Wireless Networks最新文献

Abstract

Intelligent reflecting surface (IRS) has emerged as a viable technology to enhance the spectral efficiency of wireless communication systems by intelligently controlling wireless signal propagation. In wireless communication governed by the IRS, the acquisition of channel state information (CSI) is essential for designing the optimal beamforming. However, acquiring the CSI is difficult as the IRS does not have radio frequency chains to transmit/receive signals and the capability to process the signals is also limited. The cascaded channel linking the base station (BS) and a user through the IRS does not necessarily adhere to a specific channel distribution. Conventional and deep learning-based techniques for channel estimation face challenges: the pilot overhead and compromised estimation accuracy due to assumptions of prior channel distribution and noisy signal. To overcome these issues a novel generative cascaded channel estimation (GCCE) model based on a generative adversarial network (GAN) is proposed to estimate the cascaded channel. The GGCE model reduces the reliance on pilot signals, effectively minimizing pilot overhead, by deriving CSI from received signal data. To enhance the estimation accuracy, the channel correlation information is provided as a conditioning factor for the GCCE model. Additionally, a denoising network is integrated into the GCCE framework to effectively remove noise from the received signal. These integrations collectively enhance the estimation accuracy of the GCCE model compared to the initial GAN setup. Experimental results illustrate the superiority of the proposed GCCE model over conventional and deep learning techniques when provided with the same pilot count.

Wireless medical sensor network (WMSN) is an application of the Internet of Things (IoT) that plays a very important role in today’s era for the healthcare industry, especially after the COVID-19 pandemic. To maintain the security and privacy of the real-time health information of the users or patients, the proper mutual authentication and key agreement (AKA) is the foremost necessity. In this context, Shadi Nashwan proposed an end-to-end authentication scheme for a healthcare IoT system i.e. WMSN, and claimed that their scheme could resist so many existing possible threats and could maintain a low computational cost too. Unfortunately, during this research, it is found that their scheme can be threatened by eavesdropping and jamming/desynchronization attacks and have many computational flaws, as well. Moreover, they also assumed that the gateway node (GWN) is always trustworthy, but in reality, it is not always feasible, as the GWN may act as a local server. Hence, in this article, a new AKA scheme has been proposed using the user’s physiological information like ECG data in order to make the WMSN more secure and reliable. In addition, the proposed scheme can resist many well-known threats like GWN spoofing attack, key escrow problem and can guard against GWN stolen database problem, also. To proof the superiority of the proposed scheme, the informal and formal security analysis have been performed using automated validation of internet security protocols and applications (i.e. AVISPA) and Burrows–Abadi–Needham (BAN) logic, respectively. Based on the comparative study with existing schemes concerning security features, computational and communicational cost, and storage requirement; the proposed scheme can perform better than the existing schemes and well suitable for practical implementations.

The detection of fraudulent URLs that lead to malicious websites using addresses similar to those of legitimate websites is a key form of defense against phishing attacks. Currently, in the case of Internet of Things devices is especially relevant, because they usually have access to the Internet, although in many cases they are vulnerable to these phishing attacks. This paper offers an overview of the most relevant techniques for the accurate detection of fraudulent URLs, from the most widely used machine learning and deep learning algorithms, to the application, as a proof of concept, of classification models based on quantum machine learning. Starting from an essential data preparation phase, special attention is paid to the initial comparison of several traditional machine learning models, evaluating them with different datasets and obtaining interesting results that achieve true positive rates greater than 90%. After that first approach, the study moves on to the application of quantum machine learning, analysing the specificities of this recent field and assessing the possibilities it offers for the detection of malicious URLs. Given the limited available literature specifically on the detection of malicious URLs and other cybersecurity issues through quantum machine learning, the research presented here represents a relevant novelty on the combination of both concepts in the form of quantum machine learning algorithms for cybersecurity. Indeed, after the analysis of several algorithms, encouraging results have been obtained that open the door to further research on the application of quantum computing in the field of cybersecurity.

Multi-path and multi-hop routing are multi-objective optimization problems involving multiple constraints that need to be addressed in the current scenario in wireless sensor networks. The routing process is challenging due to the constrained energy resources and transmission bandwidth. The conventional strategies possess shortcomings, like, as high computing complexity, extensive problem-solving time, complexity in achieving optimal values, and ease of falling into local solutions. Hence, the aim is to propose a hybrid metaheuristic algorithm, known as a multi-objective optimized multi-path and multi-hop routing algorithm (MMMRA). It incorporates the chimp optimization algorithm (COA) for determining the optimal multi-path route based on multi-objective function and ant colony optimization for determining the optimal multi-hop routing. The proposed MMMRA is implemented using NS-2 and to evaluate the performance, nine various scenarios are considered. The MMMRA is validated using different performance measures and compared with other benchmark algorithms. The simulation results indicate that the MMMRA exhibits percentage improvement in terms of residual energy by 1.63%, 4.96%, 6.89%, 7.51%, and 9.67% over IPSMT, BIM2RT, SCP, PSOBS, and RDICMR algorithms respectively. Moreover, the HND and FND of the MMMRA algorithm perform better in all three scenarios (center, corner, and outside positions of sink node), especially when the sink node is placed at the center position, the HND of MMRA shows a percentage improvement by 24% and 12.73% over IPSO–GWO, and COA–HGS algorithms respectively. Similarly, the FND of MMRA shows percentage improvement by 21.05% and 9.5% over IPSO–GWO, and COA–HGS algorithms respectively.

