Telecommunication Systems最新文献

Mobile edge computing (MEC) has emerged as a promising technology that can revolutionize the future of mobile networks. MEC brings compute and storage capabilities to the edge of the network closer to end-users. This enables faster data processing and improved user experience by reducing latency. MEC has the potential to decrease the burden on the core network by transferring computational and storage responsibilities to the edge, thereby reducing overall network congestion. Load balancing is critical for effectively utilizing the resources of the MEC. This ensures that the workload is distributed uniformly across all of the available resources. Load balancing is a complex task and there are various algorithms that can be used to achieve it, such as round-robin, least connection, and IP hash. To differentiate between heavily loaded and lightly loaded servers, current load balancing methods use an average response time to gauge the load on the edge server. Nevertheless, this approach has lower precision and may result in an unequal distribution of the workload. Our study introduces a dynamic threshold calculation technique that relies on a response-time threshold of the edge servers using K-means clustering. K-means based proposed algorithm classifies the servers in two sets (here K = 2), i.e., overloaded and lightly loaded edge servers. Consequently, workload is migrated from overloaded to lightly loaded servers to evenly distribute the workload. Experimental results show that the proposed technique reduces latency and improves resource utilization.

The 3GPP vehicle-to-everything (C-V2X) technology is a key solution to provide communication services for applications of intelligent transportation systems (ITS). According to the C-V2X specification, vehicles are allowed to communicate with each other without the help of cellular networks and can select subchannels (i.e. radio resources) by themselves. However, this design will induce subchannel collisions because of the mobility of vehicles. This work aims to relieve the aforementioned problem. We propose a cooperative subchannel selection (CoSS) scheme and a localized subchannel selection (LoSS) scheme, which consider vehicular mobility to prevent subchannel collisions. Based on the collected information, CoSS predicts possible subchannel collisions due to vehicles’ location changes. On the other hand, LoSS avoids subchannel collisions by organizing the usage of subchannels based on vehicles’ relative moving speeds. The simulation results indicate that the proposed schemes can effectively reduce subchannel collision ratio and increase packet delivery ratio for the network.

This paper presents an energy-efficient design of optical wireless communication (OWC) system for the indoor Internet of Things (IoT) with the assistance of machine learning (ML). A central coordinator (CC) has been proposed to interrogate the IoT devices and control the uplink formations with the prediction of transmission quality using ML classifiers. The passive reflective reflectors (PRF) are utilized in the IoT devices, which replaced the power-consuming active transmitters, formulate the zero-power consuming transmission links. The communication performance of the passive link establishments from the IoT devices have been investigated in terms of quality factor (Q-factor), bit error rate (BER), and signal-to-noise ratio (SNR) under different optical wireless channel conditions and link lengths. The ML classifiers have been evaluated on the prediction of transmission quality, and the results suggested the Euclidean K-nearest neighbor (KNN) with ten number of neighbors for the implementation. The IoT devices located within 1.2 m from the CC require a transmission power of 0.5 mW for links carrying 10 Gbps data, which increases the energy efficiency to 20 Gbps/mW with transmission energy consumption of 0.05 pJ/bit. This significant improvement in energy efficiency and passive communication ensures reliable, and green IoT links suitable for data-intensive indoor applications.

Nowadays, wireless sensor networks (WSNs) are utilised in military-based applications like border surveillance. However, existing border surveillance methods have difficulties with energy efficiency, latency, security, connectivity, optimal path selection and coverage. In this paper, a Voronoi Modified Red Fox (VMRF) algorithm is proposed as a solution to these problems. Initially, secure cluster head (CH) selection and clustering is performed using Secure Spatial Intelligence-Enhanced Voronoi Clustering (SIEVC) to boost energy efficiency, security, and extend network coverage and connectivity. The SIEVC algorithm dynamically selects CHs based on past and present trust, identity trust, and energy trust to identify malicious nodes and form optimal clusters for improved network coverage and connectivity. It also employs dynamic cluster size adjustment to maintain proximity between CHs and cluster members and utilizes node alternation to ensure equitable cluster sizes. This approach minimizes energy depletion, enhances network longevity, and improves load balancing. The algorithm introduces a node alternation mechanism to balance cluster sizes and prevent energy holes. This approach ensures secure and efficient CH selection and promotes even energy distribution. Then the proposed modified red fox (MRF) optimization method, based on the fitness metric, computes the energy-efficient and safe path for data transmission. Trust, energy, distance, link quality and traffic intensity are the factors that the fitness function takes into account. Finally, the data is transmitted to the base station (BS) through CH along the path with the highest fitness value. Then the proposed VMRF algorithm is evaluated using the NS-2 platform, and the outcomes are compared with existing protocols. Based on the evaluations, the VMRF algorithm performs better than existing ones in terms of delay, energy consumption, throughput, packet delivery ratio (PDR), malicious node detection ratio, and residual energy.

Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be replaced or easily recharged in harsh environments. Maximizing network lifetime remains a challenging issue in designing WSN routing. This paper introduces GTIACO, a novel metaheuristic clustering protocol. It employs an optimal cluster head function to determine cluster number and utilizes game theory for selecting optimal cluster heads. To optimize inter-cluster routing, improved ant colony optimization (ACO) is introduced to construct gathering paths from clusters to the base station. Both blind pathways, pheromone concentration, and angle factors are considered to improve path exploration and transmission efficiency in ant colonies. To assess network performance, various scenarios involving different base station placements and network densities are examined. Experimental results demonstrate GTIACO's superiority over LEACH, ACO, SEP, and PRESPE protocols in network lifetime, stability, energy, and throughput. The proposed GTIACO shows an improvement of at least 4.3% in network lifetime and 32.8% in network throughout. It exhibits superior stability and transmission efficiency across diverse network densities.

As a simple and inexpensive channel characteristic, received signal strength (RSS) finds extensive usage in localization applications. However, the quick changes in signal strength impact the localization precision. By averaging over access points (APs) with multiple frequencies and/or heights, this article suggests a novel approach to lowering RSS fluctuation. Initially focused on the plane-earth loss model, the study was later extended to include a multipath indoor propagation scenario that was simulated. We used ray-tracing software to model the indoor propagation situation. This research takes into account the results of three distinct methods for averaging RSS: height averaging, frequency averaging, and hybrid frequency and height (FH) averaging, which combines the two. We discovered that the Height-only strategy considerably decreased the RSS variation with distance for both settings we looked at. Using the frequency-only method even further reduced the variation. Using the Hybrid FH technique greatly enhances the results. Root mean square error values of 4.427 dB, 3.70 dB, and 3.5 dB, respectively, are provided for the averaging approaches and the ideal scenario in which no variance occurs. Another finding is that averaging with APs that have double the height or frequency will not improve the RSS distance variation.

Abstract

Distributed inference tasks could be performed by adaptive filtering techniques. Several enhancement strategies for such techniques were proposed, such as sparsity-aware algorithms, coefficients reuse and correntropy-based cost functions in the case of impulsive noise. In this paper, a general framework based on Lagrange multipliers for the derivation of sophisticated algorithms that incorporate most of these improvements is described. A new general identification algorithm is derived as an example of the proposed approach and its performance is assessed in a distributed setting.

The advent of the sixth-generation (6G) wireless communication technology brings forth immense opportunities for enhancing Intelligent Transportation Systems (ITS). We investigate the potential of 6G in revolutionizing transportation systems by analyzing the standards, technologies, and challenges associated with its implementation. Building upon the advancements of 5G, 6G introduces unprecedented capabilities, including ultra-high data rates, ultra-low latency, massive connectivity, and intelligent network orchestration. Leveraging these capabilities, 6G can facilitate seamless connectivity among vehicles, infrastructure, and pedestrians, enabling novel applications such as autonomous driving, smart traffic management, and cooperative collision avoidance. However, realizing the full potential of 6G for ITS entails addressing several challenges, including spectrum allocation, network security, resource management, and interoperability with existing infrastructures. We present a comprehensive overview of the latest developments in 6G standards and technologies, including mmWave communications, terahertz spectrum, visible light communication, Artificial Intelligence, and edge computing, emphasizing their relevance to ITS. We also describe these technologies in the context of ITS. Furthermore, we explore the potential benefits and impacts of 6G on transportation systems, highlighting the opportunities for improved safety, efficiency, and sustainability. Finally, we conclude by discussing the key challenges and research directions that must be addressed to effectively deploy 6G for ITS, thereby paving the way for the next generation of intelligent and connected transportation systems.

Abstract

Nowadays, users often opt to encrypt their sensitive data before outsourcing it to the cloud. While this encryption ensures data privacy, it compromises the search functionality. Public key encryption with keyword search (PEKS) has been posited as a potential solution to this challenge. However, many PEKS schemes are either inefficient or vulnerable to various types of attacks, such as inside keyword guessing attacks, outside keyword guessing attacks, etc. In response, we introduce a sustainable certificateless authenticated encryption system with a keyword search scheme. To the best of our knowledge, this proposed scheme is the first to consider multi-trapdoor indistinguishability within the certificateless primitive. Furthermore, our in-depth security analysis indicates that our scheme effectively protects against both online and offline keyword guessing attacksAdditionally, it ensures semantic security by offering both ciphertext indistinguishability and multi-trapdoor indistinguishability. Performance analysis results also suggest that our scheme is efficient and superior to existing alternatives.

In cognitive radio networks (CRNs), rendezvous is the vital step prior to the communication between two unlicensed secondary users (SUs), where the SUs hop on the same channel at the same time to establish a link. With the dramatic fall in the cost and size of wireless transceivers, it becomes more reasonable to apply multiple radios to achieve significant improvement in the rendezvous performance. However, most of the existing multiradio rendezvous algorithms are proposed for homogeneous CRNs where all the SUs are equipped with an equal number of radios and do not possess backward compatibility to SU with a single radio. In reality, the CRNs are heterogeneous in nature as SUs may have different numbers of radios. In this paper, a composite CH algorithm is proposed for an asynchronous and heterogeneous network to achieve blind rendezvous with full rendezvous diversity. An SU with m number radios are categorized into three groups those follow different channel hopping (CH) algorithms. The upper bound of the rendezvous latency is being evaluated with a brief theoretical and mathematical analysis. Extensive simulations have conducted for different performance metrics, and the results are compared with the state-of-art algorithms. Overall, the proposed algorithm shows better performance in heterogeneous CRNs.