Wireless Networks最新文献

Abstract

Due to its high spectral efficiency and various other advantages, filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) has long been considered as a candidate waveform for the fifth generation (5G) and beyond telecommunication technologies. On the other hand, it is possible to both increase the data rate and alleviate the channel fading effects by using the multiple-input multiple-output (MIMO) antenna structure in the FBMC/OQAM transceiver. However, since the symbol detection is an indispensable task to be fulfilled in wireless communication, it is crucial to employ an efficient symbol detector at the MIMO-FBMC/OQAM receiver. Maximum likelihood (ML) detector, which always finds the optimal symbols by trying all of the possible symbol combinations likely to be transmitted, is known for its extremely high computational complexity making it impractical to be used in any system. On the other hand, it is possible to both considerably reduce the ML complexity and achieve the near-ML performance by optimizing the symbol vectors instead of implementing an exhaustive search. Since searching for the optimal symbol combination in discrete space is a combinatorial optimization problem, we developed a novel discrete harmony search (disHS) algorithm to perform this operation. According to the simulation results, the newly developed disHS algorithm not only achieves near-ML performance with lower computational complexity, but also clearly leaves behind the other symbol detectors considered in this paper.

Abstract

To address the problem of low indoor positioning accuracy in time-of-arrival systems in the non-line-of-sight (NLOS) environments, we proposed an optimized positioning algorithm based on semidefinite programming (SDP). This algorithm reduces the NLOS error through a novelty method. Compared with the original SDP algorithm, we optimized the algorithm’s objective function by avoiding its dependence on the prior information, thereby decreasing infeasibility problems. The experiment showed that the proposed algorithm’s accuracy is superior to that of the traditional SDP algorithm in the same indoor environment.

Abstract

In urban Vehicular Ad-hoc Networks (VANETs), disseminating traffic data efficiently is challenging due to the dynamic and complex nature of the network. Multi-hop-based broadcasting approaches are commonly used to address this issue. However, selecting the optimal relay nodes poses a challenge and directly impacts network performance. Existing relay selection strategies, such as beacon-based methods, have limitations in scaling under different traffic conditions. This paper proposes a multi-attribute relay selection strategy for urban multi-hop VANETs to overcome these challenges. The strategy evaluates the rebroadcast capability of each receiving node based on its real-time status. It utilizes a fuzzy-BCM-based weight estimation strategy to determine the contribution of each attribute to the node’s capability. The node with the highest broadcasting capability is given priority to access the channel and broadcast the data packet. The proposed scheme is evaluated through simulation tests in VANET simulation environments, considering various traffic flow and speed variations. Performance comparison with four benchmark methods is conducted. The results show that the proposed scheme improves the overall dissemination efficiency by 51.1% compared to the benchmarked methods.

Abstract

Most research in the field of radar signal processing focuses on the use of time-frequency images (TFIs) to distinguish between different signal types. However, most studies have only examined the TFIs of a single signal, making it challenging to analyze and process the simultaneous reception of multiple signal components. This study proposes the use of adversarial latent separation auto encoder to separate and recognize multi-component signals, and innovatively propose a multi-network structure of feature extraction sub-network and signal separation sub-network. Thus, the problem of multi-component signal recognition is solved. Following separation, each component retains its time-frequency data while removing the influence of other components, and the separated TFIs are then subjected to parameter estimation and structural similarity (SSIM) measurements. The experimental findings demonstrate that the parameters retrieved from the separated signal have a low error with respect to the original signal, especially at low signal-to-noise ratios. The excellent SSIM and parameter estimation metrics between the separation results and the time-frequency image of the target tag imply that the separated single-component signal can be successfully reconstructed.

The longitudinal tear of conveyor belts is the most common accident occurring at the workplace. Given the limitations on accuracy and stability of current single-modal approaches to detecting the longitudinal tear of conveyor belts, a solution is proposed in this paper through Audio-Visual Fusion. According to this method, a linear CCD camera is used to capture the images of the conveyor belt and a microphone array for the acquisition of sound signals from the operating belt conveyor. Then, the visual data is inputted into an improved Shufflenet_V2 network for classification, while the preprocessed sound signals are subjected to feature extraction and classification using a CNN-LSTM network. Finally, decision fusion is performed in line with Dempster-Shafer theory for image and sound classification. Experimental results show that the method proposed in this paper achieves an accuracy of 97% in tear detection, which is 1.2% and 2.8% higher compared to using images or sound alone, respectively. Apparently, the method proposed in this paper is effective in enhancing the performance of the existing detection methods.

Insider threats remain a serious anxiety for organizations, government agencies, and businesses. Normally, the most hazardous cyber attacks are formed by trusted insiders and not by malicious outsiders. The malicious behaviors resulting from unplanned or planned mishandling of resources, data, networks, and systems of an organization constitute an insider threat. The unsupervised behavioral anomaly detection methods are mostly developed by the traditional machine learning methods for identifying unusual or anomalous variations in user behavior. The insider threat mainly originates from an individual inside the organization who is a current or former employee who has access to sensitive information about the organization. For achieving an improvement over traditional methods, the Stacked Convolutional Neural Network- Attentional Bi-directional Gated Recurrent Unit model is proposed in this paper to detect insider threats. The CNN-Attentional BiGRU model utilizes the user activity logs and user information for time-series classification. Using the log files, the temporal data representations, and weekly and daily numerical features from various sub-models of CNN are learned by the stacked generalization. Based on the chosen feature vectors, a model is trained on the CERT insider threat dataset. The stacked CNN is combined with the Attentional BiGRU model to incorporate more complex features of the user activity logs and user data during each convolution operation without raising network parameters. Thus the classification performance is improved with less complexity. The non-linear time control, chaos-based strategy, update rules, and opposite-based learning strategies are evaluated for generating the Modified-Equilibrium Optimization. The simulation outputs obtained by the model are 92.52% accuracy, 98% Precision, 95% Recall, and 96% F1-score. Thus, the proposed model has reached higher detection performance.

