公开(公告)号：US12120627B2

公开(公告)日：2024-10-15

申请号：US18530247

申请日：2023-12-06

Applicant: Sun Yat-sen University

Inventor： Yitong Liu ,  Hao Lin ,  Xingcheng Liu ,  Qiang Tu ,  Yi Xie

IPC: H04W64/00 ,  H04W84/18

CPC classification number: H04W64/00 ,  H04W84/18

Abstract: In a data processing method for node localization in wireless sensor networks, a plurality of potential coordinate calculation values for a to-be-located node are calculated based on RSSI data received by the to-be-located node from a plurality of anchor nodes. A centroid C1 of these coordinate calculation values is calculated. Then coordinate calculation values with distances to the centroid C1 less than a threshold are screened out, and a centroid C2 of the screened-out coordinate calculation values is calculated. A coordinate position of the centroid C2 is used as the position of the to-be-located node. In other words, a plurality of potential positions for the to-be-located node are calculated using multiple groups of RSSI data and these potential positions are then screened through calculation. Instead of directly processing the received RSSI data, the present disclosure processes the coordinate calculation values obtained based on the received RSSI data.

公开(公告)号：US11800425B2

公开(公告)日：2023-10-24

申请号：US17566735

申请日：2021-12-31

Applicant: Sun Yat-sen University

Inventor： Xingcheng Liu ,  Qiang Tu

IPC: H04W40/02 ,  H04L45/122 ,  H04L45/42 ,  H04L45/48 ,  G06F18/2323

CPC classification number: H04W40/02 ,  G06F18/2323 ,  H04L45/122 ,  H04L45/42 ,  H04L45/48

Abstract: A wireless sensor network (WSN) clustering routing method based on optimization with salp swarm algorithm includes: S1: constructing an objective function by comprehensively considering factors as follows: node residual energy, a distance between cluster head nodes, a distance between a cluster member node and a cluster head node, a distance from a node to a base station, and a number of cluster head nodes; S2: solving the objective function by using a salp swarm algorithm, to find an optimal clustering scheme, and thus dividing the WSN into non-uniform clusters, wherein in each cluster, the cluster member node sends data to the cluster head node in a single-hop manner; S3: constructing a minimum spanning tree with the base station as a root node by using a routing utility function ƒ(CHi); S4: sending, by the cluster head node, data to the base station through routing organized by the minimum spanning tree.

公开(公告)号：US11601138B2

公开(公告)日：2023-03-07

申请号：US17709554

申请日：2022-03-31

Applicant: Sun Yat-sen University

Inventor： Xingcheng Liu ,  Shuo Liang ,  Shizhan Cheng

IPC: H03M13/00 ,  H03M13/11 ,  H03M13/39

Abstract: A decoding method of low-density parity-check (LDPC) codes based on partial average residual belief propagation includes the following steps: S1: calculating a size of a cluster π in a protograph based on a code length m and a code rate of a target codeword; S2: pre-computing an edge residual rci→vj corresponding to each edge from a variable node to a check node in a check matrix H; S3: calculating, based on π, a partial average residual (PAR) value corresponding to each cluster in the check matrix H; S4: sorting m/π clusters in descending order of corresponding PAR values, and updating an edge with a largest edge residual in each cluster; S5: updating edge information mci→vi from a check node ci to a variable node vj, and then updating a log-likelihood ratio (LLR) value L(vj) of the variable node vj; and S6: after the updating, making a decoding decision.

公开(公告)号：US11696135B2

公开(公告)日：2023-07-04

申请号：US17566738

申请日：2021-12-31

Applicant: Sun Yat-sen University

Inventor： Xingcheng Liu ,  Zhao Tang ,  Yitong Liu

IPC: H04W12/121 ,  H04W12/00 ,  G06F17/17

CPC classification number: H04W12/121 ,  G06F17/17 ,  H04W12/009

Abstract: A malicious anchor node detection and target node localization method based on recovery of sparse terms, includes: S1: establishing an unknown disturbance term by using ranging value attack terms from an attacker to nodes in a wireless sensor network, and introducing a to-be-estimated location of a target node to the unknown disturbance term, to obtain an unknown sparse vector; S2: converting a problem of malicious anchor node detection and target node localization into a problem of recovery of the unknown sparse vector; S3: determining a location of an initial node according to a recursive weighted linear least square method, and recovering and reconstructing the unknown sparse vector with sparsity; and S4: determining a malicious anchor node determination range by approximating a threshold using a recovered value of the unknown sparse vector, to implement malicious anchor node detection, and recovering and determining location information of the target node.