Wireless Personal Communications最新文献

The demand of IEEE 802.11 based Wireless Local Area Network (WLAN) is increasing in enterprises, homes, and public access areas primarily due to its high data rates, low cost, and ease of installation. While WLAN continues to be predominantly data-centric, the necessity of Voice over Internet Protocol (VoIP) over Wireless Fidelity (WiFi) or VoWiFi cell capacity analysis in infrastructure-based WLAN plays a vital role. We employed arbitration inter-frame space (AIFS) for assessing channel state before sending real-time data from one user equipment to another, and short inter-frame space (SIFS) for transmitting Request To Send (RTS), Clear To Send (CTS) and Acknowledgement (ACK) frames to study the VoWiFi cell capacity. We have proposed analytical approaches to estimate the cell capacity of an IEEE 802.11be WLAN standard Assess Point (AP) providing VoWiFi service considering well known vocoders such as G.723.1 and G.729. To minimize the call bandwidth, we employed the compressed RTP (cRTP) protocol. Additionally, we examined the impact of retransmission on cell capacity of a WLAN standard providing VoWiFi service.

In these crucial times of COVID-19, cryptographic and nature inspired innovations help to communicate confidential data in the wireless telemedicine. The novel corona virus had entirely shattered all segments of life in the entire world. In the medical sciences, patients are more advised to take telemedicine supports. Cardiac patients are very much prone to corona virus. They need to transmit confidential medical data for their online treatments. Such heterogeneous cardiac information is to be protected with patients’ confidentiality. It is very noteworthy to impose high security features on the COVID-19 “New Normal” telecardiology. In this paper, we have designed a cryptographic system based on metaheuristic Harmony search algorithm and tree parity machines. It has been proposed to counter attack against different security hazards in online medical transactions during this tremendous flooded COVID-19 time. The proposed method has been modularized into four modules. These are: Neuro-Metaheuristic Session Key (NMSK) generation, Cryptographic Engineering, Frame format generation, and authentication and decryption phase. Harmony search with tree parity machines were used to generate the key i.e. NMSK. A new state-of-art sharing was proposed to dilute the information contents inside the telemedicine networks against the intruders. Moreover, the proposed state-of-art sharing exhibits the complete bi-partite graph property. A rigorous frame format has been placed before the encrypted message inside the COVID-19 telecardiology. Different types of statistical and mathematical tests were carried out on the proposed technique to prove its efficacy. Thus, the proposed cryptographic system acts as a reinforcement of security mechanism in COVID-19 telecardiology.

In this paper, a robust algorithm for enhancing indoor positioning accuracy utilizing time difference of arrivals is proposed. Addressing limitations of maximum likelihood estimation and traditional weighted least squares methods, which often suffer from matrix ill-conditioned problem and numerical instability, leading to significant biases and reduced accuracy, we propose a novel bias reduction technique based on ({{varvec{QR}}}) factorization. Incorporating geometric relationship information, our method improves precision. Through rigorous analysis and simulation under zero-mean white Gaussian noise, the algorithm demonstrates superior performance, overcoming matrix ill-conditioned problem, surpassing traditional methods, and closely aligning with the Cramér-Rao lower bound.

The Preservation of Variable Speed Drives at ripple and harmonic less output functions at different applications application are vital. The Variable Speed Drive applications involve the use of different converter topologies, which makes the system to reduced efficiency and leads to reduced performance. The advancement of power electronics helps to develop control schemes that are proficient at achieving Variable Speed Drives with low harmonics and ripples. Various control topologies are proposed elsewhere in the world to achieve effective variable speed drive performance. The merits and demerits of each control strategy may differ from those of others. Artificial Neural Network (ANN) based technique is made a significant importance in recent years and showing enhanced performance variation compared to other existing control strategies. This paper presents the design of 15 level inverter circuits and functioned using Artificial Neural Network (ANN) control technique to obtain the effective performance for various speed drive applications. Also, this paper presents the Space Vector Pulse Width Modulation (SVPWM) control technique and Fuzzy Logic Control Techniques to same 15 level multilevel inverter circuit. The results of ANN, SVPWM and Fuzzy Logic techniques are compared and made suggestions on suitable effective control technique for variable speed drive applications. Artificial Neural Networks are significantly better understood in terms of their effectiveness in optimizing variable speed drive systems, improving efficiency and performance.

The largest white matter structure in the brain, the corpus callosum (CC), is involved in many disorders of the central nervous system. The extent and/or severity of neurodegenerative illnesses are correlated with its size. Though numerous approaches and procedures for CC fragmentation have been offered, and the role of CC has been scrutinized more and more over the past few centuries. Nevertheless, the segmentation accuracy of the current models is not very good. This research offers a thorough analysis of various segmentation methods for CC fragmentation. Additionally, it investigates the different deep learning models focused on CC segmentation obtained from brain magnetic resonance imaging. The results show that not all of the issues with the computational methods for segmenting CC on magnetic resonance imaging have been resolved.

