Extending the lifetime of a network of battery-powered mobile devices by remote processing: a Markovian decision-based approach

Rong, P.; Pedram, Massoud

doi:10.1109/dac.2003.1219149

Cited by 32 publications

(33 citation statements)

References 14 publications

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“…Energy aware methods to take tasking decisions have been considered before for routing [28,29,30,31,32,33,34,35], data gathering [36], topology management [37] and process sharing [38,39]. Methods have been proposed to collect the residual battery status of a distributed system [40] and also to estimate the future energy consumption at various nodes [41].…”

Section: Related Workmentioning

confidence: 99%

Performance aware tasking for environmentally powered sensor networks

Kansal

Potter

Srivastava

2004

SIGMETRICS Perform. Eval. Rev.

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The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems.

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Section: Related Workmentioning

confidence: 99%

Performance aware tasking for environmentally powered sensor networks

Kansal

Potter

Srivastava

2004

SIGMETRICS Perform. Eval. Rev.

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“…Moreover, the off-board computer makes the processing of complex operations on a heavy-duty processor possible. It allows greater battery lifetime for the robot [8]. Over-rides for autonomous navigation are also more easily realized.…”

Section: Robot Design Overviewmentioning

confidence: 99%

An Economic Simultaneous Localization and Mapping System for Remote Mobile Robot Using SONAR and an Innovative AI Algorithm

Saleem¹

2013

IJFCC

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Abstract-This paper proposes an economic and effective approach towards the simultaneous localization and mapping of a mobile rescue robot using a single ultrasonic range finder. The procedure eliminates the complication involved with localizing the robot in a map while creating the map simultaneously, by employing a novel control mechanism. The problem is solved by separating the mapping and localization processes and merging the outputs after specific intervals. The data from sensory devices is processed wirelessly to map the surroundings of the robot on a computer. The innovative AI algorithm extends the map cognitively as subsequent data is acquired. Moreover, a comparison is made between conventional methodologies and the one presented in this paper along with possible enhancements applicable. Using this architecture, the economic cost, computational power and time required for SLAM are significantly reduced. The design was utilized on a mobile rescue robot for the RoboCup competitions.

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“…If the first bit of the stream is 1, the starting cell is (1,2) and if the first bit of the stream is 0, the starting cell is (2,1).…”

Section: Proposed Algorithm Based On the Dynamic Programing Tablementioning

confidence: 99%

“…Computation offloading is a method where some of the computational tasks of a mobile application can be offloaded to run on remote servers in the cloud, in order to save energy [1] [2]. However, the problem of partitioning the application tasks for offloading is NPcomplete in general.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization using Cloud Offloading Algorithm

Jyothi¹

2017

IJCATR

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Abstract-Computational offloading is an effective method to address the limited battery power of a mobile device, by executing some components of a mobile application in the cloud. In this paper, a novel offloading algorithm called 'Dynamic Programming with Hamming Distance Termination' (denoted DPH) is presented. The algorithm uses randomization and a hamming distance termination criterion to find a nearly optimal offloading solution quickly. The algorithm will offload as many tasks as possible to the cloud when the network transmission bandwidth is high, thereby improving the total execution time of all tasks and minimizing the energy use of the mobile device. Furthermore, the algorithm is extensible to handle larger offloading problems without a loss of computational efficiency.

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Extending the lifetime of a network of battery-powered mobile devices by remote processing: a Markovian decision-based approach

Cited by 32 publications

References 14 publications

Performance aware tasking for environmentally powered sensor networks

Performance aware tasking for environmentally powered sensor networks

An Economic Simultaneous Localization and Mapping System for Remote Mobile Robot Using SONAR and an Innovative AI Algorithm

Energy Optimization using Cloud Offloading Algorithm

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