Telecommunication Systems最新文献

Wireless sensor networks (WSNs) enable seamless data gathering and communication, facilitating efficient and real-time decision-making in IoT monitoring applications. However, the energy required to maintain communication in WSN-based IoT networks poses significant challenges, such as packet loss, packet drop, and rapid energy depletion. These issues reduce network life and performance, increasing the risk of delayed packet delivery. To address these challenges, this work presents a novel energy-efficient distributed neuro-fuzzy routing model executed in two stages to enhance communication efficiency and energy management in WSN-based IoT applications. In the first stage, nodes with high energy levels are predicted using a fusion of distributed learning with neural networks and fuzzy logic. In the second stage, clustering and routing are performed based on the predicted eligible nodes, incorporating thresholds for energy and distance with two combined metrics. The cluster head (CH) combined metric optimizes cluster head selection, while the next-hop combined metric facilitates efficient multi-hop communication. Extensive simulation results demonstrate that the proposed model significantly enhances network lifetime compared to EANFR, RBFNN T2F, and TTDFP by 9.48%, 25%, and 31.5%, respectively.

Software-Defined Vehicular Networks (SDVN) have been established to facilitate secure and adaptable vehicle communication within the dynamic environment of Vehicular Ad-hoc Networks (VANETs). To enhance efficiency, various optimization techniques are employed in cluster-based routing, focusing on reducing energy consumption, improving cluster stability, enhancing throughput, minimizing network overhead, increasing packet delivery ratio, and reducing latency. This work proposes enhancements to dynamic adaptive cluster-based routing to mitigate suboptimal decisions in VANETs. A centralized controller maintains Energy and Distance-Based Clustering and Dynamic Adaptive Cluster-Based Routing (EDBC-DACBR) to optimize VANET clustering and routing. EDBC utilizes energy and distance metrics between vehicles and cluster centres, or Roadside Units (RSUs), for cluster formation. A fitness model identifies Cluster Heads (CH) based on nodes with the highest fitness values, while a Location-Based Fuzzy C-Means (LBFCM) algorithm ensures optimal cluster formation. The resultant CH, chosen for their energy efficiency, stability, and dynamism, are derived by combining the LBFCM with the fitness model. Additionally, DACBR adapts to network variations, such as energy levels, communication distances, and vehicular congestion, to define the shortest path. Simulation-based evaluations demonstrate the effectiveness of the proposed approach, outperforming existing methods such as Learning-Based Cluster-Based Routing (ANFC-QGSOR), Fuzzy-Based Cluster-Based Routing (FCBR), Energy-Efficient-Based Cluster-Based Routing (EEOR), and Hierarchy-Based Cluster-Based Routing (EHCP) in terms of throughput, overhead, packet loss, latency, stability, and network lifetime. Specifically, EDACR achieves a 15% improvement in throughput, reduces network overhead by 20%, increases the packet delivery ratio by 25%, and decreases latency by 30% compared to existing approaches. Furthermore, EDACR enhances network stability, with a 10% reduction in packet loss and a 20% increase in network lifetime. These results highlight the efficacy of EDACR in enhancing the efficiency and reliability of SDVN deployments in dynamic vehicular environments.

Multiple-input multiple-output systems with spatial modulation has evolved into an energy efficient and less complex wireless transmission system due to the use of limited radio frequency chains. In this work, a novel physical layer security scheme with enhanced spatial modulation (ESM) and secret key generation using multi-level adaptive threshold binary-coded decimal quantization algorithm is introduced. The proposed method, as SPACESM, enhances spectral efficiency using ESM and achieves confidentiality by adaptively selecting and shuffling the codebook in ESM. For selection of ESM codebook, SPACESM uses physical layer signature called channel signal to noise ratio (SNR) and for shuffling of codebook and rotation of transmit vector, secret key is generated at the transmitter and receiver separately by threshold-based quantization algorithm, which uses channel state information. Adaptive modulation technique selects the modulation order for different SNR. The performance of the proposed method is evaluated in terms of confidentiality, spectral efficiency, and robustness of secret key through MATLAB simulation.The proposed SPACESM technique provides 2 bpcu improvement for both (N_t) = 2 and (N_t) = 4 with M = 64 than the existing SM. Also it provides 3 dB and 4 dB SNR gain than SM for (N_t) = 2 and (N_t) = 4 respectively. Similarly, it provides 2 dB and 4 dB SNR gain than the SMX technique for (N_t) = 2 and (N_t) = 4 respectively. In summary, the proposed method outperforms than the SMX and SM technique in all aspects.

