Results 1 - 10 of 16

Near Optimal Power and Rate Control of Multi-hop Sensor Networks with Energy Replenishment: Basic Limitations with Finite Energy and Data

by Zhoujia Mao, Can Emre Koksal, Ness B. Shroff - IEEE/ACM Transactions on Automatic Control , 2012
"... Abstract—Renewable energy sources can be attached to sensor nodes to provide energy replenishment for extending the battery life and prolonging the overall lifetime of sensor networks. For networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to ..."
Abstract - Cited by 14 (3 self) - Add to MetaCart
Abstract—Renewable energy sources can be attached to sensor nodes to provide energy replenishment for extending the battery life and prolonging the overall lifetime of sensor networks. For networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, while aggressive usage of energy may result in reduced coverage or connectivity for certain time periods. Thus, new power allocation schemes need to be designed to balance these seemingly contradictory goals, in order to maximize sensor network performance. In this paper, we study the problem of how to jointly control the data queue and battery buffer to maximize the long-term average sensing rate of a wireless sensor network with replenishment under certain QoS constraints for the data and battery queues. We propose a unified algorithm structure and analyze the performance of the algorithm for all combinations of finite and infinite data and battery buffer sizes. We also provide a distributed version of the algorithm with provably efficient performance.1 I.

Stochastic Optimization for Markov Modulated Networks with Application to Delay Constrained Wireless Scheduling

by Michael J. Neely , 2009
"... We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and sa ..."
Abstract - Cited by 13 (8 self) - Add to MetaCart
We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and satisfies the delay constraints. The problem is solved by reducing the constrained optimization to a set of weighted stochastic shortest path problems, which act as natural generalizations of max-weight policies to Markov modulated networks. We also present approximation results that do not require a-priori statistical knowledge, and discuss the additional complexity and delay incurred as compared to systems without delay constraints. The solution technique is general and applies to other constrained stochastic network optimization problems.
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Dynamic optimization and learning for renewal systems

by Michael J. Neely - Proc. Asilomar Conf. on Signals, Systems, and Computers , 2010
"... Abstract—This paper considers optimization of time averages in systems with variable length renewal frames. Applications include power-aware and profit-aware scheduling in wireless networks, peer-to-peer networks, and transportation systems. Every frame, a new policy is implemented that affects the ..."
Abstract - Cited by 11 (5 self) - Add to MetaCart
Abstract—This paper considers optimization of time averages in systems with variable length renewal frames. Applications include power-aware and profit-aware scheduling in wireless networks, peer-to-peer networks, and transportation systems. Every frame, a new policy is implemented that affects the frame size and that creates a vector of attributes. The policy can be a single decision in response to a random event observed on the frame, or a sequence of such decisions. The goal is to choose policies on each frame in order to maximize a time average of one attribute, subject to additional time average constraints on the others. Two algorithms are developed, both based on Lyapunov optimization concepts. The first makes decisions to minimize a “drift-plus-penalty ” ratio over each frame. The second is similar but does not involve a ratio. For systems that make only a single decision on each frame, both algorithms are shown to learn efficient behavior without a-priori statistical knowledge. The framework is also applicable to large classes of constrained Markov decision problems. Such problems are reduced to finding an approximate solution to a simpler unconstrained stochastic shortest path problem on each frame. Approximations for the simpler problem may still suffer from a curse of dimensionality for systems with large state space. However, our results are compatible with any approximation method, and demonstrate an explicit tradeoff between performance and convergence time. Index Terms—Stochastic processes, Markov decision problems I.
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Resource allocation in sensor networks with renewable energy

by Zhoujia Mao, Can Emre Koksal, Ness B. Shroff - in Proc. 2010 IEEE ICCCN
"... Abstract—Renewable energy sources can be attached to sensor nodes to provide energy replenishment for prolonging the lifetime of sensor networks. However, for networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, ..."
Abstract - Cited by 8 (1 self) - Add to MetaCart
Abstract—Renewable energy sources can be attached to sensor nodes to provide energy replenishment for prolonging the lifetime of sensor networks. However, for networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, while aggressive usage of energy may result in reduced coverage or connectivity for certain time periods. Thus, new power allocation schemes need to be designed to balance these seemingly contradictory goals, in order to maximize sensor network performance. In this paper, we study the problem of how to jointly control the data queue and battery buffer to maximize the long-term average sensing rate of a single communication link in rechargeable sensor networks. The coupling between the battery and data buffers does not lend itself amenable to traditional resource optimization techniques. Thus, we develop a new power and rate allocation scheme that explicitly takes this coupling into account. The new scheme is a simple myopic scheme whose performance is shown to be arbitrarily close to optimal analytically and via simulations. I.
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Power-Delay Tradeoff over Wireless Networks

by Xi Zhang, Senior Member, Jia Tang
"... Abstract—When transmitting stochastic traffic flows over wire-less networks, there exists an inherent tradeoff between average transmit power and corresponding queuing-delay bound. In this paper, we investigate such a tradeoff and show how average power increases as delay-bound requirement for wirel ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Abstract—When transmitting stochastic traffic flows over wire-less networks, there exists an inherent tradeoff between average transmit power and corresponding queuing-delay bound. In this paper, we investigate such a tradeoff and show how average power increases as delay-bound requirement for wireless network traffics becomes stringent. Specifically, we propose the resource allocation schemes to minimize the power consumption subject to a delay quality-of-service (QoS) constraint, where the delay constraint is in terms of queue-length decay rate when an arrival traffic is transmitted through the wireless networks. We focus on orthogonal-frequency-division-multiplexing (OFDM) communications under three different network infrastructures, namely, point-to-point link, multihop amplify-and-forward (AF) network, and multiuser cellular network. We derive the optimal resource allocation policies for each scenario, and compare their performances with other existing resource-allocation policies. The obtained simulation and numerical results show that using our proposed optimal resource-allocation policies, significant power saving can be achieved. Furthermore, our OFDM-based communications systems can significantly reduce the power consumption, especially under stringent delay constraint. Index Terms—Power control, statistical delay-bounded quality-of-service (QoS) guarantees, effective capacity, wireless networks, resource allocation and management, scheduling, OFDM-based communications systems, convex optimization, information the-ory. I.
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A Stable Online Algorithm for Energy-Efficient Multi-user Scheduling

