energy laterncy - Energy-Latency Tradeoffs for Data...

Info icon This preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Energy-Latency Tradeoffs for Data Gathering in Wireless Sensor Networks Yang Yu, Bhaskar Krishnamachari, and Viktor K. Prasanna Department of Electrical Engineering University of Southern California Los Angeles, CA 90089-2562 { yangyu, bkrishna, prasanna } @usc.edu Abstract — We study the problem of scheduling packet trans- missions for data gathering in wireless sensor networks. The focus is to explore the energy-latency tradeoffs in wireless communi- cation using techniques such as modulation scaling. The data aggregation tree – a multiple-source single-sink communication paradigm – is employed for abstracting the packet flow. We consider a real-time scenario where the data gathering must be performed within a specified latency constraint. We present algorithms to minimize the overall energy dissipation of the sensor nodes in the aggregation tree subject to the latency constraint. For the off-line problem, we propose (a) a numerical algorithm for the optimal solution, and (b) a pseudo-polynomial time approximation algorithm based on dynamic programming. We also discuss techniques for handling interference among the sensor nodes. Simulations have been conducted for both long-range communication and short-range communication. The simulation results show that compared with the classic shut- down technique, between 20% to 90% energy savings can be achieved by our techniques, under different settings of several key system parameters. We also develop an on-line distributed protocol that relies only on the local information available at each sensor node within the aggregation tree. Simulation results show that between 15% to 90% energy conservation can be achieved by the on-line protocol. The adaptability of the protocol with respect to variations in the packet size and latency constraint is also demonstrated through several run-time scenarios. Index terms – System design, Mathematical optimization I. I NTRODUCTION In many applications of wireless sensor networks (WSNs) [1], data gathering is a critical operation needed for extracting useful information from the operating environ- ment. Recent studies [2], [3] show that data aggregation is particularly useful in eliminating the data redundancy and reducing the communication load. Typical communication patterns in data aggregation involve multiple data sources and one data sink (or recipient). Thus, the corresponding packet flow resembles a reverse-multicast structure, which is called the data aggregation tree . Energy-efficiency is a key concern in WSNs. The large number of sensor nodes involved in such networks and the need to operate over a long period of time require careful management of the energy resources. In addition, wireless communication is a major source of power consumption.
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern