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Mobile Ad Hoc Networks (MANETs) are networks with self-organizing capabilities and without a fixed infrastructure. Wireless nodes communicate among themselves using multi-hop radio relaying, without requiring the packets to pass through a central access point or a base station. Routing is a function of speed and stability with which the nodes are able to acquire addresses. Efficient routing forms the basis of a fast and reliable communication network. In a highly mobile and infrastructure-less scenario, pre-configuration of addresses is not possible. Therefore node addresses need to be configured dynamically with minimum delay and packet loss. The main task of an address auto-configuration protocol is to manage the resource address space. It must be able to select, allocate, and assign a unique network address to an un-configured node. This paper proposes a new address auto-configuration protocol for mobile ad hoc networks. The scheme uses virtual address space for addressing new nodes joining a network. The aim is to map one point from virtual address sheet to exactly one new node. The reason for using the term "virtual" is that the whole corresponding address space is a 2D flat sheet and each point of this sheet is virtually mapped to a node in MANET. The protocol uses coordinate values for generating addresses.

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Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users' perspective, by analyzing how far a recommendation system deviates from user's expectations in receiving popular items, and (ii) by analyzing the amount of exposure that long-tail items receive, measured by overall catalog coverage and novelty. In this paper, we examine the first point of view in the book domain, although the findings may be applied to other domains as well. To this end, we analyze the well-known Book-Crossing dataset and define three user groups based on their tendency towards popular items (i.e., Niche, Diverse, Bestsellerfocused). Further, we evaluate the performance of nine state-of-the-art recommendation algorithms and two baselines (i.e., Random, MostPop) from both the accuracy (e.g., NDCG, Precision, Recall) and popularity bias perspectives. Our results indicate that most state-of-the-art recommendation algorithms suffer from popularity bias in the book domain, and fail to meet users' expectations with Niche and Diverse tastes despite having a larger profile size. Conversely, Bestseller-focused users are more likely to receive high-quality recommendations, both in terms of fairness and personalization. Furthermore, our study shows a tradeoff between personalization and unfairness of popularity bias in recommendation algorithms for users belonging to the Diverse and Bestseller groups, that is, algorithms with high capability of personalization suffer from the unfairness of popularity bias. Finally, across the models, our results show that WMF and VAECF can provide a higher quality recommendation when considering both accuracy and fairness perspectives.