In this paper, we present a novel packet delivery mechanism called Multi-Path and Multi-SPEED Routing Protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. These mechanisms for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates for local decision inaccuracies as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements up to 50 percent (12 flows versus 18 flows).

/pdf/mmspeed-multipath-multi-speed-protocol-for-qos-guarantee-of-qe6q7qybre.pdf

MMSPEED: multipath Multi-SPEED protocol for QoS guarantee of reliability and. Timeliness in wireless sensor networks

A method for distributed call admission control in a wireless network includes the steps of: initiating a communication call within the wireless network; computing a resource metric at each of a plurality of nodes along a communication route of the wireless network, wherein each of the resource metrics is representative of a network dynamic; distributing the resource metrics along a communication route to at least one call admission control point within the wireless network; and performing a call admission process for the communication call at the at least one call admission control point using the resource metrics.

Method and apparatus for distributed call admission control in a wireless network

We introduce a tractable analytic model of throughput performance for general 802.11 multi-hop multi-radio networks subject to finite loads. The model's accuracy and utility is illustrated by comparison with simulation

/pdf/modeling-802-11-mesh-networks-4tacqkhe2o.pdf

Modeling 802.11 mesh networks

Methods and apparatus related to communicating and/or using load information in support of decentralized traffic scheduling decisions are described. Individual wireless terminals corresponding to a peer to peer connection which desire to communicate traffic signals make transmitter yielding and/or receiver yielding decisions on a traffic slot by traffic slot basis. Loading information is used to intentionally skew transmitter yielding decisions in response to conditions and/or needs in the system. A link load weight value is generated based on intended transmitter loading related information and/or intended receiver loading related information. Traffic request parameters and/or link load weight values are communicated between wireless communications devices in request and/or request response signaling. As part of a transmitter yielding decision, one or more of: a spillage value, weighted SINR, and an interference cost estimate is calculated based on: one or more link load weight values and channel condition measurement information.

Methods and apparatus for communicating and/or using load information in support of decentralized traffic scheduling decisions

In this paper we introduce a tractable analytic model of throughput performance for general 802.11 multi-hop networks. We use this model to explore a number of fairness issues in 802.11 multi-hop networks that have a significant impact on performance and capacity for realistic traffic. Schemes using the functionality provided by 802.11e are proposed to mitigate this unfairness and their efficacy demonstrated.

/pdf/improving-fairness-in-multi-hop-mesh-networks-using-802-11e-2v4yzdqets.pdf

Improving Fairness in Multi-Hop Mesh Networks Using 802.11e

Ad hoc networks have been proposed for a variety of applications where support for real time, multimedia services may be necessary. This requires that the network is able to offer quality of service (QoS) appropriate for the latency and throughput bounds needed to meet the real time constraint. An important component for QoS provisioning is resource estimation and quality prediction. The paper describes a model-based resource prediction (MBRP) mechanism to support real time communication in multi-hop wireless networks. Specifically, we develop an analytical model for differentiated MAC scheduling protocols. The model can predict per-flow and system-wide throughput and delivery latency, thereby enabling admission control of the flows and providing an efficient network management utility. After describing the basic model, we propose enhanced MBRP (EMBRP) for realistic network environments. Our proposed quality prediction method is beneficial in the deployment of a real ad hoc network where knowledge of resource allocation and consumption is needed to meet the service requirements. Analytical and simulation results show that EMBRP provides accurate flow quality prediction. The results also demonstrate the effectiveness of EMBRP as an admission control solution in multi-hop ad hoc networks.

/pdf/model-based-resource-prediction-for-multi-hop-wireless-3rq0etw9in.pdf

Model-based resource prediction for multi-hop wireless networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xia Gao

Author Tools

Chat about Author

Papers

Model-based resource prediction for multi-hop wireless networks