PPubMed7 Feb 2024Cited 5×

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks †

Aida Vatankhah, Ramiro Liscano, Joan García-Haro, Antonio-Javier Garcia-Sanchez, Rafael Asorey-Cacheda, Claudia Liliana Zuniga

Abstract

The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.

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A priority‑based customized differential evolution algorithm creates shorter, collision‑free TSCH schedules that outperform the TASA method in delay, throughput and reliability.

What’s new

i.Introduced the Priority‑based Customized Differential Evolution (PCDE) algorithm for TSCH schedule optimization.
ii.Developed a collision‑ and interference‑free transmission graph to support heterogeneous sensor data rates.
iii.Performed extensive simulations (360 topologies, 18 scenarios) comparing PCDE with the Traffic‑Aware Scheduling Algorithm (TASA) across multiple metrics.
iv.Demonstrated overscheduling with reserved cells to improve robustness against packet generation timing.
v.Analyzed computational complexity and scalability of the proposed approach.

How they did it

The authors designed the PCDE algorithm, which builds a collision‑free transmission graph and uses priority‑based customized differential evolution to minimize slotframe size, delay, and maximize throughput. They evaluated the algorithm using Matlab and TSCH‑SIM simulations on 360 network topologies across 18 scenarios, measuring delay, PDR, throughput, time complexity, duty cycle, and slotframe size. Results were compared against the Traffic‑Aware Scheduling Algorithm (TASA).

What they found

In Scenario 1 (10 nodes), PCDE achieved 0.23 ms delay, 100 % PDR, throughput 1.1, and slotframe size 11, versus TASA’s 3 ms delay, 99.5 % PDR, throughput 0.9, slotframe size 15.
Across all scenarios, PCDE’s throughput reached up to 11.5 (Scenario 12) compared to TASA’s maximum of 8.4 (Scenario 12).
PCDE reduced slotframe size by up to 43 slots (Scenario 18: 244 vs 287 slots for TASA).
PCDE maintained 100 % PDR in most small‑scale scenarios, while TASA’s PDR dropped to 88.5 % in large scenarios.
PCDE’s time complexity grew sublinearly but required over a day to compute schedules for networks larger than 100 nodes.

Caveats

✕Computation time becomes prohibitive for networks larger than 100 nodes, taking a day or more.
✕The schedule is static and does not adapt to dynamic topology or link‑quality changes.
✕The generated schedule may be suboptimal, potentially causing packet loss due to unscheduled transmissions in larger networks.
✕Overscheduling can produce reserved cells that remain unused, reducing efficiency.

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