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A novel method for packet forwarding in MANETs has been proposed in this paper. A node in the network acts as both host and router. Energy utilization of the node increases as all nodes in MANET operate as source, destination, and router to forward packets to the next hop ultimately to reach destination. Routers execute a variety of functions from simple packet classification for forwarding to complex payload revision. As the number of tasks and complexity increases, processing time required also increases resulting in significant processing delay in routers. The proposed work optimizes packet header at transport and network layer by calculating Unique Identifier using pairing function for the fields which do not change for a source–destination pair. This technique optimizes the processing cost of each packet header thereby conserving energy and reducing delay. It also simplifies the task of system administration. This paper elucidates an extension to basic AODV protocol, allowing routing of most packets without an explicit header, reducing the overhead of the protocol while still conserving its basic properties. The proposed method improves the network performance significantly compared to AODV, MTPR, and S-AODV protocol.

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Recommendation systems in travel applications have a purpose to provide custom-made results to travelers while making a travel plan. These recommendation systems should be adaptable if user preferences change dynamically. To get custom-made results, the recommendation systems should be provided with traveler’s interests such as traveler’s specifications, preferences concerning destinations, type of activities they are very much interested to do in their travel plan. However, current recommendation systems are unable to fetch required features from travelers and destination places. Moreover, current systems are lacking to recommend destination places by considering social interest and their experience (i.e. recommendations by considering many traveler’s interests, for example, when two travelers interest matches the places visit by the first traveler can be suggested to the second traveler or vice-versa and travelers experience concerning particular destination place). To address the issues and problems of the current system, we propose and implement a tourist recommendation system which is termed as Average Cumulative Rating (ACR) that supports the extraction of rating and experience which is in the form of text description. The overall score is computed based on rating and traveler experience and feed to the traditional Matrix Factorization (MF) technique for providing custom results for travelers.

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