Exploring Markov models for the allocation of resources for proactive handover in a mobile environment

Exploiting Resource Contention in Highly Mobile Environments and Its Application to Vehicular Ad-Hoc Networks

“…The state diagram of the proposed proactive resource allocation using a decision system is shown in Fig. 10 and the results obtained from our previous work in [4] showed that by using this proactive model, there is a smaller mean number of jobs in the system as well as a smaller blocking probability for the same value of utilization. This was done by solving the probability states of the queueing model as shown in Fig.…”

“…We start our analysis by looking at classical handover which occurs when an MN changes its Point of Attachment (PoA) from the current wireless network to another network using a reactive approach i.e., the handover is initiated only after the MN is within the network coverage of the next wireless network. This section explains and represents the classical handover approach [4] for wireless and mobile environments where the request from MN is placed in the queue to be served i.e., waiting to use the data channel as shown in Fig. 1.…”

“…2. Assuming a system in a steady state, the state probabilities, P i 's, can be obtained as in Equation 4which has been derived in [4].…”

“…These two parameters allow us to determine the times when different nodes will need to acquire and release resources due to mobility. Therefore, it is possible to explore periods of contention for resources which, in turn, will allow us to develop heuristic algorithms to optimise resource allocation in a heterogeneous environment [4].…”

Hybrid K-Mean Clustering and Markov Chain for Mobile Network Accessibility and Retainability Prediction

“…For the Markov chain fulfilling the memoryless assumption, the transition probability is expressed as [21]:…”