Futuristic cityscape with connected vehicles and flowing data streams

Smarter Cities, Smoother Rides: How Edge Computing and Collaboration are Revolutionizing Mobile Crowdsensing

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Lena Voss in Tech & Innovation September 2025 • 4 min read.

"Discover how edge-assisted approaches are transforming mobile crowdsensing, making our cities intelligent and our commutes seamless through strategic collaboration and efficient data relaying."


The explosion of big data has fueled advancements in machine learning and data mining, underscoring the critical importance of effective data collection methods. As cities become more connected, smart vehicles emerge as crucial edge infrastructures, capable of sensing and communicating real-time urban data. This capability, known as crowdsensing, leverages the inherent mobility of vehicles to gather dynamic urban data across different times and locations.

One of the most promising applications of crowdsensing lies in creating high-definition (HD) maps. Companies like Here, TomTom, and Baidu require extensive LiDAR, camera, and IMU data to construct live maps that support autonomous driving. The sheer volume and rapid updating needed to maintain these maps present a significant challenge, often exceeding the capacity of map producers' own devices. Crowdsensing offers a solution by incentivizing private vehicles to collect and upload data, rewarding them with real or virtual currency.

This article explores how to mobilize groups of smart vehicles to accomplish sensing tasks in edge environments, where vehicles and Road Side Units (RSU) work together. This approach relies on a system of message relaying and collaboration, enabling vehicles to communicate and collaborate effectively. This article focuses on the communications and incentive mechanisms that drive vehicle collaboration.

The Building Blocks of Collaborative Crowdsensing

Futuristic cityscape with connected vehicles and flowing data streams

At the heart of this system are two key modules: a message relaying module and a collaboration motivating module. The message relaying module uses Vehicle Ad-hoc Networks (VANETs) to facilitate communication within the edge infrastructure. This module is designed around a two-stage process: a spread process, where task information is initially broadcast by a 'seed vehicle' and relayed by others, and a back process, where the message is modified and sent back to the seed vehicle, incorporating new information.

The collaboration motivating module focuses on encouraging drivers to participate in sensing tasks. By relying on the message relaying module, it facilitates information acquisition and propagation. Unlike approaches that discuss message relaying and collaboration separately, this article integrates these functions into a unified framework. The aim is to empower each networked vehicle to make informed decisions, balancing automated control with the driver's preferences. This balance ensures swift execution of tasks while maintaining high-level collaboration involving human input.

To achieve this, the system incorporates key assumptions:
  • Vehicles are equipped with Dedicated Short Range Communication (DSRC) devices, allowing communication within a specific range.
  • Drivers are assumed to be rational and self-interested, making decisions to maximize their profits.
  • The cost of participation for each driver follows a normal distribution.
To ensure seamless operations, the system model assumes a near-perfect channel without data collisions or losses. This simplification allows for a sharper focus on the collaborative and communication aspects of the system. This allows us to analyse how well communication can be done to achieve a desired goal in Mobile CrowdSensing using Collaboration techniques for participants in the network. This is what is important when designing systems for next generation networks.

Future Directions: Building Truly Intelligent Systems

This research lays a critical foundation for the future of urban data collection and management. By integrating collaborative strategies and efficient communication networks, we can move closer to creating truly intelligent systems that respond dynamically to the needs of urban environments. The key lies in refining our understanding of how technology and human behavior can be harmonized to build more responsive, efficient, and sustainable cities.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1155/2018/1287969, Alternate LINK

Title: Message Relaying And Collaboration Motivating For Mobile Crowdsensing Service: An Edge-Assisted Approach

Subject: Electrical and Electronic Engineering

Journal: Wireless Communications and Mobile Computing

Publisher: Hindawi Limited

Authors: Shu Yang, Jinglin Li, Quan Yuan, Zhihan Liu, Fangchun Yang

Published: 2018-07-29

Everything You Need To Know

1

What is mobile crowdsensing, and how are smart vehicles utilized in this process?

Mobile crowdsensing uses the inherent mobility of vehicles to gather dynamic urban data across different times and locations. Smart vehicles act as edge infrastructures, sensing and communicating real-time urban data, leveraging advancements in machine learning and data mining. This approach addresses the challenge of effective data collection in connected cities, especially for applications like creating high-definition maps.

2

What are the key modules involved in collaborative crowdsensing, and how do they function together?

The two primary modules are the message relaying module and the collaboration motivating module. The message relaying module uses Vehicle Ad-hoc Networks (VANETs) to facilitate communication within the edge infrastructure through a spread and back process. The collaboration motivating module encourages drivers to participate in sensing tasks, balancing automated control with the driver's preferences to ensure swift task execution and high-level collaboration.

3

What are the key assumptions made in the system model for collaborative crowdsensing?

The system assumes that vehicles are equipped with Dedicated Short Range Communication (DSRC) devices for communication. It also assumes that drivers are rational and self-interested, making decisions to maximize their profits, and that the cost of participation for each driver follows a normal distribution. Additionally, it simplifies the model by assuming a near-perfect channel without data collisions or losses to focus on the collaborative and communication aspects.

4

How does crowdsensing address the challenges of creating and maintaining high-definition maps for autonomous driving?

HD maps for autonomous driving rely on data from LiDAR, cameras, and IMUs, requiring constant updates. Crowdsensing offers a solution by incentivizing private vehicles to collect and upload this data, compensating them with real or virtual currency. This approach addresses the challenge of map producers needing to maintain these maps with the required volume and speed.

5

What future advancements are anticipated in urban data collection and management, and how will they contribute to building truly intelligent systems?

Integrating collaborative strategies and efficient communication networks lays the foundation for creating intelligent systems that dynamically respond to urban needs. Future progress depends on harmonizing technology with human behavior to build more responsive, efficient, and sustainable cities. This involves refining our understanding of how to effectively mobilize and incentivize smart vehicles and drivers to participate in data collection efforts.

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