TY - GEN
T1 - Optimal Energy-Delay Scheduling for Energy Harvesting WSNs via Negatively Correlated Search
AU - Jiao, Dongbin
AU - Yang, Peng
AU - Fu, Liqun
AU - Ke, Liangjun
AU - Tang, Ke
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Optimal energy-delay scheduling for capacity assignment problem in energy harvesting wireless sensor networks (EH-WSNs) with interference channel is addressed for fixed data flows and energy topologies. We formulate the optimization problem for a single time slot and multiple time slots, respectively. We focus on the optimal data rates, power allocations and energy transfers for the optimization problem. The objective is to minimize the total network delay. However, the optimization problem is non-convex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high Signal-to-Interference-plus-Noise Ratio (SINR), this paper aims to directly solve the original non-convex formulation by employing a powerful evolutionary algorithm, i.e., Negatively Correlated Search (NCS). The simulations under both no-energy-transfer scenario and energy-transfer scenario are carried out, demonstrating that solving the non-convex problem with approximated approach is a good alternative to solving the approximated convex problem with accurate optimization approaches. This idea could also be beneficial to other complex optimization problems in the wireless networks design.
AB - Optimal energy-delay scheduling for capacity assignment problem in energy harvesting wireless sensor networks (EH-WSNs) with interference channel is addressed for fixed data flows and energy topologies. We formulate the optimization problem for a single time slot and multiple time slots, respectively. We focus on the optimal data rates, power allocations and energy transfers for the optimization problem. The objective is to minimize the total network delay. However, the optimization problem is non-convex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high Signal-to-Interference-plus-Noise Ratio (SINR), this paper aims to directly solve the original non-convex formulation by employing a powerful evolutionary algorithm, i.e., Negatively Correlated Search (NCS). The simulations under both no-energy-transfer scenario and energy-transfer scenario are carried out, demonstrating that solving the non-convex problem with approximated approach is a good alternative to solving the approximated convex problem with accurate optimization approaches. This idea could also be beneficial to other complex optimization problems in the wireless networks design.
UR - https://www.scopus.com/pages/publications/85070223064
U2 - 10.1109/ICC.2019.8761296
DO - 10.1109/ICC.2019.8761296
M3 - 会议稿件
AN - SCOPUS:85070223064
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -