6,402 research outputs found
JiTS: Just-in-Time Scheduling for Real-Time Sensor Data Dissemination
We consider the problem of real-time data dissemination in wireless sensor
networks, in which data are associated with deadlines and it is desired for
data to reach the sink(s) by their deadlines. To this end, existing real-time
data dissemination work have developed packet scheduling schemes that
prioritize packets according to their deadlines. In this paper, we first
demonstrate that not only the scheduling discipline but also the routing
protocol has a significant impact on the success of real-time sensor data
dissemination. We show that the shortest path routing using the minimum number
of hops leads to considerably better performance than Geographical Forwarding,
which has often been used in existing real-time data dissemination work. We
also observe that packet prioritization by itself is not enough for real-time
data dissemination, since many high priority packets may simultaneously contend
for network resources, deteriorating the network performance. Instead,
real-time packets could be judiciously delayed to avoid severe contention as
long as their deadlines can be met. Based on this observation, we propose a
Just-in-Time Scheduling (JiTS) algorithm for scheduling data transmissions to
alleviate the shortcomings of the existing solutions. We explore several
policies for non-uniformly delaying data at different intermediate nodes to
account for the higher expected contention as the packet gets closer to the
sink(s). By an extensive simulation study, we demonstrate that JiTS can
significantly improve the deadline miss ratio and packet drop ratio compared to
existing approaches in various situations. Notably, JiTS improves the
performance requiring neither lower layer support nor synchronization among the
sensor nodes
Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints
This paper studies the problem of scheduling in single-hop wireless networks
with real-time traffic, where every packet arrival has an associated deadline
and a minimum fraction of packets must be transmitted before the end of the
deadline. Using optimization and stochastic network theory we propose a
framework to model the quality of service (QoS) requirements under delay
constraints. The model allows for fairly general arrival models with
heterogeneous constraints. The framework results in an optimal scheduling
algorithm which fairly allocates data rates to all flows while meeting
long-term delay demands. We also prove that under a simplified scenario our
solution translates into a greedy strategy that makes optimal decisions with
low complexity
Deterministic scheduling for energy efficient and reliable communication in heterogeneous sensing environments in industrial wireless sensor networks
The present-day industries incorporate many applications, and complex processes, hence, a large number of sensors with dissimilar process deadlines and sensor update frequencies will be in place. This paper presents a scheduling algorithm, which takes into account the varying deadlines of the sensors connected to the cluster-head, and formulates a static schedule for Time Division Multiple Access (TDMA) based communication. The scheme uses IEEE802.15.4e superframe as a baseline and proposes a new superframe structure. For evaluation purposes the update frequencies of different industrial processes are considered. The scheduling algorithm is evaluated under varying network loads by increasing the number of nodes affiliated to a cluster-head. The static schedule generated by the scheduling algorithm offers reduced energy consumption, improved reliability, efficient network load management and improved information to control bits ratio
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
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