Ad-hoc Mobile Computing Network Cluster Formation
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Solution Overview
Problem
Businesses and systems operating in harsh, remote, or dynamic environments often lack sufficient network connectivity to perform mission-critical tasks due to insufficient local computing and network capabilities, such as in vehicles where processing demands exceed available resources.
Innovation Solution
The creation of ad-hoc mobile computing networks that dynamically form clusters of mobile and stationary compute nodes to pool resources, enabling high-performance data processing and network connectivity at the edge of the network, allowing for advanced computations in environments with limited individual compute power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a single mobile compute node operates independently, then device complexity is reduced, but computing power and network capabilities are insufficient for mission-critical tasks
Solution Approach 1:
The patent merges multiple mobile compute nodes into a clustered network where they share computing resources, storage, and network capabilities. This allows the system to achieve higher computing power by combining individual node capabilities while maintaining manageable complexity through standardized interaction protocols and automated cluster management.
Solution Approach 2:
The compute node dynamically adjusts its operational mode, transitioning between independent operation and clustered collaboration based on task requirements. The node can form ad-hoc clusters when additional computing power is needed and dissolve them when not required, providing dynamic scalability without permanent system complexity.
2Reliability
If ad-hoc mobile computing networks are formed, then network connectivity and computing capabilities are improved, but device complexity and coordination overhead increase
Solution Approach 1:
The compute node implements self-service mechanisms for cluster formation and management. It automatically discovers other compute nodes, negotiates cluster parameters, selects master nodes, and manages its own membership status without external intervention. This self-organizing capability improves network reliability while minimizing coordination complexity through decentralized autonomous operation.
Solution Approach 2:
The system employs feedback mechanisms where compute nodes continuously monitor cluster performance, connectivity status, and resource utilization. Based on this feedback, nodes automatically adjust their participation in the cluster, optimize resource allocation, and maintain reliable connectivity through adaptive protocol selection and error correction.
3Adaptability or versatility
If mobile compute nodes form dynamic clusters, then adaptability to changing workloads is improved, but communication overhead and negotiation time increase
Solution Approach 1:
The compute node performs preliminary actions by pre-configuring its capabilities, resources, and communication protocols before cluster formation is needed. It maintains a ready-state configuration that can be quickly activated, and pre-establishes communication channels with potential cluster members, reducing the time required for negotiation and cluster formation when workloads change.
Solution Approach 2:
The system efficiently changes operational parameters during cluster formation and operation. Compute nodes rapidly negotiate and adjust cluster size, master node selection, resource allocation, and communication protocols based on current workload requirements. These parameter changes are optimized to minimize negotiation time while maintaining necessary adaptability.
Data Source
AI summary
Systems and methods are provided for generating and managing ad-hoc mobile computing networks. For example, a method includes discovering, by a first mobile compute node, an existence of a second mobile compute node within a geographic location monitored by the first mobile compute node, and exchanging data between the first and second mobile compute nodes to negotiate conditions for forming a cluster of a mobile ad-hoc network. The conditions include, for example, a target purpose for forming the cluster, criteria for compute node membership within the cluster, and designation of one of the first and second mobile compute nodes as a master compute node for the cluster. The cluster including the first and second mobile compute nodes is then formed based on the negotiated conditions.