Socio-physical based Mode Selection and Power Allocation for IoT-enabled Multi-purpose Devices
Start Date
8-11-2017 8:30 AM
End Date
8-11-2017 12:30 PM
Abstract
A new class of multipurpose devices has emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able to interchange their operation between different sensing modes providing a large mixture of information towards various IoT applications. This collection of multipurpose-devices is usually part of a sensing installation owned by an infrastructure provider within a smart facility, or smart city setting. In this work, the problem of efficient and effective device sensing mode selection is confronted, along with a power allocation problem associated with the communication needs of the IoT nodes. Each multi-purpose device acts as a learning automaton selecting the most appropriate operation mode through a machine learning mechanism. The selection aims at maximizing the profit/cost relation of the provider by simultaneously considering socio-physical parameters among the devices. In addition, towards improving the communication efficiency, the nodes create coalitions among themselves with one acting as coalition head. This formation takes into account three factors: energy availability, spatial proximity reflecting channel quality, and operation mode correlation between multi-purpose devices. Given the mode selection and coalition formation among nodes, a distributed power control mechanism is proposed to determine each devices' optimal transmission power in a Non-Orthogonal Multiple Access (NOMA) wireless network environment. This power choice fulfills each devices' Quality of Service (QoS) prerequisites represented through a holistic utility function associated with each node. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority in comparison with other related approaches is demonstrated.
Socio-physical based Mode Selection and Power Allocation for IoT-enabled Multi-purpose Devices
A new class of multipurpose devices has emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able to interchange their operation between different sensing modes providing a large mixture of information towards various IoT applications. This collection of multipurpose-devices is usually part of a sensing installation owned by an infrastructure provider within a smart facility, or smart city setting. In this work, the problem of efficient and effective device sensing mode selection is confronted, along with a power allocation problem associated with the communication needs of the IoT nodes. Each multi-purpose device acts as a learning automaton selecting the most appropriate operation mode through a machine learning mechanism. The selection aims at maximizing the profit/cost relation of the provider by simultaneously considering socio-physical parameters among the devices. In addition, towards improving the communication efficiency, the nodes create coalitions among themselves with one acting as coalition head. This formation takes into account three factors: energy availability, spatial proximity reflecting channel quality, and operation mode correlation between multi-purpose devices. Given the mode selection and coalition formation among nodes, a distributed power control mechanism is proposed to determine each devices' optimal transmission power in a Non-Orthogonal Multiple Access (NOMA) wireless network environment. This power choice fulfills each devices' Quality of Service (QoS) prerequisites represented through a holistic utility function associated with each node. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority in comparison with other related approaches is demonstrated.
