3D point clouds are data points defining the morphology of environments, and completion refers to the reconstruction of missing points. 6G Internet of Things (6G-IoT) connected with 3D mapping devices needs reliable, consistent, high-fidelity real-time point cloud completion for accurate environment registration. Trustworthy AI, modeled with dependable Deep Learning (DL), enables reliable and robust point completion with spatial-geometrical consistency for deployment with 6G-IoT devices. Although several DL-based completion techniques are integrated with 6G-IoT devices, they have reliability issues, limiting key trustworthy AI characteristics. This research focuses on the reliability and robustness aspects of trustworthy AI to propose Point-KAN, a dependable real-time 3D point cloud completion model for 6G IoT-connected 3D mapping devices. Point-KAN integrates multi-head attention and Kolmogorov-Arnold Networks (KAN) within the modules of Attention Enhanced-Embedded Feature Collector (AEFC) and KAN-Enhanced Feature Mapper (KEFM) for trustworthy point cloud completion. Empirical evaluations on the ShapeNet demonstrate the superiority of Point-KAN against state-of-the-art (SOTA). Results concrete Point-KAN’s evolution as a trustworthy AI framework ensures reliability and robustness for real-time deployment in 6G-IoT-connected devices, facilitating 3D environment mapping.

Ensuring clean water availability is critical for sustainability and health. Conventional water quality assessments are limited by manual sampling, poor temporal resolution, and centralized data processing. This study proposes HydroFedNet, a multisource water quality monitoring framework that uses Federated Learning (FL) to integrate diverse data sources, including LANDSAT satellite imagery, RGB pond images, and Internet of Things (IoT) sensor streams. The spatio-spectral transfer learning network (Spatio-Spectral TLNet), the color transfer learning network (Color TLNet) and the sensor convolutional neural network - temporal convolutional network (Sensor CNN - TCN) are fundamental models for HydroFedNet. Spatio-Spectral TLNet and Color TLNet leverage EfficientNetB3 for optimized, low-cost training, while Sensor CNN–TCN exploits improved temporal modeling. Models are trained locally and share weight updates with a central server, which builds a global model using the chosen FL strategy. FL strategies such as Federated Averaging (FedAvg), FL with Temporally Aware aggregation (FedLTA), and Federated Optimization (FedOpt) are evaluated with six objectives, including energy efficiency, fault tolerance, and handling of non-independent and identically distributed (non-IID) data. FedLTA surpasses the 90% accuracy across all three models with less communication overhead, whereas FedOpt effectively handles non-IID data. HydroFedNet allows an optimal selection of an intent-aware FL strategy, allowing robust, scalable, and efficient water quality monitoring across heterogeneous environments.

Human activity prediction is crucial for enabling intelligent smart home services, yet it is often hindered by the scarcity of high-quality, multi-dimensional datasets. Existing datasets are typically fragmented, capturing either long-term activity sequences or short-term device interactions, but rarely both in a unified manner. Traditional data collection methods are costly and time-consuming, while conventional simulation techniques struggle to generate diverse and logically coherent behavior sequences. To address these limitations, we propose SmartLLM, a novel Large Language Model (LLM)-based simulation framework for automated generation of multi-dimensional smart home datasets. SmartLLM simulates simulated agents with distinct profiles (e.g., old man, remote worker, holiday maker) performing daily activities within configurable home environments, generating temporally aligned sequences across Activity-Device-Sensor dimensions. We generate two months of simulated data for three user profiles and validated their plausibility through activity distribution visualization, statistical perplexity analysis, and case studies. Multi-dimensional feature validation experiments further demonstrate that our multi-dimensional data significantly enhances the accuracy of activity prediction models compared to using single-dimensional features. This work successfully addresses key bottlenecks in smart home data acquisition and provides a scalable, high-quality data foundation for advancing smart home algorithm research. The code is available at https://github.com/HuankeZheng/SmartLLM.

Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high specificity makes them essential for signal transduction, transport, and enzymatic activity. Traditional experimental methods for identifying protein-binding peptides are costly and time-consuming. Current sequence-based approaches often struggle with accuracy, focusing too narrowly on proximal sequence features and ignoring structural data. This study presents Deep-ProBind, a powerful prediction model designed to classify protein binding sites by integrating sequence and structural information. The proposed model employs a transformer and evolutionary-based attention mechanism, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Pseudo position specific scoring matrix -Discrete Wavelet Transform (PsePSSM -DWT) approach to encode peptides. The SHapley Additive exPlanations (SHAP) algorithm selects the optimal hybrid features, and a Deep Neural Network (DNN) is then used as the classification algorithm to predict protein-binding peptides. The performance of the proposed model was evaluated in comparison with traditional Machine Learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-ProBind achieved 92.67% accuracy with tenfold cross-validation on benchmark datasets and 93.62% accuracy on independent samples. The Deep-ProBind outperforms existing models by 3.57% on training data and 1.52% on independent tests. These results demonstrate Deep-ProBind's reliability and effectiveness, making it a valuable tool for researchers and a potential resource in pharmacological studies, where peptide binding plays a critical role in therapeutic development.

Vehicular edge computing (VEC) is an emerging computing paradigm that is rapidly advancing the development of the Internet of Vehicles (IoV). However, edge server has limited data storage capacity and computing resource, making it difficult to handle the massive offloading requests from IoV applications. Moreover, the mobility of vehicles and dynamic data traffic make it highly challenging to design optimal offloading and resource allocation strategies. To address the challenges mentioned above, we design a cloud-edge–vehicle hierarchical architecture for IoV task offloading, introducing a cloud server to assist in computation and alleviate the overload pressure on edge server. Considering the impact of vehicle mobility on task offloading, we propose a mobility detection method to predict which vehicles might leave the communication range of the base station, thereby preventing task offloading failures. Additionally, to achieve efficient task offloading and resource allocation in this complex IoV system, we propose a multiagent-reinforcement-learning-based vehicle proposal offloading algorithm (MVPOA). This algorithm enables vehicles to autonomously decide whether to process tasks locally or propose offloading to edge server. The edge server then decides whether to accept offloading requests based on task priority and sends rejected tasks to cloud server for processing, thereby maximizing the utilization of resources at each layer of the system. Simulation results demonstrate that MVPOA outperforms other baseline approaches in optimizing system delay and energy consumption.

To address the issue in zero-shot fault diagnosis (ZSFD) where fault attribute definitions (FADs) rely heavily on manual design and the accuracy of FAD depends on the expertise of developers, this article embedded expert knowledge into deep learning network, proposed a ZSFD method based on depth correlation feature extraction network (DCFEN), and automatically constructed FAD. Taking advantage of the periodic characteristics of bearing fault signals and the advantages of correlation analysis operation (CAO) in periodic signal analysis, DCFEN extracts the periodic characteristics of input signals in multiple dimensions by integrating CAO with deep learning. In addition, a soft-threshold-based feature percolation mechanism and FAD evaluation function are designed to generate the attributes related to bearing faults. The experimental results show that the FADs established by DCFEN are accurate, and the fault diagnosis performance of the proposed ZSFD is superior to the existing methods in unseen scenarios.

In real industrial scenarios, fault data are characterized by class imbalance, a major challenge for data-driven intelligent fault diagnosis. This article proposes a novel three-level regularization framework that integrates data, models, and labels to diagnose the imbalanced fault. First, a signal to image (S2I) module is introduced, which converts 1-D signals into 2-D images to conduct research and reduce model development workload and model-specific dependencies. Then, a regularization framework is proposed consisting of three submodules, inner local feature regularization (ILFR), outer local feature regularization (OLFR), and class balance margin loss (CBML), improving the faulty health state recognition accuracy without degrading the normal health state recognition performance. Finally, adequate experiments are carried out on four mechanical fault datasets. The results show that under the extremely imbalanced conditions, the proposed framework can improve the accuracy of the baseline method by 28%, 38%, and 25% on the three datasets (including PU, JNU, and UoC), respectively. Moreover, the proposed framework outperforms the SOTA method on the CWRU dataset, which validates the effectiveness and superiority of the proposed framework.

Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent addition of a chemical group to a specific protein sequence, profoundly impacts the functional diversity of proteins. Notably, identifying sumoylation sites has garnered significant attention due to their crucial roles in proteomic functions and their implications in various diseases, including Parkinson's and Alzheimer's. Despite the proposal of several computational models for identifying sumoylation sites, their effectiveness could be improved by the limitations associated with conventional learning methodologies. In this study, we introduce pseudo-position-specific scoring matrix (PsePSSM), a robust computational model designed for accurately predicting sumoylation sites using an optimized deep learning algorithm and efficient feature extraction techniques. Moreover, to streamline computational processes and eliminate irrelevant and noisy features, sequential forward selection using a support vector machine (SFS-SVM) is implemented to identify optimal features. The multi-layer Deep Neural Network (DNN) is a robust classifier, facilitating precise sumoylation site prediction. We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, employing various statistical metrics such as the Matthews Correlation Coefficient (MCC), accuracy, sensitivity, specificity, and the Area under the ROC Curve (AUC). Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction accuracy of 98.71%, surpassing existing models. The robustness and accuracy of the proposed model position it as a promising tool for advancing drug discovery and the diagnosis of diverse diseases linked to sumoylation sites.

BACKGROUND: RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier. RESULTS: The model was evaluated using two benchmark datasets, i.e., Full Transcript and Mature mRNA. Deep-m5U achieved overall accuracies of 91.47% and 95.86% for the Full Transcript and Mature mRNA datasets with 10-fold cross-validation, and for independent samples, the model attained 92.94% and 95.17% accuracy. CONCLUSION: Compared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.

In recent years, significant breakthroughs have been achieved in smart fabric technology within the healthcare sector, providing an impetus for the smart integration of wearable devices and equipment in medical applications. However, the tight coupling between fabric hardware devices and software solutions, tailored for various scenarios, has led to inefficient utilization of hardware resources and led to challenges for device upgrades and iterations. This paper focuses on the virtualization technology of smart fabric hardware resources and introduces a novel approach, termed “Big Fiber Slicing”. First, we outline the design of novel fiber devices customized for two major application scenarios: health monitoring and protection. Subsequently, we delve into the process of partitioning hardware resources into multiple “fiber slices” to better meet the unique requirements of various application scenarios and services. Next, we built a smart fabric platform, combined with 5 real multi-modal applications with different preferences, to verify the performance of the system when resources are limited and demand changes dynamically. Lastly, we explore the potential challenges that smart fabric technology may encounter in future application scenarios and provide insights into the future direction of this field.

The emergence of mobile edge computing (MEC) technology has deployed edge clouds with strong computing capabilities closer to Internet of Thing (IoT) devices, which can effectively meet the demands for computing power and latency. However, in addition to the stringent latency requirements, more and more emerging IoT applications also have higher standards for the freshness and timeliness of collected information. In order to ensure the freshness and high-information value in IoT system, we propose an Age of Information (AoI)-based optimization strategy for computation offloading and transmission scheduling. The strategy considers the AoI during the transmission phase and the execution phase, respectively, under the constraints of delay and remaining energy. Then, a joint optimization model is established based on the comprehensive benefits of AoI and computation rate. To address the strong coupling between the offloading decision and the transmission decision, the original optimization problem is divided into two stages. By the use of the deep deterministic policy gradient (DDPG) algorithm and the dueling double deep$Q$network (D3QN) algorithm, the solution is obtained in terms of the offloading decision and transmission scheduling decision, respectively. The proposed joint optimization strategy considers the impact of the transmission decision on the offloading decision and is adaptable to the dynamic changes in the channel connection between the edge cloud and the user due to user mobility. Experimental results show that compared with other offloading and transmission strategies, the proposed approach has higher overall system revenue and lower AoI.

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

The smartphone-based sensors (including accelerometer, proximity, and gyroscope sensors) are ubiquitous and emerging mobility data sources that could be used for transportation modes (i.e. bus, train, car, walking, and stationary) detection. One of the important challenges in transportation modes detection is to build an appropriate model that can extract useful data from the sensor outputs and that can reduce misclassifications. Several factors make the feature modeling difficult including inappropriate sampling frequency of input signals, wavering behavior of devices (e.g. the changing orientation of a device relative to the human body), and continuous base vibration causing similar sensor outputs for both stationary and non-stationary states and related threshold values of velocity. This paper proposes novel approaches to address these challenges by developing a robust transportation mode detector based on a convolution neural network (CNN). The proposed robust detector develops a feature modeling technique by novel feature fusion and cloning techniques. Pre-trained features are constructed using a separate vanilla neural network (VNN) framework to extract the distinguishing components from the original features that are combined with the original and cloned features. The proposed feature fusion technique is successfully able to overcome the noise from the base vibration and the minimal informative outputs from the lower sampling frequency. This enables the CNN to be trained with more efficient and discriminative features that result in a better classification model. The proposed approaches have been validated using a large volume of mobile sensor data based on the movements of travelers. Different types of mobile sensors have been used to collect data including accelerometer, proximity, and gyroscope. Experimental results demonstrate that the proposed approaches can improve the performance of the detection engine significantly over conventional techniques and reduces the misclassification rate.

