Techniques are described herein for an executable greedy algorithm of data collection to produce an optimal strategy which may decrease congestion of packets and increase overall efficiency. This may be used to divide Low power and Lossy Network (LLN) nodes into groups and arrange the regular report schedule for all the nodes. First, the Routing Protocol for LLN (RPL) tree may be pulled from a router and the topology matrix generated. Second, the constraint condition of data transmission may be listed according to network transmission characteristics. Third, the state transition equation may be solved and an optimal schedule selected. Fourth, the schedule may be implemented with a Head-End System (HES)

Jiang, Caijun

Zhang, Xiaopu

Zhang, Zaochun

Zhu, Dapeng

English

Technical Disclosure Common

Technical Disclosure CommonsDefensive Publications SeriesJuly 17, 2018DYNAMIC OPTIMAL DATA COLLECTIONBY GREEDY ALGORITHM IN LOW POWERAND LOSSY NETWORKSCaijun JiangDapeng ZhuXiaopu ZhangZaochun ZhangFollow this and additional works at: https://www.tdcommons.org/dpubs_seriesThis work is licensed under a Creative Commons Attribution 4.0 License.This Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive PublicationsSeries by an authorized administrator of Technical Disclosure Commons.Recommended CitationJiang, Caijun; Zhu, Dapeng; Zhang, Xiaopu; and Zhang, Zaochun, "DYNAMIC OPTIMAL DATA COLLECTION BY GREEDYALGORITHM IN LOW POWER AND LOSSY NETWORKS", Technical Disclosure Commons, ( July 17, 2018)https://www.tdcommons.org/dpubs_series/1329 1 5661X DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWER AND LOSSY NETWORKS  AUTHORS:   Caijun Jiang Dapeng Zhu Xiaopu Zhang Zaochun Zhang  ABSTRACT Techniques are described herein for an executable greedy algorithm of data collection to produce an optimal strategy which may decrease congestion of packets and increase overall efficiency. This may be used to divide Low power and Lossy Network (LLN) nodes into groups and arrange the regular report schedule for all the nodes. First, the Routing Protocol for LLN (RPL) tree may be pulled from a router and the topology matrix generated. Second, the constraint condition of data transmission may be listed according to network transmission characteristics. Third, the state transition equation may be solved and an optimal schedule selected. Fourth, the schedule may be implemented with a Head-End System (HES).  DETAILED DESCRIPTION In large scale meter readings of Advanced Metering Infrastructure (AMI) systems, the upstream data traffic may converge during transmission to the Head End System (HES). It is difficult to maintain the balance between congestion of packets and overall efficiency due to limited bandwidth. Figure 1 below illustrates the typical congestion/collision of packets in a Direction-Oriented Directed Acyclic Graph (DODAG) root. In a first case, congestion/collision occurs in the root when the two same hop meters (e.g., meters 7 and 12, marked in red) report at the same time. If the report traffic exceeds the capability of the root, data loss is inevitable. In a second case, congestion/collision occurs when a meter and its child (e.g., meters 10 and 14, also marked in red) transmit at the same time. 2Jiang et al.: DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWERPublished by Technical Disclosure Commons, 2018 2 5661X  Figure 1 In existing AMI systems, there are two typical meter reading approaches. One is active pulling through broadcast-request/report by HES, and the other is passive pushing triggered by a meter inner timer. Pulling data from all nodes at the same time can cause severe traffic congestion within Low power and Lossy Networks (LLNs), while pulling one by one can cost much more time. As for pushing, passive meter reporting cannot guarantee the efficient time distribution and can also cause traffic congestion within LLNs. Accordingly, provided is an executable algorithm to generate and implement an optimal schedule in meter reading. Techniques are described herein for an executable greedy algorithm of data collection to produce an optimal strategy which may decrease congestion of packets and increase overall efficiency. This may be used to divide LLN nodes into groups and arrange the regular report schedule for all the nodes. First, the Routing Protocol for LLN (RPL) tree may be pulled from a router and the topology matrix generated. Second, the constraint condition of data transmission may be listed according to network transmission characteristics. Third, the state transition equation may be solved and an optimal schedule selected. Fourth, the schedule may be implemented with a HES. Each step is discussed in turn as follows. Figure 2 below illustrates the array position associated with each node. 3Defensive Publications Series, Art. 1329 [2018]https://www.tdcommons.org/dpubs_series/1329 3 5661X  Figure 2 In Internet Engineering Task Force (IETF) Request for Comments (RFC) 6550, DODAG represents a tree-like topology. Therefore, multiway trees are depicted for simplicity, although the DODAG structure allows for each node to have multiple parents. One approach to implementing a DODAG structure is using a matrix. The elements of matrix representations are the relationships given by the full splits or bipartitions of leaves (terminal taxa) present in the trees. The DODAG version increment leads to a new DODAG matrix. Since the network RPL tree is dynamic in the LLN mesh, the schedule of application data converging upward may not only depend on deterministic topology. Each time the DODAG changed, the