Alice

Kim, Seohyang; Kim, Hyung-Sin; Kim, Chong-kwon

doi:10.1145/3302506.3310394

Cited by 78 publications

(8 citation statements)

References 37 publications

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“…In most of the recent proposed TSCH schedulers (e.g. [18], [19], [28], [34]), Orchestra was considered as the baseline for the performance evaluation. As it is shown in [17], when the traffic load is light (1 packet per minute (ppm)), Orchestra can achieve 99% packet delivery ratio.…”

Section: Evaluation Resultsmentioning

confidence: 99%

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Game-Theoretic Optimization of the TSCH Scheduling Function for Low-Power IoT Networks

Tavallaie

Taheri

Zomaya

2021

Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located With CPS-IoT Week 202

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Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-toend communication. TSCH constructs a communication schedule by combining frequency channel hopping with Time Division Multiple Access (TDMA). In recent years, IETF designed several standards to define general mechanisms for the implementation of TSCH. However, the problem of updating the TSCH schedule according to the changes of the wireless link quality and node's traffic load left unresolved. In this paper, we use non-cooperative game theory to propose GT-TSCH, a distributed TSCH scheduler designed for low-power IoT applications. By considering selfish behavior of nodes in packet forwarding, GT-TSCH updates the TSCH schedule in a distributed approach with low control overhead by monitoring the queue length, the place of the node in the Directed Acyclic Graph (DAG) topology, the quality of the wireless link, and the data packet generation rate. We prove the existence and uniqueness of Nash equilibrium in our game model and we find the optimal number of TSCH Tx timeslots to update the TSCH slotframe. To examine the performance of our contribution, we implement GT-TSCH on Zolertia Firefly IoT motes and the Contiki-NG Operating System (OS). The evaluation results reveal that GT-TSCH improves performance in terms of throughput and end-to-end delay compared to the state-of-the-art method.

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Section: Evaluation Resultsmentioning

confidence: 99%

“…It enables each node to compute its own schedule by using information of one-hop neighbors. Alice, a link-based autonomous scheduling method that allocates a unique cell for each directional link in the DAG topology, was introduced in [18]. Alice allocates more TSCH cells to nodes with a higher number of one-hop neighbors.…”

Section: Autonomous Tsch Schedulersmentioning

confidence: 99%

Game-Theoretic Optimization of the TSCH Scheduling Function for Low-Power IoT Networks

Tavallaie

Taheri

Zomaya

2021

Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located With CPS-IoT Week 202

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“…In [29] and, [30], MSF was modified to suit a particular case problem, such as heavy traffic loads on industry and efficient routing for mobile nodes. Autonomous scheduling such as Alice [31] and Orchestra, [32], uses a MAC address-based hash function. Separated slotframes are used for EB in TSCH, control packets in RPL, and independent data frames.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Fawwaz

Chung

2023

IEEE Access

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The 6TiSCH (IPv6 over IEEE802.15.4e time-slotted channel hopping mode) wireless sensor network architecture utilizes control packets to construct network formation. These control packets are essential for establishing communication links between nodes and configuring network settings. The trickle timer algorithm is utilized to broadcast the DIO control packet. DIO carries information about the available parent nodes, which is then used to form the routing tree. Sensors transmit control packets in one cell on each TSCH slotframe, called the minimal cell. This leads to the problem that RPL trickle timer algorithm encounters congestion in DIO control packet transmission with other control messages, particularly in dense networks. Moreover, high traffic transmission also leads to high queue usage, which then drops the DIO control packet. Failed DIO transmission can increase network formation time and energy consumption. To address this issue, we propose Q-Trickle, an adaptive trickle timer algorithm based on Q-learning that determines the optimal policy for transmitting or suppressing DIO based on minimal cell and transmission queue conditions. Q-Trickle adaptively selects a redundancy constant value and transmission interval that promotes fair transmission distribution and considers network condition. Additionally, a control scheme over minimal cell transmission is formulated to lower transmission congestion and faster synchronization. The proposed methods were assessed using simulation and actual testing on the FIT IoT-LAB testbed. The results indicated that Q-Trickle performed better than the benchmark methods. Q-Trickle decreases joining time, energy consumption, and number of failed DIO compared with the original algorithm by -13%, -11%, and -43%, respectively.

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“…The autonomous ALICE [18] scheduler addresses some of the shortcomings of Orchestra by assigning cells to links instead of nodes. This improves the performance for heterogeneous traffic patterns since each link, in each direction, has a dedicated cell.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Flow-Based TSCH Scheduling for Heterogeneous Traffic Patterns: Challenges, Design, Simulation, and Testbed Evaluation

Urke,

Kure,

Øvsthus

2023

IEEE Open J. Commun. Soc.

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The Industrial Internet of Things needs wireless communication with bounded latency and stronger robustness. Nodes employing the Time Slotted Channel Hopping (TSCH) MAC operate according to a schedule, and recent work on flow-based autonomous schedulers has shown they can guarantee dedicated resources to each flow of traffic. However, these works assume all nodes transmit toward one destination. Industrial applications such as process control require heterogeneous traffic patterns e.g. for sensor-to-actuator. We investigate how autonomous flow-based scheduling may support heterogeneous traffic patterns. We have previously proposed the Layered scheduler that emphasized flow scheduling and spatial reuse. In this work, we extend Layered to support heterogeneous traffic patterns. The extension includes a novel mechanism where the first application traffic packet is sent in a shared cell to inherently signal the need for scheduling dedicated cells. In adapting to heterogeneous traffic patterns, we encountered seven challenges. These include e.g. the schedule adapting to packets later found as invalid at the routing layer and MAC queues leading to packets signaling outdated routing information to neighbors. We identify a set of mitigations and key parameters to address these challenges, and we evaluate their impact using the Cooja simulator and the FIT IoT-LAB testbed. The mitigation mechanisms are essential to ensure predictable performance under all conditions. Shared cell capacity was crucial as insufficient capacity can have a detrimental impact. Lastly, the scheduler was compared to the autonomous scheduler Orchestra. In scenarios with heterogeneous traffic patterns, we found the extended Layered scheduler retained performance independent of the number of flows. However, it comes at the cost of energy per goodput. Compared to Orchestra, Layered requires approximately twice the energy to maintain the schedule, yet Layered's higher capacity allows for comparable efficiency as application traffic increases.

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Alice

Cited by 78 publications

References 37 publications

Game-Theoretic Optimization of the TSCH Scheduling Function for Low-Power IoT Networks

Game-Theoretic Optimization of the TSCH Scheduling Function for Low-Power IoT Networks

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

Autonomous Flow-Based TSCH Scheduling for Heterogeneous Traffic Patterns: Challenges, Design, Simulation, and Testbed Evaluation

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