AI-powered face recognition has become essential to various IoT applications, including home automation, security systems, and personalized services. While these systems offer significant advancements, they still face critical challenges related to accuracy and privacy. One major issue is class imbalance, which is common in face recognition systems where certain demographic groups are underrepresented. This imbalance results in biased models, compromising the accuracy and fairness of these systems. Furthermore, traditional centralized training methods can expose sensitive facial data, raising serious privacy concerns. Federated Learning (FL) has emerged as a solution to improve model training by enabling collaboration across devices without sharing sensitive data. However, it also worsens the issue of data heterogeneity. This paper proposes a Hierarchical Federated Learning (HFL) framework to address class imbalance while preserving privacy. By aggregating local models at different hierarchical levels, the framework mitigates data imbalance and enhances fairness in face recognition systems. Additionally, a privacy-preserving mechanism based on Secure Multi-Party Computation (SMPC) is implemented to ensure data security during the training process.

The proliferation of Internet of Things (IoT) devices has intensified the need for scalable and energy-efficient federated learning (FL). While Hierarchical Federated Learning (HFL) improves scalability by adding an edge aggregation tier, it still suffers from high communication costs, slow convergence, and degraded accuracy under non-IID data. Existing methods such as quantization, sparsification, and static pruning alleviate specific bottlenecks but fail to jointly optimize efficiency, robustness, and accuracy. This paper proposes an intent-driven dual-layer model pruning framework for HFL, where an Energy Management System (EMS) and an Intent-driven Pruning Orchestrator (IDPO) dynamically translate system-level intents (e.g., energy minimization or accuracy preservation) into pruning actions at both edge and cloud layers. Experiments on MNIST, CIFAR-10, and FEMNIST show up to 41% smaller models, 12× faster training, 28–35% lower energy use, and +12.9% accuracy gain under non-IID data, establishing the framework as a robust and sustainable solution for IoT learning.

The growing sophistication and frequency of cyber threats in communication networks demand Intrusion Detection Systems (IDS) that adapt to evolving attack patterns.Traditional approaches, based on static rules or purely supervised models, often fail to recognize novel attacks, leaving critical infrastructures exposed.Reinforcement Learning (RL) provides a dynamic alternative by enabling agents to refine detection policies through continuous feedback.In this work, we propose a Qlearning-based Intrusion Detection (Q-ID) system and train it on the CICIDS2017 dataset.The RL formulation defines the state as the flow's feature vector, the action as the classification decision, and the reward as +1 for correct predictions and -1 otherwise.To ensure stable convergence, the reward is integrated with cross-entropy loss in a hybrid objective, allowing continued improvement even after the supervised component has plateaued.Unlike prior IDS methods that rely solely on offline supervised training, our approach fuses reinforcement feedback with supervised optimization to support adaptive and robust detection.Experimental results, conducted under class imbalance and realistic evaluation splits, show that the proposed system achieves 99.3% accuracy, outperforming strong baselines including deep neural networks and traditional classifiers.Moreover, the RL agent demonstrates robustness under skewed traffic distributions and adaptability to previously unseen attack types.These results highlight reinforcement learning as a promising paradigm for building resilient IDS in critical communication environments.

The explosive growth of data-intensive applications and Generative AI (GAI) workloads has led to unprecedented demands on computing resources. Virtual Machine (VM) migration is pivotal in dynamically adjusting resource distribution across workloads. However, traditional reactive approaches often fall short in adapting to network changes and dynamic workloads. This paper presents a Digital Twin (DT) framework integrated with a data center to enhance resource utilization. The framework combines real-time monitoring, time-series forecasting, and Mixed-Integer Linear Programming (MILP) optimization to predict future bandwidth demands, CPU utilization, and memory usage, thereby determining optimal VM migration and placement strategies. Our analysis reveals that integrating forecasting with optimization reduces CPU load variance across servers by up to 50%. Compared to prior reactive or heuristic-based methods, our forecast-aware optimization framework achieves significantly more uniform resource distribution. Furthermore, capping server CPU load at 50% of capacity nearly eliminates variance, underscoring the effectiveness of the proposed model in stabilizing data center workloads.

We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains, with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach. The prevailing consensus mechanisms, initially crafted for cryptocurrency applications, prove unsuitable for the unique dynamics of supply chain systems. Unlike the broad inclusivity of cryptocurrency networks, our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria. The delineation of roles for supply chain participants within the consensus process becomes paramount. While reputation serves as a well-established quality parameter in various domains, its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory. Moreover, recognizing the primary role of efficient block verification in blockchain-enabled supply chains, our work introduces a comprehensive reputation model. This model strategically selects a leader node to orchestrate the entire block mining process within the consensus. Additionally, we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model. Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism, contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.

The increasing importance of intravehicular health monitoring systems (IVHMSs) necessitates robust communication protocols within vehicles, especially with the integration of the Internet of Things (IoT). This integration presents challenges in enhancing vehicle health monitoring efficiency due to the constrained space for numerous sensing devices, leading to scalability and performance issues. Existing medium access control (MAC) schemes often result in congestion, decreased throughput, and increased delays. This study proposes a scalable hybrid MAC strategy to improve throughput and reduce congestion in IVHMS. The approach features a two-phase communication model—nonemergency and emergency phases—for scalability. Our hybrid MAC strategy combines a history-based approach for emergency communication and a priority-based approach for nonemergency Communication. A Markov chain model evaluates the expected throughput and delay of the proposed MAC strategy, with numerical analysis validating the approach. Results show the hybrid MAC achieves a 66.7% throughput improvement over the history-based strategy and a 16.7% improvement over the priority-based approach while effectively reducing data collisions and delays. Furthermore, the hybrid MAC demonstrates an 8.3% increase in throughput compared to a previously proposed distributed hybrid MAC. Implementing this hybrid MAC in industrial-scale vehicular health monitoring can enhance vehicle safety, benefiting manufacturers and passengers alike.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection$({\mathrm { AP}}_{s})$is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.

The rapid expansion of the Internet of Things (IoT) and network services has revolutionized technology, enabling numerous intelligent applications. However, this interconnected environment also introduces significant security challenges, particularly the susceptibility of federated learning (FL) systems to poisoning attacks. Such attacks compromise the integrity of the global model by injecting malicious data, leading to inaccurate predictions and potentially endangering system reliability and user safety. While traditional approaches, such as early detection and secure aggregation methods, aim to prevent the aggregation of malicious updates, they are ineffective in addressing threats within systems that have already been compromised and did not initially implement these safeguards. This gap highlights the urgent need for robust post-compromise mitigation strategies in FL security. To address this challenge, we introduce “Unlearning as a Service for Safeguarding Federated Learning” (UaaS-SFL), a novel service designed to seamlessly integrate with any FL management system to remove the impact of poisoning clients and restore the integrity of the global model. UaaS-SFL effectively unlearns the contributions of malicious clients, ensuring both model security and system reliability. Our empirical evaluations, conducted in a simulated IoT environment, demonstrate that our service maintains model accuracy with less than a 10% deviation from the baseline achieved through retraining from scratch, underscoring the efficacy of our methodology in safeguarding FL systems. These results highlight UaaS-SFL as a critical service for securing FL management systems, providing a robust foundation for the continued growth of secure and intelligent IoT applications.

Packet classification plays a crucial role in numerous network services, including Quality of Service (QoS), Firewalls, and data streaming. In our research, we introduce a cutting-edge method for packet classification utilizing a modified multi-dimensional range tree, which enables effective rule storage and swift querying. This method provides a comprehensive solution for packet classification across various rulesets. Our use of the multi-dimensional tree structure significantly enhances the efficiency of packet classification, leading to better overall performance. The worst-case time complexity for our method is$O(\log^{d}n)$, with$n$representing the number of rules and$d$the number of dimensions/fields. Our method surpasses PartitionSort, which has a worst-case time complexity of$O(n^{1/d})$, underscoring the improved efficiency of our technique. Our approach includes two types of classifiers: one that identifies the first applicable rule (Classify First), and another that finds the most specific rule (Classify Specific) for a given packet. To assess the effectiveness of our method, we performed empirical tests using the Classbench dataset to simulate real-world conditions. We created standard 5-tuple rulesets of various sizes and seeds and compared our method with other techniques such as PartitionSort, Priority Tuple Space Search (TSS), HyperSplit and HyperCuts. Our findings show that our first rule classifier surpasses other techniques in all ruleset sizes, being 600% faster than PartitionSort for 64k rulesets. The specific rule classifier also shows increased efficiency, nearly matching PartitionSort for smaller rule sets and exceeding it by 30% for 64k size rulesets. Furthermore, our Classify First approach is shown to be empirically faster than other well-studied techniques such as SmartSplit, HyperSplit, HyperCuts, TSS, and others.

