Smart Luminaire Network Edge Computing Bandwidth
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Solution Overview
Problem
Current systems rely on remote servers for computational tasks, leading to bandwidth consumption and latency, especially in network-connected lighting systems where offloading tasks can cause delays and inefficiencies.
Innovation Solution
A network of smart luminaire devices that can perform computationally intensive tasks by parsing and distributing tasks among available devices based on occupancy patterns, energy usage, and connectivity requirements, leveraging local processing power and reducing reliance on remote servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computational tasks are offloaded to remote servers, then processing power is increased, but bandwidth consumption increases and latency occurs
Solution Approach 1:
The patent segments computational tasks into smaller subtasks that can be distributed to multiple luminaire devices within the network. Each luminaire processes a portion of the overall task locally, eliminating the need to transmit large amounts of data to remote servers while still achieving distributed computational power.
Solution Approach 2:
The patent transitions from a centralized remote server architecture to a distributed edge computing architecture where computation occurs at the network edge (luminaire devices). This dimensional shift in computational location reduces bandwidth consumption by keeping data processing local while maintaining collaborative processing capabilities.
2Power
If computational tasks are offloaded to remote servers, then processing power is increased, but latency increases due to network transmission
Solution Approach 1:
By dividing computational tasks into segments that can be processed locally by luminaire devices, the patent eliminates network transmission delays for large datasets. Each luminaire processes its assigned segment independently, significantly reducing latency compared to centralized remote processing.
Solution Approach 2:
The patent enables luminaire devices to perform computational preprocessing locally before any necessary data transmission to remote servers. This preliminary local processing reduces the amount of data that needs to be transmitted and processed remotely, thereby reducing overall latency.
3Loss of energy
If tasks are distributed among luminaire devices, then bandwidth is conserved and latency is reduced, but device complexity increases
Solution Approach 1:
The patent leverages the existing multi-functionality of smart luminaire devices, which already incorporate processors, sensors, and network connectivity for lighting control. By adding computational task execution to these existing functions, the system achieves distributed processing without requiring entirely new device architectures.
Solution Approach 2:
The patent implements self-service mechanisms where luminaire devices automatically assess their own computational availability and autonomously accept or reject distributed tasks. This eliminates the need for complex centralized scheduling and reduces system complexity while maintaining efficient task distribution.
4Reliability
If luminaire devices perform computational tasks, then remote server dependency is reduced, but energy consumption at device level increases
Solution Approach 1:
The patent implements dynamic task distribution that adapts to real-time energy conditions of luminaire devices. Devices with sufficient energy reserves accept computational tasks, while energy-constrained devices defer or reject tasks. This dynamic approach maintains remote server independence while preventing energy depletion.
Solution Approach 2:
The patent enables luminaire devices to discard non-critical computational tasks when energy is low and recover processing capacity when energy becomes available. This selective task acceptance ensures that essential functions maintain remote independence while non-essential tasks are deferred to preserve device energy.
Data Source
AI summary
The described embodiments relate to systems, methods, and apparatus for employing a network (410) of smart luminaires (402, 440) to perform tasks typically reserved for remote servers. The network of smart luminaires can include lighting elements for illuminating an area, as well as a computer system for processing and transmitting data. Various computational tasks can be parsed and delegated to certain smart luminaires in the network (410) in order to optimize the use of each smart luminaire in the network. Computational tasks can originate at the smart luminaires or be delegated to the smart luminaires by another device. Additionally, data that is processed by the smart luminaires can be transmitted to other devices, thereby allowing other devices to leverage the computing power of a nearby network of smart luminaires.