Data Gathering Optimization by Dynamic Sensing and Routing in Rechargeable Sensor Networks

In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be t...

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Bibliographic Details
Published in:IEEE/ACM transactions on networking 2016-06, Vol.24 (3), p.1632-1646
Main Authors: Zhang, Yongmin, He, Shibo, Chen, Jiming
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Batteries
Computer networks
Data communication
Data sensing
Data transmission
Detection
dynamic topology
Dynamics
Energy
energy allocation
Energy consumption
energy harvesting
Heuristic algorithms
Networks
rechargeable sensor networks
Resource management
Routing
Routing (telecommunications)
Sensors
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Summary:In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2015.2425146