The rise of embedded AI has introduced critical challenges, including limited RAM, storage, and energy consumption. Emerging trends, such as TinyML, offer promising solutions to these constraints. In this work, we introduce TinyFLChain, a decentralized framework that combines TinyML, Federated Learning, and blockchain to enable privacy-preserving and secure collaborative learning on low-power IoT devices. By leveraging FL, computations are decentralized and data remains on local nodes, while blockchain ensures the integrity and traceability of model updates through smart contracts. Our framework utilizes blockchain smart contracts to secure the exchange of models updates, and implement a performance-based incentive mechanism. Clients who provide high-quality updates are rewarded at each round, while less active are penalized, encouraging sustained engagement. Using Raspberry Pi devices, we evaluated TinyFLChain on three benchmark datasets (MNIST, CIFAR-10, Digits). In centralised system, the accuracy reaches 100% on MNIST, 87% on CIFAR-10, and 97% on Digits. In decentralised system without blockchain, the performance is 99.25% on MNIST, 38.9% on CIFAR-10, and 10.72% on Digits. With TinyFLChain, accuracy significantly improves on CIFAR-10, reaching 62.09% for the best client, while remaining stable on MNIST. The results show that our system maintains high accuracy while improving security, transparency, and robustness against attacks.

A data-efficient intrusion detection framework tailored for Wireless Sensor Networks (WSNs) is proposed by leveraging active learning and metaheuristic optimization techniques. This framework addresses three major limitations of traditional models: data imbalance, inefficient hyperparameter tuning, and the need for large labeled datasets. To handle class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, particularly enhancing learning in complex regions of the feature space. For hyperparameter optimization, the Sandpiper Optimization (SO) algorithm is employed to fine-tune the regularization parameter of Logistic Regression (LR), leading to improved generalization. The issue of limited labeled data is tackled using Active Learning Uncertainty (ALU) and Entropy-based Active Learning (ALE), which query the most informative samples from the unlabeled pool, maximizing learning with minimal annotation effort. Simulation results show that LRALU, LRALE, and LRSO outperform traditional models with improvements of 18.18%, 19.48%, and 9.09% in accuracy; 9.30%, 1.16%, and 9.30% in precision; 18.18%, 19.48%, and 9.09% in recall; 12.20%, 8.54%, and 7.32% in F1-score; and 14.63%, 12.20%, and 9.76% in ROC-AUC, respectively. Additionally, log loss is reduced by 6.45%, 6.45%, and 35.48% for LRALU, LRALE, and LRSO, respectively. These results demonstrate that integrating intelligent sampling, active learning, and nature-inspired optimization significantly enhances intrusion detection performance in WSNs, providing an annotation-efficient solution for practical deployment.

Robust and intelligent intrusion detection is vital for securing Internet of Things (IoT) ecosystems against evolving cyber threats. However, existing systems face challenges such as class imbalance, suboptimal model performance due to manual hyperparameter tuning, and the high cost of labeled data. These limitations are addressed using the TON IoT dataset. To resolve data imbalance, the proximity weighted random affine shadow sampling generates boundary-focused synthetic samples that preserve class distribution. Further, to tackle suboptimal performance, bayesian optimization is applied to LeNet, resulting in LeBayesNet, which discovers the optimal configuration for high-accuracy detection. Next, to mitigate the scarcity of labeled data, MargiLeNet leverages marginal-based active learning, annotating the most uncertain samples to enhance model learning efficiently. Experimental results show that LeBayesNet and MargiLeNet improve performance over existing models by 7.69% and 3.30% in accuracy, 7.69% and 3.30% in F1-score, 7.69% and 3.30% in precision, 8.89% and 4.44% in the recall, 7.69% and 6.59% in receiver operating characteristic-area under the curve, 4.88% and 7.32% in matthews correlation coefficient, and 10.34% and 11.49% in precision-recall area under the curve, respectively. Both models significantly reduce hamming loss to 75% and 37.5%, indicating better generalization in complex and imbalanced scenarios. These advancements demonstrate the potential of optimization and active learning techniques in building accurate and adaptive intrusion detection systems for modern IoT networks.

This paper proposes a DL based framework using Multilayer Perceptron (MLP) tailored for multiclass DoS attack detection in Internet of Things (IoTs). After comparative data preprocessing, class imbalance is effectively mitigated using the proximity weighted random affine shadow oversampling method, enhancing minority class representation. Moreover, feature selection based on variance threshold is employed to streamline the input space and accelerate training. To reduce dependence on large labeled datasets, the approach incorporates Diversity-Based Sampling (DBS), an active learning strategy that focuses labeling efforts on diverse, informative samples. Furthermore, the proposed model’s performance is refined through metaheuristic-driven hyperparameter tuning using the Grasshopper Optimization Algorithm (GOA). This integrated methodology ensures more efficient learning, better generalization, and improved detection across varied attack scenarios in IoT settings. A comparative analysis with traditional machine deep learning and baseline models reveals that the proposed MLP+DBS and MLP+DBS+GOA model configurations consistently deliver superior performance across all evaluation metrics. Specifically, the proposed models achieve improvements of 5.7% and 9% in accuracy, 3.5% and 8.3% in precision, 5.7% and 9% in recall, 3.4% and 8.6% in F1-score, 2.3% and 3.3% in receiver operating characteristic area under the curve, and 3.3% and 6.6% in precision recall-area under the curve, respectively. These results demonstrate that the proposed models significantly outperform the existing approaches. This paper underscores the effectiveness of combining active learning and optimization for robust intrusion detection in resource-constrained IoT settings. The proposed models show strong potential for real-time deployment in smart environments requiring proactive and reliable security solutions.

With the rapid increase of connected devices, securing IoT networks against sophisticated cyber threats has become a critical research priority. However, effective intrusion detection in IoT environments is hindered by several core challenges, including severe class imbalance in network traffic, limited availability of annotated data for supervised learning, and the sensitivity of deep learning models to hyperparameter configurations. To address these limitations, we propose a data-driven DL framework that combines data balancing, active learning, and hyperparameter optimization. We employ the proximity-weighted synthetic oversampling technique to mitigate class imbalance by generating weighted synthetic samples. To reduce labeling overhead, we propose an active learning-based, Entropy-based Convolutional Neural Network (EntroConvNet), an intrusion detector for selective annotation of the most uncertain samples. Additionally, a novel Random Search Optimized Convolutional Neural Network (RS-ConvNet) is proposed to maximize detection performance. Experimental results on the TON IoT dataset show that EntroConvNet outperforms the baseline models with improvements of 3.45% in accuracy, 3.57% in precision, 1.10% in recall, 2.30% in F1-score, 3.19% in Area Under the Receiver Operating Characteristics Curve (AUC-ROC), 6.67% in Cohen’s Kappa and Mathews Correlation Coefficient (MCC), and 16.67% reduction in log loss and Hamming loss. Furthermore, RS-ConvNet achieves superior gains of 4.60% in accuracy, 5.95% in precision, 1.10% in recall, 3.45% in F1-score, 2.13% in AUC-ROC, 8% in Cohen’s Kappa and MCC, and also reduces log loss and Hamming loss by 20% and 25%, respectively. These results validate the proposed framework’s ability to deliver accurate, and annotation-efficient intrusion detection systems in dynamic IoT network environments.

The rise of ensemble learning has led to significant advancements in machine learning, providing improved accuracy and generalization by combining multiple models. However, the centralized nature of traditional ensemble systems introduces vulnerabilities, such as single points of failure and exposure to malicious attacks. To address these challenges, we introduce secure EnsembleChain, a novel decentralized system that leverages blockchain technology to enhance the security, robustness, and trustworthiness of ensemble learning. By leveraging blockchain's distributed ledger and consensus mechanisms, secure EnsembleChain mitigates risks associated with centralized systems, ensuring trust and security in model collaboration. Smart contracts automate malicious node detection, while an immutable record fosters transparency. In this paper, we focus only on the bagging ensemble technique. Experimental results show that secure EnsembleChain improves resilience against attacks and offers an efficient, scalable solution for decentralized AI collaboration, combining the strengths of AI and the blockchain technology.

The emergence of 6G networks further increases the complexity in managing mobility and load distribution in Internet of Vehicles (IoV) systems, requiring efficient load balancing among SDN controllers. This paper proposes a dynamic load management strategy based on Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to ensure balanced load distribution across SDN controllers. The proposed method enables adaptive decision-making and promotes optimal coordination between controllers, reducing overload while enhancing Quality of Service (QoS) for connected vehicles in IoV environments. We benchmark the performance of this method against a Deep Reinforcement Learning (DRL) approach employing Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and Trust Region Policy Optimization (TRPO), which are commonly used in network management. The results indicate that MARL combined with PPO outperforms these DRL approaches in terms of overload reduction, latency minimization, energy efficiency, and overall resource optimization in IoV mobility scenarios.

The rapid evolution of wireless communication technologies has driven significant growth in network traffic, making effective load management in 6G networks increasingly challenging. This influx of data and connected devices requires advanced strategies to prevent overload and ensure efficient operation within Software-Defined Network (SDN) architectures, where controllers manage network traffic and resources. Traditional load-balancing methods often fall short in adapting to the dynamic and high-density environment of 6G networks, leading to performance issues that compromise Quality of Service (QoS) and overall network efficiency. To address these challenges, this paper introduces a novel load-balancing strategy based on Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO). This approach enables coordinated decision-making across multiple SDN controllers, allowing each agent (controller) to dynamically adjust its load management strategy in response to real-time network demands. By leveraging MARL and PPO, our approach minimizes overload, ensuring smoother traffic distribution and enhancing QoS in high-load scenarios. For performance evaluation, we benchmark this method against a Deep Reinforcement Learning (DRL) approach that utilizes Deep Deterministic Policy Gradient (DDPG), a widely adopted technique in network management. Comparative analysis reveals that our MARL-PPO approach consistently outperforms DRL-DDPG, achieving superior load distribution, reduced overload incidents, and more efficient resource utilization,demonstrating its efficacy in managing the demands of next-generation 6G networks.

Healthcare environments, particularly operating rooms, face significant challenges in maintaining optimal conditions to prevent postoperative infections. These challenges include the absence of real-time monitoring systems, the limited availability of labeled medical data, and the difficulty of deploying intelligent solutions in resource-constrained settings. Additionally, there is a growing need to explore emerging technologies in IoT and TinyML to modernize healthcare monitoring and address existing gaps in system intelligence, scalability, and privacy. This paper presents a privacy-preserving intelligent system designed to predict the compliance of operating room environments with ISO standards by continuously monitoring key parameters such as temperature, humidity, air quality, and pressure. The system employs a decentralized Tiny ensemble learning architecture, utilizing ESP32 microcontrollers as edge learners and a Raspberry Pi 4 as the aggregator node. This approach improves prediction accuracy to 98.74 % while preserving data privacy through localized processing. To address data scarcity, recent synthetic data generation techniques from the literature are employed to create comprehensive datasets for training and validation.

The Internet of Things (IoT) has transformed modern technology by interconnecting physical devices to enable intelligent automation and real-time data exchange. However, securing IoT environments remains a critical challenge due to device heterogeneity, resource limitations, and vulnerabilities in lightweight communication protocols. Traditional Intrusion Detection Systems (IDS) often struggle with issues such as imbalanced datasets, suboptimal classification accuracy, difficulty in tuning hyperparameters, and a scarcity of labeled data. To address these limitations, we propose a novel IDS framework that integrates multiple advanced techniques. Initially, categorical labels are transformed using Label Encoding to facilitate effective model training. To mitigate data imbalance, the Localized Random Affine Shadowsampling (LoRAS) technique is applied, enhancing minority class representation. A Monte-Carlo Active Learning approach implemented on the DaNet architecture, termed MALD, is introduced to improve data efficiency by selectively querying the most informative samples. Additionally, we propose Elephant Herding Optimization applied to DaNet named EHODA to autonomously tune hyperparameters and maximize classification performance. Experimental results demonstrate that the proposed MALD and EHODA models significantly outperform conventional and state-of-the-art methods, achieving up to 93 % in Accuracy, Precision, Recall, and F1-Score, along with superior values in AUC-ROC of 0.98 and PR-AUC of 0.93. These findings affirm the effectiveness of our proposed framework for robust and adaptive intrusion detection in IoT environments.

Intrusion detection in Internet of Things (IoT) environments is a critical yet challenging task due to the heterogeneous nature of devices and the complex attack landscape. Traditional machine and deep learning models often suffer from limitations such as poor class balance, irrelevant or redundant features, suboptimal classification accuracy, ineffective hyperparameter tuning, and scarcity of labeled data. To address these challenges, we propose an enhanced intrusion detection system model. Variance threshold is applied to select informative features, while the proximity weighted random affine shadow sampling technique is used to balance the dataset effectively. Capsule Network (CapsNet) is employed for robust classification due to its ability to capture spatial hierarchies in data. To further optimize CapsNet, we implement the Reptile Search Algorithm (RSA), resulting in the Reptile-Optimized Capsule Network (ROC-Net). ROC-Net is further enhanced using Margin-Based Active Learning (MBAL), forming Marginal Active-learning with Reptile-optimized Capsule Network (MARCO-Net), which efficiently annotates the most uncertain samples from the unlabeled pool. Experimental results show that the proposed ROC-Net and MARCO-Net significantly outperform traditional models, achieving improvements of 8.75 % and 12.5 % in accuracy, 13.75 % and 11.25 % in precision, 1.19 % and 9.52 % in recall,$\mathbf{7. 4 1 \%}$and$\mathbf{1 1. 1 1 \%}$in F1-score,$\mathbf{1 1. 9 4 \%}$and$\mathbf{2 0. 9 0 \%}$in Matthews Correlation Coefficient, and 13.64 % and 22.73 % in Cohen's Kappa. Additionally, there is a reduction of 36.84 % and 52.63 % in hamming loss, and 75.86 % and 82.70 % in log loss, respectively. These findings demonstrate the effectiveness of the proposed system for accurate and efficient intrusion detection in IoT environments.