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate.

The dynamic management of sensor nodes and advanced information fusion are necessary technologies to enhance the comprehensive performance of sensor networks. This paper presents a cascaded sensor dynamic activation and information fusion algorithm to simultaneously optimize the energy and sensing performance of wireless sensing networks. The proposed algorithm dynamically activates nodes that are most suitable for the current sensing task through a joint event-driven and state ranking activation algorithm that achieves a better sensing performance with lower energy costs. In addition, it further utilizes the sensing information of all the activated nodes with maximum efficiency, through an improved distributed Kalman information fusion, which achieves an extra improvement in sensing accuracy as measured by the minimum variance. Finally, the superiority of the proposed cascaded algorithm is verified by a simulation comparison, achieving almost zero dead nodes in terms of energy, and a 62.1% decrease in average error in terms of sensing.

Abstract

Heterogeneous networks are needed to meet user demands as wireless network demand rises. Network mobility management is crucial. Mobility management challenges are related to handover solutions to decrease call/packet losses in such networks. The handover is one of the most critical parts of mobility management in the Long-Term Evolution of Advanced (LTE-A) system, which relies on handover procedures to improve quality, coverage, and service in the existing network. The LTE-A future wireless communication networks consist of various femtocells, microcells, and macrocells. Therefore, designing the appropriate mechanism to perform handovers among different cells is a challenging research problem. We propose a novel handover mechanism called multi-objective handover using swarm intelligence algorithm (MH-SIA) for the future wireless communication system. MH-SIA is made of two novel features multi-objective handover and SIA for handover process optimization. The multi-objective trust parameters of each User's Equipment are computed to perform the handover decision-making and target cell selection using the SIA. The computed trust parameters are utilized as the modified fitness function in Differential Evolution (DE) optimization technique. Due to the fast convergence of DE, it performs computationally efficient handover operations. The multi-objective trust parameters are utilized in handover decision-making and target cell selection to improve network performances with minimum handover latency. The experimental result of MH-SIA reveals the efficient performance compared to underlying methods.

Abstract

Graph databases have received increased interests as many applications are handled as graph problems. Shortest distance queries are one of the fundamental operations and have been studied for recent years. To ensure the data and query privacy, researchers have introduced some secure graph encryption schemes which support the shortest distance queries on a large-scale graph database. Unfortunately, most of them only provide an approximate result by pre-computing and storing a distance oracle. To provide the exact shortest path, our solution employs a distributed two-trapdoor public-key crypto-system to perform addition and comparison operations over ciphertexts. The detailed security analysis indicates that our scheme achieves semantically secure under DDH assumption and the experiments are performed on various real-world database and random database. The experimental result shows the feasibility of our scheme.

Abstract

An industry-wide paradigm change has been sparked by the growth of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), which has made reliable and effective routing methods necessary. This thorough analysis looks at how Machine Learning (ML) techniques may be used to solve the problems that come with WSN routing. A summary of standard routing algorithms and an examination of their shortcomings comprise the first portion of the paper. The integration of ML approaches, such as reinforcement learning and supervised and unsupervised learning, is then explored in order to improve WSN routing efficiency. The article examines the difficulties and factors related to ML-based routing, including data quality, energy efficiency, scalability, and security. Applications and case studies show how ML is really used in WSN routing, offering insights into effective tactics and lessons discovered. Evaluation metrics and performance assessments are included in a separate section that uses simulation and experimental data to compare ML-based and conventional techniques. Looking forward, the study describes new breakthroughs in ML for WSNs and points out unresolved issues, providing a guide for future research paths. The important results and their consequences are outlined in the conclusion, which also highlights how ML has the potential to revolutionize WSN routing in the future.

Abstract

Trust management has been shown to be an effective technique for protecting networks from malicious nodes and ensuring wireless sensor network (WSN) security. A number of trust systems have been proposed, but most of them are not adaptative to the current state of network security and the intensity of the attacks to which they are subjected, especially in the case of collaborative attacks. They employ fixed trust metrics derived from expert opinion rather than the objective method based on the network’s current security level. Furthermore, they are complex trust systems designed for a specific application with a high attack probability. Thus, even with a low attack rate, they consume a lot of energy. This paper proposes an adaptive trust system that considers both the network’s risk level and the trust values of sensor nodes at the same time. To match the situation in the network, the proposed system employs various trust policies. In risky situations where the WSN environment remains untrustworthy, the proposed system adjusts its trust metrics based on the network attack intensity. When the attacks are eliminated and the misbehavior rate is low, the system switches to an energy efficient policy and adjusts its trust metrics to conserve sensor node energy. Simulation results show that a zero-tolerance policy achieves 95% of the detection rate and conserves 50% of nodes’ energy under the presence of 35% of malicious nodes in the network. Energy efficient policy achieves 90% of detection rate and conserves 95% of nodes’ energy under the existence of 10% of malicious nodes in the network. Normal policy achieves up to 90% of detection rate between 15 and 25% of malicious nodes while conserving 70% and 80% of energy under these percentages.