Nowadays VANETs are being used to ensure road safety and reduce traffic congestion. Vehicles exchange real time position with one another through basic safety messages and these periodic updates must be authenticated for safety purpose. Malicious vehicles can broadcast spoofed position and such an insider attack cannot be dealt with cryptography and digital signature. However, previous machine learning-based approaches that used distributed data-centric misbehaviour detection schemes could detect such attacks in most of the scenarios. However, in case of smarter attacks in a sparse network (where the attacker vehicle pretend to be stand still even though it is on the move), previous solutions could not always detect such attacks. We propose a novel detection scheme which outperforms previously published methods in this specific scenario. For evaluating the performance of this approach, we have used VeReMi dataset, a public repository for the malicious node detection in VANETs. We combine three consecutive basic safety messages and surpass currently existing methods in detecting eventual stop attacks, especially in sparse networks. Our proposed method can help in securing VANET, thereby preventing fatal accidents.

In this paper, we present a new mechanism for estimating the data sending rates of BBR congestion control algorithm using machine learning. Results show that machine learning can estimate the data sending rates of BBR with high accuracy.

Smart cities rely heavily on surveillance cameras for urban management and security. However, the extensive use of these cameras also raises significant concerns regarding data privacy. Unauthorized access to facial data captured by these cameras and the potential for misuse of this data poses serious threats to individuals’ privacy. Current privacy preservation solutions often compromise data usability with noise application-based approaches and vulnerable centralized data handling settings. To address these privacy challenges, we propose a novel approach that combines Adversarial Machine Learning (AML) with Federated Learning (FL). Our approach involves the use of a noise generator that perturbs surveillance data right from the source before they leave the surveillance cameras. By exclusively training the Federated Learning model on these perturbed samples, we ensure that sensitive biometric features are not shared with centralized servers. Instead, such data remains on local devices (e.g., cameras), thereby ensuring that data privacy is maintained. We performed a thorough real-world evaluation of the proposed method and achieved an accuracy of around 99.95% in standard machine learning settings. In distributed settings, we achieved an accuracy of around 96.24% using federated learning, demonstrating the practicality and effectiveness of the proposed solution. 1 1 The code is available at: https://github.com/farah-wahida/Privacy-Preserving-Face-Recognition-in-Distributed-Smart-City-Surveillance .

The blockchain-based Internet of Things (IoT) has served widely across various industries for authentication, cooperation, and data sharing but faced the severe challenge of storage scalability. The storage burden gets worse for IoT devices with limited resources. The state data is essential for efficient transaction issuance and validation. This article proposes the Verkle accumulator-based stateless transaction validation (VA-STV) scheme for permissionless blockchains to decrease the storage burden of the state data on each node with the acceptable overhead of computation and communication. In the scheme, the current state is summarized as the commitment maintained in the latest block header, and one witness is generated for each token to guarantee its validity. State transitions are realized by updating the commitment and witnesses so that no state is stored on nodes acting as validators and miners. Only the nodes acting as traders should maintain the tokens controlled by themselves and the witnesses locally. The VA-STV is based on the Verkle accumulator (VA), which is a combination of the Verkle tree (VT) and the KZG polynomial commitment scheme. Simulation results show that the VA-STV provides a smaller witness size ($0.6\times $–$0.74\times $) and faster commitment generation ($6\times $–$14\times$) than the existing stateless schemes in the same settings, which indicates the advantages of VA-STV in succinctness and efficiency. Besides, a tradeoff between the communication and computation requirements can be achieved by adjusting the branching factor, which improves the adaptability of the proposed scheme for different IoT scenarios.

Vehicle-to-Everything (V2X) is an important application scenario in 6G, where secure transmission is crucial in vehicular networks. Thus, this paper explores the application of an intelligent reflecting surface (IRS) in secure multipleinput multiple-output (MIMO) communication systems, which is subject to an eavesdropper equipped with multiple antennas. We formulate the secrecy rate maximization problem by jointly designing the transmit beamforming and the IRS phase-shift. Due to the coupling of the variables, the formulated problem is non-convex and thus we split the original problem into two sub-problems. For the two sub-problems, we first relax the sub-problem into a semi-definite program problem and solve it with the CVX tools. To further provide more insights into the calculation of the IRS phase-shift, we proposed the Riemannian manifold optimization (RMO) and majorization minimization (MM) algorithms to derive the closed-form solution of this subproblem. The numerical results validate that: 1) Through the proposed RMO and MM algorithms, the computation complexity is effectively reduced; and 2) the secure performance is significantly improved by the IRS.

To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.

Federated learning offers a partial safeguard for participants’ data privacy. Nevertheless, the current absence of an efficient privacy-preserving federated learning technology tailored for the Internet of Things (IoT) poses a challenge. Numerous privacy-preserving federated learning frameworks have been proposed, primarily relying on homomorphic cryptosystems, yet their suitability for IoT remains limited. Furthermore, the application of federated learning in IoT confronts two significant obstacles: mitigating the substantial communication costs and communication failure rates, and effectively discerning and utilizing high-quality data while discarding low-quality data for collaborative modeling purposes. In order to address these challenges, this paper introduces a privacy-preserving optimal aggregation federated learning framework that relies on the utilization of the multi-key EC-ElGamal cryptosystem (MEEC) and the federated sum optimization algorithm (FSOA), which are characterized by their lightweight nature and fair properties. The proposed MEEC approach aims to tackle the issue of multi-key collaborative computing within the context of federated learning, thereby resulting in reduced communication costs and enhanced communication efficiency. This is achieved through the leverage of the EC-ElGamal cryptosystem, which is known for its ability to generate short keys and ciphertexts. Furthermore, this paper presents a dynamic federated learning framework that incorporates user dynamic quit and join algorithms. The primary objective of this framework is to mitigate the adverse effects of communication failures and enhance power computation on IoT devices. Additionally, an FSOA is devised to ensure the acquisition of optimal training data, thereby preventing the inclusion of low-quality data in the training process. Subsequently, the proposed scheme undergoes rigorous security analysis and performance evaluation. The obtained results unequivocally demonstrate that our scheme outperforms existing solutions in terms of security, practicality, and efficiency with lower communication and computational costs.

Multimedia significantly enhances modern healthcare by facilitating the analysis and sharing of diverse data, including medical images, videos, and sensor data. Integrating AI for multimedia data classification shows promise in improving healthcare services, data analysis, and decision-making. However, ensuring privacy in AI-integrated healthcare systems remains a challenge, especially with data continuously transmitted over networks. Synchronous Federated Learning (FL) is designed to address these privacy concerns by allowing end devices to collaboratively train a machine learning model without sharing data. Nonetheless, FL alone does not fully resolve privacy issues and faces efficiency challenges, particularly with devices of varying computational capabilities. In this article, we introduce an Asynchronous Partial Privacy-preserving Split-Federated Learning (APP-SplitFed) approach for smart healthcare systems. This method reduces computational demands on resource-limited devices and uses a weight-based aggregation method to allow devices of differing computational power to contribute effectively, ensuring optimal model performance and rapid convergence. Additionally, we incorporate a secure aggregation method to prevent adversaries from identifying individual models owned by healthcare institutions.

Since the introduction of Bitcoin in 2008, blockchain technology has found widespread applications across various domains. While blockchain offers convenience and immense research value, it also raises privacy and security concerns among users and society at large. Notably, numerous studies have demonstrated the vulnerability of blockchain anonymity. Existing solutions based on bloom filters and SGX(Software Guard Extensions) may safeguard users' access patterns but remain susceptible to novel attacks, including protocol-level and side-channel attacks. To address these issues, we propose a Tree-based Oblivious Random Access Protocol (Tree-ORAP) that not only provides access pattern protection in privacy-preserving blockchain systems but also preserves the original blockchain performance. Furthermore, we design a Tree-ORAP State Version Controller to manage state synchronization across nodes in a multi-client blockchain network. We also analyze the system's security and implement a Tree-ORAP prototype, conducting a series of experiments to demonstrate its efficiency and technical feasibility. In summary, our protocol offers enhanced protection for blockchain systems against a wider range of attacks compared to previous methods, all while maintaining superior security performance and equal or better efficiency.

Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications.Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently.MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency.Although MPTCP has shown better performance than TCP in many applications, it has its own challenges.The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly.Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows.This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine Learning) approaches.This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications.This paper also provides details on the simulation of MPTCP and its implementations in real environments.

In recent years, multiunmanned aerial vehicular systems (MUAVs) have become prevalent in divergent applications: agriculture, spectrum utilization, transportation, forest fire monitoring, and among others, due to their flexible, robust, and autonomous operational maneuver. Battery-powered multiunmanned aerial vehicles (MUAVs) systems possess limited computation and communication resources, significantly reducing their functional dimension by limiting mission time and range. To address this issue, we propose a federated deep reinforcement learning (FDRL)-based intelligent and decentralized task offloading scheme for resource-constrained UAVs that can enhance the operational capability of the MUAV systems. Moreover, the proposed FDRL scheme can improve offloading policy quality while preserving data privacy in MUAV. However, such intelligent systems may fall prey to backdoor attacks that can intervene in the system’s regular operation causing rapid degradation of its performance. We introduce a novel triggerless backdoor attack scheme on intelligent task offloading UAVs and analyze its impact to gauge the resiliency of the offloading policy in the presence of an adversary. Then, we propose lightweight agnostic defense mechanisms to combat such backdoors in multi-UAV settings. The extensive simulation results show that the proposed attack and defense strategies are practical and efficient.

