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oai:figshare.com:article/33803251 2026-09-15T15:34:13Z portal_1197 item_type_3 month_year_09_2026

Data for Source location privacy aware routing protocols selection results Chen Gu (244594) Matthew S. Bradbury (24811657) Jack Kirton (24883243) Arshad Jhumka (21512825) Wireless sensor networks -- Security measures Location-based services -- Security measures Computer networks -- Security measures Migrated from ePrints Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Many routing protocols have been proposed that provide SLP, all of which provide a trade-off between SLP and energy. Experiments have been conducted to gauge the performance of the proposed protocols under different network parameters such as noise levels. As that there exists a plethora of protocols which contain a set of possibly conflicting performance attributes, it is difficult to select the SLP protocol that will provide the best trade-offs across them for a given application with specific requirements. In this paper, we propose a methodology where SLP protocols are first profiled to capture their performance under various protocol configurations. Then, we present a novel decision theoretic procedure for selecting the most appropriate SLP routing algorithm for the application and network under investigation. We show the viability of our approach through different case studies.<br><br>Data record consists of a data archive in .tar.gz format. This is the dataset used to generate the results for the journal paper " A Decision Theoretic Framework for Selecting Source Location Privacy Aware Routing Protocols in Wireless Sensor Networks" at Future Generation Computer Systems (FGCS) 2018. To replicate the results of the paper: All algorithms are implemented in the folder: algorithm/adaptive_spr_notify algorithm/phantom algorithm/phantom_walkabouts algorithm/ilprouting algorithm/protectionless algorithm/protectionless_ctp Set up the repository and its dependencies as specified by README.md Place all the six subfolders from the data archive into results/ in the repository. To generate graphs, run "./create.py phantom_walkabouts graph-dominating-min-max". 2017-11-10T00:00:00Z Dataset Dataset 10.82444/warw.33803251.v1 https://figshare.com/articles/dataset/Data_for_Source_location_privacy_aware_routing_protocols_selection_results/33803251 CC BY 4.0