Reusable mesh signature scheme for protecting identity privacy of IoT devices

Peer reviewedPublisher PD

A Social-Based Watchdog System to Detect Selfish Nodes in Opportunistic Mobile Networks

Detecting selfish nodes in opportunistic mobile networks can reduce the loss of network resources, thus improve the data delivery performance. Most of existing detection schemes primarily rely on the nodes\u27 contact records and do not consider their individual and social preferences in their data relaying behavior, which result in long detection time and high communication overhead. In addition, they cannot distinguish the nodes\u27 selfishness type and degree, which is important because the charge and rewarding mechanisms applied to stimulate different nodes may not be the same. In this paper, we propose a Social-based Watchdog system (SoWatch) in which watchdog nodes analyze messages received from their encountered nodes with respect to their social tie information to identify the nodes\u27 selfish behavior in message relaying. Meanwhile, the watchdog nodes apply the second-hand watchdog information received from other nodes to improve the detection time and accuracy. Next, we design a reputation system in which watchdog nodes identify selfish nodes based on their direct and indirect watchdog information and distinguish individually and socially selfish nodes. Furthermore, we design a watchdog evaluation module to protect SoWatch against wrong watchdogs disseminated by malicious nodes in which a watchdog node investigates the truthfulness of the indirect watchdogs before applying them. Our experiments using real-world datasets illustrate that SoWatch outperforms a benchmark contact-based watchdog system in terms of detection time by 45% and detection ratio by 10% with less communication overhead

Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification

Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better