Abstract Technology‐driven control measures could be an important tool to control the COVID‐19 pandemic crisis. This study evaluates the potentiality of emerging technologies such as 5G and 6G communication, Deep Learning (DL), big data, Internet of Things (IoT) etc. for controlling the COVID‐19 transmission and ensuring health safety. The healthcare sector is able to provide a unified, rapid, and incessant service to people by applying modern wireless connectivity tools like 5G or 6G during the COVID‐19 pandemic. This study has identified eight key areas of applications for the COVID‐19 management like infection detection; travel history analysis; identification of infection symptoms; early detection; transmission identification; access to information in lockdown; movement of people; and development of medical treatments and vaccines. Data have been collected from the respondents living in Sakaka city, KSA during pandemic. This study reveals that most people receive information from social networking sites, health professionals, and television without facing any challenges. The analysis shows that, during the COVID‐19 pandemic, about 42% of respondents felt tense always or most of the time in a day. Only 28.6% of respondents felt tense sometimes, whereas the remainder (about 30%) did not feel tense in relation to the COVID‐19 crisis. Satisfaction with COVID‐19‐related information is also positively correlated with COVID‐19‐related information literacy ( r = 0.53, p < 0.01) that is also positively correlated with depression or emotion, anxiety, and stress ( r = ‐0.15, p < 0.05). The long‐term pandemic is creating several psychological symptoms including anxiety, stress, and depression, irrespective of age.

In present day era use of emerging technologies has given a rise to the healthcare issues. Combination of sensors, the industrial Internet of Things (IIoT), and big data analytics to enhance patient care can lower the healthcare costs. This will enable the patients with more secure, affordable, and rising medical services. Besides problems, such as resource-constrained IoT stuff, identity theft attacks, and malicious insiders, there is a need to address smart healthcare in big data and artificial intelligence using edge computing services. To fix these concerns, we are proposing a software-defined networking (SDN)-based security compliance structure for smart healthcare load migration systems. Toward this end, the use of SDN-IIoT technology for effective and real-time protection against security attacks is being explored by researchers and professionals. In our proposed framework, there are three domains and each domain has one virtual machine and various OpenFlow virtual switches. This scenario helps in migrating the heavily loaded domain healthcare data to the lightly loaded domain to make the domain balanced and prevent the migration from happening any type of security attacks. The RYU SDN controller is used to test the simulations and effectiveness of the performance obtained in the mininet after capturing the OpenFlow packets in Wireshark. Secure data management is achieved through the proposed framework and proposed algorithm gives 80% accurate for all the fetched healthcare data packets.

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work.

Due to the outbreak of COVID-19, the Internet of Medical Things (IoMT) has enabled the doctors to remotely diagnose the patients, control the medical equipment, and monitor the quarantined patients through their digital devices. Security is a major concern in IoMT because the Internet of Things (IoT) nodes exchange sensitive information between virtual medical facilities over the vulnerable wireless medium. Hence, the virtual facilities must be protected from adversarial threats through secure sessions. This article proposes a lightweight and physically secure mutual authentication and secret key establishment protocol that uses physical unclonable functions (PUFs) to enable the network devices to verify the doctor's legitimacy (user) and sensor node before establishing a session key. PUF also protects the sensor nodes deployed in an unattended and hostile environment from tampering, cloning, and side-channel attacks. The proposed protocol exhibits all the necessary security properties required to protect the IoMT networks, like authentication, confidentiality, integrity, and anonymity. The formal AVISPA and informal security analysis demonstrate its robustness against attacks like impersonation, replay, a man in the middle, etc. The proposed protocol also consumes fewer resources to operate and is safe from physical attacks, making it more suitable for IoT-enabled medical network applications.

Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm (ACSA) and Deep Learning (DL)–based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson’s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson’s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.

