Mesh Networked Mobile Nodes for Computational Resource Sharing
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Solution Overview
Problem
Body-worn computers, due to their portability and limited battery life, have restricted computational power and communication capabilities, making it challenging to efficiently process and transfer big data in real-time, especially in resource-intensive tasks.
Innovation Solution
Implementing a mesh-networked system where mobile nodes can share computational resources by forming a squad with a coordinating node and worker nodes, allowing for the distribution of resource-intensive tasks across multiple devices based on battery level and utilization, enhancing processing power and communication range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of moving object
If body-worn computers use limited battery power for portability, then device portability is improved, but computational power and communication capabilities deteriorate
Solution Approach 1:
Multiple body-worn computing devices are merged into a mesh network where they share computational resources. The system combines the processing power, memory, and communication capabilities of multiple devices to achieve the computational capacity of a single powerful device, while each individual device remains portable and battery-powered.
Solution Approach 2:
The computational workload is segmented and distributed across multiple body-worn devices in the mesh network. Resource-intensive tasks are divided into smaller sub-tasks that can be executed by different nodes, allowing each device to perform only the computation necessary for its limited power resources while collectively achieving high computational power.
2Weight of moving object
If body-worn computers process data locally with limited computational power, then device portability is improved, but data processing capability deteriorates
Solution Approach 1:
The data processing capabilities of multiple portable devices are merged through the mesh network. Complex data processing tasks benefit from the combined CPU power, memory resources, and parallel processing ability of all nodes, achieving high productivity while maintaining device portability.
Solution Approach 2:
The system transitions from single-device sequential processing to multi-device parallel processing across the mesh network dimension. This dimensional expansion allows simultaneous execution of multiple processing operations, dramatically increasing data processing capability without adding weight to individual devices.
3Device complexity
If body-worn computers operate independently with limited range, then device simplicity is improved, but communication range deteriorates
Solution Approach 1:
The communication ranges of multiple simple body-worn devices are merged through the mesh network topology. Each device maintains simple independent operation with limited transmission range, but the network as a whole achieves extended communication range by relaying messages through intermediate nodes.
Solution Approach 2:
Intermediate mesh network nodes act as mediators to extend communication between devices that are beyond direct transmission range. Messages are forwarded through a series of intermediary devices, enabling long-range communication while each individual device remains simple and operates only within its limited communication radius.
Data Source
AI summary
The nodes of a squad of nodes include a coordinating node and a set of worker nodes for sharing computational resources to perform resource intensive tasks. A requesting worker node may send work requests to the coordinating node of a squad of nodes. In response to a work request, the requesting worker node receives from the coordinating node a list of worker nodes to assign one or more tasks associated with the work request. The list of worker nodes is selected based on a report of resources and current utilization of each node within the squad. Upon receiving the list of workers, the requesting worker node divides the tasks associated with the work request into multiple buckets, assigns each bucket to a worker node form the list of worker nodes, and sends a request to process tasks from each of the buckets to the corresponding worker node.