The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing smart devices in manufacturing and industrial processes. Industries based on IoT generate extensive data, typically analyzed using various machine learning (ML) models. Federated learning (FL) is an emerging, privacy-preserving ML method where clients train models locally and develop a global model based on the aggregation of local models, without sharing the local data set with a third party. However, FL methods struggle to achieve trustworthiness and incorporate accountable ML principles. Blockchain technologies are being developed across different industries to enhance trust and security. This article proposes a blockchain-enabled, verifiable model for securing FL within IoT systems. Our proposed framework combines a trusted execution platform (TEE) to secure each client’s local model training process, and multisignature-powered global model verification to ensure ML model verifiability. We conducted several experiments with different data sets to assess our proposed framework. The experiments demonstrated the high efficiency and scalability of the proposed framework.

The devices of the Internet of Things (IoT) are integrated and interconnected by using the traditional wireless communication technology. The unavailability of spectrum sharing occurs in traditional wireless communication due to the presence of a massive number of IoT devices. Thus, the cognitive radio network (CRN) is introduced as a promising technology to utilize the spectrum efficiently. Channel hopping sequence (CHS) is used to establish communication among CRN users. However, the majority of CHS mechanisms only focus on multiuser single-hop scenarios, which can lead to bottleneck and throughput degradation problems. It is also found that few existing CHS mechanisms still have a low percentage of rendezvous that can cause difficulties for the secondary users (SUs) to communicate. Thus, it is a challenge to design efficient CHS for establishing fast communication among SUs under multiuser, multihop scenarios, and asymmetric asynchronous environments. In this article, asymmetric synchronous and asymmetric asynchronous (ASAA) channel hopping (CH) algorithms are designed for the multiuser CRN-enabled IoT devices to share the unused spectrum in the multihop scenario. The multiuser asymmetric synchronous (MUAS) and multiuser asymmetric asynchronous (MUAA) protocols are designed in the proposed ASAA CH mechanism. The simulation results show that the proposed ASAA CHS algorithms outperform the existing CHS mechanisms in terms of throughput, channel loading (CL), channel utilization (CU), maximum time to rendezvous (MTTR), average time to rendezvous (ATTR), and maximum inter rendezvous interval (MIRI).

We propose a privacy-preserving ensemble infused enhanced deep neural network (DNN)-based learning framework in this article for Internet of Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, the edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local data set suffers from low accuracy, which can be improved by the aforementioned convergence and ensemble learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, ensemble learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a differential privacy-based privacy-preserving DNN with transfer learning for a local model generation to ensure minimal loss and higher efficiency at the edge server. We conduct several experiments to evaluate the performance of our proposed framework.

Due to advancements in semiconductor technologies, Internet of Things (IoT) applications have penetrated into a wide spectrum of aspects of human lives. This widespread penetration is also thanks to significant contributions from many emerging technologies, e.g., artificial intelligence (AI) and blockchain[1],[2]. The fast development of AI technologies like deep learning is a promising approach for extracting accurate information from massive raw sensor data in IoT applications[3]. In addition, due to its tamper-proof characteristic and distributed nature, blockchain has received increasing attentions in emerging IoT applications to tackle security and privacy issues[4],[5]. AI and blockchain have become killer technologies to advance the fast development of IoT ecosystems with incredible growth, impact, and potential.

The advancement of Industrial Internet of Things (IIoT) technology has resulted in the fourth industrial revolution, or Industry 4.0, enabling industries to enhance productivity. However, despite the benefits, there remain significant challenges, such as resource heterogeneity, communication efficiency, and data privacy, that limit the applications of IIoT in privacy-sensitive domains like healthcare. In order to protect data privacy, Federated Learning (FL) has been suggested as a solution, involving the sharing of model parameters rather than data itself. Current FL applications, however, still struggle with cost efficiency, especially when IIoT devices with heterogenous resources are involved. To address this, this paper proposes Digital Twin (DT) enabled Asynchronous SplitFed Learning (DT-ASFL) for classification tasks in the e-healthcare system over mobile networks. We first develop SplitFed Learning to introduce communication efficiency in the e-healthcare system, sending only extracted features during the learning process instead of the entire learning model. This enables resource-constrained devices to participate in the learning process by allowing the participants to train a partial learning model. DT is then employed to provide real-time statuses of IIoT devices deployed in the system, enabling asynchronous model updates in SplitFed Learning. The experimental results demonstrate the efficacy of DT-ASFL compared to the existing methods.

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL mitigates the privacy issues of the traditional collaborative learning model by aggregating multiple locally trained models without sharing any datasets among the participants. Nevertheless, the FL-based DL (FDL) model cannot be trusted as it is susceptible to intermediate results and data structure leakage during the model aggregation process. The proposed framework introduces an edge and cloud-powered service-oriented architecture identifying the key components and a service model for residual networks-based FDL with differential privacy for generating trustworthy locally trained models. The service model decomposes the functionality of the overall FDL process as services to ensure trustworthy execution through privacy preservation. Finally, we develop a privacy-preserving local model aggregation mechanism for FDL. We perform several experiments to assess the performance of the proposed framework.

The electric vehicle (EV) charging ecosystem, being a distinguishable paradigm of IIoT infrastructure, consists of distributed and complex hybrid systems that demand adaptive data-driven cyber-defense mechanisms to tackle the ever-growing attack vectors of cyber-physical systems. We propose an adaptive differential privacy-based federated learning framework for building a collaborative network intrusion detection system model for EV charging stations (EVCS). We use utility optimized local differential privacy to provide data privacy to the local network traffic data of each EVCS. Moreover, we propose a reinforcement learning-based intelligent privacy allocation mechanism at the EVCS level. The main significance of the proposed mechanism is that it can make privacy provisioning adaptive to the extent of privacy breaching rate, and dynamically optimize the privacy budget and the utility to avoid human intervention such as domain knowledge experts. The experimental results confirm the efficacy of our proposed mechanism and achieves appropriate privacy provisioning accuracy to approximately 95%.

This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of Things-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in artificial intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This article presents a secure multiparty computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

With the rapid growth in the demand for location-based services in indoor environments, wireless fingerprint localization has attracted increasing attention because of its high precision and easy implementation. However, an effective method does not exist owing to the problems of data loss, noise interference in the fingerprint database, and being time-consuming during the offline training phase. Therefore, this paper presents a novel indoor wireless fingerprint localization algorithm, termed BLS-Location, based on a broad learning system (BLS) that utilizes channel state information (CSI) to overcome the aforementioned problems. It includes an offline training phase and an online localization phase. In the offline training phase, the Kalman filter and the expectation-maximization (EM) algorithm are utilized for completing and denoising the data. Moreover, principal component analysis (PCA) is used to reconstruct the CSI data to reduce complexity and train the weights by BLS. In the online localization phase, we employ a novel probabilistic method based on the regression results of BLS to obtain the estimated location. The experimental results show that BLS-Location can significantly reduce the training time with a high accuracy, compared to several machine learning algorithms and four existing methods in two representative indoor environments.

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants’ side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.

Buffer capacities at routers are ever-increasing to accommodate the extreme-scale increase in the volume of incoming traffic. Sophisticated packet classification is being adopted to address challenges in meeting application demands. With the number of rules for classification and packets arriving at routers per second reaching 100's of thousands to millions, special hardware is also being built for packet classification to increase throughput at routers. Sorting packets (based on various conditions) in the buffers offers a significant advantage for the packet classification process. The sorting step is a time-consuming step given the large volume of packets in the incoming buffers. We propose a technique to approximately sort the packets by capping the maximum number of comparisons that can be made. We show that the performance of well-known decision tree-based packet classification methods such as HyperCuts, EffiCuts, and SmartSplits can be improved by more than 32% with approximate sorting for when the number of packets is less than 10K at any given time. If the number of packets in the arriving buffer is greater than 10K, we can divide them into smaller portions to achieve similar gains.

Mobile devices (MDs) have undergone a booming development, yet are still capacity limited in computation and energy resources and thus could face troubles when serving computation-intensive and delay-sensitive applications. Mobile-edge computing (MEC) has been proposed to accommodate MDs with both satisfactory latency and acceptable resources, by offloading MDs’ tasks to near-deployed edge servers (ESs). Whereas, offloading tasks solely to ESs are difficult to meet distinct requirements of various applications, which leads to the emergence of end–edge–cloud orchestrated computing (EECOC). However, studies on EECOC are still insufficient, most existing of which do not consider MDs’ dynamical movements partly due to the intractability of associating optimal ESs with moving MDs. To address the issue, a novel deep reinforcement learning (DRL)-based mobility-aware (MA) EECOC scheduling approach is proposed in this article. With the goal of minimizing maximal task latency, we first formulate and transform the optimization problem into a Markov decision problem (MDP). Then, we enable DRL with elaborately designed reward functions and integrate it with NoisyNet to obtain near-optimal solutions. Furthermore, a MA component based on ConvLSTM is developed to extract MDs’ temporal–spatial distribution features and predict their movements, which are further utilized to facilitate the decision making of computation-offloading and resource-allocation actions. Extensive experimental results indicate the promising performance improvements of our approach against the state-of-the-art approaches in various scenarios.

