2011/03/08 by Ranjan Pal, Pal, Ranjan, Aravind Kailas +1
Computer Science · Decision Sciences · #Auction Theory and Applications #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Social and Information Networks (cs.SI) #Wireless Networks and Protocols
paper · pdf · doi:10.48550/arxiv.1103.1544
openalex publication_date 2011/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Wireless social community networks (WSCNs) is an emerging technology that operate in the unlicensed spectrum and have been created as an alternative to cellular wireless networks for providing low-cost, high speed wireless data access in urban areas. WSCNs is an upcoming idea that is starting to gain attention amongst the civilian Internet users. By using special WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs reflect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise asimple to implement, successful a mechanism is successful if it achieves its intended purpose. For example in this work, a successful mechanism would help install routers in a locality, budget-balanced, ex-post efficient, and individually rational a mechanism is individually rational if the benefit each agent obtains is greater than its cost. auction-based mechanism that generates the optimal number of features a router should have and allocates costs to residential users in proportion to the feature benefits they receive. Our problem is important to a new-entrant SCNP when it wants to design its multi-feature routers with the goal to popularize them and increase their deployment in a residential locality. Our proposed mechanism accounts for heterogeneous user preferences towards different router features and comes up with the optimal (feature-set, user costs) router blueprint that satisfies each user in a locality, in turn motivating them to buy routers and thereby improve deployment.