Prediction method of node movement using Markov Chain in DTN

Abstract

This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.

Enhanced spectrum aggregation based frequency‐band selection routing protocol for cognitive radio ad‐hoc networks

“…A set of CR user current path states are taken as s = { s 1 , s 2 … s n }, where s 1 , s 2 represents the geographical co‐ordinates (latitude and longitude). The CR user's path from state s 1 to state s n is represented in transition matrix which are formed using Markov's Chain algorithm as follows where m is the possible number transitions.…”

An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes

“…Figures 5-7 show delivery ratio, overhead ratio, and delivery latency of the proposed protocol, PRoPHET, and RPC, for varying the buffer size of a mobile node, where delivery predictability, overhead ratio, and delivery latency are defined as follow: delivery ratio = N dm N cm (8) overhead ratio = N rm − N dm N dm (9) delivery latency = SD dm N dm (10) where N dm , N cm , N rm , and SD dm represent the number of successfully delivered messages, the number of created messages, the number of relayed messages, and the sum of the delays of the all delivered messages, respectively. Figure 5 shows delivery ratio for varying buffer size.…”

“…In related works, movement trajectory [10], movement direction, speed, estimated direction change, location [11,12], movement pattern [13], contact period [14], last contact duration, last contact time, current contact time [15], contact count, contact duration, the amount of exchanged messages [16], contact history [17], node type [20], historical throughput, historical contact time [21], encounter duration, non-encounter duration [22], rate of encounters [23], and delivery predictability of previously contact nodes [24] are used to decide a message forwarding. Most of the mentioned context information, however, reflect individual characteristics of contact nodes or node itself, and collective characteristics of nodes have not been considered well to deliver a message to a destination node.…”

“…In [10], the movement trajectory of a node is estimated by using the Markov chain based on the speed and direction of movement of nodes and the movement trajectory is used to decide whether a message is forwarded or not. In [11], the information of current location, movement direction, speed, and estimated direction change of a mobile node with GPS (global positioning system) is used to decide whether the node approaches near the destination node and a message is forwarded if a node has the closest estimated approach to the destination node.…”