Device-to-Device (D2D) relayed communication helps in extending the coverage range of cellular networks. Relay devices support multi-hop D2D communication where two devices are out of the direct D2D range. However, identifying suitable fixed relays in a network is a complex problem that needs a more efficient solution. Relay-assisted communication may also fail due to the non-cooperative nature of the users (draining battery energy for supporting other devices). This paper proposes a UAV (Unmanned Aerial Vehicle)-assisted multi-hop D2D communication scheme that serves more out-of-direct-range D2D users using the dynamic location of the UAVs (drones). Dynamic location of UAVs solves the connectivity issues with many users. We aim at maximizing the achievable throughput of the D2D users for both uplink (users to UAVs) and downlink (UAVs to users) channels simultaneously. An optimization problem is formulated for maximizing throughput subject to interference, power, and bandwidth constraints. The UAV trajectories are predicted for serving the multi-hop D2D users in the system using Neural Network (NN), and thereafter, a novel resource assignment scheme, named Dual Optimal Channel Allocation (DOCA), is proposed. DOCA optimally allocates resource blocks (RBs) for both uplink and downlink channels and ensures that the overall interference caused by resource sharing between cellular and D2D users is minimal. The spectrum efficiency has been achieved by resource sharing between cellular and D2D users. An association matrix is obtained that indicates potential resource-sharing partners of D2D and cellular users. Finally, we show the performance of the proposed technique with regard to throughput improvement, buffer requirement, and churn rate of the system.

Advanced anonymous communication schemes have been proposed to protect the privacy of network users, enabling them to communicate anonymously on the Internet. However, this can lead to the issue of misuse of anonymous communication. Most anonymous communication systems lack accountability mechanisms, allowing malicious users to engage in illegal activities on the network without facing consequences. This presents difficulties for maintaining social stability and promoting anonymous communication. To address these challenges, we develop an accountable anonymous broadcast system that combines group signatures and a secret-shared shuffle protocol. This system allows honest users to anonymously publish messages, while the identities of users who publish illegal messages will be exposed. We employ batch verification to reduce the time required for verifying group signatures, which in turn reduces system latency. Finally, we implement the system and conduct performance evaluations to demonstrate its practicality. Compared to previous accountable anonymity mechanisms, our system can directly trace and hold malicious users accountable without compromising the privacy of honest users.

The fourth-generation (4 G) cellular network is currently the dominant mobile technology used worldwide. The analysis of the network behavior can help forecast the traffic and thus improve the network. The characterization of mobile user behavior can be useful in evaluating the emerging concepts. We collected traffic traces on a commercial multi-band 4 G cell to better understand and model the network and user behavior. We evaluate the network utilization, the number of connections, and the uplink and downlink cell achieved throughput. In addition, we analyze and identify statistical models that describe the connection inter-arrival time, connection duration, and connection size. The results show daily and weekly patterns that depend not only on the time of day but also on the frequency band. We find that the frequency band and time of day have a limited impact on user behavior. On the contrary, the connection inter-arrival time strongly depends on the frequency band.

IoT is critical in many application areas, such as smart cities, health care, and surveillance systems. Each application has its own QoS requirements. Dynamic traffic management in an IoT network is essential for optimal load balancing and routing. It also allows applications to achieve their desired level of QoS. Osmotic computing is a paradigm for edge/cloud integration. In this paradigm, to balance the load of the network hosts, the services must migrate from a higher resource-utilized data center to a smaller one. According to the osmotic computing approach, each IoT application could be broken into some Micro-Elements (MELs), and each MEL resides on a resource on the edge or cloud data center. Usually, in an IoT osmotic environment, services must be executed by the edge hosts. Some remaining services must migrate to the cloud data centers if the edge hosts lack computational resources. Therefore, such data migration may produce massive traffic across the network. Moreover, the traffic sometimes must pass through a particular route, which includes some pre-specified nodes, for security or monitoring reasons. The routes must be optimized regarding QoS metrics such as delay, jitter, and packet loss ratio. Therefore, finding an optimal path between the source and the destination MEL is essential. Deep learning can facilitate this process by exploiting the massive routing data to find the optimal routes with pre-specified node(s). For this purpose, this paper proposes a new traffic management algorithm based on a deep RNN model. The algorithm predicts the alternative optimal routes, including the desired node (s), in an IoT osmotic environment. A collection of paths is generated using the minimum-distance maximum-bandwidth routing algorithm to create the dataset. The IoT osmotic environment consists of three main layers: the edge data center, Software-Defined Wide Area Network (SDWAN) infrastructure, and cloud data centers. The proposed traffic management algorithm is implemented in the controller of each layer. The simulation results showed that the osmotic approach increased the energy consumption of the edge devices and reduced the transaction time. Because the data is processed near the user, the flow size of the traffic, which is sent across the network, is reduced. The experimental results also showed that the model could achieve up to 94% accuracy. The model training and prediction time do not affect the application's total running time.

