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The authors’ choice of WSNs is motivated mainly by the use of real experiments needed in most college courses on WSNs.. The authors demonstrate the performance of the proposed algorithms that contribute to the familiarization of the learners in the field of WSNs.. They present a virtual laboratory platform (VLP) of baptized mercury, allowing students and researchers to make practical work (PW) on different aspects of mobile wireless sensor networks.. In Mobile, Wireless and Sensor Networks: A Clustering Algorithm for Energy Efficiency and Safety, the authors use an approach based on computing of the weight of each node in the network as the proposed technique to deal with this problem.. The platform presented here aims at showing the feasibility, the flexibility, and the reduced cost using the authors’ approach.. Wireless Integrated Network Sensors (WINS) technology – that began to appear with the commercialization of the Internet – has made it possible to access.. 4 Information Processing and Routing in Wireless Sensor Networks now available to integrate a rich set of sensors onto the same CMOS chip.. These usual experiments, however, require an expensive investment and many nodes in the classroom.. Commercially available sensors now include thermal, acoustic/ultrasound, and seismic sensors, magnetic and electromagnetic sensors, optical trans.. Wireless networking covers a variety of topics involving many challenges The main concern of clustering approaches for mobile wireless sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime.. For a successful clustering approach, the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. a5171a3e95