Optimal mobility patterns in epidemic networks

Abstract

Disruption Tolerant Networks or opportunistic networks represent a class of networks where there is no contemporaneous path from source to destination. In other words, these are networks with intermittent connections. These networks are generally sparse or highly mobile wireless networks. Each node has a limited radio range and the connections between nodes may be disrupted due to node movement, hostile environments or power sleep schedules, etc. A common example of such networks is a sensor network monitoring nature or military field or a herd of animals under study. Epidemic routing is a widely proposed routing mechanism for data propagation in these type of networks. According to this mechanism, the source copies its packets to all the nodes it meets in its radio range. These nodes in turn copy the received packets to the other nodes they meet and so on. The data to be transmitted travels in a way analogous to the spread of an infection in a biological network. The destination finally receives the packet and measures are taken to eradicate the packet from the network. The task of routing in epidemic networks faces certain difficulties involving minimizing the delivery delay with a reduced consumption of resources. Every node has severe power constraints and the network is also susceptible to temporary but random failure of nodes. In the previous work, the parameter of mobility has been considered a constant for a certain setting. In our setting, we consider a varying parameter of mobility. In this framework, we determine the optimal mobility pattern and a forwarding policy that a network should follow in order to meet the trade-off between delivery delay and power consumption. In addition, the mobility pattern should be such that it can be practically incorporated. In our work, we formulate an optimization problem which is solved by using the principles of dynamic programming. We have tested the optimal algorithm through extensive simulations and they show that this optimization problem has a global solution.