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Decentralized Traffic Aware Scheduling for
Multi-hop Low Power Lossy Networks in the
Internet of Things
Speaker: Chan-Yu Tsai
Advisor: Dr. Ho-Ting Wu
Date: 2015/12/10
Outline
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Introduction
Algorithm Description
Performance Evaluation
Conclusion
References
Introduction
• In Internet of Things (loT) scenarios, a potentially (very)
large number of nodes is able to establish low-power
short range wireless links, thus forming a capillary
networking infrastructure that can be connected to the
Internet .
• With reference to the MAC layer, the IEEE802.15.4e
amendment has been released on April 2012 with the aim
of redesigning the existing IEEE802.15.4-2011 MAC
standard.
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Introduction(Cont.)
• Using TSCH, nodes synchronize on a slotframe structure,
i.e., a group of slots which repeats over time.
• Each node follows a schedule which indicates its
operative mode (i.e., transmit, receive, or sleep) and, in
case it is active, the channel to use and the neighbor to
communicate with.
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Introduction(Cont.)
• Concerning the routing strategy for multi-hop Low-power
and Lossy Networks (LLNs), the IETF has recently
standardized the Routing Protocol for LLNs (RPL).
• In that case, each LLN sink assumes the role of DODAG
root.
• A more sophisticated and flexible configuration could
contain a single DODAG with a virtual DODAG root
coordinating several LLN sinks.
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Introduction(Cont.)
• The RPL topology is setup based on ranks.
• A rank specifies the individual position of a node with
respect to its (virtual) DODAG root.
• Specifically, the virtual DODAG root's rank is always 0,
while the rank of LLN sinks is 1.
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Introduction(Cont.)
• The first outcome of such investigation has been the
Traffic-Aware Scheduling Algorithm (TASA) which builds
time/frequency collisionfree patterns in a centralized
manner.
• This strategy allows a master node to collect information
about the entire network topology and on the traffic load
at each node.
• Afterwards, such master node computes the schedule for
the whole network by exploiting the graph theory'S
matching and vertex-coloring techniques.
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Introduction(Cont.)
• Such a centralized approach requires a multi-hop
signaling phase, which could impair the power efficiency.
• Moreover, TASA employs a greedy solution for allocating
transmissions for each times lot. Therefore, TASA does
not take into account queue congestion.
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Algorithm Description
• The suggested DeTAS algorithm is designed for LLN
networks organized by RPL in a single DODAG.
• Let {ns}, with s = 1, ... , Ns, be the set of all the Ns sinks,
and {nd, with i = N s + 1, ... , N s + N be the set of
source nodes. If N s = 1, the LLN sink nl is also a
DODAG root; otherwise, there is a virtual DODAG root,
n0, coordinating all the Ns LLN sinks
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Algorithm Description(Cont.)
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Algorithm Description(Cont.)
• DeTAS is a traffic-aware algorithm that builds the
schedule based on the traffic generated by each source
node.
• We assume that the network supports a multi-point-topoint traffic.
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Algorithm Description(Cont.)
• In detail, every source node in the set {ni}, with i = Ns +
1, ... , Ns + N, generates a constant integer number of
packets, i.e., the local packet number, qi.
• we define also the global packet number, Qi, as the total
amount of packets generated within a slotframe by the
nodes belonging to the sub-tree STi.
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Algorithm Description(Cont.)
A general overview
• DeTAS is able to build optimum collision-free schedules
for multi-hop IEEE802.15.4e TSCH networks, using a tiny
amount of information, locally exchanged among
neighbor nodes.
• At the same time, its decentralized nature allows a
distributed computation of such schedule, while keeping
very low the amount of signaling messages exchanged
among neighbor nodes.
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Algorithm Description(Cont.)
• Whilst scheduling the traffic, DeTAS manages queue
levels, avoiding traffic congestion and, thus, possible
packets drops due to overflow of nodes' memory buffer.
• In a IEEE802.15.4e TSCH network running DeTAS, all
devices are assumed to be synchronized with the same
slotframe, having size equal to S time slots.
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Algorithm Description(Cont.)
• In details, for each Routing Graph Gs, with s = 1, ... , Ns,
DeTAS builds a time/frequency micro-schedule, using
Ls time slots and Ws channel offsets 3.
