Ad Hoc Sensor Network Routing for Minimum-Power Data Paths
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Solution Overview
Problem
Conventional ad-hoc sensor networks primarily consider distance for data transmission routing, which can lead to increased power consumption due to environmental factors, and do not effectively manage node mobility, idle nodes, or data importance, resulting in inefficient power usage.
Innovation Solution
A routing system and method that measures power consumption between nodes, selects paths based on minimum power requirements, and considers node mobility, idle nodes, and data importance to optimize data transmission, using a node with a transmitter, signal intensity measurer, and remaining energy measurer to determine the lowest power path and update routing tables dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If nodes transmit data directly to the base node when within distance, then transmission speed is improved, but power consumption increases
Solution Approach 1:
The patent changes the routing parameter from fixed distance-based to dynamic power-based routing. Nodes continuously measure signal strength (RSSI) to determine actual power consumption characteristics, and select relay nodes that minimize total power consumption rather than simply minimizing transmission distance. This resolves the contradiction by allowing direct transmission when power-efficient but using relay nodes when it reduces overall power usage.
Solution Approach 2:
The routing path is made dynamic rather than static. The system continuously monitors signal strength and power consumption, and adjusts the routing decision in real-time. A node that was previously using direct transmission may switch to relay transmission when power consumption becomes excessive, and vice versa. This dynamic adaptation resolves the contradiction between speed and power consumption.
2Use of energy by moving object
If nodes use relay nodes to transmit data, then power consumption is reduced, but transmission delay increases
Solution Approach 1:
The patent introduces a multi-parameter decision framework that considers not only power consumption but also transmission delay, signal strength, and node capacity. The routing algorithm dynamically adjusts the weight of power consumption versus delay based on current network conditions, data priority, and node states. This resolves the contradiction by finding an optimal balance rather than extreme optimization of either parameter.
Solution Approach 2:
The patent introduces intermediate relay nodes with specific selection criteria. Rather than using any relay node, the system selects relay nodes that meet minimum signal strength thresholds and have sufficient remaining energy. This mediation ensures that while multi-hop transmission is used to reduce power consumption, the relay nodes are optimized to minimize additional delay, thus resolving the contradiction.
3Device complexity
If the system considers only distance for routing, then routing complexity is reduced, but power consumption optimization is insufficient
Solution Approach 1:
The patent extends the routing parameters from simple distance to multiple parameters including signal strength (RSSI), remaining energy, transmission history, and node capacity. However, it implements a hierarchical processing approach where simple distance-based routing is used as a first pass, and more complex power-based routing is applied only when needed, thus managing complexity while achieving power optimization.
Solution Approach 2:
The system performs preliminary measurements of signal strength and power consumption characteristics during idle periods or when establishing initial routes. This preliminary action allows the routing algorithm to make informed decisions without performing complex real-time calculations for every transmission, thus reducing operational complexity while maintaining power optimization capabilities.
4Use of energy by moving object
If the system dynamically adjusts routing based on power consumption, then power efficiency is improved, but system complexity increases
Solution Approach 1:
Each node autonomously measures its own signal strength, calculates power consumption, and makes routing decisions without requiring centralized control. Nodes exchange minimal routing information with neighbors and independently determine optimal paths. This self-service approach achieves dynamic power optimization without the complexity of centralized routing management.
Solution Approach 2:
The system implements dynamic routing adjustments based on real-time power consumption measurements, but uses a simplified update mechanism where routing tables are refreshed periodically or when significant changes occur, rather than continuously. This dynamic adaptation improves power efficiency while avoiding the complexity of continuous real-time reconfiguration.
Data Source
AI summary
In a communicating system including a base node, at least one adjacent node, and a start node transmitting data requested by the base node via the adjacent node or to the base node, data requested to the start node is transmitted by measuring a power required for data transmission between the nodes forming the communication system, selecting a path one by one depending on a minimum power consumption required for the data transmission from the base node to the start node using the measured power, and transmitting the requested data using the selected path.