The rapid expansion of Internet of Things (IoT) networks has made them increasingly vulnerable to diverse cyber threats, necessitating the development of efficient Intrusion Detection Systems (IDS). Traditional models for IDS often face challenges such as data imbalance, scarcity of labeled samples, and suboptimal performance due to manual hyperparameter tuning. To address these issues, we propose a comprehensive IDS framework comprising three key components. First, we mitigate data imbalance using the proximity weighted synthetic oversampling technique, which enhances class distribution, followed by the use of Pointer Network (PtrNet) for classification due to its ability to model variable-length sequential data. Second, to handle the scarcity of labeled data, we introduce an entropy-based active learning strategy on PtrNet, termed Entropy-based Active Learning Pointer Network (EAL-PNet). Finally, we optimize model performance through harris hawk optimization applied to PtrNet, resulting in Hawk-Pointer Attention Network (HPA-Net). Experimental results demonstrate that the proposed models significantly outperform traditional approaches. EAL-PNet achieves a performance improvement of 9.30% in accuracy, 8.14% in F1-score, 8.14% in precision, 9.30% in recall, 3.16% in Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), 13.92% in Matthews Correlation Coefficient (MCC) and Cohen's Kappa, and 45.71% reduction in log loss. Similarly, HPA-Net shows a 10.47% gain in accuracy, 10.47% in F1-score, 9.30% in precision, 10.47% in recall, 2.11% in ROC-AUC, 15.19% in MCC and Cohen's Kappa, and 51.43% decrease in log loss. These findings validate the effectiveness of the proposed framework in enhancing intrusion detection for IoT environments.

The increasing complexity and interconnectivity of Internet of Things (IoT) ecosystems have heightened the need for robust and intelligent intrusion detection mechanisms. However, the development of effective detection models is impeded by challenges such as imbalanced data distributions, limited availability of labeled samples, and the difficulty of tuning deep learning architectures to accommodate diverse threat patterns. In response to these challenges, this paper introduces two novel DenseNet-based frameworks, DN-UBS and DN-GBO, for advanced IoT intrusion detection. The proposed approach begins by applying a variance threshold technique on RT-IoT2022 dataset, to eliminate low-variance features, followed by synthetic minority oversampling technique to alleviate class imbalance and enhance minority class representation. DN-UBS integrates an uncertainty-based sampling strategy to iteratively select the most ambiguous instances for annotation, reducing labeling effort while improving model discriminability. In contrast, DN-GBO incorporates a gradient-based hyperparameter optimization using the hyperband strategy, allowing for automatic adjustment of network depth, learning rate, and regularization parameters. The DN-GBO achieved superior detection performance with an improvement of 7% in accuracy, 4% in F1-score, 12.8% in precision, 6 % in recall, 3 % in Receiver Operating CharacteristicArea Under the Curve (ROC-AUC), and 17.6 % in Matthews Correlation Coefficient (MCC). Similarly, DN-UBS also delivered high efficacy with an improvement of 5.3 % in accuracy, 3 % in F1-score, 3% in precision, 4.6% in recall, 4% in ROC-AUC, and 10.1 % in MCC, while minimizing reliance on labeled data. These findings highlight the effectiveness of the proposed models in delivering scalable, adaptive, and data-efficient solutions for securing IoT infrastructures against intrusive threats.

Finding suitable rental properties for new businesses can be a intimidating challenge due to the competitive real estate market and limited options tailored to their specific needs. Addressing this challenge, we propose a management system that facilitates the connection between upcoming business owners and landlords, utilizing blockchain technology to streamline the rental process. Through our blockchain based system, tenants can efficiently connect with landlords and access comprehensive transaction records. By leveraging blockchain, commercial real estate transactions are expedited with minimal paperwork, enabling paperless asset transfers and electronic task completion. Moreover, blockchain enhances privacy in information exchange, accelerates landlord payments, and bolsters portfolio-wide due diligence. Consequently, our system enhances productivity while saving both time and money for all parties involved, ultimately improving the quality of service and user experience.

Blockchain-based smart contracts, while transformative, pose privacy concerns due to Ethereum's transparency. To address this, we present Safe Smart Contracts (SafeSC), leveraging zk-SNARKs for privacy without compromising Ethereum's transparency. SafeSC's Python tool facilitates contract understanding and verification without accessing the source code. Our paper explores privacy preservation techniques, favoring Zero-Knowledge Proofs (ZKPs). SafeSC employs zk-SNARKs and Groth-16, achieving a delicate balance between transparency and privacy in smart contract development. The tool’s design, covering architecture, assumptions, data flow, and zero-knowledge proof workflow, marks a step toward secure smart contract solutions. We advocate for continued exploration and refinement to enhance blockchain technologies.

Due to the rapid expansion of Internet of Things (IoT) devices, massive amounts of data are generated daily, and the number of IoT devices will continue to increase. Cloud computing provides storage, processing, and analysis services for managing such large amounts of data. However, there are limitations concerning the network connectivity between the cloud and IoT devices. Nonetheless, the cloud platform has evident concerns and limitations in terms of responsiveness, latency, and overall performance for processing and accessing IoT traffic data. This process takes time, especially for large datasets, as there is back-and-forth communication between the client and the cloud. This increase in latency and energy consumption is unacceptable for real-time applications such as online gaming, smart health, video surveillance, etc. Hence the possibility of using Edge/Fog computing for optimal resource allocation. Our work aims to propose a system model for surveillance video analysis in an IoT 6G environment based on enhanced cloud architecture, Edge/Fog computing while developing an analytical model to satisfy QoS requirements (latency, energy consumption, etc.).

Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, federated learning framework faces several key challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose the Personal-ized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe). Our approach combines the strengths of Moreau En-velopes to handle optimization problems and asynchronous weight updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with the baseline PFedMe method. Our experiments demonstrate that the proposed APFedMe outperforms other meth-ods in terms of convergence speed and communication efficiency. Overall, our work contributes to developing more effective and efficient federated learning methods that can be applied in various real-world scenarios.

Federated Learning (FL) has emerged as a powerful approach to facilitate the construction of centralized models without compromising the data privacy of multiple participants. However, conventional FL methodologies do not address system heterogeneity where each participant needs to independently design its own model, a prevalent requirement in Internet of Things (IoT) applications due to the heterogeneous nature of tasks and data. Knowledge Distillation-based FL algorithms tackle this limitation by exchanging soft labels instead of model weights, thus giving each client the ability to independently design its local model architecture. While FL is inherently private, studies have indicated that exploiting gradients for a few iterations can reveal sensitive training data. To protect against privacy attacks, FL algorithms employ Differential Privacy (DP) to guarantee privacy protection, which can be applied using Local Differential Privacy (LDP). In this paper, we elaborate on preserving clients' training data privacy in KD (Knowledge Distillation)-based FL using DP, providing both privacy and flexibility. We provide theoretical analysis to extend the privacy guarantee to exchanged updates. Experimental analysis is performed utilizing Human Activity Recognition (HAR) datasets with different modalities. The results obtained demonstrate the capacity of KD-based FL to maintain a robust utility-privacy balance. Furthermore, for the same DP protection level, the utility of models trained on images was severely reduced across all FL algorithms. This suggests that the modality and complexity of a dataset are important factors for shaping the utility-privacy tradeoff of DP.

This paper proposes an innovative low-cost, dualpolarized, fully printable, and highly-dense chipless radio frequency identification (RFID) tag. The tag has a compact triangular design and covers a total surface area of 425.57mm2. The suggested tag has an encoding capacity of 28 bits, meaning that 228different objects/items can be labeled. It is configured in a way that each slot is of a different length so that it can resonate at different frequencies. The tag is realized on a flexible/bendable substrate, i.e., RT/Duroid 5880, alongside copper as a radiating material. The tag efficiently utilizes the frequency band, i.e., 4.212.5GHz. Due to the presented tag's exclusive features, i.e., flexibility, compact size, and high capacity, it can be easily deployed in various smart Internet of Things (IoT) based applications.

This research article presents a novel dual-polarized, miniaturized and fully-printable chipless RFID tag. The tag is designed using H-polarized and V-polarized slotted resonant elements in a nested loop fashion. The formulated tag has a data capacity of 29-bits over a compact physical footprint of 15 x 16 mm2, A multi-substrate performance of the tag has been examined for three different substrates ranging from rigid to bendable i.e. Rogers RT/duroid® 5870, Taconic TLX-0, and Rogers RT/duroid® 5880. The tag offers a high overall bit density of 12.08 bits/cm2. Obtained results manifest the suitability of the proposed tag to be deployed in various IoT-based applications.

We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods” while the FRL performs best among all.

Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets.

The idea of collectibles has been around for a while and is well-known in popular culture. To add, a product’s appeal to the general public has been shown to rise when it is distinctive or sold in limited quantities. Therefore, an increase in fraud and counterfeiting for those specific products is unavoidable. One of these products is the Youtooz plushies and figurines, which are sold for a limited time before they are discontinued [6]. It is inevitable to find duplicates of a product or for others to try to make money off of it if. Some may likely deceive and manipulate trusting customers to trick them or cheat them out of the real product. A blockchain approach would be beneficial, in large part, because it records all the different events pertaining to each unique figurine and greatly reduces, if not eliminate, the risk of counterfeit, fraud, or incorrect information surrounding the product. By building confidence between all parties involved in the process and guaranteeing that each client is purchasing from a reputable supplier, the solution will safeguard the ordering process.

This paper introduces a simple Wireless Sensor Network (WSN)-based framework that uses Proteus sensors of Libelium Smart Water Xtreme IoT platform to detect e-Coli in wastewater, uses an efficient priority-based routing protocol for timely notification of the detection e-Coli at the COVID-19 detection lab to identify the existence of SARS-CoV-2, the virus that currently causes the COVID-19 pandemic. These sensors use fluorescence to monitor coli forms in real-time, determining if the water is polluted and contaminated with SARS-CoV-2 once tested at the lab. The framework also includes an efficient Packet Priority Routing Protocol (PPRP) that prioritizes data packets transmission related to detecting COVID-19 over other data packets for timely and emergency measures. Simulation results show that the proposed PPRP routing protocol is more efficient in terms of end-to-end data transmission delay and network energy consumption than existing LEACH and CPWS protocols.

The perfume industry’s complex and multi-faceted supply chain is challenged by issues of transparency, accountability, and efficiency. To address these issues, this paper proposes a ScentTrack blockchain-based solution for the perfume manufactory, that provides benefits such as improved product quality, supply chain efficiency, and increased trust among supply chain partners. The system was created in collaboration with a perfume manufacturer based in Saudi Arabia, and it incorporates multiple chaincodes distributed across multiple channels. These chaincodes serve to monitor and track the various stages of the perfume supply chain. The proposed solution leverages the automation capabilities of blockchain to enhance trust and accountability among participants. To facilitate this, each participant will have access to their own application portal, enabling them to manage and monitor their respective supply chain. Moreover, the system includes extensive event notifications that keep all stakeholders informed about the multiple steps within each stage of the supply chain they are interested in.Based on the findings, the study suggests that a blockchain system for perfume manufacturing supply chains could transform the industry by establishing a more efficient, secure, and transparent network, which lowerultimately lowering costs while improving quality. Additionally, the proposed solution could be adapted for other supply chains, although further research is required to assess its applicability in different industries. The implementation of this blockchain-based solution in the perfume industry could offer a practical demonstration of how blockchain technology can address the challenges of supply chain management.

Digital twin (DT) provides a real-time digital representation of electric device state for energy dispatching and control (EDC) model training in power system. However, the large age of information (AoI) deteriorates the consistency of DT and the precision of EDC model. In this paper, we investigate the global loss function minimization problem underthe long-term AoI constraint through coordinated resource management. The optimization problem is decoupled based on telescoping sum and Lyapunov optimization, and solved by the proposed AoI-aware DT-assisted intelligent resource management algorithm named AoI-DT. AoI-DT achievesa balanced tradeoff between AoI guarantee and EDC model precision through device scheduling and channel allocation. Simulation results verify the superior performance of AoI-DT in terms of global loss function and AoI compared withtwo state-of-the-art algorithms.

As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical.

The recent COVID-19 (novel coronavirus disease) pandemic induced a deep polarization among regional as well as global communities. The sentiments regarding the pandemic and its impact on lifestyle and economy, often expressed via social networks, are regarded as critical metrics for capturing such polarization and formulating appropriate intervention by the relevant authorities. While there exist a myriad of Natural Language Processing (NLP) models for mining social media data, we demonstrate the shortcomings of the individual models in this paper, and explore how to improve the COVID-19 sentiment analysis in social media network data via two hybrid predictive models based on a Long-Short-Term-Memory (LSTM)-based autoencoder and a Convolutional Neural Network (CNN) model coupled with a bi-directional LSTM. Through extensive experiments on the recently acquired Twitter dataset, we compare the COVID-19 sentiments exhibited in the USA and Canada using our proposed hybrid predictive models and demonstrate their superiority over individual Artificial Intelligence (AI) models.

In this paper, we propose a blockchain patient electronic health record system called Etmean (اِطْمَئِن) to handle medical records sharing and transfer in healthcare centers. Our main goals are to help ease the challenges faced in the healthcare sector: multiple records for one patient, difficulty prioritizing emergency rooms (ER) cases, and lack of medical records security. Blockchain is a promising, trusted technology that has a digital ledger of transactions supported by a network of computers in a form that makes it hard to hack or modify. Our implemented system fulfills privacy, security, and accessibility requirements in a healthcare sector such as hospitals and clinics.

As a consequence of the global pandemic, many restrictions and rules were enforced. One predicament was the travel restrictions and requirements put into place with regard to vaccinations. Countries worldwide now require people to be vaccinated upon entry. The process of validating vaccine doses requires lots of paperwork and is inefficient. Blockchain is an uprising technology that is secure and fast at carrying out transactions. We propose implementing vaccine dose verifications between countries through vaccine certificates using Blockchain as an effective solution. The need for a common shared database, avoiding a trusted third party to administrate the network, having several countries involved, ensuring privacy and security, and accountability logs make Blockchain needed in this scenario. Digital vaccine certificates are very sensitive information that must be kept private and secure but accessible to several entities. Blockchain ensures the aforementioned requirements are met while preserving the integrity of the VDCs. This paper describes blockchain technology and its application in digital vaccine certificates.

