There are many real time environment in which the use of wireless sensor networks (WSNs) is growing, especially the applications like military, health monitoring, security monitoring etc. WSN is nothing but collection of small, tiny sensor nodes which is having resource constraints like battery life. Therefore many research comes on WSNs are targeted on improving the energy efficiency of WSNs and extend the lifetime. In this paper, we are focusing to work on packet scheduling scheme which plays vital role to improve the energy efficiency and QoS performances. Currently there are many packet scheduling schemes used by researchers in WSNs applications such as First Come First Serve (FCFS), preemptive priority scheduling, nonpreemptive priority scheduling methods. However this method suffered from limitations like higher routing overhead, more end to end delay and hence more energy consumption. In this paper, we are aiming to investigate new algorithm which overcomes the limitations of this existing method and achieves the better QoS and less energy consumption results. The investigated algorithm is called Dynamic Multilevel Priority (DMP) scheduling method. As the name indicates, this method works dynamically and as per the requirement of packet scheduling. There are three queues used by this algorithm for priority scheduling and applications like real time, non-real time. In first queue, real time packets processed with highest priority. In second queue, non-real time data packets processed with highest priority than third queue. In third queue, non real time data packets those are sensed at local are processed. The practical evaluation of this method is done using NS2.
Mobility in communication is an important factor now-adays.The mobile telecommunication service providers are trying to achieve maximum mobility. In mobile network there are many factors affecting mobility. It is thus necessary to look after the bandwidth strength, RSS, load balancing and many other vital factors. This paper has explained a very important mechanism of mobile networks which is handoff and its various types. The prediction part and the various existing strategies come into play and have a role in deciding the activities based on user movement. It is immensely important to predict the user movement and keep the resources ready which will be helpful to achieve a seamless handoff. Such predictions can be useful to minimize the delay, call drops and have real life application in traffic management and disease spread prediction.
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