The deployment of billions of machine‐to‐machine (M2M) devices is expected to have an enticing effluence in cellular networks. Data aggregation is an attractive scheme to decrease power consumption; however, it introduces longer delay to the aggregated data. M2M devices have disparate traffic types with different delay requirements. In this study, the author proposes a priority‐based data aggregation scheme at the M2M gateway that effectively maintains a good trade‐off between the power consumption and delay requirement. An analytical model that considers the idle and busy states behaviour of the aggregator using preemption M/G/1 queuing with priority disciplines is developed and analysed. The performance measures in terms of system delay and power consumption are derived and used to quantify the trade‐off between M2M delay sensitive traffic and low power consumption provisions. Based on the numerical and simulation results, the proposed scheme provides a good trade‐off between delay and power consumption.

Efficient radio access strategies are necessary and critical to manage an long‐term‐evolution (LTE) network system where machine‐to‐machine (M2M) devices and human‐to‐human (H2H) users coexist. In this study, the authors propose a cognitive‐based access strategy with a priority‐based queuing model that is designed for LTE with M2M/H2H coexistence, where the M2M communications have real‐time (M2M‐RT) and non‐real‐time (M2M‐NRT) traffic. Radio access gives the highest priority to H2H, while M2M‐RT has higher priority than M2M‐NRT. A continuous‐time Markov chain model is developed to evaluate the system performance in terms of service completion rate, blocking and forced termination probabilities, and mean queuing delay of the M2M traffic. In addition, resource utilisation by the M2M traffic is also evaluated. Analytical results reveal that, while protecting the H2H services, the proposed queuing model could increase the capacity of the M2M traffic network while decreasing the blocking probability. Additionally, allowing an interrupted M2M‐NRT to be inserted back into its queue can further decrease the forced termination rate. For these reasons, it can be concluded that the proposed model can be used to improve the system performance of multi‐channel M2M communication networks.

In this paper, we propose and investigate an efficient multichannel Medium Access Control (MAC) protocol for Cognitive Radio Ad Hoc Networks (CRAHNs). The proposed protocol supports Secondary Users' (SUs) dynamic utilization of the licensed channel of Primary Users (PUs) to enable the SUs' performance of a multichannel data transmission simultaneously and without interfering with each other. The proposed protocol can be cooperative or non-cooperative. The cooperative protocol scheme enables a SU to sense PU's presence independently in a multichannel architecture and then broadcasts the report to its neighbors in a proactive fashion. The performance of the cooperative and non-cooperative protocol schemes is simulated and compared. The performance demonstration focuses not only on the SU performance but also on the PU's performance with an Ad hoc On-demand Distance Vector (AODV) routing protocol. The simulation results show that the proposed protocol enhances the network utilizations in general and that the cooperative scheme significantly enhances the Packet Delivery Ratio (PDR) and decreases the end-to-end delay of SUs with high traffic PU connections.

A cognitive radio ad hoc network (CRAHN) is a group of autonomous users that work in ad hoc mode. This network enables a secondary user (SU) to use the frequency spectrum opportunistically when the primary user (PU) does not utilize it. CRAHNs can be deployed ubiquitously, and SUs of any CRAHN could co-exist when utilizing the spectrum. This situation leads to the fairness issue of spectrum resource sharing between SUs. CRAHN deployment is a large challenge in the medium access control (MAC) protocol design in which it must actively encourage each SU to operate for a high fairness. Most of the developed multichannel MAC protocols for CRAHN have not deliberated a fair resource sharing mechanism between co-existing SUs. Therefore, we propose an efficient multichannel (FMC) MAC protocol to address the dynamic availability of the spectrum and which orientates to the fairness in resource sharing. In this proposed protocol, the SU keeps the current backoff (KCB) counter when a PU appears to claim the intended channel. We compare the proposed fair protocol to the renewal backoff (RB) counter approach and the modified existing 802.11 protocol. The performance evaluation exhibits our protocol by providing a higher fairness than others while maintaining a high throughput.

