Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multi-hop Wireless Networks

Eswaran, Sharanya; Johnson, Matthew; Misra, Archan; Porta, Thomas F. La

doi:10.1007/978-3-642-02085-8_7

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…When this happens, the network stays idle until the next task arrival. Similar models for task processing networks have been considered in [6]- [9].…”

Section: Basic Modelmentioning

confidence: 99%

“…Then E {J k (γX + Q)} is also convex and increasing and has maximum slope given by γ < 1. Therefore, using (6), it can be seen that J k+1 (X), which is the maximum of X and −β + λ(γX + Q) + (1 − λ)E {J k (γX + Q)}, is also convex and increasing. Further, it has a maximum slope of max[1, λγ +(1−λ)γ] = 1.…”

Section: A Geometric Arrivalsmentioning

confidence: 99%

“…Similarly, the QoI attribute of accuracy and its impact on rate control at the transport layer has been considered in [4], [5] in the context of event detection applications in wireless sensor networks. Reference [6] considers network utility maximization for sensor networks capable of in-network processing, while [7] focuses on the effect of corroboration, where the belief in information is reinforced by several sources reporting the same (or similar) information in social swarming applications. [10], [11] consider rate allocation and scheduling for distortion-sensitive compressible data.…”

Section: Introductionmentioning

confidence: 99%

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Quality of Information aware scheduling in task processing networks

Urgaonkar

Ciftcioglu

Yener

et al. 2011

2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks

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Abstract-We investigate Quality of Information (QoI) aware scheduling in task processing networks. Specifically, we consider the scenario where a network sequentially receives tasks from an end user, utilizes its resources to process them, and sends back its response. The utility derived by the end user from this response depends on both the accuracy and the freshness of the information. There is often a trade-off between these two attributes and we present a model that quantifies this dependence. Using dynamic programming and optimal stopping theory, we characterize the optimal scheduling policy that maximizes the time average utility delivered by the network. We show that for many scenarios of practical interest, the optimal policy has a simple threshold structure. We also propose a method to approximately compute the threshold in closed-form. This work takes a step towards incorporating application aware objectives in making optimal scheduling decisions.

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“…When this happens, the network stays idle until the next task arrival. Similar models for task processing networks have been considered in [6]- [9].…”

Section: Basic Modelmentioning

confidence: 99%

Section: A Geometric Arrivalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quality of Information aware scheduling in task processing networks

Urgaonkar

Ciftcioglu

Yener

et al. 2011

2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks

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show abstract

“…Addressing the challenges of such networks, we developed a family a protocols that optimizes the performance of mission-oriented wireless networks by dynamically adapting parameters such as data rate and compression ratios to meet the bandwidth and energy constraints at the network level, 9 and the mission utilities, dynamics and priorities, at the application level. [10][11][12] In order to verify the applicability of our suite of utility-based optimization protocols to real-time WSNs, we implemented one the protocols, which provides optimal rate adaptation for congestion control, on an 802.11b-based network using a middleware called the ITA Sensor Fabric 13 for communication. The ITA Sensor Fabric, which was developed as part of the International Technology Alliance, 14 is a popular pub/sub-based communication infrastructure that was built to primarily to address the unique set of challenges resulting from the diverse sensor types and networking technologies commonly used in tactical WSNs.…”

Section: Introductionmentioning

confidence: 99%

Implementation of utility-based resource optimization protocols on ITA Sensor Fabric

Eswaran

Misra²,

Bergamaschi

et al. 2010

SPIE Proceedings

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Utility-based cross-layer optimization is a valuable tool for resource management in mission-oriented wireless sensor networks (WSN). The benefits of this technique include the ability to take application-or mission-level utilities into account and to dynamically adapt to the highly variable environment of tactical WSNs. Recently, we developed a family of distributed protocols which adapts the bandwidth and energy usage in mission-oriented WSN in order to optimally allocate resources among multiple missions, that may have specific demands depending on their priority, and also variable schedules, entering and leaving the network at different times. 9-12 In this paper, we illustrate the practical applicability of this family of protocols in tactical networks by implementing one of the protocols, which ensures optimal rate adaptation for congestion control in mission-oriented networks, 9 on a real-time 802.11b network using the ITA Sensor Fabric. 13 The ITA Sensor Fabric is a middleware infrastructure, developed as part of the International Technology Alliance (ITA) in Network and Information Science, 14 to address the challenges in the areas of sensor identification, classification, interoperability and sensor data sharing, dissemination and consumability, commonly present in tactical WSNs. 15 Through this implementation, we (i) study the practical challenges arising from the implementation and (ii) provide a proof of concept regarding the applicability of this family of protocols for efficient resource management in tactical WSNs amidst the heterogeneous and dynamic sets of sensors, missions and middle-ware.

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“…In-network query processing on wireless sensor networks [1], [2], [3], [4], [5], [6] provides an efficient way of query evaluation along wireless sensor nodes. In in-network query processing, when a query is committed to a node, it is decomposed into subqueries to be evaluated on some specified nodes where the decomposed subqueries or the related data (if needed) have to be routed for evaluation, then the output data of the subqueries are aggregated while being routed to the query node.…”

Section: Introductionmentioning

confidence: 99%

Ripple routing: An on-demand routing protocol for in-network query processing on wireless sensor networks

Zhang

Grumbach²,

Yang

et al. 2010

2010 IEEE International Conference on Mechatronics and Automation

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In-network query processing on wireless sensor networks handles queries in a distributed manner by decomposing them into subqueries, which are evaluated on groups of hosts (nodes) on the network. Reducing the amount of nodes to which (sub)queries as well as data are sent in the network is essential to reduce the communication cost, crucial for restricted devices. This paper proposes an on-demand routing protocol, called ripple routing, which pushes ripple after ripple when necessary to reduce communication in the network. Ripple routing accomplishes a distributed three-phase on-demand route discovery mechanism at the price of a small overhead for route discovery traffic. Our performance analysis shows the benefits of ripple routing over DSR in terms of communication.

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Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multi-hop Wireless Networks

Cited by 16 publications

References 16 publications

Quality of Information aware scheduling in task processing networks

Quality of Information aware scheduling in task processing networks

Implementation of utility-based resource optimization protocols on ITA Sensor Fabric

Ripple routing: An on-demand routing protocol for in-network query processing on wireless sensor networks

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