Providing air-time guarantees across a group of clients forms a fundamental building block in sharing an access point (AP) across different virtual network providers. Though this problem has a relatively simple solution for downlink group scheduling through traffic engineering at the AP, solving this problem for uplink (UL) traffic presents a challenge for fair sharing of wireless hotspots. Among other issues, the mechanism for uplink traffic control has to scale across a large user base, and provide flexible operation irrespective of the client channel conditions and network loads. In this study, we propose the SplitAP architecture that address the problem of sharing uplink airtime across groups of users by extending the idea of network virtualization. Our architecture allows us to deploy different algorithms for enforcing UL airtime fairness across client groups. In this study, we will highlight the design features of the SplitAP architecture, and present results from evaluation on a prototype deployed with: (1) LPFC and (2) LPFC+, two algorithms for controlling UL group fairness. Performance comparisons on the ORBIT testbed show that the proposed algorithms are capable of providing group air-time fairness across wireless clients irrespective of the network volume, and traffic type. The algorithms show up to 40% improvement with a modified Jain fairness index.
Abstract-One of the most compelling and immediate applications of pervasive computing would be to use RF technology to support low-cost, long-lived and continual tracking of assets. Unfortunately, initial solutions have not yet led to widespread deployment. We believe that meeting the economic and system requirements of this application requires a redesign of the tag, the transmission protocol, and the algorithms used by basestations to identify tags, all with the underlying goal of reducing cost and power consumption through simplification. In this paper, we propose a new inventory tracking system, called RollCall, in which a transmit-only RFID tag will be attached to every item, and these tags will report their presence to the readers periodically by broadcasting the tag IDs so that a missing tag/item can be quickly identified. The power conservation obtained from short transmissions on a very simple MAC layer combined with the hardware cost and size reduction from having a simple radio stack on the tag provides considerable economic, dimensional and tag lifetime benefits. In this paper, we present the design and architecture of the RollCall system, and conduct preliminary studies to examine the feasibility of building such a system by tweaking offthe-shelf signal processing algorithms. Initial studies and simulation results suggest that it is possible to monitor about 5000 tags in a store with networked basestations at a low error rate with an extended tag life time of at least a year based on conservative estimates with non-custom tag radio and micro-controllers.
Abstract-Many asset tracking applications demand long-lived, low-cost, and continuous monitoring of a large number of items, which has posed a significant challenge to today's RFID design. In order to satisfy these requirements, we propose to adopt transmit-only tags without a receiver, which can offer both low power and low cost. In spite of their great potential, such a platform faces many challenges since it cannot sense the channel, causing the collisions among tag transmissions to be high. It is thus crucial to employ effective multi-user detection schemes at the tag reader to extract valid information from collided signals. Traditional detection schemes, such as successive cancelation, cannot be directly applied to the targeted system. Firstly, due to the simplicity of receiver-less transmit-only tags, there is no mechanism for feedback to the tags that is traditionally needed for accurate multi-user detection. More importantly, these schemes impose serious processing and memory requirements on the underlying system, which makes real-time tracking impossible. In this study, we address these challenges by performing a statistical estimation of the signal amplitude, and by dividing the received signal sequence (from all the tags) and assigning each block to one reader. We also adopt an online learning mechanism so that readers can anticipate the tags that belong to them. We show that the proposed detection algorithm can achieve low detection error under realistic system conditions.
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