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme.

In this paper, we employ stochastic geometry to analyze ground-to-unmanned aerial vehicle (UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with uplink transmissions, where the distribution of UEs follows the Poisson Cluster process (PCP) and each UAV is dedicated to a specific cluster. In particular, we characterize the Laplace transform of the interference caused by multiple UEs in terms of the distribution of UEs as well as the transmission probability of each UE. We then derive analytical expressions of the successful transmission probability. We next conduct a comprehensive numerical analysis with consideration of different system parameters. The results show that four factors (i.e., the geographical surroundings, the transmission powers, the Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, and the UAV height) have main influences on ground-to-UAV communications.

COVID-19 (Coronavirus) is a very contagious infection that has drawn the world public’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system's design is a challenging problem. In this paper, motivated by the outstanding performance of deep learning (DL) in many solutions, we used DL based approach for computer-aided design (CAD) of the COVID-19 detection system. For this purpose, we used a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness by using two benchmark publicly available datasets (Covid-Chestxray-Dataset and Chex-Pert Dataset). The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a 10-fold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed system are high, and it performs better than the existing state-of-the-art systems. The proposed system based on DL will be helpful in medical diagnosis research and health care systems.

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

Multihop device-to-device (D2D)-enabled relay networks are envisaged to be utilized by the Internet of Things (IoT) and massive machine-type communication (mMTC) traffic for the purpose of offloading data in beyond fifth-generation (B5G) networks. The emerging challenge of spectrum scarcity and overloading of cellular base stations can be addressed using such relay nodes in terms of spectrum and energy efficiency. In order to improve spectral efficiency, in this article, we intend to employ several frequency bands concurrently in the relay node rather than the traditional concept of specifying one channel on a specific band at a time. A deep learning-based predictive channel selection method is leveraged to unravel the potential challenges associated with the dynamic channel conditions in the multiband relay networks. For predicting the most appropriate channel based on its quality, signal-to-interference-plus-noise-ratio (SINR) is adopted as the metric, which is predicted by the proposed convolutional neural network (CNN) model. The best modulation and coding rates of the predicted band are attained in order to transmit the packets received from the source or previous relay node to the successive relay node/destination. Two proactive channel assignment strategies, referred to as controlled and smart prediction schemes, are employed to exhibit the performance of the shallow and deep-CNN models. The proposed model is evaluated on multiple publicly available data sets from diverse network systems and compared with several machine/deep learning methods. Our proposal leads to encouraging results for proactively predicting the conditions of the channels and choosing the most suitable ones in multiband relay systems.

Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV.

Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%.

In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.

In this paper, we deal with some major energy issues related to the charging of vehicles in vehicular network. The exponential increase of Electric Vehicles (EVs) has led to the more complex problems. In general, there are two major issues related to th EVs. First, its difficult to find a nearest charging station with required energy. Second, how much energy is needed to reach charging station from current location. In traditional systems, the energy trading between charging station and EVs is not secured due to centralized girds. To deal with this problem, a consortium blockchain based secure energy trading system is proposed. Blockchain is used for secure energy trading with moderate cost. The main purpose of the proposed system is resource reduction and find out the present state of charging stations. Simulations and results show that the proposed schemes outperform the conventional schemes in terms of minimizing the charging cost of battery and expenses of EVs.

An Intelligent Transportation System (ITS) aims to achieve efficiency of traffic by minimizing its problems, such as traffic congestion, road accidents, etc. It is not only limited to control traffic congestion but also enhances the safety and comfort of the commuters. For road traffic safety and efficient infrastructure usage, vehicles need to communicate with each other to disseminate information related to traffic. However, vehicles cannot directly communicate with each other and other infrastructure because of privacy and security concerns. In the proposed work, blockchain is implemented on Road Side Units (RSUs) that are used to provide reliable communication between vehicles. Furthermore, cloud and edge servers are used to tackle the storage issue. We proposed a Transactions Filtering Pattern Matching Scheme (TFPMS) to filter the transactions before sending them to the blockchain network. In this way, it saves storage space and reduces computational overhead of blockchain. Moreover, we are exploiting consortium blockchain to implement our proposed scheme. Simulations are performed based on the number of transactions and cost to achieve high-quality data sharing between vehicles, which result in a reduction in storage overhead as compared to the existing schemes.

In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness.

Traditionally, the Internet of Things (IoT) devices, deployed on the ultra-edge of the network, lack computation, and energy resources. In this paper, we press on the need to go beyond the realms of traditional edge computing (e.g., limited to user-smartphones) and investigate how to incorporate intelligence into the ultra-edge IoT sensors. Among numerous use-cases, we select a mobile Health (mHealth) scenario where we conceptualize a smart IoT sensor to collect and intelligently process single-channel Electrocardiogram (ECG) signals to detect arrhythmia, a heart-condition often associated with morbidity and even mortality. The arrhythmia detection can be regarded as a non-linear Delay Differential Equation (DDE) time-series analysis problem, and the conventional solutions to this problem are not suitable for integration with IoT sensors due to rigorous pre-processing steps. As a solution, a Convolutional Neural Network (CNN)-based, lightweight Arrhythmia classification system is proposed in the paper without the need for noise-filtering and feature extraction steps. Four classes of the heartbeats are considered to comply with the ANSI/AAMI EC57:1998 standard. The proposed system's performances and generalization potential are assessed using three datasets from PhysioNet trained on a deep learning workstation and then transferred to virtualized micro-controllers connected to IoT sensors. The proposed deep learning model exhibits encouraging performance (accuracy 95.27%) in heartbeat classification. Experimental and numerical results demonstrate that the proposed deep learning technique outperforms conventional DDE-based optimization techniques and machine learning techniques such as K-Nearest Neighbor (KNN), and random forest (RF).

In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques.

Secure software engineering entails that security concerns needs to be considered from the early phases of development, as early as the requirements engineering phase. Misuse cases is a well-known security analysis and specifications techniques, based on the popular use case modeling technique, that takes place in the requirements engineering phase. Similar to use case modeling, misuse case modellers are prone to committing modeling mistakes and applying antipatterns. As a result, misuse case models need to be analysed to determine if they contain fallacious design decisions. Changes, known as refactoring, to the misuse case diagrams are then required to remedy any design issues and such changes which would normally be manually applied. However, manual application of such changes in misuse case models are prone to human error, further compounding the design issues in a given misuse case model. To this end, this paper presents a model transformation approach to systematically apply changes to misuse case models. A case study related to a book store is presented to illustrate the application and feasibility of the approach.

A rapid development has been seen in the Vehicular ad hoc networks (VANETs) because of their applicability and significance in the fields of traffic management, road monitoring and safety, infotainment, and on-demand services. Route planning in vehicular networks based on efficient collection of real-time data can effectively mitigate traffic congestion problems in urban areas. Furthermore, real-time data is shared by using an effective sharing mechanism to avoid redundancy of the collected information. However, dynamic route replanning and effective sharing mechanisms based on real-time data are still challenging problems. Therefore, based on the aforementioned constraints, this paper describes a route discovery technique that uses real time data collected from various vehicles using the Internet of Things. The proposed scheme is based on the novel data dissemination technique for information sharing among the roadside units. RTRD is comprised of VANETs, vehicular traffic servers, and a 5G-based cellular system of public transportation. By considering the traffic congestion in urban areas, the optimal path is calculated to re-plan routes based on the k shortest path algorithm, and a load balancing technique is adopted to avoid further congestion.

Continuous increase in urban population causes enormous pressure on the limited resources of cities including transport, energy, water, housing, public services, and others. Hence, the need to plan and develop smart cities-based solutions for enhanced urban governance is becoming more evident. The technological framework for smart cities services connect hundreds of data collecting device networks (e.g., sensor networks) with central server or cloud through Internet. Scheduling of these enormous data (or big data) both at the device networks and central server/cloud is significantly important to facilitate timely and priority-based smart city services. This paper introduces a cloud- based big data framework and Priority-based, Dynamic and Time sensitive data processing and Scheduling (PDTS) approach that works both for the device networks and cloud-based big data framework. Simulation results demonstrate that the proposed PDTS approach reduces the number of data transmission and data processing time as opposed to data scheduling only in cloud-based big data framework.

In the communication scheme for Internet of Things (IoT) systems, reliability and low power are essential in ensuring the connection of a large lot of terminals. Multi-Hop based cooperative communication is a promising tool towards achieving that goals. Moreover, in many works of research, the channel fading is limited to multi-paths fading modeled by Rayleigh or Nakagami-m distributions, or a combination of the two with pathloss fading. In this paper, we have explored the Generalized K fading which offers a more general distribution encompassing multi-paths fading, pathloss fading and shadowing. Thus, we investigate the Decode and Forward relaying strategy over Generalized K channels for multi-hops IoT networks. We derive analytical expressions of the average symbol error probability and the outage probability. Finally, we show the validity of the theoretical results by simulation.

The problem of managing congestion and overcrowding in a critical situation has largely been studied over the last decades. The problem is how to safely direct pedestrians with suitable velocity to the nearest floor in order to avoid the bottleneck. For example, in Hajj there is a ritual practice where millions of pilgrims arrive in a certain area and we should indicate the nearest and lowest congestion path that leads to the ritual. In this paper, we address this problem during the Hajj season and propose a crowd management service based on the game theory model. We modeled the problem as a Mean-Field-Game (MFG) where the solution is a system composed of a Backward Hamilton-Jacobi-Bellman equation and a Forward Transport (Kolmogorov) equation. Simulation results show the efficiency of the MFG solution on the total time out of the pilgrims to reach the ritual.

Sensors, RFID, Wi-Fi, and other technologies embedded with devices and items such as home appliances, vehicles, and grocery items improve the quality of life by exchanging information among each other under a common network platform that defines the emerging future of the Internet, also known as Internet of Things (IoT). Sensors or Wireless Sensor Networks (WSNs) consist of an integral part of IoT since sensors can be controlled by end users and data can be transmitted to distant sites through Internet. Moreover, thousands of sensors and similar devices poses a great challenge in routing that raises the need for zone-based (or cluster) based routing protocol. Most existing routing protocols are not designed considering the dense architecture of IoT. It is a great challenge to render these algorithms adaptive to the changing requirements of sensor-based IoT applications since their routing policies are mostly predetermined. Thus, they are not energy efficient and fault tolerant for such mobility centric IoT. In this article, we provide a brief introduction to IoT with the current state-of- the-art research and classify routing protocols based on several factors. We then introduce a Multiple Base station and Packet Priority-based Clustering scheme (MBPP) for IoT and evaluate its performance through simulations.

The next generation of mobile broadband technologies, namely the Fifth Generation (5G), will include an integration of legacy technologies like the Long Term Evolution (LTE), the Worldwide Interoperability for Microwave Access (WìMaX), the Wireless Fidelity (Wi-Fi), etc. However, Quality of Service (QoS) details are not fully defined for the integrated networks, and the subject is left open to be explored by the vendors. Provisioning of QoS guarantees for a heterogeneous network is contingent upon the execution of an appropriate mapping among the QoS classes of the participating technologies. LTE and WiMAX are two flourishing technologies in the Fourth Generation (4G) and will remain so in 5G. Therefore, an adaptive QoS class mapping strategy is proposed for the incorporated LTE-WiMAX 5G Network. Simulation results revealed a satisfying performance in terms of QoS measures such as response time and Packet Loss Rate (PLR) for almost all types of traffic.

In the Era of the Internet of Things (IoT) the validity of sensing coverage is of utmost importance as it affects the reliability of sensing services. The presence of anchor misplacement poses a challenge on the validity of sensing coverage. This kind of challenge has generally been overlooked in sensing coverage research. In this paper, we investigate the sensing validity under several scenarios of anchor misplacement with different displacement values. We also investigate the error components of measurement and anchor misplacement, and their resultant impact on sensing validity. We model the problem using computational geometry. We provide a theoretical approach to test such validity. Then we propose an algorithm that implements the suggested approach. Our results are further validated through extensive simulation. This study shows interesting results that could be used to mitigate the negative impact anchor misplacement has on sensing coverage.

Hajj is a ritual practice where millions of pilgrims arrive in a limited area and required to transfer through dedicated paths to several locations. Congestions and overcrowded have been one of the biggest challenges to the authorities, and hence, encourages researchers to proposed practical solutions to preventing congestions and their consequences from happening. In this paper, we propose an Expert Crowd Monitoring and Management Framework as a substitution to the current manual crowd monitoring and management system. The framework was designed to be proactive in predicting potential problems accurately by utilizing smart monitoring of each path of rituals locations' paths. The framework consists of Pilgrim Sensor Units, Data Collection Units, Database Unit, Expert Crowd Monitoring and Management Unit, Dashboard Units and Notification Unit.

Small cell networks are considered one of the key technologies in the next generation wireless networks. One of the main challenges in this area of research is how to tackle the problems of interference and resource allocation, especially for the hyper dense deployment of small cells. In this paper, we study the problem of cooperative interference mitigation in a small cell network. In particular, we develop a novel algorithm for encouraging small cell base stations to cooperate with their neighbors. Considering coalitional structure, small cells can mitigate the co-tier interference within a coalition and thus increase the system capacity. A cooperative approach among the adjacent small cells is formulated as a coalitional structure generation with characteristic forms. Meanwhile, during the coalition being formed, the associated frequency resources are also assigned. In contrast to the previous small cells' cooperative algorithms, a new feature for the proposed algorithm requires the computation for pairs of players. The proposed algorithm will raise the efficiency and is particularly applicable to the mass small cells' deployment scenario.