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

The complex factory environment of the Industrial Internet of Things (IIoT) greatly increases the energy consumption of sensor nodes and reduces production profits. Especially, in mines, the terrain will change continuously as the mining progresses. Additionally, the heavy traffic load on a single sink node and the unbalanced load on multiple sink nodes also reduce the battery life. Therefore, how to build an energy-efficient topology based on the unique mine terrain characteristics is a critical issue. To address this problem, this article proposes a 3-D topology evolution scheme with self-adaption for mining areas (3D-TES) to reduce energy consumption. We build the multipeak terrain model according to the characteristics of the mining environment. Blocked by the undulating peaks on the mine, the strength of the node signal is quantified by the slope and aspect. The 3D-TES is then applied to determine the optimal number of sink nodes and find the best data transmission path between sensor nodes and multiple sink nodes. The experimental results show that 3D-TES outperforms the directed angulation toward the sink node model (DASM) in terms of reliability, average path length, and data load on sink nodes.

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET ++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.

The Internet of Things (IoT) is transforming major industries, including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations, such as the amalgamation of software-defined networks (SDNs) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. The existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: 1) convolutional module (CNM); 2) randomization module; and 3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g., 91%-96%) with high model quality even under low privacy budgets (e.g., ε = 0.5).

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.

In Vehicular Ad Hoc Networks (VANETs), the unreliable wireless environment and highly dynamic topology make multi-content distribution inefficient due to the large amount of content requests from vehicles. This gives rise to the need for new content distribution schemes in VANETs. Current researches on content distribution for VANETs generally focus on the single type of content. In this paper, we consider both elastic (with no hard delay requirement) and inelastic (with a hard deadline) contents and propose a joint content distribution scheme in VANETs. We model the content distribution as an optimization problem which jointly minimizes the waiting delay of elastic requests and the failure ratio of inelastic requests. Then, we propose an efficient clonal selection-based approximate algorithm to solve the optimization problem. The performance of our scheme is evaluated by simulation using realistic vehicular traces. Simulation results show that our proposed scheme has better performance than previous solutions.

Underwater wireless sensor networks (UWSNs) have become crucial for many different applications, such as marine pastures, which needs stratified aquaculture according to the habitat and range of activities of marine organisms. This type of application poses a significant challenge to network topology because strengthening the monitoring of the living conditions and collecting underwater data on organisms in different regions is necessary. In this paper, a new concept of "subregional monitoring" is proposed, and subregions with frequent activities of marine organisms are taken as the key monitoring areas, i.e., the areas of interest (AOIs). Then, SFRG, an energy-balanced and robust topology based on scale-free network and rigid graph theory, is proposed to strengthen the monitoring of the AOIs. The topology of a rigid graph is constructed in AOI, and the entire network is scale-free network with power-law degree distribution, when the rigid graph is regarded as a "node". Furthermore, a transmission algorithm based on the SFRG is suggested to prolong the network lifetime. The algorithm guarantees that 90% of the received packets are from the AOIs. The simulation results demonstrate that the SFRG effectively prolongs the network lifetime and improves the network robustness, which provides a strong support in strengthening the monitoring of AOIs and balancing the energy consumption.

Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications, such as environmental monitoring, smart navigation, and smart healthcare come with new requirements, such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

Wireless sensor network (WSN) is an indispensible part of Internet of Things that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology, and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly toward security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this paper field based on our thorough survey and conclude this paper with possible future research directions.

In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

As one of the key services for non-safety applications in Vehicular Ad-hoc Networks (VANETs), the Popular Content Distribution (PCD) has become a hot issue in recent years. In popular content distribution, the On-Board Units (OBUs) passing the Area of Interest (AoI) receive popular content broadcast by the RoadSide Units (RSUs). However, due to the high speed of OBUs, limited bandwidth, and unstable wireless connections, only a portion of the popular content can be received by OBUs. To address this issue, in this paper, a cooperative V2V-aided transmission scheme based on a coalitional game (CVCG) is proposed. The scheme allows the OBUs to cooperate with their neighbors to provide the missing popular content. In addition, a coalition graph game algorithm is designed for optimizing the cooperative behaviors among OBUs. The performance of our CVCG scheme is evaluated by different metrics compared to other three content distribution schemes. The numerical results show that the proposed CVCG scheme could outperform the three schemes in terms of the number of iterations for 99 percent finished PCD, the average content completion percentage, and the number of completed OBUs.

Malware is one of the biggest threats for the privacy and security of the smart-phone users. Android is currently the most popular operating system for smart-phones; consequently, many malwares are directed toward Android devices. Existing techniques for malware detection tend to compromise between accuracy and computational complexity. In this paper, we have proposed a novel technique to monitor the behavior of both malwares and benign applications using system calls and have developed a mathematical model that can detect mobile mal-wares. We have extracted features by sequencing the system calls of these applications. We have proposed a novel way of feature reduction using Gaussian dissimilarity and compared our feature selection technique with existing methods. Using the extracted set of features, we have implemented a machine learning classifier, namely Gaussian Bayes classifier, on two different malware data-sets (obtained from Malware Genome Project and Android Malware Dataset by Arguslab) and on non-malware samples (obtained from Google Play Store). We have found that our model is quite lightweight yet powerful to detect malwares with significant accuracy of 98%.

Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due to obstacle shadowing regions with high vehicle densities, which leads to frequent disconnection problems and blocks radio wave propagation between vehicles. Previous researchers used multi-hop, vehicular cloud or roadside infrastructures to solve the routing issue among the vehicles, but they suffer from significant packet delays and frequent packet losses arising from obstacle shadowing. We proposed a vehicular cloud based hybrid technique called Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. The novelty of our approach lies in the fact that our proposed technique dynamically adapts between obstacle shadowing and non-obstacle shadowing regions. Simulation based performance analysis of Hybrid-Vehcloud showed improved performance over Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high vehicle densities.

The ubiquitous sensor-rich mobile devices (e.g., smartphones, wearable devices, and smart vehicles) have been playing a vital role in the evolution of the Internet of Things (IoT), which bridges the gap between digital and physical spaces. The powerful computing/communication capacities, huge population, and inherent mobility make mobile device networks a much more flexible and cost-effective IoT solution than traditional wireless sensor networks. However, the energy issue of mobile terminals poses significant challenges to the widespread use of IoT: not only the mobile terminals have short lifetime with the proliferation of mobile applications but also the current networking and communication technologies are not adequately taking the energy efficiency into account. Therefore, the sustainable issue of IoT has attracted considerable attention from both academia and industry. Wireless energy harvesting (EH), and transfer technology was recently proposed as an effective mean to address this issue. It enables the mobile terminals to harvest energy from the ambient environment to prolong its battery. Although some forms of EH have been applied to WSNs, networking and communication solutions must be redesigned for wireless powered IoT with massive number of mobile terminals.

Over the past couple of decades, transportation systems have begun to receive widespread attention from the scientific community and emerged toward Intelligent Transportation Systems (ITS). Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, and alleviate the impact of congestion; devising the ITS experience. Furthermore, during the past decades, the volume and density of vehicles increased significantly, especially the road traffic; this lead to a dramatic increase in the number of accidents and congestion, with negative impacts on the economy, environment, and quality of people’s lives. In particular, according to the World Health Organization (WHO), road traffic injuries are estimated to be the leading cause of death for young people aged 15–29 and the ninth cause of death worldwide in 2015. The enabling communication technologies are intended to realize the frameworks that will spur an array of applications and use cases in the domain of road safety, traffic efficiency, and driver’s assistance. Although these applications will allow the dissemination and gathering of useful information among vehicles and between transportation infrastructure and vehicles in pursuance of assisting drivers to travel safely and comfortably, much effort is required to implement these practices for the success of these applications.

Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behavior towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10 to 30 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.

Under the environment of the big data, the correlation between the data makes people have a greater demand for privacy. Moreover, the world has become more diversified and democratic than ever before. Freedom of speech is considered to be very important; thus, anonymity is also a very important security demand. The research of our paper proposes a scheme which can ensure both the privacy and the anonymity of a communication system, that is, the protection of message privacy while ensuring the users’ anonymity. It is based on anonymous identity‐based encryption (IBE), by which the users’ m e t a d a t a are protected. We implement our scheme in JAVA with Java pairing‐based cryptography library (JPBC); the experiment shows that our scheme has significant advantage in efficiency compared with other anonymous communication system. Internet‐of‐Things (IoT) involves many devices, and privacy of devices is very significant. Anonymous communication system provides a secure environment without leaking metadata, which has many application scenarios in IoT.