Long Term Evolution (LTE) is a cellular technology developed to support diversity of data traffic at potentially high rates. 3GPP's LTE is defined by the standardization body's Release 8 and 9. A key mechanism in the LTE traffic handling is the packet scheduler, which is in charge of allocating resources to active flows in both the frequency and time dimension. The scheduling scheme used largely impacts the throughput of individual users as well as throughput of the cell. It is worthwhile to evaluate the throughput and fairness conditions for different scheduling schemes before the actual deployment of LTE scheduler. Our main contribution in this study is to evaluate and compare the performance of six scheduling schemes designed for LTE network in terms of user's throughput and fairness. The findings from our performance evaluation presented to draw conclusions on the performance of the six schedulers, and point out the strengths and weakness that are common to schedulers under study. This would help design the scheme of the scheduler at the eNodeB appropriately.

Long Term Evolution (LTE) packet scheduling plays an essential role as part of LTE's Radio Resource Management (RRM) to support successful implementation of new data services across the LTE network. The main contribution of this paper is to model and propose a novel scheduling scheme for LTE networks and to compare its performance with the performances of both the Best-CQI and RR Uplink schedulers. The Best-CQI scheduler is characterized by high data rates at cell level, but poor fairness. On the other hand, the round robin (RR) scheduler is characterized by low data rates at cell level, but good fairness. The main goal of our proposed scheme is to process these two conflicts terms in a better way. Performance modeling results presented in the paper show that the newly proposed scheduling scheme allows fair distribution of available LTE resources while at the same time keeps the system's capacity utilization as good as possible.

A Terrestrial Trunked Radio Enhanced Data Services (TEDS) networks can be securely partitioned so that different public safety agencies such as police, fire, ambulance, etc. can share a single network's infrastructure including base stations and main switches. Without an efficient radio resource management (RRM) policy, one agency can exhaust the capacity of others. This study proposes a radio resource allocation scheme to provide maximum system throughput and proportional fairness in accordance with agency capacity, which is shared through an adaptive resource allocation scheme. The performance analysis of this scheme in terms of throughput, bounded delay and queue size are obtained. Numerical and simulation results show that the proposed adaptive rate sharing RRM allocation scheme improves both system throughput and average delays.

Packet reservation multiple access (PRMA) protocol is an adaptation of the Reservation ALOHA protocol to the wireless environment. In this paper, a new modified version of the PRMA protocol called PRMA with adaptive permission probability is proposed, which represents a natural enhancement of PRMA. Unlike traditional PRMA, this proposed protocol allows adaptive permission probability to take place based on the instantaneous traffic loads in the system and slot availability provided by the base station. Therefore, instead of relying on a predefined permission probability to access the first available slot, each contending wireless terminal randomly selects an available slot to use from the current pool of idle slots based on an adaptive permission probability. The simulation results presented show quantitative improvements in the performance of proposed protocol with respect to the performance of traditional PRMA and PRMA with random contention.

Packet reservation multiple access (PRMA) can be considered as a merge of slotted ALOHA protocol and time division multiple access (TDMA) protocol. Independent terminals transmit packets to base station by contending to access an available time slots. A terminal that succeeds in reserving a certain time slot keeps on this reservation for transmitting its subsequent packets. Speech activity detection is used in PRMA to improve system capacity. In this work we propose a simpler contention mechanism that does not depend on a predetermined permission probability as in the original PRMA. In the new method, terminals select the contention slot uniformly from the pool of remaining free slots in the current frame. We evaluate the performance of the new contention mechanism in terms of various metrics including maximum number of carried voice calls and packet delays for a given acceptable drop rate of voice packets. We show that the new mechanism is superior to that of the original PRMA for loaded systems and is expected to be insensitive for traffic source burstiness.

The wide-band code division multiple access (WCDMA) based 3G and beyond cellular mobile wireless networks are expected to provide a diverse range of multimedia services to mobile users with guaranteed quality of service (QoS). The main contribution of this paper is to design and analyze a novel dynamic priority call admission control (DP-CAC) that can be able to achieve a better balance between system utilization and quality of service provisioning. More importantly, the analytical model of this system is valid for the real-time (RT) and non-real-time (NRT) calls having different bit- rate ( i.e. different bandwidth requirements), channel holding time , time out, and Eb/Norequirements. Its performance is compared with another two call admission control strategies, referred to as the complete partitioning CAC (CP-CAC) and the queuing priority CAC (QP-CAC). The DP-CAC analytical model can be used easily to derive the analytical model of the QP-CAC and to the CP-CAC. We present numerical examples to demonstrate the performance of the proposed CAC algorithms and we show that analytical and simulation results are in total agreement. Results also indicate the superiority of DP-CAC as it is able to achieve a better balance between system utilization and quality of service provisioning.

