TY - GEN

T1 - Energy-based adaptive multiple access in LPWAN IoT systems with energy harvesting

AU - Michelusi, Nicolo

AU - Levorato, Marco

N1 - Publisher Copyright:
© 2017 IEEE.

PY - 2017/8/9

Y1 - 2017/8/9

N2 - This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. Due to fluctuations in energy availability and, possibly, energy outages, the number of nodes attempting channel access is random and varies over time. Thus, each node must carefully adapt its access probability to the network state to optimize network performance. In order to reduce the complexity of network control, a lightweight and flexible design framework is proposed where energy storage dynamics are replaced by dynamic average power constraints at each node, induced by the time correlated energy supply. The BS adapts the access probability of the 'active' nodes (those currently under a favorable energy harvesting state) so as to maximize throughput. The resulting policy takes the form of access probability as a function of the local energy harvesting state and number of active nodes. The structure of the throughput-optimal policy is analytically derived for the genie-aided case of non-causal knowledge of the number of active nodes. Inspired by it, a Bayesian estimation approach is presented for the more practical scenario where the BS estimates the number of active nodes. The proposed scheme is shown to outperform by 20% a scheme in which the nodes operate based only on local state information, and to be robust against the impact of energy storage dynamics at a fraction of the complexity.

AB - This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. Due to fluctuations in energy availability and, possibly, energy outages, the number of nodes attempting channel access is random and varies over time. Thus, each node must carefully adapt its access probability to the network state to optimize network performance. In order to reduce the complexity of network control, a lightweight and flexible design framework is proposed where energy storage dynamics are replaced by dynamic average power constraints at each node, induced by the time correlated energy supply. The BS adapts the access probability of the 'active' nodes (those currently under a favorable energy harvesting state) so as to maximize throughput. The resulting policy takes the form of access probability as a function of the local energy harvesting state and number of active nodes. The structure of the throughput-optimal policy is analytically derived for the genie-aided case of non-causal knowledge of the number of active nodes. Inspired by it, a Bayesian estimation approach is presented for the more practical scenario where the BS estimates the number of active nodes. The proposed scheme is shown to outperform by 20% a scheme in which the nodes operate based only on local state information, and to be robust against the impact of energy storage dynamics at a fraction of the complexity.

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U2 - 10.1109/ISIT.2017.8006701

DO - 10.1109/ISIT.2017.8006701

M3 - Conference contribution

AN - SCOPUS:85034055583

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 1112

EP - 1116

BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Symposium on Information Theory, ISIT 2017

Y2 - 25 June 2017 through 30 June 2017

ER -