The next generation ubiquitous wireless networks will comprise of mobile users moving between heterogeneous networks with terminals having multiple access interfaces and services. Vertical handover techniques deal with the scenario where a user seamlessly moves between different network access technologies. The most crucial issue in such environment is seamless connectivity. Earlier works demand for infrastructural modification with cumbersome implementations. Here, we propose an automated, transparent, seamless, hybrid decision model based easy-to-implement vertical handover mechanism between WiFi and cellular network. The implementation of the mechanism does not supplant the current infrastructure. We carry out experiments on a testbed to demonstrate the efficacy of our proposed scheme. Experimental results show that a handover-held communication slightly differs from a handoverless communication in terms of diversified network metrics.

Automatic multi-document abstractive summarization system is used to summarize several documents into a short one with generated new sentences. Many of them are based on word-graph and ILP method, and lots of sentences are ignored because of the heavy computation load. To reduce computation and generate readable and informative summaries, we propose a novel abstractive multi-document summarization system based on chunk-graph (CG) and recurrent neural network language model (RNNLM). In our approach, A CG which is based on word-graph is constructed to organize all information in a sentence cluster, CG can reduce the size of graph and keep more semantic information than word-graph. We use beam search and character-level RNNLM to generate readable and informative summaries from the CG for each sentence cluster, RNNLM is a better model to evaluate sentence linguistic quality than n-gram language model. Experimental results show that our proposed system outperforms all baseline systems and reach the state-of-art systems, and the system with CG can generate better summaries than that with ordinary word-graph.

Large civil structures, such as bridges, buildings, and aerospace vehicles form the backbone of our society are critical to some catastrophic events such as damage. Wired sensor networks are usually adopted for structural health monitoring (SHM) applications. This is also an important Smart City application. Recent wireless sensor networks (WSNs) technology promises the eventual ability to cover such a structure and continuously monitor its health. However, researchers from both engineering and computer science domains face numerous hurdles, such as application-specific requirements, in reaching this goal. These hurdles have a cumulative effect on severely resource-constrained WSNs. This article provides a comprehensive investigation of WSN-based SHM applications with an emphasis on networking perspectives to get insights into a cyber-physical system (CPS) design. First , we provide the SHM philosophy and conduct extensive comparative studies regarding various aspects of benefits and hurdles of going wireless for SHM. Second , we propose a taxonomy of SHM techniques and their applicability to WSNs. Third , we show a transition from the WSN-based SHM towards the CPS design, expecting that such a design will mitigate WSN resource constraints and satisfy SHM application-specific requirements to a great extent. For each of these, we discuss a surge of existing schemes with an emphasis on limitations of the state-of-the-art, and we point out open issues. Finally , we propose a series of design guidelines for a potential CPS. This article will help both engineering and computer science domain researchers/engineers and respective communities in designing future CPS to ensure the economic benefit and public safety in functioning civil structures.

NEtwork MObility (NEMO) is a host-based mobility management protocol, which efficiently manages Internet connectivity of multiple nodes on the move. Proxy Mobile IPv6 (PMIPv6), a network-based mobility management protocol, tries to offload low-power mobile devices though managing mobility signaling by the infrastructure devices. To support network mobility in PMIPv6-domain, several schemes have been proposed: NEMO-enabled PMIPv6, PMIPv6-based NEMO and Network-based NEMO. However, there is no work which performs entity-wise cost analysis of PMIPv6 domain though the mobility management entities have limited resources. In this paper, we have developed an analytical framework to perform entity-wise cost evaluation of the above three NEMO schemes in PMIPv6 domain. The performance is evaluated with respect to location update cost, session continuity cost, packet delivery cost and handover cost. Our results show that Network-based NEMO performs better as it distributes its overall load among different network entities. The cost analysis framework presented in this paper will help in choosing the best PMIPv6-based NEMO scheme that imposes least load on the network infrastructure.

The last decade has witnessed a dramatic growth of social networks, such as Twitter, Sina Microblog, etc. Messages/short texts on these platforms are generally of limited length, causing difficulties for machines to understand. Moreover, it is rarely possible for users to read and understand all the content due to the large quantity. So it is imperative to cluster and extract the viewpoints of these short texts. To solve this, the representation of a word is enriched with additional features from external, but it is demanding in terms of computational and time resources. In this paper, we proposed OnSeS, a novel short text summarization method which makes full use of word2vec to represent a word and utilizes neural network model to generate each word of the summary. OnSeS consists of three phrases: 1) clustering short texts using the K-means algorithm; 2) ranking content of each cluster by building a graph-based ranking model using BM25; 3) generating main point of each cluster with the help of neural machine translation model on the top ranked sentence. The experimental results reveal that our proposed fully data-driven approach outperforms state-of-the-art method.

Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al. [1] and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.

Scheduling of real-time tasks on a multi-core processor is challenging due to the execution time being a nondeterministic value. Through studying the relationship between task affinity and execution time, we propose an accelerated multi-core real-time scheduling algorithm (RM-λ) for periodic and dependent real-time tasks on a homogeneous multi-core processor based on acceleration between tasks to obtain a real-time scheduling scheme with less resource utilization. We adopt an acceleration factor matrix to represent the degree of affinity and develop a real-time scheduling model to find the best accelerated pair. The heterogeneous multi-core architectures can execute tasks by sharing their dependent data on L1 Cache. The results demonstrate our approach can loosens the schedulability constraints of RM (maximum improvement of 25%) so that an un-schedulable real-time tasks set on a single-core processor might be schedulable, and for those still hard to be scheduled, could be made schedulable on a multi-core processor.

Abstract Several great features offered by wireless sensor networks (WSN) result in its wide deployment in various remote and continuous monitoring applications. As such, managing huge collected readings in this domain posted many challenges due to its design limitations. In order to provide seamless data transmission, which is of utmost importance in those delay‐sensitive applications, minimum delay and packet loss occurrence should be considered. Specifically, this paper addresses the common issue of congested networks in WSN with the combination technique of variance‐based distributed contention control (DCC‐V) and packet size optimization. The proposed integration technique, which operates on medium access control layer, takes into consideration the packet size advantages as it plays a key role in determining successful data delivery, given the error‐prone nature of WSN. While ensuring fewer corrupted packets, the proposed contention window (CW) in DCC‐V minimizes the chances of packet collisions and so alleviates congestion. In this technique, CW is determined based on slot utilization and average collision values, which also involve standard deviation measurements. Simulation analysis using network simulator‐2 shows outstanding performance of the proposed solution compared with the existing IEEE 802.15.4 protocol. Copyright © 2014 John Wiley & Sons, Ltd.

The usage of Unmanned Aerial Vehicles (UAVs) is increasing day by day.In recent years, UAVs are being used in increasing number of civil applications, such as policing, firefighting, etc in addition to military applications.Instead of using one large UAV, multiple UAVs are nowadays used for higher coverage area and accuracy.Therefore, networking models are required to allow two or more UAV nodes to communicate directly or via relay node(s).Flying Ad-Hoc Networks (FANETs) are formed which is basically an ad hoc network for UAVs.This is relatively a new technology in network family where requirements vary largely from traditional networking model, such as Mobile Ad-hoc Networks and Vehicular Ad-hoc Networks.In this paper, Flying Ad-Hoc Networks are surveyed along with its challenges compared to traditional ad hoc networks.The existing routing protocols for FANETs are then classified into six major categories which are critically analyzed and compared based on various performance criteria.Our comparative analysis will help network engineers in choosing appropriate routing protocols based on the specific scenario where the FANET will be deployed.

Internet usage over mobile devices is on the rise. The bandwidth demand for mobile Internet access is also increasing with the number of mobile users. To answer users' demand, carrier aggregation is proposed in LTE-A system. In carrier aggregation, the best available one or more component carriers of each band are assigned to each user to provide efficient services. Several works have been reported in the literature on mandatory and periodic component carrier assignment methods. Although the previous works, especially periodic component carrier assignment methods, have significantly improved performance of LTE and LTE- A systems, many limitations still exist. One limitation of previous works is that data transfer is interrupted during periodic component carrier assignment operation which can decrease performance of the system. Therefore, in this paper, selective periodic component carrier assignment technique which allows continues data transfer during periodic carrier assignment operations is proposed. Results show that the proposed technique increases throughput rate up to 25% and decreases average delay time up to 35%.

Large multimedia files are accessed by a high number of mobile users over the Internet. Therefore, the bandwidth demand for mobile Internet access is increasing exponentially. To answer users' demand, carrier aggregation is proposed in LTE-A. In carrier aggregation, the best available one or more component carriers of each band are assigned as primary and secondary component carriers to each user for efficient services. The previous works have significantly improved the performance of LTE-A by using efficient component carrier assignment methods. The previous works, however, have two limitations. First, they have not investigated which of joint or partial secondary component carrier assignment techniques shows better performance. Second, overall system performance is analyzed in order to evaluate methods by ignoring system behavior such as packet drops and delay metrics during carrier reassignment operations. Therefore, in this paper, firstly, packet drops and delay which are experienced by users during the carrier reassignment process are investigated. Secondly, joint and partial secondary component carrier assignment techniques are compared. Results show that the partial carrier assignment technique increases efficiency of resource usage, throughput rate up to 15% and decreases average delay time up to 12%.

