Maximizing Rewards in Wireless Networks with Energy and Timing Constraints for Periodic Data Streams

Power efficiency is an important design issue in mobile devices with limited power supplies. In this paper, we study a reward-based packet scheduling problem in wireless environments. We consider a general scenario in which a transmitter communicates with multiple receivers periodically. To guarantee timely transmission of data, each packet is associated with a delay constraint. The periodic data streams have different importance levels, power functions, and levels of data sizes.

Existing System:

Existing work focus on the minimization of the total energy consumption under the timing constraints. Meanwhile, more and more embedded systems are being built with renewable energy sources, such as solar power, wind power, and mechanical power, from the environment. Wireless nodes powered by these energy sources are subjected to limited amount of energy which is collected in each period. Generally, a wireless node may generate a Significant amount of data in a networked environment in periodic cycle. Due to the limitation in both delay and energy, it is often impossible for a wireless node to deliver all data in the transmission buffer at a time. Instead, the node tends to transmit data collected in the buffer selectively under time and energy constraints.

Proposed System:

The more data a transmitter delivers the more rewards it obtains. Our objective is to develop schemes that selectively transmit data streams of different data sizes at different transmission rates so that the system reward can be maximized under given time and energy constraints. We show that the problem is NP-hard and develop a dynamic programming algorithm for the optimal solution in pseudo polynomial time. A fast polynomial-time heuristic approach based on clustering of states in state space is presented to achieve close approximation.


  • Data Model

  • Power Consumption Model

  • Time-Efficient Approximation

  • Performance Evaluation