Journal of High Speed Networks

Opportunistic Delay Tolerant Networks also referred to as Opportunistic Networks (OppNets) are a subset of wireless networks having mobile nodes with discontinuous opportunistic connections. As such, developing a performant routing protocol in such an environment remains a challenge. Most research in the literature have shown that reinforcement learning-based routing algorithms can achieve a good routing performance, but these algorithms suffer from under-estimations and/or over-estimations. Toward addressing these shortcomings, in this paper, a Double Q-learning based routing protocol for Opportunistic Networks framework named Off-Policy Reinforcement-based Adaptive Learning (ORAL) is proposed, which selects the most suitable next-hop node to transmit the message toward its destination without any bias by using a weighted double Q-estimator. In the next-hop selection process, a probability-based reward mechanism is involved, which considers the node’s delivery probability and the frequency of encounters among the nodes to boost the protocol’s efficiency. Simulation results convey that the proposed ORAL protocol improves the message delivery ratio by maintaining a trade-off between underestimation and overestimation. Simulations are conducted using the HAGGLE INFOCOM 2006 real mobility data trace and synthetic model, showing that when time-to-live is varied, (1) the proposed ORAL scheme outperforms DQLR by 14.05%, 9.4%, 5.81% respectively in terms of delivery probability, overhead ratio and average delay; (2) it also outperforms RLPRoPHET by 16.17%, 9.2%, 6.85%, respectively in terms of delivery ratio, overhead ratio and average delay.

In modern society, multi-agent consensus is applied in many applications such as distributed machine learning, wireless sensor networks and so on. However, some agents might behave abnormally subject to external attack or internal faults, and thus fault-tolerant consensus problem is studied recently, among which Q-consensus is one of the state-of-the-art and effective methods to identify all the faulty agents and achieve consensus for normal agents in general networks. To fight against Q-consensus algorithm, this paper proposes a novel strategy, called split attack, which is simple but capable of breaking consensus convergence. By aggregating all the states of neighboring nodes with an extra perturbation, the normal nodes are split into sub-groups and converge to two separate values, so that consensus is broken. Two scenarios, including the introduction of additional faulty nodes and compromise of the original nodes, are considered. More specifically, in the former case, two additional faulty nodes are adopted, each of which is responsible to mislead parts of the normal nodes. While in the latter one, two original normal nodes are compromised to mislead the whole system. Moreover, the compromised nodes selection is fundamentally a classification problem, and thus optimized through CNN. Finally, the numerical simulations are provided to verify the proposed schemes and indicate that the proposed method outperforms other attack methods.

Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited battery power in smart vehicles, the concept of energy consumption is one of the main and critical challenges of the IoV environments. Optimizing resource management strategies for improving the energy consumption using AI-based methods is one of important solutions in the IoV environments. There are various machine learning algorithms for selecting optimal solutions for energy-efficient resource management strategies. This paper presents the existing energy-aware resource management strategies for the IoV case studies, and performs a comparative analysis among their applied AI-based methods and machine learning algorithms. This analysis presents a technical and deeper understanding of the technical aspects of existing machine learning and AI-based algorithms that will be helpful in design of new hybrid AI approaches for optimizing resource management strategies with reducing their energy consumption.

With the development of big data technology, medical data has become increasingly important. It not only contains personal privacy information, but also involves medical security issues. This paper proposes a secure data fitting scheme based on CKKS (Cheon-Kim-Kim-Song) homomorphic encryption algorithm for medical IoT. The scheme encrypts the KGGLE-HDP (Heart Disease Prediction) dataset through CKKS homomorphic encryption, calculates the data’s weight and deviation. By using the gradient descent method, it calculates the weight and bias of the data. The experimental results show that under the KAGGLE-HDP dataset,we select the threshold value is 0.7 and the parameter setting is (Poly_modulus_degree, Coeff_mod_bit_sizes, Scale) = (16384; 43, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 43; 23), the number of iteration is 3 and the recognition accuracy of this scheme can achieve 96.7%. The scheme shows that it has a high recognition accuracy and better privacy protection than other data fitting schemes.

In the next generation wireless networks, the number of connected terminals to the network, communication protocols, and the channels available will be increased, thus network slicing will become more important. Also, vehicles, buses, trains and motorcycles are considered communication terminals. These communication terminals should have independent network management considering their movement such as joining and leaving the networks. Therefore, Delay-Disruption-Disconnection Tolerant Networking (DTN) has been attracting attention for their potential support of inter-vehicle communication. In this paper are presented the Contact-Time (CT) based and Adaptive-Timer (AT) based Message Suppression (MS) methods for Vehicular DTN. For the CT-based MS method are used three DTN protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. For AT-based MS are used conventional Epidemic and two proposed Epidemic-based methods for V2V communication. We compare MS method, Message Suppression Controller (MSC) and MSC with Adaptive Threshold (MSC-ATh). The simulation results show that MSC-ATh performs better than other approaches. The storage consumption is improved when the number of vehicles increases and there is no reduction in PDR even if the message suppression is enabled. For Epidemic, when the number of Road-Side Units (RSUs) is 16, the results of PDR are the best compared with other DTN protocols. The MSC-ATh method is about 22% better than Epidemic for storage consumption. Also, the delay performance of MSC-ATh is improved by increasing the Suppression Coefficients (SCs) and number of vehicles.

Fault diagnosis is an important link in intelligent development of industrial robots. Aiming at the problem of weak fault diagnosis performance caused by insufficient training samples, a fault diagnosis model based on triplet network is proposed. Firstly, we combine the multiscale convolutional neural network (MSCNN) with channel attention networks (squeeze-and-excitation network, SENet), and use it to construct a triple sub-network structure MS-SECNN, which can adaptively extract features from the original fault signal. Then, the feature similarity is calculated by triplet loss in the low dimensional space to realize the fault classification task. The experiments are based on the real industrial robot operation data set. In this model, we use Few-shot learning strategy to test the diagnostic performance under small samples, and compare it with WDCNN, FDCNN and MSCNN models. Experimental results show that the proposed model has more effective fault classification ability under small samples. In addition, when the training sample size is 1400, the average accuracy of MS-SECNN reaches 99.21%.