An energy-efficient data-driven power management for wireless sensor networks

ABSTRACT

Providing energy-efficient continuous data collection services is of paramount importance to Wireless Sensor Network (WSN) applications. This paper proposes a new power management framework called Data-Driven Power Management (DDPM) as the infrastructure for integrating various energy efficient techniques, such as approximate querying and sleep scheduling. By utilizing the beneficial properties of these techniques, we can achieve not only better energy efficiency but also meet specific criteria, such as data accuracy and communication latency. The distinguished feature of DDPM is that it generates a precision-guaranteed estimation for each sensor node as its maximum sleep time to make deterministic schedules. Furthermore, two decentralized algorithms are proposed to avoid undesirable communication delays caused by staggered local sleep schedules. The experimental results show that nodes' sleep times can be significantly increased while incurring only a minor rise in latency.

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INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.1 Network Architecture and Design
          Subjects: Wireless communication

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems

General Terms:
Algorithms, Design, Experimentation, Management, Performance

Keywords:
approximate query, sleep scheduling, wireless sensor networks