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Enhancement of Energy Consumption of Real-Time Wireless Sensor Networks by Data Aggregation Techniques

Mahmoudifard, Mehdi | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43120 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jahangir, Amir Hossein
  7. Abstract:
  8. Prolonging the lifetime of wireless sensor networks (WSN) is one of the most important goals in the sensor network research. A lot of work has been done to achieve this goal. Since sensor nodes normally have limited resources in terms of energy, bandwidth and computation capability, efficiency is a key design goal in sensor network research. Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in dense WSNs. Previous research on data aggregation has demonstrated its effectiveness in reducing traffic, easing congestion and decreasing the energy consumption. However, few are actually designed for a real world application and implemented in a running system.Supporting real-time feature in wireless sensor networks faces severe challenges due to the wireless nature, limited resource, low node reliability, distributed architecture and dynamic network topology. There are tradeoffs between different application requirements including energy efficiency and delay performance.In this dissertation, we propose a new adaptive scheduling algorithm for real-time data aggregation in wireless sensor networks. Achieving an effective method, data aggregation is applied to reduce the traffic loads especially in the congested areas. Therefore, using our method in the intermediate nodes can balance the traffic as well as the energy consumption and improve real-time data delivery potential through the network. Using extensive simulations in Castalia, we investigate the performance of our algorithm in terms of miss ratio, energy consumption, and end-to-end delay for WSNs
  9. Keywords:
  10. Wireless Sensor Network ; Real-Time Procces ; Energy ; Scheduling ; Data Aggregation

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