Combining distributed queuing with energy harvesting to enable perpetual distributed data collection applications

Abstract

This paper presents, models, and evaluates energy harvesting–aware distributed queuing (EH‐DQ), a novel medium access control protocol that combines distributed queuing with energy harvesting (EH) to address data collection applications in industrial scenarios using long‐range and low‐power wireless communication technologies. We model the medium access control protocol operation using a Markov chain and evaluate its ability to successfully transmit data without depleting the energy stored at the end devices. In particular, we compare the performance and energy consumption of EH‐DQ with that of time‐division multiple access (TDMA), which provides an upper limit in data delivery, and EH‐aware reservation dynamic frame slotted ALOHA, which is an improved variation of frame slotted ALOHA. To evaluate the performance of these protocols, we use 2 performance metrics: delivery ratio and time efficiency. Delivery ratio measures the ability to successfully transmit data without depleting the energy reserves, whereas time efficiency measures the amount of data that can be transmitted in a certain amount of time. Results show that EH‐DQ and TDMA perform close to the optimum in data delivery and outperform EH‐aware reservation dynamic frame slotted ALOHA in data delivery and time efficiency. Compared to TDMA, the time efficiency of EH‐DQ is insensitive to the amount of harvested energy, making it more suitable for energy‐constrained applications. Moreover, compared to TDMA, EH‐DQ does not require updated network information to maintain a collision‐free schedule, making it suitable for very dynamic networks.

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

“…Additionally, the same researchers designed two protocols based on the DQ algorithm: these were the Low Power Distributed Queuing (LPDQ) [7], and the Energy Harvesting Distributed queuing (EHDQ) [2]. The first protocol was developed for low power wireless networks with bursty traffic, whereas the second was designed for data collection networks with devices equipped with energy harvester.…”

“…The choice of Aloha-based algorithms is motivated by its easy implementation in low power, inexpensive and small size devices that are intended to dominate those networks. However, as many studies [2]- [5] pointed out, the Aloha-based algorithms are inefficient in terms of energy consumption, throughput, and access delay when the network size is high. Thus, there is a need for new access schemes to address the scalability issue of those networks with a potentially massive number of connections per session.…”

“…Recently, as an alternative to the Aloha-based access schemes, several works [2]- [16] proposed a Distributed Queuing (DQ) algorithm as a potential candidate to many of the challenges posed by the massive Machine-to-Machine (M2M) connectivity. In [6], it is predicted as one of the possible solutions to the majority of Media Access Control (MAC) issues in the IoT.…”

“…Most of the studies reviewed have been found to put an emphasis on the evaluation of these four performance metrics: the access delay [3]- [5], [9], [10], [12]- [16], the throughput [2]- [6], [15], [16], the energy consumption [2], [3], [5], [8], [14], and the number of attempts per node [3], [4], [10], [11]. Besides, it is a fact that the algorithm outperforms the Aloha-based schemes in terms of those performance metrics.…”

“…Recently, the DQ algorithm has interested several IoT researchers [2]- [16]. The protocol is seen as a solution to many M2M communication challenges present in both unlicensed and licensed networks.…”