scholarly journals An Advanced Optimization Protocol for Cross Layer Routing in MANET

Through advancements in communication techniques, there have been significant advances in information technology. Information exchange is captive from infrastructure-based to infrastructure-free techniques. Development in wireless technology and portable computing systems has brought interest in the mobile communication field. The increasing flexibility of people around the network has generated demand for mobile networks such as MANET that can be deployed rapidly and without infrastructure. When users of MANET expect effective communication, seamless reliability is currently crucial across heterogeneous mobile wireless systems. The main challenges in adhoc networks are regular topology changes due to flexibility and limited battery capacity for mobile devices. Depletion of the power source may cause early links in the network to be unavailable. Often, due to frequent breaks in path and affects the performance adversely needed for applications as well as node flexibility. This research paper aims to test and suggest a cross-layer interaction model between transport layer, routing layer, data link layer, and physical layer with power-efficient routing intentions. Using the proposed link prediction model, the article modified the incorporated AODV routing protocol by the link prediction algorithm to predict the accessibility time and even before the connection breaks. The proposed algorithm increases the service quality of the network and NS2 simulator checked the model. The simulation results indicate that the performance of the AODV routing algorithm is much more effective than the current algorithm.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.


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