Energy harvesting is an emerging technology for enabling green, sustainable, and autonomous wireless networks. In this paper, a large-scale wireless network with energy harvesting transmitters is considered, where a group of transmitters forms a cluster to cooperatively serve a desired receiver amid interference and noise. To characterize the link-level performance, closed-form expressions are derived for the transmission success probability at a receiver in terms of key parameters such as node densities, energy harvesting parameters, channel parameters, and cluster size, for a given cluster geometry. The analysis is further extended to characterize a network-level performance metric, capturing the tradeoff between link quality and the fraction of receivers served. Numerical simulations validate the accuracy of the analytical model. Several useful insights are provided. For example, while more cooperation helps improve the link-level performance, the network-level performance might degrade with the cluster size. Numerical results show that a small cluster size (typically 3 or smaller) optimizes the network-level performance. Furthermore, substantial performance can be extracted with a relatively small energy buffer. Moreover, the utility of having a large energy buffer increases with the energy harvesting rate as well as with the cluster size in sufficiently dense networks.

Wireless machine-to-machine (M2M) communications are widely considered as part of the Internet of Things (IoT) infrastructure. Resource management is crucial for heterogeneous M2M networks. In this paper, we present new distributed sleep management techniques to prolong network lifetime for multi-hop heterogeneous wireless M2M networks, which consist of battery-powered nodes and mains-powered nodes. We also propose two novel battery energy aware routing metrics to efficiently select routes that satisfy performance guarantees. Finally, we present an extensive performance evaluation of our sleep management techniques and routing metrics. Simulation results show that our schemes achieve high packet delivery rate, long network lifetime and low energy consumption even in very low percentage of mains-powered nodes.

The IETF standardized the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) to meet routing requirements of the emerging applications. RPL is a distributed routing protocol and shows good scalability and fast network setup. However, RPL does not support sleep operation well. To provide efficient energy management and enhance RPL for sleep operation support, this paper presents battery energy management solutions for heterogeneous wireless machine-to-machine networks containing both battery powered nodes and mains powered nodes. We introduce a distributed sleep model for battery powered nodes to manage their own sleep schedules based on their internal parameters and observed network conditions. We propose two broadcast message delivery methods for battery operated networks that use distributed sleep control. Two battery node aware routing metrics are introduced to discover more battery energy efficient routes. We also present a battery energy efficient routing protocol called B-RPL to leverage distributed sleep model and introduced routing metrics. A battery energy efficient data packet transmission and forwarding method is provided to select the most battery energy efficient route among multiple active routes to transmit and forward data packets. Simulation results show that compared with standard RPL, the proposed B-RPL can extend network lifetime by two times and improve data packet delivery rate by 75%.