In heterogeneous scenarios, nodes greatly differ with respect to their capabilities and mobility patterns. Moreover, episodic connectivity in opportunistic networks further aggravates the problem of finding a suitable next-hop to obviate unnecessary utilization of network resources. In this paper, we present a Multiattribute Routing Scheme (MARS) based on “Simple Multiattribute Rating Technique” (SMART) that collects samples of vital information about a node's different characteristics. This stochastic picture of a node behavior in multiple dimensions is then effectively employed in calculating its next-hop fitness. We also devise a method based on learning rules of neural networks which dynamically determines relative importance of each dimension to maximize next-hop utility of a node. With simulations, using synthetic and real mobility traces against well-known utility-based schemes, we show that MARS can achieve better delivery ratios despite introducing limited redundancy within the network.

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node synthetic and real mobility traces nexthop delivery ratios network multiattribute routing scheme opportunistic networks mars episodic connectivity method wellknown utilitybased redundancy stochastic picture vital information simple multiattribute rating technique learning rules of neural networks scenarios nodes

Sadaf Yasmin,Rao Naveed Bin Rais,Amir Qayyum,.Resource Aware Routing in Heterogeneous Opportunistic Networks. 2016 (),.

Sadaf Yasmin,Rao Naveed Bin Rais,Amir Qayyum,"Resource Aware Routing in Heterogeneous Opportunistic Networks" 2016

Sadaf Yasmin,Rao Naveed Bin Rais,Amir Qayyum,"Resource Aware Routing in Heterogeneous Opportunistic Networks"