In 5G, heterogeneous broadband networks will emerge to a common set of objectives to offer the end users higher capacity, robustness, security, QoS, and more in a unified and converged manner. In order to provide end-to-end QoS support to application services over the heterogeneous networks, a common QoS framework is proposed in this paper. The framework consists of a two-level scheduling scheme: a Class-Based Weighted Fair Queuing (CBWFQ) discipline and a Rate-Controlled Priority Queuing (RCPQ) discipline. Mapping of integrated QoS classes among heterogeneous networks was defined using a mapping table. Simulation results show seamless QoS deliverables over a vertical hand-off between different types of technologies such as LTE, WiMAX, WiFi, and a wireline IP-based DiffServ-based network. The results also show that the proposed solution improves performance for both Real Time (RT) and Non-Real Time (NRT) applications.

Many authors have considered the performance analysis of M-QAM modulated signals over the Generalized-K channel but only at High Signal-to-Noise Ratio (SNR) range. Furthermore, in the cooperative context, only Amplify and Forward protocol has been considered for this kind of fading channel, and it was limited to high SNR range for M-QAM modulated signals. In this paper, we present performance analysis of both the direct link and the adaptive Decode and Forward (DF) relaying protocol with multiple relays over independent and identical (i.i.d) Generalized K fading channels. Besides, we handle with the whole SNR range for M-QAM modulated signals; we give accurate analytical expressions for the Average Symbol Error Probability (ASEP) and the outage probability. Results for high SNR are derived as particular cases. We have provided Monte-Carlo simulation results which match well theoretical ones. The obtained results are very useful to tackle the QoS based on cooperative communications for wireless sensor networks.

Long Term Evolution (LTE) is rapidly flourishing in the global market due to its speed, coverage, relative ease of deployment, and consistency. However, Quality of Service (QoS) provisioning scheme is not fully defined for LTE, and it is left open for researchers to explore. In this paper, we propose a QoS provisioning technique for LTE. This technique involves a two-level scheduling scheme, consisting of a Rate-Controlled Priority Queuing Discipline (RCPQD) and a Deficit Weighted Round Robin Queuing Discipline (DWRRQD). To provision fairness, the scheduler considers the buffer content of the non-real-time applications, while priority is used to take differentiation of RT applications into account. The results show relative improvement in the performance of both real-time and non-real-time applications.

The energy cost of large-scale datacenters is increasing rapidly as a result of the rising demands of user and application computation and transactions. A datacenter is expected to serve clients' requests and satisfy their constraints at a minimum cost and maintain the best performance according to the needs. To satisfy different requirements efficiently, there is a need to develop and evaluate efficient, cost effective, and green computing technologies. In this paper, an optimization approach using dynamic placement of virtual machines in cloud computing is presented. The approach is evaluated in simulation environment and compared with traditional semi-static methods.

The execution of sensing services within the Internet of Things (IoT) mandates considering IoT characteristics which include heterogeneity, scalability, dynamicity, randomness, and multiple ownership. In such environment new types of sensing coverage holes posed by anchor misplacement arise. The first type is actual coverage holes that have been falsely hidden and unreported. The second one is perceived coverage holes that have been falsely generated by anchor misplacement. These types have generally been overlooked in sensing coverage research. We study these types of coverage holes in the locality of the affected sensing objects. Then we calculate the ratio of the area of each coverage holes to the total area of coverage. We utilize Delaunay Triangulation (DT) to partition the sensing region into triangles. Then we apply the concept of history in graph theory to characterize the DT structure before and after anchor misplacement. We locally detect the intra-triangle coverage hole, determine its type, and then provide the ratio of the area of this hole to the total triangle area.

In recent decades, staggering number of mobile applications have been developed in the wireless technology arena. The Wireless Fidelity (WiFi) and the Long Term Evolution (LTE) have become the most common wireless technologies used for those applications. Mobile applications are classified into Real-Time (RT) and Non Real-Time (NRT). The growth in the number of users burdens the network with more congestion which requires techniques to carry different types of traffic simultaneously in order to alleviate the problem. Different types of traffic (RT or NRT) are managed by Quality of Service (QoS) provisioning techniques. The main issue with the dominant services (RT applications) is the fact that they are delay sensitive, and this is the motivation for building a scheme that enables both LTE and WiFi systems to achieve reasonable values for delay, jitter, and packet loss. In this paper, we propose a QoS provisioning method that could be adopted by both LTE and WiFi, separating the traffic into RT and NRT applications. In the first stage, Class-Based Weighted Fair Queuing (CBWFQ) and Round Robin (RR) disciplines are used for RT and NRT applications respectively. In the second step, Deficit Weighted Round Robin Queuing (DWRRQ) is used for all applications. In this paper, we have analyzed the results of implementation of the proposed scheme in LTE and WiFi networks, and have made a comparison between their results (jitter, end-to-end delay, and traffic received). Simulation results demonstrate improvement in delay for RT in both networks.

Rapid growth and demand for computational power by scientific, business and web-applications has led to the creation of large-scale datacenters consuming enormous amounts of power. Improving the efficiency of datacenters, with a focus on power consumption and carbon emission, is a topical theme on which we are witnessing an increasing amount of academic and industrial research. The objective of this paper is to design and implement an energy efficient computing framework for green cloud datacenters that improves energy efficiency, reduces operational costs, and meets required Quality of Service (QoS). A dynamic migration algorithm is proposed to minimize the cost of energy in consideration of SLAs. The proposed approach utilizes one of the most promising technologies in the areas of server virtualization research area, namely Software Defined Networking (SDN) using OpenFlow technology. Obtained results demonstrate that the efficiency of the resource usage and reduced power consumption of the cloud can coexist with Service Level Agreements (SLAs) while keeping the cost of penalties and power consumption to a minimum.

Sensing coverage in Wireless Sensor Network (WSN) research has received significant attention. The usage of WSNs within the Internet of Things (IoT) mandates taking IoT characteristics into account when considering sensing coverage. These characteristics include heterogeneity, ultra-large scale, dynamicity, randomness, and multiple ownership. This paper provides an analytical study of sensing coverage in IoT where sensing resources (sensors) are: random, mobile or static, belong to different owners, and which are heterogeneous in terms of sensing and communication capabilities. We utilize Delaunay Triangulation (DT) to partition the target sensing region into triangles. The vertices of these triangles are IoT sensors. Since intra-triangle coverage holes are not uniform, our goal is to locally detect each hole and provide its bounds. First we determine the existence of an intra-triangle coverage hole, and then we provide a computation of lower and upper bounds of each local coverage hole. Our results are promising, and can be utilized in a multiplicity of coverage applications regardless of the sensors or deployment types.

Road fatalities in Saudi Arabia are expected to reach an alarming rate of one death per hour in 2015. In this paper, we describe a framework for vehicular sensing that is directly aimed at instilling safe driving. The presented architecture utilizes access to vehicle's CAN-Bus through an OBD-II connector. The access is processed using both on-vehicle smartphone and in-the-cloud processing. Road conditions (e.g., potholes, speedbumps, slowdowns, etc.) are recognized and utilized to classify roads using a threshold-based engine. The proposed framework is designed with strong emphasis on extendability, and a service application providing drivers with Quailty of Road (QoR) status is demonstrated.

This paper evaluates the potential use of hierarchal entropy analysis to detect covert timing channels and determine the best time-scale that reveals it. A data transmission simulator is implemented to generate a collection of overt and covert channels. The hierarchical entropy analysis approach is then utilized to detect the covert timing channels and identify the type-scale that provides the highest evidence that the underlying channel is covert. Hierarchical entropy divides the stream of inter-arrival times greedily to identify the time-scale the best reveals the existence of a covert-timing channel. The lowest entropy in the sequence is the best indicator that identifies non-random patterns in the given data stream. The results show that hierarchal entropy analysis performs significantly better than the classical flat entropy approach in the detection of covert timing channels. Furthermore, the hierarchical entropy analysis provides details about the best time-scale that reveals the features of the covert timing channel.

Localization errors posed by anchor misplacement have generally been overlooked in localization research. Previous studies in this field were focused on measurement errors. In this paper, we study the effects of anchor node misplacement, in terms of distance rather than orientation, on the localization error. We propose a distributed and deterministic detection algorithm to identify misplaced anchor nodes and to discard them. We evaluate the performance of our proposed algorithm, and compare it to the algorithm in [1]. Results show that our proposed algorithm is far more conducive to wireless sensor networks (WSNs), results in higher detection rates of misplaced anchors, and provides more effective mitigation.

Among the major challenges in 3GPP LTE-A (3rd Generation Partnership Project Long Term Evaluation) and beyond networks are the convergence of services towards IP technology, the quality of service (QoS) and the maintenance of multimedia flows transmitted on wireless networking for mobile and heterogeneous users. In this environment, multimedia services must face various disadvantages generated by the unreliability of the wireless channel shared by several users. These new characteristics impose new requirements when designing wireless protocol, such as in LTE-A networks, where protocols must be dynamically adapted with the continuous changes of the radio operator state channel and seek at the same time to meet the QoS requirements with the supported applications. In this context, we will propose a new cross layer and performances optimization of the 3GPP LTE-A system. Our major contribution consists to set up coordinated interactions between high layers applications representing for example video streaming and those of low layers, such as network, MAC (Medium Access Control) and physical layer. The information exchange between these layers will lead to optimize the application operation according to the changes observed on the radio operator channel. Thus, this solution would reduce the probabilities of the application dysfunction or interruption, while respecting the QoS constraints (time, throughput, PLR (Packet Loss Ratio) and maintain a constant flow for the user.

Abstract Towards satisfying the requirements of International Mobile Telecommunications–Advanced, both the Institute of Electrical and Electronics Engineers (IEEE) and Third Generation Partnership Project (3GPP) introduced revolutionary wireless technologies, exploiting advanced technologies and architectures. Both IEEE's 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)) and 3GPP's Long Term Evolution have been introduced to accommodate the increasing demand for mobile services and applications. To realize the true potential of these technologies, however, opportunistic frameworks for radio resource management must be designed to exploit the adaptive nature of mobile traffic. The utility optimized quality‐of‐service (QoS) framework proposed in this paper for the mobile WiMAX networks achieves this objective. To maintain support for QoS guarantees, the framework capitalizes on the adaptive nature of WiMAX traffic by individually linking connections with a utility function designed to both uphold the end users’ perceived performance and determine bandwidth allocations by a search tree maximization algorithm. In doing so, bandwidth utilization is maximized for all active connections, and blocking and dropping probabilities for new and handover calls, respectively, are minimized. The framework is evaluated through an extensive simulation model and is shown to outperform state‐of‐the‐art solutions. Copyright © 2014 John Wiley & Sons, Ltd.

We examine the impact of primary and secondary interference on opportunistic relaying in cognitive spectrum sharing networks. In particular, new closed-form exact and asymptotic expressions for the outage probability of cognitive opportunistic relaying are derived over Rayleigh and Nakagami-m fading channels. Our analysis presents revealing insights into the diversity and array gains, diversity-multiplexing tradeoff, impact of primary transceivers' positions, and the optimal position of relays. We highlight that cognitive opportunistic relaying achieves the full diversity gain which is a product of the number of relays and the minimum Nakagami-m fading parameter in the secondary network. Furthermore, we confirm that the diversity gain reduces to zero when the peak interference constraint in the secondary network is proportional to the interference power from the primary network.

Recently, many natural disasters have occurred (e.g., the 2011 tsunami in Japan). In response to these disasters, Wireless Sensor Networks have been deployed to improve their detection level. This important technology has several significant challenges, subsequently, this paper focuses on the problem of routing. Especially, a new set of dynamic versions of Sensing Sphere close to the Line:Smallest Angle to the Line (SSL:SAL) (El Salti et al.) is proposed. These versions are the SSL:SAL version 1 and version 2 (SSL:SALv1 and SSL:SALv2, respectively). This paper also conducts some experiments where it demonstrates the following: 1) packet delivery is a control factor that impacts several metrics, 2) the two versions of SSL:SAL increase the ability to improve the packet delivery even though the regions are partially covered, 3) the SSL:SALv1 and SSL:SALv2 achieve short hop-based paths, and 4) the SSL:SALv1 achieves short Euclidean-based paths. The proposed protocols are compared to some existing position-based protocols. Moreover, the experiments show generally that trade-offs exist between these metrics.

Wireless Sensor Networks (WSNs) have significant potential in many application domains, and are poised for growth in many markets ranging from agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large scale commercial adoption\nof sensor networks are the lack of available network management and control tools for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at exact location, and scheduling data packets so that data are sent based\non their priority and fairness. Moreover, due to the limited communication range of sensors, a large geographical area cannot be covered, which limits sensors application domain. Thus, we investigate a scalable and flexible WSN architecture that relies on multi-modal nodes equipped with IEEE 802.15.4 and IEEE 802.11 in order to use a Wi-Fi overlay as a seamless gateway to the Internet through WiMAX networks. We focus on network management approaches such as sensors localization, data scheduling, routing, and data aggregation for the WSN plane of this large scale multimodal network architecture and find that most existing approaches are not scalable, energy efficient, and fault tolerant. Thus, we introduce an efficient approach for each of localization, data scheduling, routing, and data aggregation; and compare the performance of proposed approaches with existing ones in terms of network energy consumptions, localization error, end-to-end data transmission delay and packet delivery ratio. Simulation results, theoretical and statistical analysis show that each of these approaches outperforms the existing approaches. To the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Hence, we e±ciently integrate all network management components so that it can be used as a single network management solution for a large scale WSN, perform experimentations to evaluate the performance of the proposed framework, and validate the results through statistical analysis. Experimental results show that our proposed framework outperforms existing approaches in terms of localization energy consumptions, localization accuracy, network energy consumptions and end-to-end data transmission delay.

