4 research outputs found
Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks over Unreliable Channels
Wireless sensor networks have been increasingly used for real-time
surveillance over large areas. In such applications, it is important to support
end-to-end delay constraints for packet deliveries even when the corresponding
flows require multi-hop transmissions. In addition to delay constraints, each
flow of real-time surveillance may require some guarantees on throughput of
packets that meet the delay constraints. Further, as wireless sensor networks
are usually deployed in challenging environments, it is important to
specifically consider the effects of unreliable wireless transmissions.
In this paper, we study the problem of providing end-to-end delay guarantees
for multi-hop wireless networks. We propose a model that jointly considers the
end-to-end delay constraints and throughput requirements of flows, the need for
multi-hop transmissions, and the unreliable nature of wireless transmissions.
We develop a framework for designing feasibility-optimal policies. We then
demonstrate the utility of this framework by considering two types of systems:
one where sensors are equipped with full-duplex radios, and the other where
sensors are equipped with half-duplex radios. When sensors are equipped with
full-duplex radios, we propose an online distributed scheduling policy and
proves the policy is feasibility-optimal. We also provide a heuristic for
systems where sensors are equipped with half-duplex radios. We show that this
heuristic is still feasibility-optimal for some topologies
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
Proportional Fair Coding for Wireless Mesh Networks
We consider multi–hop wireless networks carrying
unicast flows for multiple users. Each flow has a specified
delay deadline, and the lossy wireless links are modelled as
binary symmetric channels (BSCs). Since transmission time, also
called airtime, on the links is shared amongst flows, increasing
the airtime for one flow comes at the cost of reducing the
airtime available to other flows sharing the same link. We
derive the joint allocation of flow airtimes and coding rates that
achieves the proportionally fair throughput allocation. This utility
optimisation problem is non–convex, and one of the technical
contributions of this paper is to show that the proportional
fair utility optimisation can nevertheless be decomposed into
a sequence of convex optimisation problems. The solution to
this sequence of convex problems is the unique solution to the
original non–convex optimisation. Surprisingly, this solution can
be written in an explicit form that yields considerable insight
into the nature of the proportional fair joint airtime/coding rate
allocation. To our knowledge, this is the first time that the utility
fair joint allocation of airtime/coding rate has been analysed,
and also, one of the first times that utility fairness with delay
deadlines has been considered