-time consultant by the UNDP for the Smart School Pilot Project on the year 2003. He was the Vice President of the Arab Computer Society (ACS) in 2011. He held number of management position during the years 2006-2009. He was the Vice Dean of Prince Abdullah Bin Ghazi Faculty of Science and Information Technology, Al-Balqa Applied University (BAU), Jordan (2008-2009) and the Dean Assistant for Planning and Development, College of Information Technology, BAU, Jordan (2006-2008). His scientific research interests include Evolutionary Computation, Software Reliability Modeling, Software Cost Estimation, Modeling and Simulation of Dynamical Nonlinear Systems, Image Processing, Robotics, Swarm Intelligence, Automatic Control, Fuzzy Logic and Neural Networks.