Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels.

Area of Science:

• Wireless Sensor Networks (WSNs)
• Energy Harvesting
• Communication Networks

Background:

• Energy harvesting WSNs face capacity allocation challenges due to interference.
• Optimizing data rates, power, and energy transfer is crucial for network efficiency.
• Non-convex optimization problems hinder direct solutions in these networks.

Purpose of the Study:

• To investigate capacity allocation in energy harvesting WSNs with interference channels.
• To minimize total network delay by optimizing data rates, power, and energy transfer.
• To analyze the impact of energy transfer versus no energy transfer on network performance.

Main Methods:

• Formulation of an optimization problem for fixed topologies and constant data flow.
• Transformation of a non-convex problem into a convex one using convex approximation for high SINR networks.
• Application of Lagrange duality and the CVX solver to find optimal solutions.

Main Results:

• Energy harvesting WSNs with interference channels exhibit greater total delay than those with orthogonal channels.
• Increasing data flow elevates total network delay when energy arrival rates are fixed.
• Energy transfer effectively reduces the total network delay.

Conclusions:

• Energy transfer is a key strategy for mitigating delay in interference-constrained energy harvesting WSNs.
• The proposed convex approximation method provides an effective approach to solve complex optimization problems in WSNs.
• System design should consider the trade-offs between data flow, energy harvesting, and energy transfer for optimal performance.