Probabilistic Beacon Filtering for IoT Energy Efficiency
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Solution Overview
Problem
In systems with a large number of broadcasting devices, the increased airwaves with broadcast signals lead to inefficiencies, including excessive communication bandwidth consumption and energy drain for observing entities, as most signals are irrelevant and require unnecessary communication with a central verifying entity.
Innovation Solution
Implementing probabilistic filters, such as Bloom filters, at observing entities to locally determine the membership of received beacon identifiers in predefined sets, reducing the need for unnecessary communication by allowing initial or complete beacon identifier processing and grouping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the observing entity communicates with the central verifying entity at each signal reception instance, then the identity and semantic information of beacon devices can be obtained, but communication bandwidth is consumed and energy is drained due to the vast majority of signals being irrelevant noise
Solution Approach 1:
The system performs preliminary filtering of beacon signals by maintaining a probabilistic filter data structure that pre-stores identifiers of relevant beacon devices. When a signal is received, the observing entity first checks if the beacon identifier exists in the probabilistic filter before initiating communication with the verifying entity. This preliminary action eliminates the need to communicate for irrelevant signals, dramatically reducing energy consumption while maintaining reliable information acquisition for relevant beacons.
Solution Approach 2:
The probabilistic filter serves as an intermediary between the received beacon signals and the communication with the verifying entity. Instead of directly communicating with the verifying entity for every signal, the observing entity uses the probabilistic filter as a mediator to pre-screen signals. This intermediary structure allows the system to efficiently distinguish relevant from irrelevant signals without losing the ability to obtain accurate information when needed.
2Reliability
If the observing entity communicates with the central verifying entity at each signal reception instance, then the identity and semantic information of beacon devices can be obtained, but communication bandwidth is overwhelmed due to the large number of unnecessary communications
Solution Approach 1:
The system performs preliminary filtering of beacon signals by maintaining a probabilistic filter data structure that pre-stores identifiers of relevant beacon devices. When a signal is received, the observing entity first checks if the beacon identifier exists in the probabilistic filter before initiating communication with the verifying entity. This preliminary action eliminates the need to communicate for irrelevant signals, dramatically reducing energy consumption while maintaining reliable information acquisition for relevant beacons.
Solution Approach 2:
The probabilistic filter serves as an intermediary between the received beacon signals and the communication with the verifying entity. Instead of directly communicating with the verifying entity for every signal, the observing entity uses the probabilistic filter as a mediator to pre-screen signals. This intermediary structure allows the system to efficiently distinguish relevant from irrelevant signals without losing the ability to obtain accurate information when needed.
3Use of energy by moving object
If probabilistic filters are implemented at observing entities, then unnecessary communication and energy consumption are reduced, but the complexity of the observing entity increases due to local filtering processing
Solution Approach 1:
The patent employs a probabilistic filter, specifically a Bloom filter, which is a space-efficient and computationally simple data structure. The filter uses a fixed-size bit array and multiple hash functions to store and query beacon identifiers. This approach provides a cheap and lightweight filtering mechanism that can be implemented with minimal computational resources, making the increased complexity acceptable given the substantial energy savings from reduced communications.
Solution Approach 2:
The patent replaces the traditional approach of centralized verification for every signal with a decentralized probabilistic filtering mechanism. Instead of relying on complex communication protocols and centralized processing, the system substitutes a simple local data structure that uses hash functions and bit manipulation. This substitution dramatically simplifies the filtering operation at the observing entity while maintaining effective signal discrimination.
Data Source
AI summary
Systems and methods for generating and using probabilistic filters are provided. One example method includes obtaining a plurality of beacon identifiers respectively associated with a plurality of beacon devices. The operations include determining a plurality of filter shards for each beacon identifier by applying a plurality of hash functions to each beacon identifier. The operations include providing the plurality of filter shards for each beacon identifier for local storage in a probabilistic filter at an observing entity, such that the observing entity can query the probabilistic filter to receive an indication of whether a received identifier is a member of a set that includes the plurality of beacon identifiers. One example system includes a plurality of beacon devices, at least one observing entity, and at least one verifying entity.