The number of mobile users is rapidly increasing. Therefore, the bandwidth demand of mobile users significantly growing. To answer users' demand, Carrier Aggregation is proposed in LTE systems. In Carrier Aggregation, communication between users and base stations are achieved by multi bands which have different coverage areas and mobile users can simultaneously connect one or multi carrier components in each band. Because of mobility of users and quality of carriers, the best available Carrier Components of each band should be assigned to each user in order to provide desired service to users. Several works have been proposed in the literature to address Carrier Components assignment methods in LTE systems by using Channel Quality Indicator, quality of service and service types. Although the previous works on carrier assignment methods significantly increase the performance of LTE system, continuously increasing bandwidth demand of users forces the operators to manage resource allocation more intelligently. Therefore, we have proposed a novel Carrier Component assignment method which considers user profiles and channel quality indicator to increase quality of services and experiences getting by mobile users. Results show that the proposed method uses system resources efficiently and can improve performance of LTE systems. Our method will help service providers build efficient carrier components assignment methods through considering user profile and performance metrics, such as band usage, throughput and delay.

Abnormalities in sensed data streams indicate the spread of malicious attacks, hardware failure and software corruption among the different nodes in a wireless sensor network. These factors of node infection can affect generated and incoming data streams resulting in high chances of inaccurate data, misleading packet translation, wrong decision making and severe communication disruption. This problem is detrimental to real-time applications having stringent quality-of-service (QoS) requirements. The sensed data from other uninfected regions might also get stuck in an infected region should no prior alternative arrangements are made. Although several existing methods (BOUNDHOLE and GAR) can be used to mitigate these issues, their performance is bounded by some limitations, mainly the high risk of falling into routing loops and involvement in unnecessary transmissions. This paper provides a solution to by-pass the infected nodes dynamically using a twin rolling balls technique and also divert the packets that are trapped inside the identified area. The identification of infected nodes is done by adapting a Fuzzy data clustering approach which classifies the nodes based on the fraction of anomalous data that is detected in individual data streams. This information is then used in the proposed by-passed routing (BPR) which rotates two balls in two directions simultaneously: clockwise and counter-clockwise. The first node that hits any ball in any direction and is uninfected, is selected as the next hop. We are also concerned with the incoming packets or the packets-on-the-fly that may be affected when this problem occurs. Besides solving both of the problems in the existing methods, the proposed BPR technique has greatly improved the studied QoS parameters as shown by almost 40 percent increase in the overall performance.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. On the contrary, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, there are several proposed scheduling algorithms in the literature. However, these scheduling algorithms are limited because they do not consider different types of customers. Our objective is to satisfy performance expectations of customers by proposing an efficient Dynamic Dedicated Server Scheduling (DDSS) while considering different types of customers. Results show that the customer drop rate and throughput can be significantly improved by DDSS. Our proposed scheduling and related analysis will help cloud service providers build efficient cloud computing service architectures through considering different types of priority class performances such as, drop rate, throughput, and utilization.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. However, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, several scheduling algorithms have been proposed in the literature. However, these scheduling algorithms do not consider heterogeneous servers or different priority classes of customers. Our objective is to satisfy performance expectations of customers by proposing a Heterogeneous Dynamic Dedicated Server Scheduling (h-DDSS) while considering heterogeneous servers and different priority classes of customers. Results show that h-DDSS architecture can provide improved customer throughput and drop rate over homogeneous DDSS in cloud computing. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient cloud service architectures which are adaptable to homogeneous and heterogeneous environments by considering different types of priority class performances, such as, drop rate, throughput, and utilization.

To facilitate higher bandwidth for multimedia traffic, modern routers support simultaneous multi-band communication, leading to less interference, higher capacity and better reliability. However, there is lack of quantitative evaluation to judge whether multi-band is better than single band router in realistic scenarios. Our objective is to propose a scheduling algorithm for multi-band routers and compare single band and multi-band system with different allocation policies. We have used different scheduling algorithms for multi-band routers which transmit different classes of traffic through different frequency bands, thereby achieving improved performance. By comparing multi-band and single band mobile router performances, we have found out that one of them is not always better than the other although multi-band is expected to have better performance.

Recent trends in Internet usage have seen large amount of multimedia data due to increasingly large number of mobile users. To facilitate higher bandwidth, modern mobile routers are capable of supporting simultaneous multi-band, leading to less interference, higher capacity. However, there exists no previous work that attempts to maximize utilization through sharing of traffic among different frequency bands. In this paper, we have proposed a novel scheduling algorithm for multi-band mobile routers which transmits different classes of traffic through different frequency bands to achieve improved performance. We have developed an analytical model to perform queuing analysis of the proposed multi-band system and derived various performance metrics, validated by extensive simulations. Our results show that the proposed architecture can ensure maximum possible utilization through sharing of capacities among the bands. Our proposed scheduling algorithm and related analytical model can help network engineers build next generation mobile routers with higher throughput and utilization ensuring least packet loss for different classes of traffic.

Partitioning and offloading some parts of mobile applications onto remote servers is a promising approach to extend the battery life of mobile devices. However, since available network bandwidths vary in a wireless environment, static partitionings proposed by previous works with a fixed bandwidth assumption are unsuitable for mobile platforms, while dynamic partitionings result in high overhead due to continuously partitioning. Targeting this problem, we propose a novel partitioning scheme taking the bandwidth as a variable to improve static partitioning and avoid high costs of dynamical partitioning. Based on the application Object Relation Graph, we propose a partitioning optimization model and two bandwidth-adaptive partitioning algorithms: Branch-and-Bound based Application Partitioning (BBAP) and Min-Cut based Greedy Application Partitioning (MCGAP). BBAP is suitable for obtaining the optimal partitionings for small applications, while MCGAP is applicable to large-scale applications by quickly obtaining suboptimal solutions. Experimental results demonstrate that both algorithms can adapt to bandwidth fluctuations well, and significantly reduce the execution time and energy consumption by optimally distributing components between mobile devices and servers.

Network mobility (NEMO) protocols are required to maintain connectivity of ongoing sessions for mobile networks that can be formed in bus, train, aircrafts with a wide variety of on-board IP-enabled devices. However, basic NEMO suffers from large delay and packet loss and fails to ensure seamless handover. Multi-homed network mobility solutions can facilitate seamless handover of mobile networks by making use of multiple network interfaces of NEMO. Previous works have not evaluated and compared the performance of multi-homed network mobility protocols that work in two different layers: network layer and transport layer. In this work, we compare the performance of two multi-homed network mobility protocols that exploits make-before-break strategy to ensure seamless handover for NEMO. Using experimental testbed, we have presented a detailed performance evaluation of these two multihomed NEMO protocols. Results demonstrate that transport layer-based mobility solution performs better than network layer-based protocol when the handing off between heterogeneous-capacity networks.

To facilitate seamless Internet connectivity to mobile nodes, we earlier proposed SIGMA, a transport layer mobility management protocol. However, frequent handoff of the mobile nodes produce excessive signaling overhead in the bandwidth-limited air interfaces and cause large handover delay, thereby degrading the performance. Hierarchical mobility management for SIGMA aims at resolving this problem by introducing micro-mobility agents in the hierarchy. In this paper, we perform a comprehensive cost and efficiency analysis of the key mobility management entities of Hierarchical SIGMA taking into account all possible costs. We present numerical results comparing Hierarchical SIGMA with standard hierarchical mobility protocol. Our results demonstrate that Hierarchical SIGMA requires less cost and yields better performance. Our analysis can be used as a framework to compare performance and signaling overheads of mobility protocols for next generation networks.

Mobile networks can be formed in vehicles, ships, satellites with a wide variety of on-board IP-enable devices. In spite of the fact that the connection configuration of the components inside the mobile network can influence its total cost and performance regarding mobility management, there exists no standard topological configuration of the mobile network. In this paper, we have performed a cost analysis of the key components of the nested mobile network considering all possible costs. We have chosen several topological configurations to evaluate their cost and performance. Our analysis will help in selecting a mobile network configuration that minimizes the total costs while maximizing efficiency for data communication.

Mobile networks can be formed in bus, train, aircrafts, satellites with a wide variety of on-board IP-enabled devices and Network Mobility (NEMO) protocols are required to support uninterrupted services to ongoing sessions. Earlier works have not demonstrated seamless handover for NEMO architecture. In this work, we proposed a handover scheme for NEMO that exploits the multi-homing feature of the Mobile Router and uses make-before-break strategy to ensure seamless handover for NEMO. Using experimental testbed, we have presented a thorough handoff performance evaluation of multihomed NEMO and compared it with basic NEMO. Results demonstrate that the proposed multihomed NEMO outperforms the basic NEMO while achieving seamless handover.

Network Mobility (NEMO) efficiently manages the mobility of multiple nodes that moves together as a mobile network. A major limitation of the basic protocol in NEMO is the inefficient route between end hosts. A number of prefix delegation-based schemes have been proposed in the literature to solve the route optimization problem in NEMO. Approaches used by the schemes trade off delivery of packets through the partially optimized route with signaling and other processing overheads. Cost of delivering packets through the partially optimized route along with signaling and processing cost need to be measured to find out the gain from tradeoff. However, cost analysis performed so far on NEMO protocols consider only the cost of signaling. In this paper, we have developed analytical framework to measure the costs of the basic protocol for NEMO, and four representative prefix delegation-based schemes. Our results show that cost of packet delivery through the partially optimized route dominates over other costs. Therefore, optimizing the route completely is preferable to reduction of signaling as far as cost of network mobility is concerned. Our cost analysis framework will help in decision making to select the best route optimization scheme depending on the load imposed by the scheme on the infrastructure.

