Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Abstract

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round.LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

“…Data aggregation is the great technique to solve that problem. Immense studies have been investigating how to apply the data aggregation technique to sensor networks for energy efficiency [15]- [19] or time efficiency (Minimum Latency Aggregation Scheduling (MLAS)) [11], [13], [20]- [23]. With any of the above targets, at the end, the research helps the sensor network span their lifetime.…”

“…The above multi-channel aggregation scheduling methods work in always-on networks only. Therefore, the researchers in [11], [14] present novel data aggregation scheduling schemes for multi-channel duty-cycled IoT sensor networks. Jiao, et al [14] proposed two schemes, namely EDAS and NDAS, both schemes split into two phases that are aggregation tree construction based maximal independent set (MIS) and nodes scheduling based on Candidate Active Conflict Graphs (CACG) and Feasible Active Conflict Graphs (FACG) that they proposed to show the relationship of transmissions in one working period so that the schemes can avoid interference while scheduling.…”

“…Using MIS for tree construction causes bottlenecks for dominators in dense networks as claimed by [29]. Consequently, our previous work [11] presented a new way to build the aggregation tree for sensors active at multiple timeslots in a working period, the scheme leverages the waiting time of receivers when they are active with their neighbors so that the number of child nodes of non-leaf nodes throughout the aggregation tree is balanced. In addition, the constructed tree consists of pipeline links together with reinforcement scheduling algorithm that schedules more nodes to the unused channels and timeslots left off at every scheduling round.…”

“…Nevertheless, duty-cycling technique comes with a downside that the long dormant time occurs higher data aggregation delay [7]. At this point, using multi-channel technology to allocate sensor nodes further reduces the aggregation time because of the increment of simultaneous interference-free transmissions [8]- [11].…”

“…In this paper, we investigate MLAS problem in multichannel duty-cycled IoT sensor networks (dc-MLAS). Previous works propose approaches that consist of aggregation tree construction and node scheduling phases [11]- [14]. Herein, the tree construction algorithm builds directed links (senderreceiver links) from the communication network, then the node scheduling algorithm allocates channels and assigns scheduled time to sensor nodes such that there is no interference occurred.…”