Low Power Wide Area Networks (LPWAN) are prominent option of wireless communication technology for dense Internet of Things (IoT) applications. With a growing population of resource-constrained IoT devices, meeting various communication requirements in dynamic and dense wireless networks has become a significant problem. Long Range (LoRa) was designed for LPWAN, which features long-distance communication, low-power consumption, and simultaneous transmission of multiple end devices. However, LoRa deployment in dense IoT networks facing several challenges like interference, scalability, security, and reliability. In recent times numerous techniques have been developed for interference mitigation. As these techniques used a range of methodologies to address the interference challenge, it is necessary to thoroughly analyze current solutions. This paper presents a comprehensive overview of the existing literature on interference issues and the solution approaches in LoRa. Initially, the challenges in dense IoT networks are discussed. We next present the fundamentals of LoRa and the classification of interference in the different categories. In each type of interference, the available methodologies are categorized based on their solution approaches. The analysis of different solution approaches is summarized by examining various issues of the LoRa network. Finally, the open issues and future directions related to the interference in the LoRa network are discussed.

Multi-server authentication, called single sign-on, enables users to easily access the necessary services from multiple servers with a single registration. Users generally hesitate to sign up individually with each service provider due to the challenge of remembering numerous credentials and trust. Through real-time consumer validation on a public channel, the multi-server authentication allows speedy access to services. Several multi-server authentication techniques have been introduced. However, the existing efficient and robust authenticated key exchange (AKE) schemes are not secure against quantum attacks as they are constructed on traditional cryptographic primitives, integer factorization, and discrete log problems. Due to the advent of scalable quantum computers, these schemes will be breakable as per the application of Shor’s algorithm. To address this issue, we propose a quantum secure ring learning with error based AKE mechanism for multi-server networking to establish a secure connection between users and multiple servers. Our suggested technique provides robust mutual authentication and fulfils the desired security attributes, as presented by the rigorous security analysis in the random oracle model. Additionally, we demonstrate a detailed comparative analysis with other AKE schemes currently in practice for multi-server environments to describe further our proposed scheme’s increased efficiency and quantum security.

This study presents a thorough evaluation of satellite downlink performance in a Free Space Optics (FSO) system with a Low-Density-Parity-Check (LDPC) based Multiple-Input-Multiple-Output (MIMO) configuration. Atmospheric turbulence is characterized using a generalized K-distribution and a negative exponential distribution, with specified parameters. Key performance metrics including Bit Error Rate (BER), outage probability, and accuracy are measured. To address pointing-errors (PEs) and atmospheric turbulence (AT), a novel decoding methodology for Non-Recursive Convolutional Polynomial Encoding (NRCPE)-based Pulse Position Modulation (PPM)-Gaussian Minimum Shift Keying (GMSK)-modulated FSO transmissions is introduced, leveraging Support Vector Machines (SVM). The study introduces a sophisticated Meijer-G function for MIMO statistical analysis and proposes a power series-based Probability Density Function (PDF) with non-recursive GMSK modulation. This PDF allows closed-form derivation of BER and Outage Probability expressions, showcasing improved MIMO link performance in the presence of PEs and AT. Simulations validate the models, offering insights into their effectiveness across varying turbulence levels. The findings assist FSO-MIMO designers in minimizing PEs,AT and achieving optimal results.Subsequently, authors perform a suppression to BER, Particularly, the optimum beam width factors for (left{ptimes q|1times 2times 2, & 2times 3right}), diversity degrees by a (left{ptimes q|1times 1right}), as a reference are 81.24%, 87.32%, and 89.61%, respectively, at ({varepsilon }_{nj}=4.02) and ({I}_{o}=10text{ dBm}).The proposed MIMO/FSO provides accuracy and an irradiances gain of 13.34dBm,i.e.,({varepsilon }_{nj}=5.03) at BER ({10}^{-9}) for downlink satellite transmission over SIMO and SISO FSO links. This study provides a comprehensive framework for optimizing FSO communication systems, considering atmospheric turbulence and pointing errors.