Energy Management And Cross-layer Optimization In Heterogeneous Powered Wireless Sensor Networks

The development and use of renewable energy provide an effective way to solve the energy limit in wireless sensor networks. However, due to the unstable supply of renewable energy, it cannot guarantee the sustainable network operation. Hence, in this paper, a heterogeneous energy supply model, jointly considering renewable energy, electricity grid and wireless energy transfer, is proposed.Based on the in-depth analytical of research status and practical application on the wireless sensor network at home and abroad, a series of energy management and cross layer optimization problems focusing on the heterogeneous energy supply model are designed. The following works are carried out:(1) A new system model suitable for WSN is proposed, taking into account multiple energy consumption due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multidimensional stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method are applied to obtain a fully distributed and low-complexity cross-layer algorithm and to achieve an explicit trade-off between the optimization objective and queue backlog. The simulation results verify the performance of our algorithm, and show that the heterogeneous supply mode can significantly improve network performance in the case of renewable energy scarcity.(2) An energy management and cross-layer optimization model is proposed for the multiple multicast heterogeneous powered wireless multimedia sensor networks with correlated sources, jointly considering the distributed source coding and network coding. The source coding technique is used to remove the correlation between sources, and to reduce the amount of data transmission and energy consumption. The network coding technique is used to increase the throughput of the multicast transmission and to reduce the energy consumption. Based on the Lyapunov drift-plus-penalty with perturbation technique and dual decomposition technique, a fully distributed dynamic cross-layer algorithm is proposed to achieve the explicit trade-off between the queue backlog and optimization objective. Then, the simulation results verify the performance of our algorithm.(3) Focusing on the heterogeneous powered supply model with the coexistence of renewable energy and wireless energy transfer, two optimization problems of rate utility maximization are formulated for unicast/multicast multi-hop wireless sensor networks, respectively. By using the wireless energy transmission technology, each node not only receives the energy transferred from other nodes, but also transfers the energy to other nodes through its outgoing links. Hence, two proposed optimization problems aim to achieve the joint optimization of information flow and energy flow. In addition, a nonlinear energy consumption model of the data compression is expanded from the simple linear model. The simulation results show that wireless energy transfer and network coding can improve the network performance.