• Such micro
schedule is setup minImIzing the number
of active slots, Ls, needed for delivering to the LLN sink,
ns, the traffic load generated by the source nodes
belonging to Gs, during a single slotframe.
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Algorithm Description(Cont.)
• Afterwards, the virtual DODAG root, n0, collects the
length values Ls of each of the N s micro-schedules, as
computed from the LLN sinks it coordinates.
• It arranges such micro-schedules into a macroschedule within the network slotframe.
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Algorithm Description(Cont.)
• This set is divided in K subsets, so that the sums
computed over such subsets are as close as possible.
• Once the partition into K subsets has been done, all the
micro-schedules belonging to the k-th subset will be
activated sequentially, and they will use the values 3(k 1), 3(k - 1) + 1 and 3(k - 1) + 2 as channel offsets.
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Algorithm Description(Cont.)
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Algorithm Description(Cont.)
Decentralized Micro-scheduling
• Note that every source node ni can locally compute its
global packet number, Qi, as sum of its local packet
number, qi, and the global packet numbers of its children,
and then forward such information to its parent node, Pi.
• Starting from the aforementioned traffic information,
exchanged at I-hop distance, the micro-schedule is built
in a distributed fashion, with each node, ni, allocating
some slots within the schedule to its children.
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Algorithm Description(Cont.)
• To avoid buffer overflow, in DeTAS each node ni assigns an
alternate sequence of "transmit" and "receive" slots (i.e.,
slots for transmission and reception of packets, respectively)
to every child node.
• Definition 1: A subtree STi is even-scheduled, if all nodes ∈
STi with even DODAGrank transmit in even time slots, and
those with odd DOD AGrank transmit in odd time slots.
• Definition 2: A subtree STi is odd-scheduled, if all nodes ∈
STi with even DODAGrank transmit in odd time slots, and
those with odd DODAGrank transmit in even time slots.
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Algorithm Description(Cont.)
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Algorithm Description(Cont.)
Micro-schedule length Ls
• For every randomly scattered physical topology, the
proposed DeTAS scheme is able to find the optimum
schedule with the minimum length, given by:
• DeTAS assumes a different behavior depending on the
traffic loads of the children of the LLN.
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Performance Evaluation
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We highlight that the maximum queue occupation has been
selected as performance parameter because it indicates how
much traffic load can be managed by source nodes, avoiding
packet discards due to possible buffer overflows.
We consider only single sink topologies, i.e., with a single LLN
sink acting as DODAG root, in order to picture also a fair
comparison between DeTAS and TASA.
area of 200 x 200 m.
coverage range R = 50 m
varying the total number of nodes, N in the interval [60, 100].
For each network size, we have also considered 25 different
random displacements for the source nodes.
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Performance Evaluation(Cont.)
• We test the algorithm under different traffic conditions:
• we assume that the average number of packets, npkt,
generated by each node within a single slotframe can
vary in the range [3, 5] .
• To better characterize the performance comparison, for
each average number of packets, we have also
considered 25 different random arrangements for the
traffic load on each source node.
• Finally, we assume for TASA a number of 3 available
channels.
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Performance Evaluation(Cont.)
• These results have been obtained, averaging on 625
simulations runs, derived from the combination of 25
different network-topologies and 25 different traffic load
sets.
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Performance Evaluation(Cont.)
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Performance Evaluation(Cont.)
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Performance Evaluation(Cont.)
• This inference let us guess that DeTAS would totally
avoid packet drops due to buffer overflows, allowing the
use of smart devices with smaller memories.
• DeTAS shows also a great improvement with respect to a
centralized solution like TASA.
• In fact, the TASA maximum buffer occupation increases
with a decreasing rank, and with an increasing network
size.
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CONCLUSION
• A new scheduling scheme, namely DeTAS, has been
described.
• Enabling I-hop signaling, the proposed algorithm
naturally addresses some pendent issues that limit a
concrete deployment of multihop LLN networks.
• Furthermore, DeTAS exploits very well the
time/frequency resources available with TSCH, being
already suitable for complex LLN topologies.
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References
• ACCETTURA, Nicola, et al. “Decentralized traffic aware
scheduling for multi-hop low power lossy networks in the
internet of things.” In: World of Wireless, Mobile and
Multimedia Networks (WoWMoM), 2013 IEEE 14th
International Symposium and Workshops on a. IEEE, 2013.
p. 1-6.
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