Markov fluid queue model of an energy harvesting IoT device with adaptive sensing

Abstract

Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP.

Energy-Efficient LoRaWAN for Industry 4.0 Applications

“…In addition to the aforementioned literature, some other works (e.g., [17]- [20]) also discuss the lifetime of sensor nodes through duty-cycled operation [19], [20]) on the cost of communication delays but their studies neither take into account the complex LoRa deployments nor operating costs (e.g., battery replacement and damage penalty) in industrial settings where trying to curtail the one, compliments the other type of cost. To the best of author's knowledge, this work is the premier to thoroughly investigate LoRaWAN and its carbon footprints for the industry 4.0 services in the presence of several harvesting sources to pare the reliance on batterypowered operation.…”

Performance optimization of LoRa nodes for the future smart city/industry

“…In [31], a different approach is taken to derive transmission policies. Instead of finding the optimal configuration under which an event should be reported, authors modeled an energy harvesting sensing unit that must determine the rate at which events should be reported to prevent nodes from quickly depleting their batteries.…”

A Product-Form Model for the Performance Evaluation of a Bandwidth Allocation Strategy in WSNs

“…The motes forming a WSN are typically simple systems powered by batteries with a limited memory and computational power. Many research efforts are devoted to developing new technologies or protocols that can extend the lifetime of a network, such as the capability of nodes of harvesting energy from the environment and hence extend their life (see, e.g., Gelenbe and Marin (2015), , , and Tunc and Akar (2017) for a survey of the state of the art of the technology and the available models). Although in this article we will mainly focus on the bandwidth allocation problem, the solution that we propose can be also used for balancing the energy consumption of the nodes forming a network.…”