Energy efficiency in specific clustering protocols is highly desired in wireless sensor networks. Most existing clustering protocols periodically form clusters and statically assign cluster heads (CHs) and thus are not energy efficient. Every non-CH node of these protocols sends data to the CH in every time slot of a frame allocated to them using the time division multiple access scheme, which is an energy-consuming process. Moreover, these protocols do not provide any fault tolerance mechanism. Considering these limitations, we have proposed an efficient fault-tolerant and energy-efficient clustering protocol for a wireless sensor network. The performance of the proposed protocol was tested by means of a simulation and compared against the low energy adaptive clustering hierarchy and dynamic static clustering protocols. Simulation results showed that the fault-tolerant and energy-efficient clustering protocol has better performance than both the low energy adaptive clustering hierarchy and dynamic static clustering protocols in terms of energy efficiency and reliability. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless Sensor Networks (WSNs) have been achieved widespread applicability in water quality monitoring. However, existing WSN-based monitoring systems are not adequate for monitoring pond and lake water, city water distribution and water reservoir. Moreover, these frameworks cannot be reused in other monitoring applications since they use static and application specific sensor nodes and are not dynamic to the changing requirements. Thus, we introduce a reusable, self-configurable, and energy efficient WSN-based water quality monitoring system that integrates a Web-based information portal and a sleep scheduling mechanism of sensor nodes. The testbed and simulation results show that the framework can monitor the water quality in real-time and the sleep scheduling mechanism increases the network lifetime, respectively.

This paper introduces a Mobility-centric Energy efficient and Fault tolerant Clustering protocol (MEFC) for Wireless Sensor Network that minimizes the number of cluster heads and active nodes to provide network coverage. Most existing routing protocols of WSN do not support mobile sensor nodes and are not fault tolerant. Considering the real world mobility-centric sensor applications the MEFC protocol allows the base station to control the mobility of sensor nodes so that they move to a new location to provide network coverage to be used in coverage-sensitive applications, e.g., battlefield surveillance. It also allows nodes to move inside or outside of a cluster and join a new cluster in data-centric applications, e.g., health monitoring for elderly people. The base station and cluster heads transmit beacon messages to discover the failure of cluster heads and member nodes of a cluster, respectively. Experimental results demonstrate that the proposed MEFC protocol reduces network energy consumptions and increased network lifetime as compared to the existing FT-EEC, DSC and LEACH protocols.

Scheduling different types of packets, such as realtime and non-real-time data packets, at sensor nodes with resource constraints in Wireless Sensor Networks (WSN) is of vital importance to reduce sensors' energy consumptions and end-to-end data transmission delays. Most of the existing packet-scheduling mechanisms of WSN use First Come First Served (FCFS), non-preemptive priority and preemptive priority scheduling algorithms. These algorithms incur a high processing overhead and long end-to-end data transmission delay due to the FCFS concept, starvation of high priority real-time data packets due to the transmission of a large data packet in nonpreemptive priority scheduling, starvation of non-real-time data packets due to the probable continuous arrival of real-time data in preemptive priority scheduling, and improper allocation of data packets to queues in multilevel queue scheduling algorithms. Moreover, these algorithms are not dynamic to the changing requirements of WSN applications since their scheduling policies are predetermined. In this paper, we propose a Dynamic Multilevel Priority (DMP) packet scheduling scheme. In the proposed scheme, each node, except those at the last level of the virtual hierarchy in the zone-based topology of WSN, has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. We evaluate the performance of the proposed DMP packet scheduling scheme through simulations for real-time and non-real-time data. Simulation results illustrate that the DMP packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay.

Although Wireless Sensor Networks (WSNs) have significant applications in monitoring, security and other areas, they still lack network management solutions to enable large scale adoption. Such solutions would help in determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately, scheduling and routing data by reducing end-to-end delay, and energy consumptions. Moreover, to the best of our knowledge, no integrated network management framework consisting of efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Thus, we introduce an integrated management framework comprising sensors Localization, Routing, data Scheduling, and Aggregation (LRSA) for a large scale WSN. Simulation results show that LRSA outperforms other approaches in terms of localization energy consumptions and error, end-to-end delay, and network energy consumptions.

Co-channel interference models are increasingly becoming important in wireless networking. This is especially true in heterogeneous networks where a single device may cause unintended interactions between multiple radios using difference radio access technologies, even though they are located on the same device. Currently simulation tools provide varying levels of modelling of this phenomenon. This paper tries to quantify how well popular wireless simulation tools capture these effects using qualitative techniques while providing quantitative evaulation of the effects of interference between two radio access technologies - Bluetooth and Wi-Fi. Each tool is compared according to level of interference modelling offered, the technique for interference modelling and the types of RATs which are supported. The effect of co-channel interference between Bluetooth and Wi-Fi is evaluated with respect to throughput using devices equipped with both Blutooth and Wi-Fi radios.

Scheduling real-time and non-real time packets at the sensor nodes is significantly important to reduce processing overhead, energy consumptions, communications bandwidth, and end-to-end data transmission delay of Wireless Sensor Network (WSN). Most of the existing packet scheduling algorithms of WSN use assignments based on First-Come First-Served (FCFS), non-preemptive priority, and preemptive priority scheduling. However, these algorithms incur a large processing overhead and data transmission delay and are not dynamic to the data traffic changes. In this paper, we propose three-class priority packet scheduling scheme. Emergency real-time packets are placed into the highest priority queue and can preempt the processing of packets at other queues. Other packets are prioritized based on the location of sensor nodes and are placed into two other queues. Lowest priority packets can preempt the processing of their immediate higher priority packets after waiting for a certain number of timeslots. Simulation results show that the proposed three-class priority packet scheduling scheme outperforms FCFS and multi-level queue schedulers in terms of end-to-end data transmission delay.

In recent years a growing interest in information hiding in multimedia data as the host has been observed in the research community. This hidden information can be used for many different purposes, including source identification, copyright protection and covert data transmission. In this paper, an optical crypto technique with adaptive steganography (AS) is proposed for audio/video sequence encryption and decryption. The optical crypto technique is based on double random phase encoding algorithm to encrypt and decrypt the intended audio/video sequences. The main purpose of steganography algorithms is to hide as much information within the cover media as possible. Therefore, for steganography algorithms, the tradeoff is between the amount of covert information being embedded, called stego-data, and that the ensurance for its presence to remain undetected. While their purposes may seem different, recent advances allow more and more the use of advanced watermarking techniques to embed large amounts of covert information that is also robust against removal and detection.

This paper proposes a simple Wireless Sensor Network (WSN)-based water level and water quality monitoring system for fish ponds. The proposed architecture uses RGB color sensors and provides a low cost and real-time monitoring system to grow healthy fish and avoid anomalies such as overflow or low water level and the death or disease of fishes for unhealthy water (e.g., rise of acid level due to the change of pH and lack of oxygen in water) in a pond. In this simple monitoring system, sensors monitor the water level, dissolved oxygen, temperature and pH level of the water of the fish ponds at some predefined sensing interval. We also introduce a simple but efficient Clustering Protocol for Water Sensor network (CPWS) for the proposed fish pond monitoring framework in terms of network energy consumptions, network lifetime and number of data communications.

Designing energy efficient and reliable routing protocols for mobility centric applications of wireless sensor network (WSN) such as wildlife monitoring, battlefield surveillance and health monitoring is a great challenge since topology of the network changes frequently. Existing cluster-based mobile routing protocols such as LEACH-Mobile, LEACH-Mobile-Enhanced and CBR-Mobile consider only the energy efficiency of the sensor nodes. However, reliability of routing protocols by incorporating fault tolerance scheme is significantly important to identify the failure of data link and sensor nodes and recover the transmission path. Most existing mobile routing protocols are not designed as fault tolerant. These protocols allocate extra timeslots using time division multiple access (TDMA) scheme to accommodate nodes that enter a cluster because of mobility and thus, increases end-to-end delay. Moreover, existing mobile routing protocols are not location aware and assume that sensor nodes know their coordinates. In this study the authors, we propose a location-aware and fault tolerant clustering protocol for mobile WSN (LFCP-MWSN) that is not only energy efficient but also reliable. LFCP-MWSN also incorporates a simple range free approach to localise sensor nodes during cluster formation and every time a sensor moves into another cluster. Simulation results show that LFCP-MWSN protocol has about 25–30% less network energy consumptions and slightly less end-to-end data transmission delay than the existing LEACH-Mobile and LEACH-Mobile-Enhanced protocols.

Radio Resource Management (RRM) techniques used in the Worldwide Interoperability for Microwave Access (WiMAX) can provide for many of the services and features promised by 4G wireless networks, such as supporting multimedia services with high data rates, and wide coverage area, as well as all-IP with security and Quality of Service (QoS) support. The IEEE 802.16 standard, associated with the WiMAX, leaves the details of RRM components open for the vendors to explore. In this paper we propose a novel scheme for the QoS support in WiMAX including packet scheduling and bandwidth allocation strategies. The proposed solution efficiently enhances performance and utilizes resources, while being fair, practical and in compliance with the IEEE 802.16 standard specifications. Our solution provides QoS support to all traffic classes defined by the standard, and it dynamically changes the bandwidth allocation based on the traffic characteristics and service demands. Simulation results show that the proposed solution can deliver QoS support and be fair to all classes of service in a WiMAX network.

Designing energy efficient and reliable routing protocols for mobility centric Wireless Sensor Networks (WSN) applications such as wildlife monitoring, battlefield surveillance and health monitoring is a great challenge since topology of the network changes frequently. Existing cluster-based routing protocols such for LEACH-Mobile, LEACH-Mobile-Enhanced, CBR-Mobile that are designed for mobile sensor network only consider the energy efficiency of the sensor nodes. Moreover, these protocols allocate extra timeslots using TDMA scheme to accommodate nodes that enter a cluster due to mobility and hence, increases end-to-end delay. In this paper, we propose a Fault Tolerant Clustering Protocol for Mobile WSN (FTCP-MWSN) that is not only energy efficient but also reliable by introducing fault tolerance mechanism. Moreover, FTCP-MWSN does not use any extra timeslot for calculating mobility and thus, reduces end-to-end delay. Simulation results show FTCP-MWSN protocol has more network lifetime, reliability than the existing LEACH-Mobile and LEACH-Mobile-Enhanced protocols.

Distribution of radio resources in fourth generation (4G) networks requires sophisticated Radio Resources Management (RRM) techniques to guarantee a sustainable level of Quality of Service (QoS) support. By analyzing the adaptive nature of the project applications and their traffic it is possible to substantially enhance broadband service delivery. In this paper, we propose a Utility Optimized QoS (UOQoS) scheme for mobile WiMAX. The scheme maintains a high level of QoS support by maximizing bandwidth utilization and minimizing the rejection of both new and handover calls. It takes advantage of the adaptive nature of WiMAX traffic by associating each connection with a unique and appropriate utility function. The performance of the UOQoS is validation through simulation.

Routing and topology control for Wireless Sensor Network (WSN) are significantly important to achieve the following: 1) energy efficiency in resource constrained WSN and 2) High speed packet delivery. In this paper, we propose a framework for WSN which combines three design approaches: 1) clustering, 2) routing, and 3) topology control. In this framework, we implement an energy efficient zone-based topology and routing protocol. Afterwards, we propose for this framework a new set of graphs referred to as the Mini Gabriel (MG) graphs. The simulation results show that the framework based on the new set of graphs outperforms an existing geometric graph. This is in terms of the transmission energy consumptions of the network and the end-to-end data transmission delay. In addition, the proposed framework generally demonstrates the best performance in terms of the network energy consumption. Moreover, the MG demonstrates that it achieves the connectivity property. Achieving this property is critical for WSNs.

Wireless sensor networks (WSNs) have significant potential in many application domains such as agriculture, health, environmental monitoring, battlefield surveillance, and wild fire detection. They, however cannot be used in large geographical areas due to the short communication range of sensors.. In addition, sensor networks have been the lack of available network management and control tools, such as for determining the degree of data aggregation prior to transforming it into useful information. Designing different network management tools such for routing, localization, and data aggregation are, therefore, required in large scale WSNs. Only a few of the existing data aggregation methods have been developed for a large scale WSN. In this paper, we propose an efficient data aggregation scheme for large scale WSNs that considers the tradeoff between energy efficiency and end-to-end delay. Simulation results show that the proposed scheme has better performance than an existing standard data aggregation approach, namely SUMAC.

Efficient sensor localizations are techniques for efficiently identifying sensors' positions for different Wireless Sensor Networks (WSNs) applications (e.g., environmental monitoring). Most existing localization techniques are designed for low scale sensor networks. Moreover, existing localization approaches are mostly range based that use some powerful nodes equipped with expensive GPS and/or extra hardware (or techniques) for distance estimations. On the other hand, most researchers focus only on the energy efficiency of sensor networks when designing localization method though 1) cost, 2) accuracy, and 3) scalability should also be considered as major design factors. In this paper, we propose Range-free Energy efficient, Localization technique using Mobile Anchor (RELMA) for large scale WSNs that improves accuracy and energy efficiency by reducing the number of anchor nodes. Simulation results demonstrate these properties where RELMA Outperforms NBLS an existing localization approach in terms of localization accuracy and energy efficiency. Moreover, rigid statistical analysis is used to validate these results.

For an efficient usage of the sensor technology, several design factors (e.g., topology and sensing coverage) should be taken into account. In this paper, we focus on the underlying topology of sensor networks in two-dimensional environments and enhance a set of recently proposed graphs. The new enhanced graphs are referred to as the Derived Circles version 2 (DCαv2) graphs. We show that DCαv2 graphs are locally constructed, connected, have the rotation-ability property, and have the Euclidean Minimum Spanning Tree (EMST) as their subgraphs. Moreover, we show that the new set of graphs has a bounded Euclidean/length and power dilation when 0.5 ≤ α ≤ 1. Furthermore, via simulations, we confirm most of these properties, and demonstrate that the DCαv2 graphs also have bounded Euclidean and power dilations when 0αv2 graphs outperform the Half Space Proximal (HSP) and the Relative Neighbourhood Graph (RNG) graphs in terms of the network dilation, Euclidean dilation, and power dilation. This, in turn, increases the speed for message delivery, reduces the energy consumption of nodes and accordingly prolongs the network lifetime.

