Ad Hoc Networks最新文献

Device-to-Device (D2D) communication plays a prominent role in mobile data offloading from the cellular infrastructure (e.g., base station). This paradigm empowers user equipment to communicate with each other directly, offering an efficient resort for data communication that eliminates the need for the base station. However, significant challenges, such as interference, resource allocation, and energy efficiency, impede the performance of D2D communication. In the context of resource allocation, most of the existing work primarily focuses on game and graph theoretical models, which raises the computational complexity as the number of D2D users increases. In this article, we formulated a sum rate maximization problem, which is solved using a combinatorial scheme comprised of Whale Optimization Algorithm (WOA) and Federated Learning (FL). First, we discover the optimal CUs-D2D Groups (D2DGs) pairs by utilizing the social behavior of whales in the WOA. Only these optimal links are permitted to participate in the FL-based resource allocation, ensuring a physical layer access control. Next, we generated a dataset from the WOA-based optimal CU-D2DG links, which is employed by the Convolutional Neural Network (CNN) model for decentralized learning. FL offers a proactive decision for resource assignment, i.e., whose CU resources will be used by the D2DG. The proposed scheme is evaluated by considering different performance parameters, such as convergence rate, statistical measure (accuracy, loss), fairness (0.72), and overall sum rate ($\approx 25\text{Mbps}$).

Millimeter-Wave (mmWave) communication can be highly affected by blockages, which can drastically decrease the signal strength at the receiver side. To overcome the impact of blockages, predicting the optimal mitigation technique and accurately estimating the duration of the blockage events are crucial for maintaining reliable and high-performance mmWave communication systems. Prior works on mitigating blockages have proposed a variety of model and protocol-based blockage mitigation solutions that concentrate on a singular technique at a time, like switching the current beam to an alternative beam at the current base station or client. In this paper, we tackle the overarching question: what blockage mitigation technique should be employed? and what is the optimal sub-selection within that technique? We also address the blockage duration estimation problem. We solve these problems by developing a Gated Recurrent Unit (GRU) model, trained on data from periodic message exchanges in mmWave systems. We tested our neural network models by utilizing a mmWave simulator that is commercially available and widely used in wireless communication to compile a large amount of dataset for this purpose. Our findings reveal that our proposed method introduces no extra communication overhead, while achieving remarkable accuracy, exceeding 91%, in predicting the optimal blockage mitigation technique. Moreover, the blockage duration estimation model achieves a very high accuracy with a residual mean error of less than 0.04 s. Finally, we demonstrate that our proposed blockage mitigation method substantially boosts the volume of data transferred in comparison to various other blockage mitigation strategies.

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

Recent Internet of Things (IoT) research aims to develop generic objects to learn, reason, and perceive their environment. Therefore, a new area has emerged known as cognitive IoT (CIoT). The cognitive Internet of Things integrates IoT with intelligence and behaves as well as humans through intelligent functionality. Several inferential tasks in CIoT require multiple hypothesis testing. The situation becomes cumbersome when the data is massive and heterogeneous. Thus, this research suggests a novel technique for multiple-hypothesis testing that uses a copula function to deal effectively with massive heterogeneous data. In addition, these data may contain missing or corrupted entries. Hence, it introduced probabilistic clustering, which reduces model inefficiency and takes control over the false discovery rate (FDR). Most of the variance from each cluster was extracted using kernel principal component analysis (KPCA) to reduce the processing burden at the fusion centre. Subsequently, it computes the p-value of each cluster's first principal component data and employs the Bonferroni method for multiple hypothesis testing. Finally, this research study evaluates the performance of the proposed algorithm on six-month environmental data, revealing that the proposed technique is efficient in terms of accuracy and computation time compared to other methods in the presence of massive heterogeneous data.

WSNs have various uses across numerous industries and are one of the essential technologies of modern life. Energy consumption is the issue that has drawn the greatest attention and still has to be resolved because the nodes that comprise WSNs have a limited amount of energy. Numerous factors influence energy consumption, and our algorithm design considerations are centered on extending the network lifetime and energy efficiency through the resolution of imbalance and hotspot issues related to WSNs clustering. Because of this, we suggest a partitioned uneven cluster routing algorithm based on gray wolf optimization. To find the ideal cluster head, we first divide the network into areas with distinct important influence factors, then we improve the final cluster head election function and the candidate cluster head competition radius. Subsequently, to reduce the energy consumption resulting from multiple rounds of clustering, similarity determination is introduced. Finally, the optimal transmission path in the multi-hop process is obtained by combining the Gray Wolf optimization algorithm with the relay node selection function. Simulation results show that the network lifetime of the proposed algorithm is extended by 54.6 %, 46.2 %, 58.6 %, and 18.5 % compared to LEACH, DEBUC, LEACH-EDP, and LEACH-IM, respectively. The energy efficiency of the proposed algorithm is extended by 40.8 %, 7.1 %, 22.7 %, and 34.0 %, respectively. The proposed algorithm significantly extends the network lifetime and improves the energy efficiency of the network.

