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From Cloud to Edge: Maximizing Performance and Efficiency in Wearable IoT Devices

In the era of IoT, wearable devices have gained significant popularity, offering various applications for fitness tracking, health monitoring, and more. These devices often incorporate 9DOF (9 Degrees of Freedom) sensors, such as the MPU9250, which can provide valuable motion and orientation data. However, a common architectural approach of uploading this sensor data to the internet cloud for processing raises several feasibility concerns. In this blog, we will explore the challenges associated with streaming 9DOF sensor data to the cloud in real-time and discuss alternative solutions for effective data processing.


  • Excessive Battery Power Consumption:

One of the major drawbacks of streaming sensor data to the cloud is the considerable power consumption associated with wireless communication. Transmitting data at a high frequency, such as every 10ms, over RF (Radio Frequency) connections significantly drains the device's battery. This high power consumption limits the device's battery life, rendering it impractical for long-term use without frequent recharging.

  • Cost and Scalability of Cloud Storage:

Storing vast amounts of sensor data in the cloud is not only a storage concern but also a financial consideration. Cloud storage services often charge based on data usage, which can quickly accumulate when dealing with continuous high-frequency data streams. Additionally, the scalability of cloud storage becomes crucial when dealing with a large number of connected wearable devices. These costs and scalability challenges need to be carefully evaluated during the product development stage to avoid unforeseen financial burdens.

  • Delay in Real-time Processing:

Real-time processing is often a requirement for wearable IoT applications. However, when relying on cloud-based processing, there is a notable delay in sending the data to the cloud, processing it, and receiving the results back to the device. This delay can hinder time-sensitive applications, such as gesture recognition or real-time feedback systems, which heavily depend on near-instantaneous processing. Relying on the cloud for data processing introduces latency that may not be acceptable for these applications.

  • Underutilization of Local Computation:

Wearable devices are equipped with System-on-a-Chip (SoC) units capable of performing local computations. However, when streaming sensor data to the cloud, the computational capabilities of the SoC remain underutilized. By offloading all processing tasks to the cloud, the potential efficiency and processing power of the device's local resources are not fully utilized, resulting in suboptimal performance and increased dependence on cloud services.

  • Network Connectivity Issues:

Using traditional mobile network technologies like 2G or 4G to directly send large amounts of 9DOF sensor data poses additional challenges. These networks are primarily designed for transmitting smaller data packets, such as text messages or browsing data. Uploading continuous high-frequency sensor data to the cloud can overload these networks, leading to potential network congestion, increased latency, and unreliable data transmission. This approach is not a viable alternative due to the inherent limitations of mobile network technologies.

  • Large Data Volume:

When streaming 9DOF sensor data at a high frequency, the amount of data generated can quickly become overwhelming. For instance, if we assume data is collected from the sensor at 250Hz and each data reading requires 12 registers, the estimated data size that needs to be sent to the cloud is substantial. With these numbers, the data volume reaches approximately 10.30MB per hour, 0.24GB per day, and 1.69GB per week. Dealing with such a large amount of data poses significant challenges in terms of storage, transmission, and processing efficiency.

  • Bandwidth Constraints:

Transferring large volumes of 9DOF sensor data to the cloud over limited bandwidth connections can result in network congestion. This congestion not only affects the wearable device itself but also impacts other devices connected to the same network. The resulting slowdowns and network instability can compromise the overall user experience and lead to potential data loss or corruption.

  • Cost of Data Transmission:

In addition to the storage costs mentioned earlier, there are financial implications associated with data transmission itself. Uploading substantial amounts of data to the cloud in real-time requires a robust and reliable internet connection, often necessitating expensive data plans or broadband subscriptions. These costs can add up significantly, especially when scaling up to a large number of connected wearable devices.

  • Privacy and Security Concerns:

Transmitting sensitive 9DOF sensor data to the cloud raises privacy and security concerns. Data breaches or unauthorized access to this personal information can have severe consequences, both for individuals and for the companies involved. Storing and processing data locally on the wearable device, with appropriate security measures, can mitigate these risks and provide users with better control over their data.

  • Offline Functionality:

Reliance on real-time cloud processing for wearable IoT devices poses challenges when connectivity is lost or disrupted. Many wearable applications require continuous functionality, even in the absence of a stable internet connection. By designing the device to process sensor data locally, users can still access critical features and functionality offline, ensuring a seamless user experience even in challenging network environments.

  • Edge Computing as an Alternative:

An alternative approach to cloud-based processing is edge computing, where data processing tasks are performed on the device itself or at the network edge. By leveraging the computational capabilities of the wearable device's SoC, edge computing reduces latency, conserves battery power, and minimizes reliance on cloud resources. This approach enables faster response times, improved privacy, and reduced network bandwidth requirements.


Conclusion:

While the idea of streaming 9DOF sensor data to the internet cloud for processing might seem appealing initially, it presents numerous practical challenges. The excessive power consumption, cost and scalability concerns, delays in real-time processing, underutilisation of local computation capabilities, and network connectivity issues make this approach unfeasible for most wearable IoT devices. As product development companies venture into IoT, careful consideration must be given to the architectural design choices, exploring alternative solutions that balance power efficiency, data processing, and network connectivity to deliver optimal performance and user experience. By addressing these challenges proactively, IoT product developers can ensure the successful integration of wearable devices into our daily lives. By considering the challenges related to data size, bandwidth constraints, costs, privacy, and security, along with exploring alternatives like edge computing, wearable IoT product developers can address the feasibility concerns associated with uploading 9DOF sensor data to the internet cloud. Taking these factors into account during the product development stage will lead to more efficient and successful implementations of wearable devices in the IoT ecosystem.

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