Seamless IP-diversity based NEtwork MObility (SINEMO) was proposed to address a number of drawbacks of the Network Mobility (NEMO) protocol for mobility management of a number of hosts moving together in a Local Area Network. Increasing number of mobile hosts results in higher level of signalling cost on the mobility agents in a mobility protocol. Previous works on cost analysis of mobility protocols have considered the cost on some of the mobility agents, but did not consider all possible costs for mobility management, resulting in incomplete cost estimation. In this paper, we have developed analytical models to estimate costs of mobility management as functions of network size, mobility rate, traffic rate and data volume for all the entities of SINEMO. Numerical results, comparing the cost between mobility entities of SINEMO and NEMO, reveal that SINEMO has higher efficiency and lower cost than NEMO. Our comprehensive cost model can be used as a framework for estimating total cost of key mobility management entities of different handover protocols, and can aid in decision making to choose the most efficient protocol for future all-IP mobile and wireless networks.

The term mobile FSO refers to wireless optical nodes with mobility capabilities. Mobile FSO has emerged as a way to combat some of limitations facing FSO links, i.e. incompatibility of transmitters and receivers relative to movement and misalignment. In this paper we utilize an angular diversity model for characterizing the performance of mobile FSO over atmospheric turbulence. Switch-and-examine combining (SEC) technique is our suggestion for the diversity combining solution in such unbalanced multi-transceiving configuration. The channel fading is modeled as a lognormal distribution with spatially correlated samples. Analytical/ statistical discussion on the resultant output is presented and bit error performance and processing load are numerically evaluated and compared to a selection combining (SC) diversity.

Typical intelligent transportation systems (ITS) are comprised of geographically distributed ITS devices including sensors, cameras and dynamic message signs (DMS). There are several options for providing data communication between these field devices and traffic management centers (TMC). Wireless networks are attractive due to their relatively lower cost and ease of deployment compared to fixed networks. However, these face unique security and signal interference problems, and deploying new wireless networks can require significant equipment investment. In this work, a new extremely low-cost wireless strategy for ITS network communication is presented. This approach applies control channels of the Network Trunking System (NTS), a licensed radio frequency to support the voice communication, to control DMS operation under the National Transportation Communications for ITS Protocol (NTCIP). Because the NTS network has already installed in the metropolitan, urban, and rural areas in Oklahoma, it provides a cheap deployment solution requiring only additional adapter devices. Long term operation and maintenance costs can further be amortized between voice and data services. The proposed technique is compared to the existing ITS wireless networking strategies and hybrid network strategies merging wired and wireless networks deployed in Oklahoma. Details of the strategy for using wireless networking in the Oklahoma ITS and experiences with wireless ITS device deployment are also provided.

ABSTRACT Wireless optical nodes with mobility capabilities (mobile free space optics [FSO]) have emerged as a way to overcome limitations inherent in FSO links, for example, transmitter–receiver restrictive alignment requirement. This paper proposes the use of an angular diversity model for characterizing the performance of mobile FSO over atmospheric turbulence, and transmitter–receiver misalignment switch‐and‐examine combining technique is suggested for a diversity combining under unbalanced multireceiving configuration. A laser beam is modeled as spatial Gaussian profile, and channel fading is modeled as a lognormal distribution with spatially uncorrelated samples. Analytical and statistical discussion on the resultant output is presented, and bit error performance and processing load are numerically evaluated and compared with those obtained using selection combining diversity schemes. Copyright © 2011 John Wiley & Sons, Ltd.

Hybrid Free Space Optics (FSO)/Radio Frequency (RF) communication systems have emerged as a way to improve network performance by providing enhanced availability and reliability. In an effort to mitigate individual drawbacks in the optical link during adverse weather conditions, network traffic flows simultaneously between channels. Based on the Shannon-Hartley theorem, channel capacity is dependent, among others, on both the signal to noise ratio (SNR) and channel bandwidth. As such, combined link throughput may be affected by channel state conditions. Because atmosphere turbulence can be modeled as a time-varying fading channel, capacity analysis can be investigated. In this paper, authors apply various availability scenarios of channel state information (CSI) on the optical link to derive closed-form expressions for combined link capacity. Numerical simulation provides a comparison to the results.

Internet mobility protocols are designed to support handover between different wireless networks. Many of them, such as Mobile IPv6, suffer problems such as high handover latency, high packet overhead, high packet loss during handoff, requirement for infrastructure change, etc. To solve these problems, SEamless MObility using SHIM6 (SEMO6), a multihoming based mobility protocol framework for host mobility, has been proposed. The goal of this paper is to experimentally validate that SEMO6 can perform better than MIPv6. We show that SEMO6 can improve the performance of applications in IP-based mobile networks.

The traffic characteristics in recent years have changed considerably having more video and audio traffic with critical real-time constraints. This situation is compounded by the increased number of mobile nodes communicating over Internet. Due to such high data traffic, the access routers are often overloaded with packets with different priority. The access routers have to act quickly enough to avoid loss of connection of mobile nodes' ongoing communication. However, there is a lack of analytical models that focus on the internal queue management access router of IP-mobility protocols. In this paper, we have developed an analytical model using multi-class non-preemptive priority queues to measure average queuing delay, queue occupancy, and packet drop probability for different classes of data and signaling traffic in the access router. We present numerical results that reflects the impact of node density, service rate and traffic pattern on these measures. The analytical framework presented in this paper will be helpful to better manage access routers with different classes of data and signaling traffic causing least queuing delay and packet loss.

To facilitate mobile computing, mobility protocols have been proposed and mobility management involves signaling costs. Widespread use of IP-enabled mobile devices have resulted in increase in number of mobile users in the network and the signaling cost on underlying mobility entities have increased significantly, which will result in performance degradation of the mobility protocols. However, there has been no comprehensive cost analysis of mobility protocol entities that considers all possible costs for mobility management. In this paper, we have developed analytical models to estimate total costs of mobility management entities of SIGMA, an IP-diversity based seamless handover protocol. We have presented numerical results to demonstrate the impact of increased of network size, mobility rate, traffic rate and data volume on these costs. Our results show that a significant amount of resources are required by the mobility entities for transmission, processing of various signaling messages, as well as searching location database. Our cost analysis will thus help network engineers estimate actual resource requirements of the network in future design.

A number of prefix delegation-based schemes have been proposed to solve the route optimization problem in NEMO, where a group of hosts move together as a mobile network. The schemes trade off between inefficiency of routes and various overheads. With the rapid growth of mobile computing, this overhead will give rise to the scalability issue of these schemes. However, there has been no quantitative study on the asymptotic scalability analysis of these schemes. In this paper, we have developed analytical models for scalability analysis of these schemes in terms of network size, mobility rate, distance between mobility agents, and traffic rate. Our analysis shows that the prefix delegation-based schemes exhibit asymptotically identical overhead on the network, and they show better asymptotical scalability in terms of number of mobile routers. The analytical framework for scalability analysis presented in this paper will help in visualizing the effects of future network expansion on the performance of these route optimization schemes of NEMO.

Ad hoc networks are characterized by nodes which are usually connected by low data rate Radio Frequency (RF) links when compared to optical links. RF links make it challenging for transmitting video and running high bandwidth applications over ad hoc networks. The objective of this paper is to develop a novel routing protocol for Ad hoc networks consisting of hybrid nodes using high bandwidth Free Space Optical (FSO) and RF links, with FSO being the primary link and RF as backup in case of FSO link failures. Our protocol, called Ad hoc On-demand Distance Vector hybrid (AODVH), computes multiple "FSO only" paths to ensure high data rate communication, and uses "hybrid paths" consisting of FSO and RF links as backups. AODVH can be used to design a rapidly deployable, reliable and high bandwidth communication infrastructure for re-establishing communication following a disaster. We evaluate the performance of AODVH using ns-2 simulations and compare with three other Ad hoc routing protocols. Results show that AODVH performs better in terms of packet loss, end-to-end delay, overhead, packet delivery ratio, route discovery frequency and throughput when compared to the other three protocols.

The purpose of devising a hybrid FSO/RF (Free Space Optics/Radio Frequency) system is to allow for reconfiguration, which enables an increased availability and reliability grade of the communication system. In the event of loss of an active FSO link, path reconfiguration ensures utilization of the FSO path queued next in priority. Thus, communication can be maintained in the optical domain with increased efficiency for a longer period. However, for all its benefits, path reconfiguration may inadvertently cause increased system delay and lower throughput, as well as packet loss. Given these advantages and disadvantages, path reconfiguration studies for hybrid systems are of great importance. A mathematical investigation and statistical consideration of path reconfiguration probability is the focus of this theoretical paper. A closed-form expression of the reconfiguration probability will be derived. The first-order Markov chain will provide a mathematically tractable model for atmosphere turbulence fading channels, as it uses only the received SNR of the FSO symbol immediately preceding the current one. Results show that increasing the intensity of turbulence-induced lognormal fading for FSO link will not necessarily increase the reconfiguration probability.

