Energy-Efficient Wireless Sensor Routing With Multi-Agent Deep RL
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Solution Overview
Problem
Energy consumption in wireless sensor networks is high due to inefficient routing, leading to battery depletion and network paralysis, especially when forwarding paths are not optimally chosen and residual energies of sensor nodes are uneven.
Innovation Solution
A method for energy efficient routing using multi-agent deep reinforcement learning, which dynamically adjusts routing policies based on sensor node residual energies and hops, involving a cooperative routing decision system with decision networks on each sensor node and a sink module, trained through a multi-agent deep reinforcement learning algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional routing algorithms are used, then the routing decision is simple, but the energy consumption increases dramatically and network lifetime is reduced
Solution Approach 1:
Each sensor node autonomously makes routing decisions based on its own residual energy and observed network conditions. The routing algorithm allows nodes to self-manage their data forwarding without centralized control, enabling energy-efficient autonomous routing while avoiding the complexity of centralized management
Solution Approach 2:
The routing algorithm dynamically adjusts forwarding decisions based on changing network parameters including residual energy levels, transmission success rates, and hop counts. By continuously adapting routing parameters to current network conditions, the system optimizes energy consumption without requiring overly complex predetermined routing tables
2Reliability
If sensor nodes with higher residual energy relay data, then the network connectivity is maintained longer, but the routing policy becomes more complex and requires continuous energy monitoring
Solution Approach 1:
The routing algorithm incorporates feedback mechanisms where sensor nodes continuously monitor their residual energy and transmission success rates, then use this information to adjust their routing decisions. This feedback loop enables the network to adapt to changing energy conditions and maintain connectivity while using relatively simple routing logic at each node
Solution Approach 2:
The routing policy dynamically adapts to changing network conditions including residual energy distribution and transmission reliability. Rather than using static predetermined routing tables, the system continuously adjusts forwarding decisions based on current network state, maintaining connectivity without requiring complex static planning
3Use of energy by moving object
If the transmission cycle is increased to reduce frequent routing decisions, then the energy consumption is reduced, but the routing policy cannot adapt quickly enough to energy changes
Solution Approach 1:
The routing algorithm uses periodic transmission cycles where nodes collect environmental data and evaluate routing decisions at regular intervals. This periodic action reduces the frequency of routing computations, lowering energy consumption while still allowing the system to adapt to energy changes through accumulated observations over multiple cycles
Data Source
AI summary
A method for energy efficient routing in wireless sensor network based on multi-agent deep reinforcement learning, predefines a to-be-deployed wireless sensor network and creates a cooperative routing decision system including A decision networks and one sink module, A decision networks deployed on the agents ai, i=1, 2, . . . , A, of the sensor nodes, the sink module deployed on the sink node n0. The decision network obtains a probability vector according to its local observation and position vectors. The sink module calculates a routing for each sensor node according the probability vectors of A decision networks and sends the routings to corresponding sensor nodes. A multi-agent deep reinforcement learning algorithm is adopted to train the decision networks of A agents ai, i=1, 2, . . . , A of the cooperative routing decision system, deploys the to-be-deployed wireless sensor network into an environment and updates the routing policy of the deployed wireless sensor network at each update cycle of routing.