by Nitin Salodkar, Abhay Karandikar, Vivek S. Borkar
"... In this paper, we consider the problem of energy-efficient uplink scheduling with delay constraint for a multiuser wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
In this paper, we consider the problem of energy-efficient uplink scheduling with delay constraint for a multiuser wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard constraint on another (average delay). We do not assume the arrival and channel statistics to be known. To handle state-space explosion and informational constraints, we split the problem into individual CMDPs for the users, coupled through their Lagrange multipliers; and a user selection problem at the base station. To address the issue of unknown channel and arrival statistics, we propose a reinforcement learning algorithm. The users use this learning algorithm to determine the rate at which they wish to transmit in a slot and communicate this to the base station. The base station then schedules the user with the highest rate in a slot. We analyze convergence, stability, and optimality properties of the algorithm. We also demonstrate the efficacy of the algorithm through simulations within IEEE 802.16 system.

Exploiting Ephemeral Link Correlation for Mobile Wireless Networks

by Wei Liu, Yunhuai Liu, Ziguo Zhong, Limin Sun, Hongsong Zhu, Tian He
"... In wireless mobile networks, energy can be saved by using dynamic transmission scheduling with pre-knowledge about channel conditions. Such pre-knowledge can be obtained via profiling as proposed by several existing systems which as-sumed that the existence of spatial link correlation makes the meas ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
In wireless mobile networks, energy can be saved by using dynamic transmission scheduling with pre-knowledge about channel conditions. Such pre-knowledge can be obtained via profiling as proposed by several existing systems which as-sumed that the existence of spatial link correlation makes the measured channel status at one location reusable over a long period of time. Our empirical data, however, tell-s a different story: spatial link correlation only maintains well within a short duration (from seconds to tens of sec-onds) while decreases significantly afterwards, a phenomena we call ephemeral link correlation. By leveraging this ob-servation, we design and implement a real-time transmission scheduling system, named PreSeer, on the railway platfor-m for cargo transportation, where the transmission of a sink
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A Stable On-line Algorithm for Energy Efficient Multi-user Scheduling

by Nitin Salodkar, Abhay Karandikar, V. S. Borkar
"... In this paper, we consider the problem of energy efficient uplink scheduling with delay constraint for a multi-user wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
In this paper, we consider the problem of energy efficient uplink scheduling with delay constraint for a multi-user wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard constraint on another (average delay). We do not assume the arrival and channel statistics to be known. To handle state space explosion and informational constraints, we split the problem into individual CMDPs for the users, coupled through their Lagrange multipliers; and a user selection problem at the base station. To address the issue of unknown channel and arrival statistics, we propose a reinforcement learning algorithm. The users use this learning algorithm to determine the rate at which they wish to transmit in a slot and communicate this to the base station. The base station then schedules the user with the highest rate in a slot. We analyze convergence, stability and optimality properties of the algorithm. We also demonstrate the efficacy of the algorithm through simulations within IEEE 802.16 system.

Power Efficient Scheduling under Delay Constraints over Multi-user Wireless Channels

by Nitin Salodkar, Abhay Karandikar, V. S. Borkar , 2007
"... In this paper, we consider the problem of power efficient uplink scheduling in a Time Division Multiple Access (TDMA) system over a fading wireless channel. The objective is to minimize the power expenditure of each user subject to satisfying individual user delay. We make the practical assumption ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
In this paper, we consider the problem of power efficient uplink scheduling in a Time Division Multiple Access (TDMA) system over a fading wireless channel. The objective is to minimize the power expenditure of each user subject to satisfying individual user delay. We make the practical assumption that the system statistics are unknown, i.e., the probability distributions of the user arrivals and channel states are unknown. The problem has the structure of a Constrained Markov Decision Problem (CMDP). Determining an optimal policy under for the CMDP faces the problems of state space explosion and unknown system statistics. To tackle the problem of state space explosion, we suggest determining the transmission rate of a particular user in each slot based on its channel condition and buffer occupancy only. The rate allocation algorithm for a particular user is a learning algorithm that learns about the buffer occupancy and channel states of that user during system execution and thus addresses the issue of unknown system statistics. Once the rate of each user is determined, the proposed algorithm schedules the user with the best rate. Our simulations within an IEEE 802.16 system demonstrate that the algorithm is indeed able to satisfy the user specified delay constraints. We compare the performance of our algorithm with the well known M-LWDF algorithm. Moreover, we demonstrate that the power expended by the users under our algorithm is quite low.

1CBM: Online Strategies on Cost-aware Buffer Management for Mobile Video Streaming

by Author(s He, Jian Xue, Zheng Wu, Di Wu, Oliver Dapeng Wen, Jian He, Zheng Xue, Di Wu, Dapeng Oliver Wu, Yonggang Wen
"... © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to s ..."
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© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version of this article is available at [DOI:
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