1 Introduction
1.1 Related work
1.1.1 Communication-aware route selection
1.1.2 Position-aware optimization
1.2 Motivation and contribution
Statement of contribution
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Communication-aware optimization: The optimal route with the highest throughput is identified using a communication quality-oriented route selection metric based on reception probability.
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Position-aware optimization: The optimal position with better communication quality and higher throughput for the router involved in the hop with lowest link quality is determined using particle swarm optimization (PSO). The controller is designed to move the router from its initial position to the optimal position. The channel map in general has a lot of local optimal points, and it is almost impossible to find the global optimal point directly. We show that our framework performs well by avoiding local optimal points with extremely low link quality.
1.3 Paper outline
2 Network model
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Find a suitable metric to represent the link quality in a realistic communication environment, taking into account noise, path loss, multipath fading, and interference.
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Select an optimal route on the basis of the chosen metric.
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Identify the link with the lowest throughput, as route performance is limited by that link.
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Exploit multipath fading and position information to heuristically find optimal position for the receiving router of the lowest quality link.
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Use mobility control to guide the router from the initial position to the optimal position, such that end-to-end throughput of the route is improved.
2.1 Communication link model
2.2 Link quality estimation using reception probability
Parameter | Description | Value |
---|---|---|
P
| Transmit power | 0 dBm |
N
o
| Noise variance | −85 dBm |
ζ
t
| SINR threshold | 10 dBm |
λ
| Wavelength | 0.12 m |
α
| Path loss exponent | 4 |
N
| Total nodes | 150 |
k1 and k2 | Controller parameters | 0.2 and 1 |
2.3 Performance measure
3 COMPARE route optimization
3.1 Communication-aware route selection
3.2 Position-aware optimization
3.2.1 PSO-based optimal position search
3.2.2 Feedback mobility control of mobile agents
3.3 Computational complexity analysis
4 Performance evaluation
4.1 Simulation setup
4.1.1 Assumptions
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Each node has a unique identification (ID).
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Every node knows the relative distance to its neighboring nodes.
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A priori channel information is available.
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The transmission power is the same for all nodes.
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Single-path routing is considered, and only the route chosen and optimized by the COMPARE framework is used for data transmission from the source to the destination.
4.2 Results from illustrative scenarios
4.2.1 Scenario 1: sparse network
4.2.2 Scenario 2: dense network
Scheme | Route |
---|---|
RP | 1→37→56→7→80→81→13→83→18→ |
100→104→144→145→124→130→150→147 | |
RP-PSO-MC | 1→37→56→7→80→81→13→83→ |
18→104→144→145→124→130→150→147 |