Reliable Crowdsourced Last Mile Delivery: Blockchain-Enabled Framework With Feedback

This paper addresses the challenges of Last Mile Delivery (LMD) in crowdsourced platforms under time and budget constraints. LMD service providers face a continuous increase in demand with limited resources, such as workers and budgets. With tasks that vary in urgency, limited resources often lead to task failures. Furthermore, the increasing number of requesters and workers complicates the governance of the LMD platform, making it difficult to maintain credibility, integrity, and transparency. Current LMD solutions generally focus on route optimization and service acceleration, overlooking the challenge of combined time-critical and budget-constrained tasks. This creates the need for an agile, accelerated, and decentralized LMD framework that can 1) handle tasks instantly upon submission, 2) motivate successful completion based on urgency, and 3) compensate for tasks with deficient budgets to fortify long-term success and avoid failure due to constrained resources. For this purpose, this paper introduces the first Blockchain Hybrid Crowdsourced Auction-based LMD framework (BHCA-LMD), which is designed to effectively manage time and budget constraints. Built on-chain for transparency and decentralization, the framework uses drones and ground vehicles as a hybrid delivery mode for their capabilities in speeding up deliveries. BHCA-LMD incorporates an auctioning system to alleviate failures due to limited budgets, with a feedback mechanism to prevent entities from abusing the platform’s profit for their benefit, ensuring the credibility and reliability of the framework. The evaluation shows an on-time task completion rate of up to 74% with the presence of malicious behavior and a 15% lower gas consumption compared to the closest benchmark.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-06-03
DOI
https://doi.org/10.1109/tits.2025.3565821
Primary Topic
Urban and Freight Transport Logistics
Type
article
Field-Weighted Citation Impact
0.00
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article

Reliable Crowdsourced Last Mile Delivery: Blockchain-Enabled Framework With Feedback

Jamal Bentahar, Shakti Singh, Rabeb Mizouni, Hadi Otrok et al.
IEEE Transactions on Intelligent Transportation Systems
Urban and Freight Transport Logistics
article

Reliable Crowdsourced Last Mile Delivery: Blockchain-Enabled Framework With Feedback

Jamal Bentahar, Shakti Singh, Rabeb Mizouni, Hadi Otrok, Esraa Odeh
article en

Abstract

This paper addresses the challenges of Last Mile Delivery (LMD) in crowdsourced platforms under time and budget constraints. LMD service providers face a continuous increase in demand with limited resources, such as workers and budgets. With tasks that vary in urgency, limited resources often lead to task failures. Furthermore, the increasing number of requesters and workers complicates the governance of the LMD platform, making it difficult to maintain credibility, integrity, and transparency. Current LMD solutions generally focus on route optimization and service acceleration, overlooking the challenge of combined time-critical and budget-constrained tasks. This creates the need for an agile, accelerated, and decentralized LMD framework that can 1) handle tasks instantly upon submission, 2) motivate successful completion based on urgency, and 3) compensate for tasks with deficient budgets to fortify long-term success and avoid failure due to constrained resources. For this purpose, this paper introduces the first Blockchain Hybrid Crowdsourced Auction-based LMD framework (BHCA-LMD), which is designed to effectively manage time and budget constraints. Built on-chain for transparency and decentralization, the framework uses drones and ground vehicles as a hybrid delivery mode for their capabilities in speeding up deliveries. BHCA-LMD incorporates an auctioning system to alleviate failures due to limited budgets, with a feedback mechanism to prevent entities from abusing the platform’s profit for their benefit, ensuring the credibility and reliability of the framework. The evaluation shows an on-time task completion rate of up to 74% with the presence of malicious behavior and a 15% lower gas consumption compared to the closest benchmark.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(9)
Khalifa University of Science and Technology (AE)
Openalex Percentile: Top 88%
Urban and Freight Transport Logistics
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