Middleware for Mobile Device Community Sensing
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Solution Overview
Problem
Community-driven sensing technologies face challenges in reducing infrastructure costs for traditional sensor networks, particularly in designing mobile device-resident middleware for opportunistic and objective-oriented sensing of heterogeneous phenomena.
Innovation Solution
A client-side middleware is developed that enables a unified sensing architecture and expressivity constructs to efficiently control and coordinate sensor networks by processing sensor data requirements and user preferences, determining sensing strategies, and scheduling sensor duty cycles and sampling frequencies based on these requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor networks are used for community-driven sensing, then sensing coverage and reliability are improved, but infrastructure costs increase
Solution Approach 1:
The patent uses mobile devices as copies of traditional sensor nodes. Instead of deploying expensive physical sensor infrastructure, the system leverages existing mobile devices (phones, tablets) that already have sensors, treating them as virtual sensor nodes in the crowd-sensing network. This copying approach maintains sensing capabilities while dramatically reducing infrastructure costs.
Solution Approach 2:
The patent makes mobile devices universal sensing platforms that can perform multiple sensing functions. The same mobile device can serve different sensing applications (environmental monitoring, event detection, location tracking) by dynamically configuring and reconfiguring sensor usage, eliminating the need for application-specific dedicated sensors and reducing overall infrastructure requirements.
2Adaptability or versatility
If multiple sensing applications run simultaneously on mobile devices, then sensing coverage is improved, but device resource constraints are worsened
Solution Approach 1:
The patent merges the resource management functions of multiple sensing applications into a unified middleware layer. Instead of each application independently managing its own sensors and consuming resources separately, the middleware consolidates resource allocation decisions, combining sensor access requests from multiple applications and optimizing their execution to share device resources more efficiently.
Solution Approach 2:
The patent implements dynamic resource allocation where sensor access and processing priorities are adjusted in real-time based on current device conditions and application needs. The system dynamically modifies sensing strategies, sampling frequencies, and resource distribution to adapt to changing resource constraints while maintaining comprehensive sensing coverage across multiple applications.
3Measurement precision
If sensor sampling frequency is increased to meet application requirements, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The patent applies different sampling frequencies and sensing intensities to different sensors and different sensing tasks based on their specific requirements. Instead of uniformly high sampling across all sensors, the system tailors local sensing quality to match application needs, using higher precision only where necessary and reducing sampling rates for less critical measurements, thereby lowering overall energy consumption.
Solution Approach 2:
The patent dynamically changes sensing parameters including sampling frequency, duty cycle, and sensor activation thresholds based on current conditions and application priorities. By adjusting these parameters flexibly rather than maintaining fixed high values, the system optimizes the balance between measurement precision and energy consumption, reducing power usage while maintaining sufficient data quality for sensing objectives.
Data Source
AI summary
Techniques, systems, and articles of manufacture for application and situation-aware community sensing. A method includes processing one or more sensor data requirements for each of multiple sensing applications and one or more user preferences for sensing, determining a sensing strategy for multiple sensors corresponding to the multiple sensing applications based on the one or more sensor data requirements and the one or more user preferences for sensing, wherein said sensing strategy comprises logic for executing a sensing task, and scheduling a sensor duty cycle and a sampling frequency for each of the multiple sensors based on the sensing strategy needed to execute the sensing task.