Recently, cloud computing has become a growing technology in the information technology industry because of its several smooth delivery services. In cloud computing, multi-tenancy is one of the primary features that affords economic and scalability significance to the service providers and end-users by distributing a similar cloud platform. Due to the increasing demand for cloud computing, cloud usage has increased, so various vulnerabilities and threats have also been enhanced. Hence, data security and privacy are considered the major issues of multi-tenant environments in the cloud. Several existing studies have developed different mechanisms to solve security issues in multi-tenant cloud environments. However, they faced various problems while improving security, and this led to a lack of confidentiality, authenticity, and data integrity. Thus, this research paper intends to propose an efficient encryption approach for securing data delivery in the cloud with reduced time. For secure data delivery, homomorphic encryption is utilized to encode the cloud server’s data. In homomorphic encryption, four stages are available for data delivery: key generation, encryption, decryption, and evaluation. The main problems in this homomorphic encryption mechanism are key sharing and key management. Due to these problems, the performance of homomorphic encryption is diminished. Thus, the proposed work introduces an Aquila optimizer for the key generation process. In this, optimal keys are selected, and it provides improved data security and privacy for cloud users. Finally, the selected keys are generated for the encryption and decryption process. The efficiency of the proposed approach is proved by comparing the performance in terms of encryption time, decryption time and throughput over the existing schemes like Rivest, Shamir and Adleman, ElGamal, Algebra Homomorphic Encryption scheme based on ElGamal (AHEE) and modified AHEE. The experimental results reveal that the proposed model achieves reduced encryption and decryption time of 972 ms and 4261 ms for the data size ranges from 5 to 25 mb.

In Wireless Sensor Networks (WSNs), malicious nodes can infiltrate the network at any time, compromising network performance. Developing a secure routing protocol is critical for ensuring reliable data transmission. LEACH (Low-Energy Adaptive Clustering Hierarchy) is a well-established hierarchical routing protocol within the WSN paradigm. However, LEACH lacks robustness against security attacks due to the absence of integrated security mechanisms, making it vulnerable in the presence of malicious nodes. On the other hand, Blockchain, an emerging technology, is best suited for Wireless Sensor Networks due to its distributed, decentralized, immutable, and traceability features. Integrating blockchain with WSNs significantly enhances security, data integrity, and decentralization within the network. Integrating blockchain technology with WSNs significantly enhances security, data integrity, and decentralization within the network. In this paper, we introduce a Blockchain-enabled secure LEACH protocol to ensure reliable data transmission in wireless sensor networks. The proposed hybrid blockchain framework includes three phases: Registration, Authentication, and Malicious Node Detection. By utilizing the autonomous execution capabilities of smart contracts without third-party intervention, we develop specific smart contracts for the registration of cluster heads and ordinary nodes, for authentication between cluster heads and their corresponding ordinary nodes, and for the identification and elimination of malicious cluster head nodes. A detailed security analysis of the proposed BSLEACH framework is provided to provide its robustness against different network attacks such as Man-in-the-Middle attack, Sybil attack, Replay attack and non-repudiation.In performance evaluation, it is observed that the proposed Blockchain-based Secured LEACH (BSLEACH) protocol outperforms other versions of LEACH such as Sleach (Secure low-energy adaptive clustering hierarchy) and Ms-leach (A routing protocol combining multi-hop transmissions and Single-hop transmissions) in the presence of malicious cluster heads and Sybil nodes in the network. The proposed solution improves the performance of the traditional LEACH protocol by identifying and removing malicious Cluster head units from the Private Blockchain network.

Delay Tolerant Network (DTN) provides communication in environments composed of nodes with heterogeneous characteristics such as frequent disconnections, limited transmission range, dynamic topology, and scarce resources. Data delivery is achieved via the mobility of nodes by employing multi-copy protocols in which each node is allowed to create and transmit multiple copies of each message. The data is delivered at a higher consumption of node energy, buffer space, and network bandwidth. Similarly, buffer space is a critical resource to store and carry messages. Effective management can reduce message drops and increase delivery rate. The existing techniques observe message transmissions to evaluate energy consumption and basic buffer management policies to overcome congestion. In this paper, we have proposed an Energy Estimation based Routing Protocol (EERP) in which instead of number of transmissions the energy quota has been assigned to each message. In addition, buffer management of DTN node has been considered more critical. Therefore, we have proposed a buffer management policy in which messages are dropped based on their level of energy consumption. The simulation results prove that the proposed EERP has performed better in terms of increasing delivery ratio, buffer time average, and reducing message transmission, drop, and hop count average.

As the comming of the sixth generation (6 G) era, the Low-Earth-Orbit (LEO) satellite communications are expected to provide better service for massive terminals. With the rapid increasing of users, it will make the wireless spectrum resource more scarce. In this paper, in order to improve the spectrum efficiency, Multi-carrier Faster-than-Nyquist (MFTN) signaling is applied into downlink of LEO satellite communications. Further, we have proposed a Doppler frequency offset estimator, which can minimize the performance loss of MFTN signaling in the LEO satellite channel. For reducing computation complexity, the proposed estimator has been simplified by polynomial approximation. In LEO satellite channel, simulation results demonstrated that the proposed estimator can achieve satisfied estimation performance. It is also verified that our proposed estimator can reduce transmission performance loss for MFTN with different packing factors.

Badarneh and da Costa (IEEE Wirel Commun Lett, 2024. https://doi.org/10.1109/LWC.2024.3353620) introduced the fluctuating fading distribution by taking the signal envelope to be the ratio of two independent random variables, one having the Nakagami m distribution and the other a uniform random variable. The Nakagami m distribution corresponds to signals following the normal distribution which may not always hold in practice. In this paper, we derive twenty other fluctuating fading distributions. For each distribution, we give explicit expressions for the average channel capacity and the average bit error rate. Their correctness is checked numerically.