&lt;title&gt;Resource allocation and congestion control in task-oriented distributed sensor networks&lt;/title&gt;

Zhang, Jinsong; Kulasekere, E.C.; Premaratne, Kamal; Bauer, Péter

doi:10.1117/12.456076

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…video, for which queuing delay (not accounted for in [11]) is often the largest component. Other works ([12]- [14]) derive delay bounds in wireless (sensor) networks, using Network Calculus [15], i.e. the same technique exploited in this paper.…”

Section: Introductionmentioning

confidence: 99%

Optimal link scheduling for real-time traffic in wireless mesh networks in both per-flow and per-path frameworks

Cappanera

Lenzini

Lori

et al. 2010

2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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In this paper we investigate link scheduling for Wireless Mesh Networks (WMNs) carrying real-time (i.e., delayconstrained) traffic. We show that the problem of computing a conflict-free link schedule with end-to-end delay constraints can be formulated as a mixed-integer non linear problem that can be optimally solved in reasonable time (i.e., minutes) for relatively large WMNs (up to 20-30 nodes). We use the above result to explore the schedulability region of a WMN with a given routing and input traffic, assessing whether and when aggregating flows which traverse the same path makes a given input flow set schedulable. Furthermore, we devise a heuristic solution strategy, which computes good suboptimal solutions within up to few seconds, thus being amenable for online admission control.

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Section: Introductionmentioning

confidence: 99%

Optimal link scheduling for real-time traffic in wireless mesh networks in both per-flow and per-path frameworks

Cappanera

Lenzini

Lori

et al. 2010

2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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“…The service curves β(t) of the sensor nodes are given in Eq. (10), where R is interpreted as the service rate and T as the latency of the service curves of the sensor nodes. The fixed delay between two neighbor sensor nodes is marked by d.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Obviously, the arrival curves of the flows are given by Eq. (10). Remark 7: The units of buffer queue length Q, effective bandwidth e and ee are Mb, the units of delay D and DD, the time t, the latency T and the fixed delay d are ms and the unit of the service rate R is Mbps except the units that are given.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The maximum endto-end delay, for a given flow in any feed-forward network under blind multiplexing, was calculated [9]. Resource allocation and congestion control was investigated in distributed sensor networks using the network calculus [10]. An analytical framework was presented, based on the network calculus, to analyze worst-case performance and to dimension the resource of sensor networks [11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

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Modelling the guaranteed QoS for wireless sensor networks: a network calculus approach

Zhang

Deng

2011

J Wireless Com Network

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Wireless sensor networks (WSNs) became one of the high technology domains during the last ten years. Real-time applications for them make it necessary to provide the guaranteed Quality of Service (QoS). The main contributions of this paper are a system skeleton and a guaranteed QoS model that are suitable for the WSNs. To do it, we develop a sensor node model based on virtual buffer sharing and present a two-layer scheduling model using the network calculus. With the system skeleton, we develop a guaranteed QoS model, such as the upper bounds on buffer queue length/delay/effective bandwidth, and single-hop/ multi-hops delay/jitter/effective bandwidth. Numerical results show the system skeleton and the guaranteed QoS model are scalable for different types of flows, including the self-similar traffic flows, and the parameters of flow regulators and service curves of sensor nodes affect them. Our proposal leads to buffer dimensioning, guaranteed QoS support and control in the WSNs.

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“…The work given in [14] presents a resource allocation and congestion control in distributed sensor networks-a network calculus approach. The establishment of the overall objectives of a distributed sensor network is a dynamic task so that it may sufficiently well 'track' its environment.…”

Section: Related Workmentioning

confidence: 99%

Context Aware Resource Allocation in Distributed Sensor Networks

Bhajantri¹,

Nalini²

2012

IJWMN

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Distributed Sensor Networks (DS'.s) have been an area for active research over the past few years, due to potentially widespread applications in creating smart computing environment. In this work, we have proposed new smart computing environment for super/hyper market. The proposed system uses Distributed Sensor Networks (DSN's) for real-time tracking of the products. While new technologies are making sensors nodes smarter, smaller, and cheaper, it is very challenging to find an optimal to allocate sensors and other network resources. Context awareness is one of the methods to improve resource utilization in Distributed Sensor Networks (DSN's). We have studied Indian retail industry as case study and we listed out the drawbacks of Indian retail industry. But proposed system can be applied to most of the retail industries across the globe to overcome similar problems. As a part of proposed system, we have derived many contexts in retail industry based on human perspective. At a basic level of design, sensors are deployed in each rack which provides the real time information about available products in each rack. This information can be used by warehouse manager, store manager for day to day activity in super/hyper market. R&D team can also use this information to understand consumer behaviour. The context information derived from sensor node is used in allocation sensor node or resources. We have tested our proposed system using Castalia simulation tool, a derivative of OMNeT++. The results obtained from our experiment shows advantages of context aware system. As DSN's are data centric, analyzing and implementing system in user context prospective will greatly contribute in terms of cost, performance. We have simulated the context aware resource allocation in distributed sensor networks to test the operation scheme in terms of performance parameters. The DSN's promise to revolutionize sensing in a wide range of application domains. This is because of their reliability, accuracy, flexibility, cost effectiveness and ease of deployment, Smart sensors can offer vigilant surveillance and can detect and collect data.

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<title>Resource allocation and congestion control in task-oriented distributed sensor networks</title>

Cited by 7 publications

References 3 publications

Optimal link scheduling for real-time traffic in wireless mesh networks in both per-flow and per-path frameworks

Optimal link scheduling for real-time traffic in wireless mesh networks in both per-flow and per-path frameworks

Modelling the guaranteed QoS for wireless sensor networks: a network calculus approach

Context Aware Resource Allocation in Distributed Sensor Networks

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