Sharing of 3G network infrastructure among operators offers an alternative solution to reducing the investment in the coverage phase of WCDMA. For radio access network (RAN) sharing method each operator has its own core network and only the RAN is shared. Without an efficient RRM, one operator can exhausts the capacity of others. This paper proposes and analyzes an efficient uplink-scheduling scheme in case of RAN sharing method. We refer to this new scheme as multi-operators code division generalized processor sharing scheme (M-CDGPS). It employs both adaptive rate allocation to maximize the resource utilization and GPS techniques to provide fair services for each operator. The performance analysis of this scheme is derived using the GPS performance model. Also, it is compared with static rate M-CDGPS scheme. Numerical and simulation results show that the proposed adaptive rate M-CDGPS scheduling scheme improves both system throughput and average delays.

For current and next 3G and beyond wideband code-division multiple access (WCDMA) cellular networks, sharing the radio access network has become an important issue for 3G mobile operators. Sharing of network infrastructure among operators offers an alternative solution to reducing the investment in the coverage phase of WCDMA. In radio access network (RAN) sharing method, which is our focus in this study, each operator has its own core network and only the RAN is shared. It implies that multiple operators fully share the same RAN. This paper tackles an uplink efficient scheduling to provide maximum system throughput and proportional fairness in accordance with operator capacity share through adaptive resource allocation scheme. We refer to this new scheme as multi-operators code division generalized processor sharing scheme (M-CDGPS). It employs both adaptive rate allocation to maximize the resource utilization and GPS techniques to provide fair services for each operator. The simulation results show that the proposed scheme improves both system utilization (throughput) and average delays.

This paper studies the uplink performance of multi-operator WCDMA cellular networks. Allocating the resource of a cell between different operators and the deployment of a QoS-aware uplink admission control for each operator is studied and simulated. The contribution of this work lies in granting higher priority to soft handoff calls. We introduce queuing techniques and the idea of 'soft guard channels', which is represented by reserving a small fraction of the cell load for the higher priority calls. The performance of this admission control with different scenarios is studied. The Grade of Service (GoS) is considered here to evaluate the system performance. Based on the simulation results we can conclude that this algorithm can reduce the dropped soft handoff calls and improve the over all system performance. Hence, this algorithm is able to satisfy the network operator by allowing more traffic (more revenue) and at the same time satisfying the user by guaranteeing lower handoff call drop.

The next wireless generation is calling for seamless integrated networks where users can roam freely among different 4G network operators. To accomplish this challenging objective, an effective resource sharing mechanism among 4G multi- operator networks should be implemented. This paper investigates a new method of dynamically prioritized resource allocation for multi-operator WCDMA networks. This method is based on assigning uplink shared resources among users from different operators based on their current priority level which is a function of their current assignments as well as their previous assignments. More, the system under consideration assumes both original calls besides handoff calls. However, to ensure higher priority to soft handoff calls, queuing and 'soft guard channels', are introduced. The performance of this proposed allocation method is studied using simulation. It has been examined against the fixed resource allocation method under different traffic load situations. The Grade of Service (GoS) and the expected carried traffic are considered here to evaluate the system performance. The simulation results have shown the outstanding performance of the dynamic method compared with the fixed one. Moreover, the dynamically prioritized resource allocation method has shown fairness in assigning resources among networks operators' customers.

In current and next 3G and beyond mobile wireless systems, sharing the radio access network has become an important issue for 3G mobile operators. Sharing network infrastructure amongst operators offers an alternative solution to reduce the investment in the coverage phase of WCDMA, allows increased coverage, reduces time to market, and allows earlier user acceptance for WCDMA and its related services. In this paper a novel radio resource management strategy known as adaptive partitioning with borrowing (APB) is proposed to cope with the implied new architectural changes. This strategy is devoted to achieve an efficient usage of the available pool of radio resources while satisfying the required quality of service (QoS) in heterogeneous traffic 3G wireless networks. Grade of service (GoS) and resources utilization are considered in this study to evaluate the network performance. Simulation results indicate that the proposed APB resource allocation provides higher resource utilization under all load conditions leading in turn to increased revenue. Moreover it provides the best balance between the system utilization and the required QoS