Extending the lifetime of NB-IoT devices through Energy-Harvesting techniques

Abstract

For the Internet of Things (IoT) applications that send a few bytes of sensor information infrequently, several long-range IoT technologies have been conceived. Narrowband IoT (NB-IoT) is one of them that stands out due to its extended coverage, high penetrability, and high reliability features. Envisaged
long device lifetime and the extended coverage are important aspects of the NB-IoT radio technology that has garnered attention. With the increase in applications to connect a large number of devices over long distances, it becomes crucial for the technology to meet the lifetime expectations set in the standards. However, the empirical lifetime estimations, done as part of this work, indicate that the current devices do notmeet the lifetime estimates targeted in the 3GPP standards, lasting for 2.38 yearswhen exchanging 200 bytes of data at a rate of 1 message per day. Potential solutions include
energy-harvesting to augment the battery lifetime. However, due to the spatiotemporal variation in the amount of energy harvested, the supplemented energy levels will be less than the energy consumption. Therefore, it becomes essential for the sensor device enabled with NB-IoT communication technology to implement energy management techniques to maximize the utility to the application it serves based on its energy situation. Further, the coverage enhancement techniques inbuilt in the technology forms a major contributor to the low device lifetime. With the NB-IoT radio block considered as a ‘black box’ with no access to the software stack, innovative solutions are required to reduce
the energy consumption of sending payloads without losing out on coverage.
To this end, this work proposes a framework called xTEND to increase the utility of the device while being energy-efficient in deep coverage scenarios. In xTEND, the decision to transmit a payload is done by modeling the problem as a 0/1 incremental knapsack problem. A threshold policy is proposed that optimally schedules payloads in a given finite horizon, with a low complexity method to compute it on the device. Furthermore, as the coverage enhancement techniques deplete the battery soon for deep coverage scenarios, we propose to increase the transmission power adaptively based on the channel conditions. The framework is evaluated with an existing scheme in the literature that
performs energy management and is shown to outperform in different traffic distributions. Further, with the framework, an average 3.8 J of energy savings and 8.027 s of time savings is obtained in the shared and control channels of the physical layer. This results in an average lifetime extension of 20 % in deep coverage scenarios. With the only requirement of access to the power amplifier embedded on the NB-IoT chipset, the framework can be implemented on the deployed sensor device.