Abstract-Recently, sensor network technology has brought the attention of many researchers due to its scalability and efficiency. However, to improve its efficiency level, several design challenging factors should be taken into account. Among these challenges, we focus on the underlying topology of sensor networks in three-dimensional environments and extend a new set of graphs referred to as the Derived Sphere (DSα) graphs from their 1-D version Il][30]. We show that DSαgraphs are locally constructed, connected, and have the rotation-ability property. Achieving the connectivity and the rotation-ability properties imply strong reliability for these graphs. Moreover, we show that the new set of graphs has a bounded Euclidean (or length) when 0αgraphs outperform 3-D Half Space Proximal (HSP) and 3-D Gabriel Graph (GG) graphs in terms of network and Euclidean dilations. This, in turn, increases the speed for message delivery. Therefore, the DSαgraphs are considered timely-efficient graphs.

In this paper we propose a system architecture for telemonitoring system. The proposed architecture is not limited to only indoor environment. By employing a mobile phone that implements a personal server, the system also supports the monitoring of old people while away from home. In addition to the system architecture, we also propose the middleware architecture for the personal server and the cross-layer protocol stack architecture for the sensor node. The middleware and the cross-layer stack improve the flexibility and expansibility of the system.

The arousal of emotion might have consequences for physical health is a broadly acknowledged idea. Therapy for depression, prevention for heart pathologies, and rehabilitation treatments for drug addiction are just a few examples of application domains that may benefit from technologies capable of monitoring, detecting, representing, and disseminating information pertaining to patients' physical and psychological/emotional states. However, the design and development of healthcare applications of this kind is a rather challenging issue that requires to integrate sensor infrastructures, which are able to detect changes in patients' physiological and emotional states, and of sharing this information to interested caregivers, such as professional medical staff, relatives, and friends. This paper proposes the Pervasive Environment for AffeCtive Healthcare (PEACH) framework, a middleware level support for affective healthcare that incarnates these ideas and describes its effective functions in a drug addiction treatment application scenario.

Abstract Secure time synchronization is one of the key concerns for some sophisticated sensor network applications. Most existing time synchronization protocols are affected by almost all attacks. In this paper, we consider heterogeneous sensor networks (HSNs) as a model for our proposed novel time synchronization protocol based on pairing and identity‐based cryptography (IBC). This is the first approach for time synchronization protocol using pairing‐based cryptography (PBC) in HSNs. The proposed protocol reduces the communication overhead of the nodes as well as prevents from all the major security attacks. Security analysis shows, it robust against reply attacks, masquerade attacks, delay attacks, and message manipulation attacks. Copyright © 2009 John Wiley & Sons, Ltd.

Energy efficiency in the clustering protocols in highly desired in Wireless Sensor Network (WSN). The Dynamic Static Clustering (DSC) protocol is an energy efficient clustering protocol; however, it does not provide any fault tolerance mechanism. Moreover, the non-Cluster Head nodes send data to the Cluster Heads (CH) in every time slot of a frame allocated to them using TDMA scheme, which is an energy consuming process. Considering these limitations of the DSC protocol, we have proposed a more energy efficient and fault tolerant Dynamic Static Clustering (FT-DSC) protocol of WSN to enhance the performance of DSC. The performance of the proposal protocol has been tested by means of simulation and compared against the original DSC protocol. Simulation results show that the FT-DSC protocol has better performance than the DSC protocol in terms of energy efficiency and reliability.

Recently, different types of sensors have been developed to detect environmental changes (e.g., instability of the earth's crust) and to reduce the associated damage. For an efficient usage of the sensor technology, several design factors (e.g., topology and sensing coverage) should be taken into account. In this paper, we focus on the underlying topology of sensor networks in two-dimensional environments and propose a new set of graphs referred to as the Derived Circles (DCalpha) graphs. We show that DCalphagraphs are locally constructed, connected, power efficient, and orientation-invariant. We also show that DCalphagraphs have a minimum degree of one and an Euclidean dilation of one. Furthermore, via simulations, we demonstrate that DCalphagraphs outperform the half space proximal (HSP) graph in terms of the network dilation, Euclidean dilation, and power dilation. This, in turn, reduces the energy consumption of nodes and accordingly prolongs the network lifetime.

In this paper, we propose a Call Admission Control-based dynamic pricing scheme that aims at preventing congestion and maximizing the utilization of broadband wireless access systems. The main aim of our scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally; hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically; hence, ensuring a congestion-free system. The proposed scheme is general and can be implemented with different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.

In the modern era of Internet, providing Quality of Service (QoS) is a challenging issue, particularly in resource-constrained wireless networks with delay-sensitive multimedia traffic. Real-time and multimedia services are now available to end-users over wired networks, Wireless Local Area Networks (WLANs), and Wireless Personal Area Networks (WPANs). While the usual trend is to provide the best possible QoS for these services, it is also imperative to deploy security requirements along with the QoS parameters. In this paper, we argue that the existing approaches for including security parameters (such as encryption/decryption key lengths) with QoS parameters (e.g., end-to-end delay requirements) lead to further security risks and consequently fail to provide an adequate solution. Through simulations, we point out the pitfalls of integrating delay and security support in the contemporary approaches. We also envision QoS2, a framework integrating both quality of security and QoS, in order to provide possible solutions for solving these problems. We also demonstrate via simulation the effectiveness and strength of our adopted approach.

Radio resource management (RRM) techniques used in Worldwide Interoperability for Microwave Access (WiMAX) can provide for many of the services and features promised by 4G wireless networks, such as supporting multimedia services with high data rates, and wide coverage area, as well as all-IP with quality of service (QoS) support and security. In this paper we propose a new scheduling scheme and admission control policy for WiMAX that efficiently enhances performance and utilizes resources, while being fair, practical and in compliance with the IEEE 802.16 specifications. Our scheduler is dynamic and based on hierarchical and ad-hoc models. Our solution provides QoS support to all traffic classes defined by the standard, and it dynamically changes the bandwidth allocation depending on the traffic characteristics and service demands. Simulation results show that the proposed solution can deliver QoS support and be fair to all classes of service in a WiMAX network.

Recent advances in wireless technology, sensors and portable devices offer interesting opportunities to enable ubiquitous assistance to individuals in need of prompt help. Providing healthcare services to mobile users, such as, patients, elders, or potential drug abusers, is a rather challenging task. Novel middleware-level supports are required to integrate sensor infrastructures capable of detecting changes in the monitored subjects' health conditions and of alerting medical personnel, and the victim's relatives and friends in case of emergency situations. Along this line, the paper envisions a context-aware middleware-level solution dubbed Pervasive Environment for Affective Healthcare (PEACH). PEACH integrates together various sensors in a Wireless Body Area Network (WBAN) to detect alterations of monitored subjects' affective and physical conditions, aggregate the sensed information, and also detect potentially dangerous situations for the monitored subject. Finally, PEACH aims at providing outdoor assistance to the victim/patient by quickly promoting the formation of ad hoc rescue groups comprising nearby volunteers. Through encouraging results obtained from both simulations and a practical drug-rehabilitation application testbed, the effectiveness of the envisioned PEACH framework is verified.

The success of emerging Broadband Wireless Access Systems (BWASs) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature, and hence, diverse Quality of Service (QoS) requirements of different applications that these systems support. Therefore, QoS provisioning is crucial for the success of such wireless access systems. In this paper, we propose a novel downlink packet scheduling scheme for QoS provisioning in BWASs. The proposed scheme employs practical economic models through the use of novel utility and opportunity cost functions to simultaneously satisfy the diverse QoS requirements of mobile users and maximize the revenues of network operators. Unlike existing schemes, the proposed scheme is general and can support multiple QoS classes with users having different QoS and traffic demands. To demonstrate its generality, we show how the utility function can be used to support three different types of traffic, namely best-effort traffic, traffic with minimum data rate requirements, and traffic with maximum packet delay requirements. Extensive performance analysis is carried out to show the effectiveness and strengths of the proposed packet scheduling scheme.

Next-generation wireless communication systems aim at supporting wireless multimedia services with different quality-of-service (QoS) and bandwidth requirements. Therefore, effective management of the limited radio resources is important to enhance the network performance. In this paper, we propose a QoS adaptive multimedia service framework for controlling the traffic in multimedia wireless networks (MWN) that enhances the current methods used in cellular environments. The proposed framework is designed to take advantage of the adaptive bandwidth allocation (ABA) algorithm with new calls in order to enhance the system utilization and blocking probability of new calls. The performance of our framework is compared to existing framework in the literature. Simulation results show that our QoS adaptive multimedia service framework outperforms the existing framework in terms of new call blocking probability, handoff call dropping probability, and bandwidth utilization.

Abstract Next generation wireless networks (NGWNs) will utilize several different radio access technologies, seamlessly integrated to form one access network. This network has the potential to provide many of the requirements that other previous systems did not achieve such as high data transfer rates, effectives user control, seamless mobility, and others which will potentially change the way users utilize mobile devices. NGWN will integrate a multitude of different heterogeneous networks including (a) cellular networks, passed through multiple generations—1G, 2G, 3G, and 3.5G; (b) wireless LANs, championed by the IEEE 802.11 wireless fidelity (WiFi) networks; and (c) broadband wireless access networks (IEEE 802.16, WiMAX). In this paper, a new adaptive quality of service (QoS) oriented CAC scheme is proposed to limit the occurrence of hard IEEE 802.11 WLAN‐UMTS handovers to mobile users using real time (RT) applications. This scheme is hybrid, based on the service class differentiation, the location in the heterogeneous infrastructure and a vertical handoff decision function as well. Simulation results show that our policy achieves significant performance gains. It maximizes the utilization of the resources available at the WLAN cells, and meets as much as possible the QoS requirement of higher priority users. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we present an analytical model to assess the blocking capacity of multi‐service code division multiple access (CDMA) systems. We include smart antenna systems in our model and show how the capacity of CDMA systems can be improved if smart antennas are employed at the base stations. Applying smart antennas can actually transform CDMA systems from being interference limited to being channel/code limited. To investigate this effect, we extend our model to include the limitation of channelization codes in CDMA‐based universal mobile telecommunication system (UMTS) systems. From the point of view of the call admission control (CAC) in a smart antenna CDMA system, we can either accept the capacity loss due to code limitation, or we can additionally apply space division multiple access (SDMA) techniques to re‐use channelization codes and thus re‐approach the capacity which is obtained if no code limitation is considered. Copyright © 2008 John Wiley & Sons, Ltd.

The increasing demand for wireless heterogeneous multimedia applications presents a real challenge to mobile service providers. Even with the substantial increase in the supported bandwidth in future broadband wireless systems such as 3.5G wireless cellular networks and 802.16 broadband wireless networks (WiMAX), these systems suffer from the same inherited problem in wireless networks, which is limited spectrum. Therefore, bandwidth provisioning is crucial for the success of such broadband wireless systems. In this paper, we propose a novel dynamic bandwidth provisioning scheme for broadband wireless communication. The proposed scheme spans multiple time slots/frames and optimally allocates them to the different classes of traffic depending on their weights, the real-time bandwidth requirements of their connections and their channel quality conditions. Simulation results show that satisfactions of different classes of traffic can be improved by implementing our scheme.

In this paper we propose a handover and class-based Call Admission Control algorithm for UMTS/WLAN heterogeneous networks. Its main objective is to limit the occurrence of hard WLAN-UMTS handovers to mobile nodes moving across cells and using real time applications. Our mechanism is based on the service class differentiation, the call origination point, and a vertical handoff decision function as well. It aims at maximizing the utilization of the resources available at the WLAN cells, and meeting the QoS requirement of higher priority users as much as possible while maintaining the minimum requirements of lower priority users, especially when the UMTS and the WLAN networks suffer from congestion. The new CAC policy achieves a good performance and capacity gain.

Secure time synchronization is one of the key concerns for some sophisticated sensor network applications. Most existing time synchronization protocols are affected by almost all attacks. In this paper we consider heterogeneous sensor networks (HSNs) as a model for our proposed novel time synchronization protocol based on pairing and identity based cryptography (IBC). This is the first approach for time synchronization protocol using pairing-based cryptography in heterogeneous sensor networks. The proposed scheme reduces the key spaces of nodes as well as it prevents from all major security attacks. Security analysis indicated that the proposed scheme is robust against reply attacks, masquerade attacks, delay attacks, and message manipulation attacks.

Sensor networks is a promising technology that can be used to avoid disasters such as fire, storm, etc. This technology needs to be improved in terms of several issues. In this paper, we focus on two issues: coverage and routing. For coverage problem, we introduce a new approach for obtaining a static covered network in the 3-Denvironment. This technique is referred to as the Chipset model. This would be accomplished by using a small number of sensor nodes in order to save up some energy. For routing issue, we propose a new position-based routing protocol referred to as the 3-DSensing Spheres close to the line routing algorithm (3-DSSL). We show that this protocol achieves 100% delivery rate on top of our 3-Dmodel. We also show that even this protocol has similar performance with the 3-DGFin terms of hop dilation and total running time, the 3-DSSLoutperforms the 3-DGFin terms of Euclidean dilation. Thus, this might reduce the energy consumption of the nodes. Therefore, prolonging the lifetime of the network.

The success of emerging wireless broadband communication systems such as 3.5 G wireless cellular systems and 802.16 broadband wireless systems (WiMAX) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature and, hence, diverse bandwidth requirements of applications that these communication systems support and the reliance on high speed shared channels for data delivery instead of dedicated ones. Therefore, bandwidth provisioning is crucial for the success of such communication systems. In this paper, we propose a novel dynamic bandwidth provisioning scheme for broadband wireless communication. The proposed scheme spans multiple time slots/frames and optimally allocates them to the different classes of traffic depending on their weights, the real-time bandwidth requirements of their connections, their channel quality conditions and the expected obtained revenues. Simulation results are provided to show the potential and effectiveness of our scheme.

Most of the proposed routing protocols for wireless sensor networks (WSN) are concentrating on efficiently using extremely constrained resources, especially the energy. However, one important factor of the routing protocols, quality of service (QoS) routing has not been paid enough attention from researchers. Transmission collision occurred at nodes that receive packets from multiple nodes at the same time greatly reduces the network performance. Therefore, the services provided by the sensor network are also greatly impacted. In this paper, we propose a novel mechanism to find multiple-paths between one sink and multiple-sources with the consideration of reducing collision occurred at nodes that are receiving and forwarding packets on behalf of the source nodes. Simulation results are provided to show the potential and effectiveness of our solution.

