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Providing End-to-End Delay Guarantees forMulti-hop Wireless Sensor Networks
I-Hong Hou
Motivation
• Wireless sensor networks are being deployed for real-time surveillance
Challenges
• Wireless sensor networks can be deployed over a large area
• Multi-hop transmissions are required to deliver sensed data
• Need to provide end-to-end delay guarantees• Sensors are limited in transmission capacity
and may suffer from low transmission reliability
Contributions of this Work
• Study the problem of providing end-to-end delay guarantee and throughput guarantee for multi-hop wireless sensor networks
• Develop scheduling policies for two kinds of networks
• Provide simulation results to justify the performance
Network Model
• A number of sensors transmitting data to a base station through multi-hop transmissions
• A routing tree is formed by the routing protocol, with the base station being the root
• h(n) = parent of n• h(6) = 4• h(5) = 2
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Traffic Model
• Time is slotted and grouped into intervals of length T time slots
• Each sensor may generate several flows• Packets generated in an interval need to be
delivered before the end of the interval, or they are dropped
T Flow 1 Flow 2 Deadline
Channel and QoS Model
• When a sensor n transmits to its parent, the transmission is successful with probability pn
• A flow f requires its throughput to be at least qf
• A scheduling policy is feasibility optimal if it satisfies requirements of all flows whenever feasible
Communication Model
• Consider two types of sensor networks
• Orthogonal relay system: Sensors can transmit and receive simultaneously– Sensors are equipped with full-duplex radio, or
they use OFDMA
• Half-duplex system: Sensors can either transmit or receive. They can receive one transmission at a time
Solution Overview
• Debt of flow f at interval k:
• Theorem: A policy that maximizes
in every interval is feasibility optimal
Indicator function of packet delivery
Orthogonal Relay System
• Greedy Forwarder: Each sensor transmits the packet with the largest debt among the available ones in each time slot
• Theorem: Greedy Forwarder is feasibility optimal for orthogonal relay system
Half Duplex System
• Closest Sensor First: Order packets by the number of hops between their current sensor and the base station, break ties by their debts
• Use this ordering to greedily select a maximal set of packets that can be transmitted simultaneously
• Theorem: Closest Sensor First is feasibility optimal for line topologies– Line topology: all flows are originated at the same
sensor
Simulation Setup
• 12 flows generated by sensors 3, 5, 6, 7, 8, 9• Channel reliability is randomly selected from
[0.4, 0.9]• Half of the flows require qf = α, others require qf = β
• Compare two policies– Random policy– Static priority
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Results for Orthogonal Relay Systems
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1Greedy Forwarder
Static Priority
Random
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Impact of Delayed Information
• Sensors notify their children information about debts periodically
• Sensors far away from the base station has stale information
00.10.20.30.40.50.60.70.80.9
1Instant knowledgeUpdate period = 100Update period = 200
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Results for Half Duplex Systems
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0.8Closest Sensor First
Static Priority
Random
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Conclusion
• Study the problem of providing end-to-end delay guarantees for wireless sensor networks with unreliable transmissions
• Develop scheduling policies for both orthogonal relay system and half duplex system
• They offer provable performance guarantees• Simulation results show that they are superior
than other policies