A rapid joining scheme based on fuzzy logic for highly dynamic IEEE 802.15.4e time-slotted channel hopping networks

Abstract

The IEEE 802.15.4e standard is an amendment of the IEEE 802.15.4-2011 protocol by introducing time-slotted channel hopping access behavior mode. However, the IEEE 802.15.4e only defines time-slotted channel hopping link-layer mechanisms without an investigation of network formation and communication scheduling which are still open issues to the research community. This article investigates the network formation issue of the IEEE 802.15.4e time-slotted channel hopping networks. In time-slotted channel hopping networks, a joining node normally takes a long time period to join the network because the node has to wait until there is at least one enhanced beacon message advertised by synchronized nodes (synchronizers) in the network on its own synchronization channel. This leads to a long joining delay and high energy consumption during the network formation phase, especially so in highly dynamic networks in which nodes join or rejoin frequently. To enable a rapid time-slotted channel hopping network formation, this article proposes a new design for slotframe structure and a novel adaptive joining scheme based on fuzzy logic. Our proposed scheme enables a synchronizer to be able to adaptively determine an appropriate number of enhanced beacons it should advertise, based on the number of available synchronizers in the network, so that joining nodes can achieve a short joining time while energy consumption of enhanced beacon advertisement at the synchronizers is optimized. Through extensive mathematical analysis and experimental results, we show that the proposed scheme achieves a significant improvement in terms of joining delay compared to state-of-the-art studies.

Adaptive Scheduling Design for Time Slotted Channel Hopping Enabled Mobile Adhoc Network

“…Moreover, we compare our model with [8] since they considered the decentralized network; it is observed that AS technique achieves the satisfactory trade-off between energy overhead and routing performance. Hence from discussion it is clear that our model outperforms the various state-of-art technique [8], [10], [11], [12], [13], and [14] considering the carious parameter such as access fairness, packet delivery rate and energy overhead.…”

“… The main advantage of Adaptive Scheduling is that it provisions for the high traffic application and this does not have any effect on the energy consumption of MANET, furthermore this is achieved through activating the additional slots dynamically and it does not require the reorganizing of new schedule.  The Adaptive model outperforms various state-of-art technique such as [8], [10], [11], [12], [13] and [14] in terms of access fairness, packet transmission and energy efficiency.…”

“…Moreover, [8] proposed a scheduling technique, which considers the decentralized network, and this technique achieves the 44.5% improvisation in throughput, however the energy consumption and packet routing performance is ignored. Similarly, [10] and [12] performed the evaluation and achieves the energy consumption minimization of 27.08% and 25.6%, respectively; whereas [13] achieved the energy overhead reduction of 22.38% and packet dropt rate of 25% when compared to the existing technique. Moreover, we compare our model with [8] since they considered the decentralized network; it is observed that AS technique achieves the satisfactory trade-off between energy overhead and routing performance.…”

Collision Minimization Beacon Scheduling Scheme using RPL in Dense TSCH-based IoT Environment

“…Duy et al [6]. divided slotframe into advertisement plane and communication plane to avoid the EB collisions in congested situations.…”

“…As the number of slots used to transmit EBs in the entire Timeslot is smaller, the number of slots to which data can be transmitted is increased, and the data rate of the entire network is increased. In this experiment, Rapid [6], a comparison algorithm, compares our algorithm with the technique of changing the number of EBs according to neighbor nodes. The experiment was divided into 5 nodes and 20 nodes.…”