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in next generation Broadband Wireless Access Systems (BWASs), it is expected that these systems will severely suffer from congestion due to the rapid increase in demand of bandwidth intensive applications. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer immense amount of revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of Call Admission Control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the arrival rates to the system are less than or equal to the optimal ones computed dynamically, hence, guaranteeing a congestion-free system. Simulation results show the effectiveness and strengths of our proposed approach.

Presents the introductory welcome message from the conference proceedings.

Key management poses a main concern for security operation in sensor network. Most existing key management schemes try to establish shared keys for all pairs of neighbor sensors, no matter whether these nodes communicate with each other or not, and causes large overhead. Recent trend of research mainly consider homogeneous sensor network and a few consider heterogeneous sensor network for key management. In this paper, we consider heterogeneous sensor network (HSN) as a model for our proposed novel key agreement protocol based on pairing identity based encryption (IBE). The proposed scheme reduces the key spaces of the nodes, in fact nodes do not need to store any key of the other nodes rather it computes secret sharing key by using pairing and IBE properties. Security analysis shows, it also robust against different attacks such as replay attack, masquerade attack, and integrity attack.

In recent years, sensor networks have been proposed to improve the detection level of natural disasters (e.g. volcanoes, tornadoes, tsunamis). However, this technology has several issues that need to be improved. We, therefore in this paper, focus on two main issues: coverage and routing. For coverage problem, we introduce a new approach for obtaining a static covered network in 3-D environment. This technique is referred to as the chipset coverage model. This would be accomplished by using a small number of sensor nodes in order to save up some energy. For routing issue, we propose several new position-based routing protocols which are the 3-D sensing spheres close to the line routing algorithm (3-D SSL), the 3-D smallest angle to the line routing algorithm (3-D SAL), and the 3-D SSL:SAL routing protocols. We show that the 3-D SAL and the 3-D SSL:SAL routing protocols guarantee the delivery of packets. In our simulation, we show that the 3-D SSL:SAL protocol has similar performance in terms of network (hop) dilation and routing delay to these for an existing 3-D progress-based routing protocol. Moreover, the 3-D SSL:SAL and the 3-D SAL routing protocols outperform an existing 3-D progress-based routing protocol in terms of Euclidean dilation. Thus, the new protocols reduce the energy consumption of the nodes and, therefore, prolong the life of the network.

Abstract The main characteristic of a mobile ad hoc network (MANET) is its infrastructure‐less, highly dynamic topology, which is subject to malicious traffic analysis. Malicious intermediate nodes in MANETs are a threat concerning security as well as anonymity of exchanged information. In this paper, we propose an anonymous on‐demand routing protocol, called RINOMO, to protect anonymity and achieve security of nodes in MANETs. After successful authentication of the legitimate nodes in the network they can use their pseudo IDs for secure communication. Pseudo IDs of the nodes are generated considering pairing‐based cryptography. Nodes can generate their pseudo IDs independently and dynamically without consulting with system administrator. As a result, RINOMO reduces pseudo IDs maintenance costs. Only trust‐worthy nodes are allowed to take part in routing to discover a route. To ensure trustiness each node has to make authentication to its neighbors through the designed anonymous authentication process. Thus, RINOMO safely communicates between nodes without disclosing node identities. It also provides different desirable anonymous properties such as identity privacy, location privacy, route anonymity, and robustness against several attacks. Mathematical analysis of privacy loss is also evaluated and it shows there is no loss of privacy with respect to time. Thus, RINOMO is an anonymous robust protocol in MANETs. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this article we present an optimal Markov Decision‐based Call Admission Control (MD‐CAC) policy for the multimedia services that characterize the next generation of wireless cellular networks. A Markov decision process (MDP) is used to represent the CAC policy. The MD‐CAC is formulated as a linear programming problem with the objectives of maximizing the system utilization while ensuring class differentiation and providing quantitative fairness guarantees among different classes of users. Through simulation, we show that the MD‐CAC policy potentially achieves the optimal decisions. Hence our proposed MD‐CAC policy satisfies its design goals in terms of call‐class‐differentiation, fairness and system utilization. Copyright © 2006 John Wiley & Sons, Ltd.

The emergence of high-speed wireless cellular networks such as high speed downlink packet access (HSDPA) and 1x evolution data optimized (1xEV-DO) will enhance the support of existing applications and will enable the development of a wide range of heterogeneous "content rich" multimedia applications that have different quality of service (QoS) requirements. However, due to scare wireless resources and high traffic demands, new resource management techniques are needed in order to satisfy the QoS requirements of the different heterogeneous applications and maximize the network capacity at the same time. In this paper, we propose a novel resource management scheme through packet scheduling for high-speed wireless cellular networks. The proposed scheme utilizes utility and opportunity cost functions to satisfy the needs of mobile users and service providers. Simulation results are provided to show the effectiveness and potential of our proposed scheme.

Future wireless cellular networks such as high speed downlink packet access (HSDPA) and 1x EVolution data optimized (1xEV-DO) promise to revolutionize the mobile user's wireless experience by offering high downlink data rates that are much more beyond what 2.5 G and 3 G cellular systems could offer. In order to support as many users as possible, these systems exploit the bursty nature of the data traffic by utilizing high speed downlink shared channels that are shared among the users according to the packet scheduling scheme being used. However, to support acceptable performance levels especially at peak loads, these systems require much more efficient downlink packet scheduling schemes than ever before. In this paper, we propose a novel generic centralized downlink (GCD) packet scheduling scheme for next generation wireless cellular networks. The proposed scheme utilizes novel utility and opportunity cost functions to satisfy the mobile users as well as the service providers. We show that our scheme converges to two well known schemes, Max CIR and PF. Simulation results are provided to show the effectiveness and strength of the GCD scheme.

The access link contention can severely constrain the end- to-end throughput of the path between a source and destination mobile node connected through the backbone of the wireless mesh network (WMN). In this paper, we propose an integrated routing system for WMN that includes both the backbone paths and the ad-hoc paths formed as a result of direct communication among mobile nodes without going through the backbone. In our proposed routing system, an alternative ad-hoc path can be used only when the primary backbone path is severely constrained due to access links contention. We propose a scheme that allows the source mobile node to evaluate the throughput of the backbone and ad-hoc paths, and select one path for communicating with the destination. We implemented the proposed routing system in OPNET simulator, and evaluated the performance of our scheme under variety of conditions. Simulation results show that the alternative ad-hoc path is effective in delivering higher throughput when backbone path is severely constrained.

As mobile wireless ad hoc networks have different characteristics from wired networks and even from standard wireless networks, there are new challenges related to security issues that need to be addressed. Many intrusion detection systems have been proposed and most of them are tightly related to routing protocols, such as Watchdog/Pathrater and Routeguard. These solutions include two parts: intrusion detection (Watchdog) and response (Pathrater and Routeguard). Watchdog resides in each node and is based on overhearing. Through overhearing, each node can detect the malicious action of its neighbors and report other nodes. However, if the node that is overhearing and reporting itself is malicious, then it can cause serious impact on network performance. In this paper, we overcome the weakness of Watchdog and introduce our intrusion detection system called ExWatchdog. The main feature of the proposed system is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation results show that our system decrease the overhead greatly, though it does not increase the throughput obviously.

Major research challenges in the next generation of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end quality of service (QoS), supporting high data rates over wide area and enabling users to specify their personal preferences. The integration and interoperability of this multitude of available networks will lead to the emergence of the fourth generation (4G) of wireless technologies. 4G wireless technologies have the potential to provide these features and many more, which at the end will change the way we use mobile devices and provide a wide variety of new applications. However, such technology does not come without its challenges. One of these challenges is the user's ability to control and manage handoffs across heterogeneous wireless networks. This paper proposes a solution to this problem using artificial neural networks (ANNs). The proposed method is capable of distinguishing the best existing wireless network that matches predefined user preferences set on a mobile device when performing a vertical handoff. The overall performance of the proposed method shows 87.0 % success rate in finding the best available wireless network. To test for the robustness and effectiveness of the neural network algorithm, some of the features were removed from the training set and results showed a significant impact on the overall performance of the system. Hence, managing vertical handoffs through user preferences can be significantly affected with the selection of features used to provide the closest match of the available wireless networks.

Next generation wireless communication systems target to provide guaranteed quality of service (QoS) for multimedia applications. In this paper, a multiple-threshold bandwidth reservation scheme combined with a call admission control algorithm is proposed and analyzed. The objective of our work is to be able to achieve better QoS provisioning for mobile users while achieving efficient utilization of the available limited bandwidth. The proposed scheme is modelled as M/M/C/C queueing system and the performance measures, call blocking probability and call dropping probability are computed. The performance of our scheme is compared to the complete sharing (CS) policy. Simulation results show that our scheme surpasses the CS policy in terms of call blocking probability, call dropping probability and bandwidth utilization.

In this paper a new channel allocation scheme is proposed and analyzed for real time traffic. There is no division of channels into groups. Two thresholds (lower and higher) are taken depending on the signal strength of the mobile host in the cell. The handoff calls have the right of preemption over the originating calls with signal strength less than the lower threshold value. The performance of the system is evaluated in terms of handoff dropping probability, originating call blocking probability and the channel utilization.

High-Speed Downlink Packet Access (HSDPA) is an emerging wireless cellular-based system that promises a peak data rate of up to 14 Mbps. HSDPA relies on new technologies that make it possible to achieve such high data rate. These new technologies include: adaptive modulation and coding, hybrid automatic repeat request, fast cell selection and fast packet scheduling. In this paper, we propose an efficient packet scheduling algorithm for HSDPA to provide priority scheduling between non-real time services of different Quality of Service (QoS) classes and fairness between users within the same class. Simulation results show that the proposed algorithm meets QoS requirements of different non-real time traffic classes, with the proposed algorithm having a better performance compared to existing algorithm, Max-CIR, in the literature.

HSDPA is a 3.5G wireless cellular system that can offer data rates of up to 14.4 Mbps which is much beyond what 2.5G and 3G cellular systems could offer. Due to its high supportable data rate, HSDPA can support a wide range of applications with diverse QoS. Data generated by these applications consists of different traffic classes; including conversational, streaming, interactive and background; each having certain QoS requirements. HSDPA scheduling schemes need to accommodate these traffic classes as well as provide certain QoS provisions for them. This paper proposes a delay-based scheduler (DBS) that prioritizes traffic based on its delay requirements. In addition, the DBS is designed to achieve a high throughput and a fair allocation of resources among users. Simulation results indicate that the DBS achieves a lower average queuing delay while maintaining other performance metrics when compared with existing HSDPA scheduling schemes.

Abstract Effective support of real‐time multimedia applications in wireless access networks,viz. cellular networks and wireless LANs, requires a dynamic bandwidth adaptation framework where the bandwidth of an ongoing call is continuously monitored and adjusted. Since bandwidth is a scarce resource in wireless networking, it needs to be carefully allocated amidst competing connections with different Quality of Service (QoS) requirements. In this paper, we propose a new framework called QoS‐adaptive multimedia wireless access (QoS‐AMWA) for supporting heterogeneous traffic with different QoS requirements in wireless cellular networks. The QoS‐AMWA framework combines the following components: (i) a threshold‐based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold‐type connection admission control algorithm, and (iii) a bandwidth adaptation algorithm that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. The framework can be modeled as a multi‐dimensional Markov chain, and therefore, a product‐form solution is provided. The QoS metrics—new call blocking probability (NCBP), handoff call dropping probability (HCDB), and degradation probability (DP)—are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability and maintaining the bandwidth utilization efficiently. Copyright © 2006 John Wiley & Sons, Ltd.

Channel dependant scheduling schemes for High Speed Downlink Packet Access (HSDPA) system such as Max CIR and Proportional Fairness (PF) have been proven to provide a significant throughput gain by exploiting the channel fluctuations of the users. However, their inability to ensure a fair distribution of the radio resources among the mobile users has been a major concern. In earlier study [6], we proposed a Fair and Efficient Channel Dependent (FECD) algorithm for HSDPA to provide a priority scheduling between users based on their instantaneous channel conditions and their average throughputs. The FECD algorithm aims at increasing the data rates of the users by exploiting the variations of their channel conditions while at the same time ensure a fair distribution of the radio resources. We evaluated the performance of the FECD algorithm in Pedestrian A environment. In this paper, however, we extended our simulation model to include evaluating the proposed algorithm in Vehicle A environment. Simulation results show that the proposed algorithm in both environments outperforms the maximum CIR and the Proportional Fair schemes in terms of providing minimum throughput assurance and, therefore, it has a better degree of fairness.

In this paper, we propose a novel packet scheduling algorithm, based on the CBQ scheduling policy for QoS provisioning in multimedia cellular networks. CBQ, based on service class differentiation, aims at maximizing the use of available radio resource and meeting the QoS requirement of higher priority users as much as possible while maintaining the minimum requirements of lower priority users, especially when the system suffers from congestion. In this policy, CBQ is combined to a Dynamic Priority Function. It considers the realistic behaviour of traffic, taken into account while considering the spatial variation of the system characterizing both the user mobility and the signal propagation impairments due to the surrounding effects. The new scheduling policy provides enhanced traffic performance in heterogeneous environments and achieves a good level of capacity gain.

In this paper, we use the stochastic control technique Markov Decision Process (MDP) to study and examine the relationship between optimal decision, which the service provider should apply, and traffic parameters in wireless heterogeneous networks. The MDP model is formulated as a linear programming problem with objectives of maximizing the system utilization while ensuring that mobile users are experiencing low dropping rates which is translated in satisfying their Quality of Service (QoS) requirements. Simulation results show the strength of our contribution.

Channel dependant scheduling schemes for high speed downlink packet access (HSDPA) system such as Max CIR and proportional fairness (PF) have been proven to provide a significant throughput gain by exploiting the channel fluctuations of the users. However, their inability to ensure a fair distribution of the radio resources among the mobile users has been a major concern. In this paper, we propose a novel Medium Access Control Packet Scheduler (MAC-PS) scheme for HSDPA that is motivated by realistic economic models to satisfy the mobile users as well as the service providers through the use of utility and opportunity cost functions. Simulation results reveal the superiority of the MAC-PS in terms of fairness, user satisfaction and flexibility.