corresponding matrix and schedule may refresh as well. A mathematical model is built as follows. Xi=[...]1xn represents the matrix of the selected nodes from n total nodes at time i: one is selected, and zero are not. T is the transition matrix generated from the topology. Si=[...]1xn represents the active transmitting nodes at time i: one is active, and zero are not. According to this definition, the following formula is workable: Si+1 = Si T + Xi T (i = 0, 1, ..., m). Parameter m represents the length of the schedule. The sets of X (X = [X1, X2, ..., Xi, ..., Xm]) represent the optimal schedule at time i. Xi Tk represents the transmitting state after k time cycles from selective nodes Xi. This may be zero after several time cycles, which means the data transmitting process has finished in the LLNs. As the interconnects in the LLNs are characterized by high loss rates, low data rates, and instability, the data transmitting upward may have constraint conditions. The Single Input Single Output (SISI) node transmit schedule may limit received multi-input application data so as to avoid congestion of packets (e.g., if the selected meters are 7 and 12, a collision may occur in the root after three time cycles). This may be formulated as 4Jiang et al.: DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWERPublished by Technical Disclosure Commons, 2018 4 5661X max(SiTk) <= B. The function maximum represent the largest elements in the matrix. Parameter k is 0, 1, 2, ... until all SiTk elements are zero. Parameter B is a constant for the maximal inputs from child nodes simultaneously. A single node may avoid simultaneously sending and receiving (e.g., if meters 9 and 13 are selected, congestion may occur at meter 1 after two time cycles). This may be formulated as (Si + Xi)Tk(1+TH) <= C. In this equation, all the elements on the left side are less than or equal to the ones on the right side. Parameter H is made up of the application data size (Sa) and maximal size per transmit (Sm). H is the smallest integer greater than Sa / Sm + 1. Parameter C represents the ability to simultaneously send and receive. C is 1 in SISO and n in multiple-input and multiple-output (MIMO). Because the solution of the Xi equation is unlikely to be unique, the greedy algorithm always makes the choice that seems to be the best at that moment. This means the more nonzero elements, the better. This choice may lead to a globally-optimal solution by identifying how to make a locally-optimal choice. The state transition equations may have multiple optimal solutions, which means the best schedule may not be unique, and the approaches to obtain the solutions may be implemented by equation-solving with mathematics and local optimum with an iterative method. For example, assume that maximal one input and application size is smaller than per transmit size, and that B and H are both two. The selected meters may void the same hop meters and continuous hops under the same branch (same hop two). According to the greedy principle, the maximal selected number is four and one of the possible X0 is [0,0,1,1,0,0,0,0,0,0,0,1,0,1,0], in which the left child is selected preferentially. Thus meters 2, 3, 11, 13 are selected at time zero. At a subsequent time, the new state formulas may be S1=S0 T + X0T=[1,1,0,0,0,0,1,0,0,0,1,0,0,0,0], in which root and meters 1, 6 and 10 are active. At time 1 no selected meters satisfy the rules, so all elements in X1 are zero. Similarly, for S2=[1,0,1,0,1,0,0,0,0,0,0,0,0,0,0], the optimal schedule X2 is [0,0,0,0,0,0,0,0,0,0,0,0,1,0,1]. When all the meters are selected for iterating, the sets of Xi make up the global schedule X. Each time HES updates the schedule when a new DODAG is formed, the most effective report mechanism is prepared. Two typical approaches may take full advantage of the scheduler. First, in the solution of each Xi, the non-zero elements mapping the mesh 5Defensive Publications Series, Art. 1329 [2018]https://www.tdcommons.org/dpubs_series/1329 5 5661X nodes may be divided into groups Gi. Nodes may be informed that the group identity to which they belong and the broadcast-request/report paradigm were archived group by group. According to the size of application data and network bandwidth, the time may be approximately calculated hop by hop. The HES may send the schedule to nodes and start a timer to actively trigger the report. The algorithm may be extended to support more complicated LLN topology. This may be extended to various kinds of networks other than LLN. In summary, techniques are described herein for an executable greedy algorithm of data collection to produce an optimal strategy which may decrease congestion of packets and increase overall efficiency. This may be used to divide LLN nodes into groups and arrange the regular report schedule for all the nodes. First, the RPL tree may be pulled from a router and the topology matrix generated. Second, the constraint condition of data transmission may be listed according to network transmission characteristics. Third, the state transition equation may be solved and an optimal schedule selected. Fourth, the schedule may be implemented with a HES. 6Jiang et al.: DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWERPublished by Technical Disclosure Commons, 2018

DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWER AND LOSSY NETWORKS

https://www.tdcommons.org/cgi/viewcontent.cgi?article=2394&amp;context=dpubs_series

DYNAMIC OPTIMAL DATA COLLECTION BY GREEDY ALGORITHM IN LOW POWER AND LOSSY NETWORKS

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