Decentralized Traffic Aware Scheduling for Multi-hop Low Power Lossy Networks in the Internet of Things

Speaker: Chan-Yu TsaiAdvisor: Dr. Ho-Ting Wu

Date: 2015/12/10

Outline

• 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.)

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• 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-to-point traffic.

Algorithm Description(Cont.)

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• 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.

Algorithm Description(Cont.)

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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.

Algorithm Description(Cont.)

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• 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.

Algorithm Description(Cont.)

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• 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.

Algorithm Description(Cont.)

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• 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 macro-schedule within the network slotframe.

Algorithm Description(Cont.)

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• 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.

Algorithm Description(Cont.)

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Algorithm Description(Cont.)

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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.

Algorithm Description(Cont.)

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• 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.

Algorithm Description(Cont.)

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Algorithm Description(Cont.)

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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.

Performance Evaluation

• 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 mvarying 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. 21

Performance Evaluation(Cont.)

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• 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.

Performance Evaluation(Cont.)

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• 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.

Performance Evaluation(Cont.)

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Performance Evaluation(Cont.)

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Performance Evaluation(Cont.)

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• 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.

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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