In this paper, we propose a new distributed weighted clustering algorithm with local cluster-heads election (WCA-L) based on an on-demand distributed clustering algorithm for multi-hop packet radio networks. The multi-hop packet radio networks, also named mobile ad hoc networks (MANETs) have a dynamic topology due to the mobility of their nodes. This mobility makes the challenge harder for routing protocol. Moreover, the well known routing protocols are not able to offer QoS that is why we need to manage MANETs. Such task can be done using clustering techniques but the association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The nodes called cluster-heads form a dominant set and determine the topology and its stability. Simulation experiments are conducted to evaluate the stability of the dominant set in terms of updates of the dominant set, handovers of a node between two clusters and the QoS in terms of packet delivery rate and overhead provided by both our algorithm (WCA-L) and the weighted clustering algorithm (WCA), which does not consider prediction and local election. Results show that our algorithm performs better than WCA.

The Publish-Subscribe middleware has been widely used to provide asynchronous communication functionality in distributed applications. Due to lack of communication infrastructure, frequent node mobility and limited resources, asynchronous publish-subscribe message middleware is a suitable communication model for mobile ad hoc networking environment, hi this paper, we propose an asynchronous publish-subscribe message middleware scheme for mobile ad hoc networks. The scheme supports disconnected operation and spontaneous communications. The proposed publish- subscribe mechanism is based on topics. The message publisher broadcasts an advertisement to its neighbor brokers, and receivers utilize subscription packets to set up connections with publisher. With the spread of advertisements, the system can also use advertisements to do the network reconfiguration. The main features of the proposed middleware scheme are: efficient, flexible, mobility awareness, network reconfiguration and cross-layer interaction support. The performance of the protocol is evaluated using simulation in terms of packet delivery ratio and end-to-end packet delay. These metrics are investigated as a function of node moving speed. The results show that our scheme can support intermittent connectivity and asynchronous communication environment that characterizes mobile ad hoc networks.

Major research challenges in the next generation (4G) of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end Quality of Service (QoS) and enabling users to specify their personal preferences. Under this motivation, we design a novel cross-layer architecture that provides context-awareness, smart handoff and mobility control in heterogeneous wireless IP networks. We develop a Transport and Application Layer Architecture for vertical Mobility with Context-awareness (Tramcar). Tramcar is tailored for a variety of different network technologies with different characteristics and has the ability of adapting to changing environment conditions and unpredictable background traffic. Furthermore, Tramcar allows users to identify and prioritize their preferences. Simulation results demonstrate that Tramcar increases user satisfaction levels and network throughput under rough network conditions and reduces overall handoff latencies.

The allocation of scare spectral resources to support as many user running multimedia applications as possible while maintaining acceptable quality of service (QoS) is a fundamental problem in next generation wireless cellular networks. In this paper, we propose an adaptive bandwidth framework which aims at providing an effective management of the limited radio resources in wireless cellular networks. The framework consists of two integrated components, call admission control module and adaptive bandwidth allocation (ABA) module. The framework is designed to take advantage of the ABA module with new calls and handoff calls in order to provide an acceptable trade off between new call blocking and handoff call dropping probabilities. The elaborate simulation is conducted to verify the performance of the framework in terms of new call blocking probability, handoff call dropping probability, and bandwidth utilization

Next generation wireless cellular networks aim to provide guaranteed Quality of Service (QoS) for multimedia applications. However, bandwidth is an extremely scarce resource in such networks that required an efficient and effective management to enhance the network performance and provide better services for mobile users. In this paper, we propose a QoS adaptive multimedia service framework for controlling the multimedia traffic in cellular-based multimedia wireless networks. The frame operates at the connection-level where the bandwidth of ongoing connections can be dynamically adjusted to provide an acceptable trade off level between connection blocking and dropping probabilities for different traffic class. The proposed framework is designed to take advantage of the Adaptive Bandwidth Allocation (ABA) algorithm with new calls in order to enhance the system utilization and blocking probability of new calls. The performance of our framework is compared to a framework previously proposed in [4]. Simulation results show that our QoS adaptive multimedia service framework outperforms the previous framework in terms of connection blocking probability, connection dropping probability, and bandwidth utilization.

Hierarchical routing and clustering mechanisms in Wireless Sensor Networks (WSN) help to reduce the energy consumption and the overhead created when all the sensor nodes in the network are sending information to the central data collection point or base station. Most of the routing and clustering protocols currently used or proposed for WSN assume that the nodes are stationary. However, in applications like habitat monitoring or search and rescue, that assumption makes those clustering mechanisms invalid, since the static nature of sensors is not real. In this thesis, we present a Cluster-based ROuting protocol for MObile Sensor networks (CROMOS). The protocol considers the following design aspects: mobility of sensors, zones and routes maintenance, information update and communication between sensor nodes. A simulation model has been designed and developed to evaluate the performance of the proposed protocol. Simulation results and comparisons with different scenarios show the effectiveness and strengths of the CROMOS protocol. CROMOS shows a low routing and mobility overhead, while achieving a good performance in WSN using small zone sizes and sensors with low speed.

A Wireless Sensor Network (WSN) is a collection of wireless sensor nodes forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, due to the limited range of each node's wireless transmissions, it may be necessary for one sensor node to ask for the aid of other sensor nodes in forwarding a packet to its destination, usually the base station. One big issue when designing wireless sensor network is the routing protocol to make the best use of the severe resource constraints presented by WSN, especially the energy limitation. In this paper, we propose a new scheme called EBMR: Energy-Balancing Multipath Routing Protocol that uses multipath alternately to prolong the lifetime of the network.

In this paper, we propose a new distributed mobility prediction-based weighted clustering algorithm with local cluster-heads election (MPWCA-L) based on an on-demand distributed clustering algorithm for multi-hop packet radio networks. The multi-hop packet radio networks, also named mobile ad hoc networks (MANETs) have a dynamic topology due to the mobility of their nodes. This mobility makes the challenge harder for routing protocol. Moreover, the well known routing protocols are not able to offer QoS that is why we need to manage MANETs. Such task can be done using clustering techniques but the association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The nodes called cluster-heads form a dominant set and determine the topology and its stability. Simulation experiments are conducted to evaluate the stability of the dominant set in terms of updates of the dominant set, handovers of a node between two clusters and the QoS in terms of packet delivery rate, end-to-end delay and overhead provided by both our algorithm (MPWCA-L) and the weighted clustering algorithm (WCA), which does not consider prediction and local cluster-heads election. Results show that our algorithm performs better than WCA

Summary form only given. In this paper we present a call admission control (CAC) policy for multimedia services that characterize the next generation of wireless cellular networks. The well-known CAC guard-channel policy is modified to maintain a pre-specified level of quality of services for multimedia calls. A semi-Markov decision process (SMDP) is used to represent the multi-class guard channel CAC policy with constraints on the dropping probabilities of multimedia handoff calls. The SMDP is formulated as a linear programming problem with the objectives of maximizing the system utilization and guaranteeing QoS of multiple classes of handoff calls with each class having particularly different QoS requirements. We show numerically that the multi-class guard channel policy deploying SMDP outperforms the existing upper-limit CAC policy as it improves the service quality by maximizing the bandwidth utilization while stratifying the quality of service constraint to upper bound of the handoff dropping probability.

The 3G UMTS is currently undergoing inherent changes. The expected release 5&6 of UMTS will contain a new set of features known collectively as high speed downlink packet access (HSDPA). In this paper, we propose two packet scheduling algorithms for HSDPA, shortest queue first (SQF) and longest queue first (LQF), to provide priority scheduling between services of different quality of service (QoS) classes and fairness between users within the same class. Simulation results show that the proposed algorithms meet QoS requirements of different traffic classes, with the SQF algorithm having a better performance compared to the LQF algorithm.

In this paper, we propose an adaptive bandwidth framework for supporting multiple classes of multimedia services with different quality of service (QoS) requirements in the next generation of wireless cellular networks. Three related components comprise the main building blocks of the framework. The core component is a threshold-based bandwidth allocation policy. The other two components are a call admission control algorithm and a bandwidth adaptation algorithm. The two algorithms are integrated into the framework to manage the network resources. We develop an analytical model to derive the QoS metrics new call blocking probability, handoff call dropping probability, and degradation probability. The model is based on a multi-dimensional Markov chain. The accuracy of the model is verified by comparison with simulation results. The simulation results show that the overall performance of our framework is very attractive in that the handoff call dropping probability is near zero (negligible).

Next generation wireless cellular networks aim at supporting wireless multimedia services with different classes of traffic that are characterized by diverse quality of service (QoS) and bandwidth requirements. They will use micro/picocellular architectures in order to provide higher capacity. However, small-size cells increase the handoff rate drastically. As a result, it is a challenge to provide stable QoS in these networks. In this paper, we present a novel dynamic call admission control (DyCAC) framework for next generation wireless cellular networks. The framework consists of the following components: (i) a threshold-based bandwidth reservation policy; (ii) a threshold update processing module; and (iii) an admission controller module. In this work, each base station locally, independently of other base stations in the network, differentiates between new and handoff calls for each class of traffic by assigning a threshold to each class according to its QoS requirements. The threshold values change dynamically and periodically in order to respond to the varying traffic conditions. The main feature of the proposed framework is its ability to simultaneously achieve several design goals. Numerical results show that our proposed DyCAC framework can guarantee the connection-level quality of service of individual traffic classes while maximizing resource utilization. As well, DyCAC requires low communication overhead, and is highly scalable.

Providing multimedia services with quality of service (QoS) guarantees in next generation wireless cellular networks poses great challenges due to the scarce radio bandwidth. Effective call admission control (CAC) is important for the efficient utilization of the limited bandwidth. In this paper we present an optimal Markov decision-based call admission control (MD-CAC) policy for the multimedia services that characterize the next generation of wireless cellular networks. A Markov decision process (MDP) is used to represent the CAC policy. The MD-CAC is formulated as a linear programming problem with the objectives of maximizing the system utilization while ensuring class differentiation and providing quantitative fairness guarantees among different classes of users. Through simulation, we show that the MD-CAC policy upholds the handoff call dropping probability required by each traffic class and provides fairness for all classes while maximizing the bandwidth utilization.

This paper examines quality of service (QoS) guarantees for mobile users in future wireless cellular networks supporting multiple classes of traffic with focus on reducing dropped handoff connections. We achieve this by proposing a threshold-based bandwidth reservation policy. The policy gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls according to their QoS constraints by reserving a maximum occupancy, i.e., a threshold, to each call class. The policy can be modeled as a multidimensional Markov chain where each dimension is represented as M/M//spl infin/ queuing system, and therefore, a product form solution is provided. The QoS metrics - new call blocking probability, handoff call dropping probability, and probability of unsuccessful call completion - are derived. The analytical results are supported by simulation and show that the policy is able to reduce the connection-level QoS handoff call dropping probability for each class of traffic. Thus, it satisfies mobile user's needs and thus resulting in a stable performance levels during heavy load periods.

The next generation of wireless cellular networks (WCNs) is expected to support real-time multimedia applications with different classes of traffic and diverse bandwidth requirements. Bandwidth is a scarce resource in wireless networking that needs to be carefully allocated amidst competing connections with different quality of service (QoS) requirements. In this paper, we propose an adaptive framework for supporting multiple classes of multimedia services with different QoS requirements in WCNs. The framework combines the following components: (i) a threshold-based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold-type call admission control (CAC) algorithm, and (iii) a bandwidth adaptation algorithm (BAA) that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. Numerical results show that the performance of our adaptive multimedia framework outperforms that of existing non-adaptive schemes in terms of the handoff call dropping probability and effective utilization.

In this paper, we propose an adaptive bandwidth framework for supporting multiple classes of multimedia services with different quality of service (QoS) requirements in the next generation of wireless cellular networks. The framework combines the following components: (i) a threshold-based bandwidth allocation policy. (ii) an efficient threshold-type call admission control (CAC) algorithm, and (iii) a bandwidth adaptation algorithm (BAA). The framework can be modeled as a multi-dimensional Markov chain, and therefore, a product form solution is provided. The QoS metrics - new call blocking probability, handoff call dropping probability, and degradation probability are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability.

Efficient call admission control (CAC) techniques are of paramount importance in UMTS networks to satisfy the quality of service (QoS) requirements of different traffic classes and to utilize the system resources in an efficient manner. In this paper, we propose a novel uplink CAC framework to enhance existing UMTS networks on three related accounts. First, we introduce a measurement-based component to calculate the current load of the system; second, this measurement-based component is integrated with a power prediction module to estimate the load increment that the new call will bring into the system; and third, the proposed framework feeds the results obtained to a call admission control algorithm with a QoS-enforcing mechanism that gives each class of traffic different treatment based on the QoS requirement of the connections. To the best of our knowledge, ours is a first attempt towards combining the above components into one uplink CAC framework that aims to enhance system performance and to achieve per-class QoS objectives. Simulation results show that the framework is able to reduce dropping ratio for active users to zero level. Thus, it satisfies mobile users' needs resulting in stable performance levels during heavy load periods. Furthermore, the framework provides a low blocking ratio for new calls, which translates into high resource utilization. This is a highly desirable property from the service provider point of view.

In this paper we develop an analytical threshold-based bandwidth allocation policy for 3G multi-class cellular networks. We consider the effects of user mobility when the cellular network supports multiple classes of connections having different QoS bandwidth requirements. The policy gives priority to handoff connections over new calls and prioritizes between different classes of handoff connections according to their QoS constraints by assigning a maximum occupancy, i.e. a threshold, to each connection class. The policy can be modeled as a multi-dimension Markov chain, and therefore, a product farm solution is provided. The QoS metrics - new call blocking probability, handoff call dropping probability, and probability of unsuccessful call completion are derived. We show numerically that this policy improves the service quality by minimizing handoff dropping probability and maximizing the bandwidth utilization i.e. by minimizing the new call blocking probability.