Distributed Edge Computing with Data Segmentation and Privacy
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Solution Overview
Problem
Distributed computing environments face challenges in optimizing resource usage and preserving privacy when utilizing resource-constrained edge devices, such as smart phones and residential gateways, due to limitations in computing power, memory, bandwidth, and energy constraints.
Innovation Solution
A system and method that incorporates a cluster monitoring module, privacy module, and communication module to optimize resource usage and ensure privacy preservation by segregating data into smaller sets, allocating them efficiently across edge devices, and using randomized network encoding to optimize bandwidth and energy consumption while maintaining data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared with edge devices for distributed computation, then computational productivity is improved, but data privacy is compromised
Solution Approach 1:
The patent segments data into smaller data sets before distributing to edge devices. This segmentation allows the system to perform computation on divided data portions while maintaining privacy, as individual edge devices process only fragments rather than complete datasets, thus reducing the risk of privacy breach while preserving computational productivity.
Solution Approach 2:
The patent applies randomized network encoding to transform data parameters during transmission and processing. This parameter transformation obscures the original data while preserving computational functionality, enabling productivity improvement without compromising privacy as the encoded data maintains utility while hiding sensitive information.
2Productivity
If additional computational nodes are added for parallel computation, then computational productivity is improved, but device complexity increases
Solution Approach 1:
The patent employs smart devices as multi-functional computational nodes that can perform various computation tasks. These universal devices can be dynamically allocated for different computational workloads, improving productivity without requiring specialized hardware for each function, thus managing system complexity through software-based flexibility.
Solution Approach 2:
The patent implements dynamic allocation and scheduling of computational tasks to edge devices based on real-time availability and capabilities. This dynamic approach allows the system to adapt to changing conditions, adding or removing nodes from active computation as needed, which improves productivity while preventing permanent complexity accumulation.
3Productivity
If data is transferred to edge devices for computation, then computational productivity is improved, but bandwidth consumption increases
Solution Approach 1:
The patent segments large datasets into smaller data sets before transmission to edge devices. This segmentation reduces the amount of data transferred in each batch, thereby lowering bandwidth consumption while maintaining the ability to perform comprehensive computation through parallel processing of multiple smaller datasets across different devices.
Solution Approach 2:
The patent uses randomized network encoding to transform data parameters during transmission. This encoding optimizes the data representation for efficient transmission over networks with limited bandwidth, reducing the energy consumption associated with data transfer while preserving the computational integrity needed for productivity improvement.
4Adaptability or versatility
If smart devices are used as computational nodes, then device versatility is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic scheduling that assigns computational tasks to smart devices based on their energy availability and current workload. This dynamic approach ensures that energy-intensive computations are distributed to devices with sufficient energy reserves, maintaining device versatility while managing overall energy consumption through intelligent task allocation and load balancing.
Data Source
AI summary
This disclosure relates generally to the use of distributed system for computation, and more particularly, relates to a method and system for optimizing computation and communication resource while preserving security in the distributed device for computation. In one embodiment, a system and method of utilizing plurality of constrained edge devices for distributed computation is disclosed. The system enables integration of the edge devices like residential gateways and smart phone into a grid of distributed computation. The edged devices with constrained bandwidth, energy, computation capabilities and combination thereof are optimized dynamically based on condition of communication network. The system further enables scheduling and segregation of data, to be analyzed, between the edge devices. The system may further be configured to preserve privacy associated with the data while sharing the data between the plurality of devices during computation.