Underwater acoustic sensor networks (UASNs) are usually deployed in unattended, opaque and even hostile environments; thus, they may face serious threats. In the last few years, physical layer security (PLS) has emerged as a new technique that can improve the security performance of networks. In this paper, we research the physical layer security scheme of UASNs by actively jamming against eavesdropping attacks. Utilizing the large propagation delay of the underwater acoustic (UWA) signal, the jamming signals can interfere with legitimate signals to prevent eavesdropping. Therefore, we propose an active jamming-based helper deployment scheme (AJHDS) for UASNs, which deploys the helpers to the target water area and realizes the secure transmission of the network. Both the simulation and field test results show that the scheme can significantly reduce the interception capability of eavesdroppers. Furthermore, the field sea experiment evaluates that the location of helpers affects the eavesdropper’s acquisition of legitimate data packets.

The aeronautical ad-hoc network (AANET) aims to provide a wider network connection for aircraft by creating links between them. However, the high speed of flight of aircraft leads to poor sustainability of AANET topology. In order to solve the problem of poor network topological stability, this study proposes a clustering method, called distributed multi-hop clustering algorithm (DMCA), for AANET. DMCA is based on the defined node stability weight and the concept of multi-hop clustering, which can effectively ensure the coverage and stability of clustering. In this study, we divided DMCA into three phases: information table generation stage, cluster head selection and cluster formation stage, and cluster maintenance stage. In order to verify the effectiveness of the proposed method, we compare the proposed method with N-hop, DMCNF, and K-means algorithms from four aspects: average cluster head duration, average cluster member duration, number of cluster head changes, and average number of link broken. According to the simulation results, it can be concluded that the proposed method has improved in these four performance indicators.

In recent years, there has been a significant increasing demand for wireless networks, particularly mobile communication. Additionally, smart devices like mobile phones and tablets are able to use multiple interfaces simultaneously. In this regard, the concept of Multipath TCP (MPTCP) has been introduced, enabling the utilization of multiple interfaces for concurrent communication. However, packet loss and subflow heterogeneity, especially in wireless networks, cause an increase in Out-of-Order (OfO) packets at the receiver node, which leads to a decrease in the total MPTCP throughput. To address these performance-related challenges, numerous schedulers have been proposed. However, most existing methods have primarily focused on improving performance without adequately considering the impact of packet loss. This research paper provides a comprehensive overview of MPTCP schedulers. Subsequently, we propose a Practical and Robust Scheduler for MPTCP (PRS-MPTCP) with the specific aim of minimizing OfO packets to improve MPTCP performance in wireless systems. The PRS-MPTCP scheduler takes into account various characteristics of each subflow, including RTT, CWND, and the number of packet losses, to effectively decide which packets should be assigned to which subflow. By using Mininet-WiFi emulation, fair comparisons between PRS-MPTCP and the state-of-the-art schedulers have been conducted, considering throughput, the number of OfO packets, and retransmission rates as performance metrics. The evaluation results reveal the profound impact of selected parameters on the behavior of the schedulers and the overall performance of MPTCP. Ultimately, the results demonstrate that PRS-MPTCP guarantees acceptable throughput and achieves lower retransmission rates and fewer OfO packets compared to other methods. In the PRS-MPTCP, the number of OfO packets has decreased by 38 %, 37 %, 45 %, and 44 % compared to BLEST, ECF, RR, and the Default scheduler, respectively.

Single event upset (SEU) refers to the change in the state of a digital circuit caused by a single, energetic particle passing through the sensitive area of semiconductor components. In the space environment, cosmic rays and high-energy particles may lead to SEUs, which can cause failures of routing functions, such as forwarding errors, in satellite networks. This paper focuses on the impact of SEUs on satellite networks from the perspective of distributed routing. The influence of SEUs on a single satellite can easily spread to the entire satellite network through distributed routing, which can influence the reliability of the satellite network. We propose a framework for the reliability analysis of satellite networks under the impact of SEUs. According to the influence of SEUs on the execution of routing programs in different routing phases, we present the error propagation model of satellite networks for understanding SEUs in satellite network routing. We found that all the influences on program execution ultimately converge on data forwarding and can be reflected in the proposed path topology graph. Therefore, we propose an algorithm that can evaluate the impact of SEUs on satellite networks by analyzing the path topology graphs in both normal and SEU scenarios. The feasibility and effectiveness of the proposed method is demonstrated by experiments. The proposed method could provide a new perspective and theoretical basis for research on secure routing mechanisms and program/instruction reinforcement.

For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission.