L-MAC: A wake-up time self-learning MAC protocol for wireless sensor networks

Abstract

This paper analyzes the trade-off issue between energy efficiency and packet delivery latency among existing duty-cycling MAC protocols in wireless sensor networks for low data-rate periodic-reporting applications. We then propose a novel and practical wake-up time self-Learning MAC (L-MAC) protocol in which the key idea is to reuse beacon messages of receiver-initiated MAC protocols to enable nodes to coordinate their wakeup time with their parent nodes without incurring extra communication overhead. Based on the self-learning mechanism we propose, L-MAC builds an on-demand staggered scheduler to allow a node to forward packets continuously to the sink node. We present an analytical model, and conduct extensive simulations and testbed experiments to show that L-MAC achieves significant higher energy efficiency compared to the state-of-the-art asynchronous MAC protocols and a similar result of latency compared to synchronous MAC protocols. In particular, under QoS requirements with an upper bound value for one-hop packet delivery latency within 1 s and a lower bound value for packet delivery ratio within 95%, results show that the duty cycle of L-MAC is improved by more than 3.8 times and the end-to-end packet delivery latency of L-MAC is reduced by more than 7 times compared to those of AS-MAC and other state-of-the-art MAC protocols, respectively, in case of the packet generation interval of 1 minute. L-MAC hence achieves high performance in both energy efficiency and packet delivery latency.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications

“…In our evaluation, we focus on energy efficiency and network lifetime of WSNs. In both cases, we assume that WSNs operate in duty cycle mode using a low power listing (LPL) protocol [13] and the base station is always on.…”

“…The reason is that duty cycle is mostly hardware independent and easier to reproduce results: $DC=D{C}_{lx}+D{C}_{tx}+D{C}_{rx}+D{C}_{sensing}+D{C}_{computing},$ where $D{C}_{lx},D{C}_{tx},D{C}_{rx},D{C}_{sensing},andD{C}_{computing}$ are ratio of amount of time per a second a sensor node spends for listening, transmitting, receiving, sensing, and computing. The detailed medium access control (MAC) configuration and traffic model for sensors are presented in our previous work [13]. …”

“…To keep the analysis tractable, we use a concentric circular ring network (CCR) model [13] with the sink as the center for the network deployment in each region. The sink node interconnects sensors to the cloud.…”

“…Although some methods like request aggregation [3] or data caching [12] may help reduce data transmission, sensors are still required to transmit their data regularly. For example, in the conventional periodic sensing data collection model [3,13], sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. Such a continuous sensing report without awareness of sensing demands leads to unnecessary energy loss due to redundant transmission.…”

Adaptive Sleep Efficient Hybrid Medium Access Control algorithm for next-generation wireless sensor networks

“…The self-learning receiver-initiated MAC (L-MAC) [27] protocol works on low data rate. It follows a tree structure to collect data within a network, where a child node adjusts its sleep time according to its parent.…”