AirTag, You're It: Reverse Logistics and Last Mile Dynamics
- URL: http://arxiv.org/abs/2502.17447v1
- Date: Mon, 03 Feb 2025 02:21:23 GMT
- Title: AirTag, You're It: Reverse Logistics and Last Mile Dynamics
- Authors: David Noever, Forrest McKee,
- Abstract summary: This study addresses challenges in reverse logistics, particularly in disaster relief scenarios where infrastructure disruptions demand adaptive solutions.<n>Using 20 Apple AirTags embedded in packages, this research provides empirical insights into logistical flows, capturing granular spatial and temporal data through Bluetooth LE (BLE) 5 trackers integrated with the Apple Find My network.<n>These trackers demonstrated their value in monitoring dynamic cargo movements, enabling real-time adjustments in mobile hub placement and route optimization, particularly in disaster relief contexts like Hurricane Helene.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This study addresses challenges in reverse logistics, a frequently overlooked but essential component of last-mile delivery, particularly in disaster relief scenarios where infrastructure disruptions demand adaptive solutions. While hub-and-spoke logistics networks excel at long-distance scalability, they often fail to optimize closely spaced spokes reliant on distant hubs, introducing inefficiencies in transit times and resource allocation. Using 20 Apple AirTags embedded in packages, this research provides empirical insights into logistical flows, capturing granular spatial and temporal data through Bluetooth LE (BLE) 5 trackers integrated with the Apple Find My network. These trackers demonstrated their value in monitoring dynamic cargo movements, enabling real-time adjustments in mobile hub placement and route optimization, particularly in disaster relief contexts like Hurricane Helene. A novel application of discrete event simulation (DES) further explored the saddle point in hub-spoke configurations, where excessive hub reliance clashes with diminishing spoke interaction demand. By coupling simulation results with empirical AirTag tracking, the study highlights the potential of BLE technology to refine reverse logistics, reduce delays, and improve operational flexibility in both routine and crisis-driven delivery networks.
Related papers
- Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
arXiv Detail & Related papers (2025-01-07T03:23:28Z) - Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks.
Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
arXiv Detail & Related papers (2024-11-01T00:37:00Z) - Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data [55.70071704247794]
Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs)
This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner.
Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques.
arXiv Detail & Related papers (2024-05-10T08:04:27Z) - Deep Reinforcement Learning for Trajectory Path Planning and Distributed
Inference in Resource-Constrained UAV Swarms [6.649753747542209]
This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm.
The formulated problem is NP-hard so finding the optimal solution is quite complex.
We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.
arXiv Detail & Related papers (2022-12-21T17:16:42Z) - Correlating sparse sensing for large-scale traffic speed estimation: A
Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging.
We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
arXiv Detail & Related papers (2022-10-21T07:25:57Z) - Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks
Relying on Real Flight Data: From Single-Objective to Near-Pareto
Multi-Objective Optimization [79.96177511319713]
We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs)
A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop.
We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
arXiv Detail & Related papers (2021-10-28T14:18:22Z) - Space Meets Time: Local Spacetime Neural Network For Traffic Flow
Forecasting [11.495992519252585]
We argue that such correlations are universal and play a pivotal role in traffic flow.
We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor.
The proposed STNN model can be applied on any unseen traffic networks.
arXiv Detail & Related papers (2021-09-11T09:04:35Z) - Spatial-Temporal Conv-sequence Learning with Accident Encoding for
Traffic Flow Prediction [17.94199362114272]
In intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation.
We propose the Spatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal block uses unidirectional convolution to effectively capture short-term periodic temporal dependence.
We conduct extensive experiments on large-scale real-world tasks and verify the effectiveness of our proposed method.
arXiv Detail & Related papers (2021-05-21T17:43:07Z) - Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line
and Off-policy Bandit Solutions [30.606518785629046]
In a fast-varying vehicular environment, the latency in offloading arises as a result of network congestion.
We propose an on-line algorithm and an off-policy learning algorithm based on bandit theory.
We show that the proposed solutions adapt to the traffic changes of the network by selecting the least congested network.
arXiv Detail & Related papers (2020-08-14T11:48:13Z) - Object Tracking through Residual and Dense LSTMs [67.98948222599849]
Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative.
DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances.
Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
arXiv Detail & Related papers (2020-06-22T08:20:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.