Real-time and accurate location detection is a key link to ensure the safety of operating machines and workers in production and life. Compared with traditional static multi-anchor nodes, mobile anchor node assisted localization is greener and more energy-saving. In this paper, we first propose a static trajectory based on a light reflection model. Compared with other static models, this model has fewer times in the field, overcomes the collinearity problem and uniform beacon distribution, and ensures that all sensor nodes can receive good enough beacon quality for localization. Secondly, an RSSI-based improved weighted centroid localization algorithm and an RSSI-based improved weighted centroid collaborative localization algorithm are proposed. The two-strategy optimal location beacon set screening method is used to reduce location misjudgment. In order to improve the accuracy of centroid localization, a weighted centroid localization algorithm based on distance and hop number is designed. Moreover, a collaborative localization strategy is aiming at improving beacon density. Experimental results show that both the algorithm and static trajectory can guarantee better beacon coverage rate and localization success rate under different experimental conditions, and at the same time have higher accuracy.

Wireless sensor networks (WSNs) hold the promise of delivering new intelligent, cost-effective, and collaborative applications with the potential to have a great impact on our daily life. WSNs are often employed for detecting and tracking a wide range of entities involved in realistic scenarios where security is of vital importance. While selecting energy-efficient Cluster Heads (CHs) is the primary focus of the majority of clustering approaches currently in use in WSNs, researchers have not given adequate consideration to the security aspects of CHs when developing a CH selection strategy. Estimating the trust between the nodes not only makes the WSN secure, but also improves communication between nodes and makes the WSN more reliable. In this paper, we develop a secure and energy-aware clustering approach (SEACDSC) for WSNs by adapting sand cat swarm optimization algorithm (SCSO). SEACDSC incorporates a novel mechanism for determining secure and energy-efficient CHs among the WSN nodes. In particular, we propose a Discrete SCSO method, a variant of the traditional SCSO, to facilitate the secure and efficacious selection of CHs. The fitness function is designed by considering nodes’ remaining energy and trust values for choosing CH efficiently. Furthermore, the exponential weighted moving average (EWMA) is used for dynamically updating the predefined threshold values following the network state. As demonstrated by the simulation results, SEACDSC outperforms the existing BAT-Based, MG-LEACH, Enhanced-LEACH, Improved-Leach, and RCH-LEACH techniques in terms of network stability, number of alive nodes, energy efficiency, reliability, average trust value of CHs and network lifetime.

This paper presents a lowpass-bandpass diplexer with ultra-wide stopband and low insertion loss using hexagon-shaped resonators. The proposed diplexer consists of a bandpass (BPF) and a lowpass filter (LPF), representing the core concept of the proposed design method that aims to concurrently design BPF and LPF. In this proposed design method, the influence of the LPF filter on the BPF's design has been identified through coupling matrix analysis for the first time. Initially, an LPF is designed based on three coupled hexagon-shaped elliptical resonators. Subsequently, a novel model for BPF design, utilizing coupled high-impedance lines, has been introduced. Following this, the BPF model is developed using coupling matrix analysis while considering the impact of LPF resonators. The LPF have a 1.32 GHz cut-off frequency and ultra-wide stopband up to 17.42 GHz. The BPF consisted of four resonators and the hexagon-shaped structure is used instead of low impedance lines. The utilization of hexagon-shaped resonators serves the purpose of enhancing the precision of the coupling effect, aligning with the proposed coupling matrix analysis. Additionally, hexagon-shaped resonators exhibit a greater capacitive effect, leading to a reduction in insertion loss within the passband when compared to rectangular-shaped resonators. The BPF has narrow passband with center frequency of is 2.25 GHz and 0.31 GHz bandwidth. The measured insertion losses of LPF and BPF are < 0.75 dB and 0.81 dB, respectively in 60% of passbands.

In recent years, service provided based on the location has brought a tremendous change in our lives. However, one of the biggest challenges is to preserve users’ privacy which upon leakage could have disastrous consequences. Privacy preservation has gained remarkable consideration as a notable number of users have started being conscious about privacy protection. Most solutions that have been developed in such a distributed scenario need a third party for data anonymization. In a system of public data sharing, one of the most popular and useful anonymization techniques is local differential privacy (LDP). Without requiring a third party to perturb the data, LDP allows users to perturb their data locally and individually, resulting in stronger privacy guarantees. Based on this principle the proposed system provides anonymity and integrity during communication and independent key generation by using secure authentication mechanism i.e., Physical Unclonable Function (PUF) with elliptical curve cryptography and remove third party dependency for data anonymization by using LDP with Hadamard count mean sketch (HCMS) protocol. For scalability, and quantum secrecy IOTA ledger is used on top of LDP anonymization technique. Our experimental results show that using PUF with ECC for authentication can reduce the computational overhead and increase the secrecy of the communication, LDP with HCMS achieves high privacy while also showing the tradeoff between utility and privacy. Furthermore, the IOTA ledger provides more scalability than the existing technique. Hence, the privacy of an individual will be preserved without compromising accuracy while sharing information to the third party for using location-based services.