Among the route optimization schemes in NEtwork MObility (NEMO), the prefix delegation-based schemes perform better than other schemes. Since the prefix delegation-based schemes are designed for communication between a mobile network and a wired network, they lack evaluation for the case of intra mobile network communication involving MIPv6 incapable hosts in a mobile network. We evaluate prefix delegation-based schemes to reveal their inefficiencies for the case where both the communicating hosts are mobile and MIPv6 incapable, propose extensions, and compare them. Since both the communicating hosts are mobile, handoff latency in extended schemes are large resulting in significant performance loss when the speed of the mobile network is high. Results reveal that the effect of speed of the mobile network dominates the performance in such cases. We conclude that for slow moving mobile networks, extended schemes are preferable.

Node mobility has a direct impact on the performance evaluation of various network mobility protocols. Unfortunately, most of the analysis on mobility protocols used Random Waypoint mobility model which does not represent real-world movement patterns of mobile nodes. In this paper, we have analyzed City Section mobility model, a realistic mobility model for the movement in the city streets. We have developed an analytical model to derive certain stochastic properties, such as, expected epoch length, expected epoch time, expected number of subnet crossings and subnet residence time of this model. Finally, we have applied the model to calculate the signaling cost of NEMO BSP and compared it with Random Waypoint mobility model. Results show that the use of realistic mobility model leads to better estimation of the signaling cost of network mobility protocol.

In network layer based mobility protocols, such as mobile IP, the correspondent node is not aware of handovers and therefore continues to send data at the same rate as before the handover which may exhibit network unfriendliness, especially when the capacities of the two subnets differ significantly. On the contrary, transport layer based mobility protocols, such as SIGMA, do not show such phenomena. The objective of this paper is to evaluate and compare the network friendliness of network and transport layer based mobility protocols. Experimental results obtained from laboratory prototypes show that network unfriendliness is inevitable in the network layer based mobility protocols during handover when there is a large difference in capacities between the two networks. Transport layer based mobility protocols are, however, more network friendly than network layer based protocols.

Most current web application use HTTP (Hyper Text Transfer Protocol) and TCP (Transmission Control Protocol) to retrieve objects from the Internet. SCTP (Stream Control Transmission Protocol) is recently proposed transport protocol with congestion control mechanism similar to that of TCP. Waiting delay is an important performance criterion for when transferring web object over the Internet. In this paper, we present an analytical model of mean waiting delay for object transfers over the Internet in a wireless using the SCTP as the transport protocol and compare with the mean waiting in using TCP. Validation of the model using experimental results show that the mean waiting delay for HTTP over SCTP is less than that for HTTP over TCP. This is caused by the small slow-start time of SCTP.

Among the schemes, proposed in the literature to solve the route optimization problem in NEtwork Mobility (NEMO), the prefix delegation-based schemes perform better than other schemes. Depending on the way the prefix are delegated, the prefix delegation-based schemes result in difference in performance under different speed at various distance from the home network (network to which mobile network usually belongs). Therefore, there is a need to evaluate the performance of individual prefix delegation-based scheme to find an appropriate scheme based on speed and distance from home network. In this paper, we identify the differences of the prefix delegation-based schemes using simulation. Results reveal that performance of the schemes depends on speed of the network and distance from home network.

In this study, the authors address a mobile web agent framework based on stream control transmission protocol (SCTP) to overcome the deficiencies, which the typical mobile web agent framework based on transmission control protocol (TCP) suffers from, such as performance degradation, head-of-line blocking and unsupported mobility in mobile wireless environment. The proposed SCTP-based mobile web agent framework supports seamless transport layer mobility in a ubiquitous environment. It consists of an application engine and a transport engine, to use the hypertext transfer protocol and to deploy SCTP with dynamic address reconfiguration, respectively. The authors explore and describe the components necessary to implement the proposed mobile web agent framework in an ubiquitous environment. The performance of the proposed SCTP-based mobile web agent is compared with that of a typical TCP-based mobile web agent using ns-2 simulator. The simulation results show that the proposed web agent based on SCTP has a remarkably lower mean response time than a typical web agent based on TCP.

To increase the availability of free space network (FSO) networks, the optical link is provided with a redundant backup network, such as RF, to form a hybrid network. The immediate switching from FSO to RF in hybrid networks leads to suboptimal utilization of FSO network. In this work, a network of hybrid nodes, where each node is equipped with multiple transceivers and has multiple associations with other nodes of the network is proposed. Alternate FSO paths are established and prioritized through these multiple associations via intermediate nodes. On the loss of an active FSO link, path reconfiguration takes place, and the FSO path next in priority is utilized. Thus, communication can be maintained in the optical domain for a longer period, and efficiency is increased. A hardware testbed with four hybrid nodes having multiple IR transceivers to establish optical links was constructed to validate proof of concept. Dynamic path reconfiguration in the optical domain was demonstrated in the testbed by blocking different transceivers of the nodes. We measure network performance in terms of end-to-end delay along alternative FSO paths. Results show how the proposed path reconfiguration algorithm can provide performance improvement in hybrid networks.

IETF has proposed mobile IPv6-based network mobility (NEMO) basic support protocol (BSP) to support network mobility. NEMO BSP inherits all the drawbacks of mobile IPv6, such as inefficient routing path, single point of failure, high handover latency and high packet overhead. To address these drawbacks, a new network mobility scheme, called seamless IP-diversity based network mobility (SINEMO), has been proposed. The goal of this paper is to validate with experimental data that SINEMO performs better than NEMO BSP. We show that SINEMO can improve the performance of IP based mobile networks.

Existing terminal oriented handover mechanisms capable of meeting the strict delay bounds of real time applications such as VoIP do not consider the QoS of candidate handover networks. In this paper ECHO - a QoS based handover solution for VoIP- is proposed. ECHO is endpoint centric and does not require any network support; it leverages the SCTP transport protocol. ECHO incorporates network metrics that directly affect VoIP quality into the handover decision process. A dynamic variant of the ITU-T E-Model is used to calculate how the network metrics map to a user perceived voice quality metric known as the MOS. The MOS value is then used to make handover decisions between each of the available access networks. The results show that the addition of the QoS capabilities significantly improves the handover decisions that are made.

Wireless networks are becoming more heterogeneous; different classes of networks co-exist and users want to connect to any available network, anytime. So, it is important to have a mobility management scheme that can manage handoff for both inter-class and intra-class mobility so that the users can connect to and roam between any network. We propose an end-to- end mobility management scheme, Multi-class SIGMA (mSIGMA), that performs soft handoff for inter-class and intra-class mobility in wireless network. Our analysis shows mSIGMA performs seamless handoff across networks with low delay and packet loss. We have also shown though experimental analysis that mSIGMA is implementable with existing networking technologies and can perform handoff efficiently.

SIGMA is an IP diversity-based mobility management scheme that aims to reduce handoff latency and packet loss of mobile IP. In this paper, we propose a power-aware version of SIGMA (called PEARS) which reduces power consumption by intelligently switching off network devices, and improves handoff performance by reducing the number of unnecessary handoffs. The effectiveness of PEARS and SIGMA have been evaluated on a Linux-based experimental testbed. PEARS has been found to require considerably less power than SIGMA. The schemes used in PEARS can also be used to reduce power consumption of multihomed mobile nodes and routers.

IETF has proposed mobile IPv6-based network mobility (NEMO) basic support protocol (BSP) to support network mobility. NEMO BSP inherits all the drawbacks of mobile IPv6, such as inefficient routing path, single point of failure, high handover latency and packet loss, and high packet overhead. To address these drawbacks, we proposed an IP diversity-based network mobility management scheme called seamless IP-diversity based network mobility (SINEMO). In this paper, we develop analytical models to analyze and compare the performance of SINEMO and NEMO BSP. Our analysis shows that SINEMO enhances the performance of network mobility compared to NEMO BSP. We have implemented a testbed for SINEMO to support our claim of SINEMO's high performance.

We present a new multi-rate architecture for decoding irregular LDPC codes in IEEE 802.16e WiMax standard. The proposed architecture utilizes the value-reuse property of offset min-sum, block-serial scheduling of computations and turbo decoding message passing algorithm. The decoder has the following advantages: 55% savings in memory, reduction of routers by 50%, and increase of throughput by 2times when compared to the recent state-of-the-art decoder architectures.

Current Web applications use HTTP (hyper text transfer protocol) over TCP (transmission control protocol) to retrieve objects over the Internet. SCTP (stream control transmission protocol) is a recently proposed transport protocol with very similar congestion control mechanisms as TCP, except the initial congestion window during the slow start phase. In this paper, we present an analytical model of object transfer latency during the slow start phase for HTTP over SCTP and compare with the latency of HTTP over TCP. Validation of the model using experimental results, show that the average object transfer latency for HTTP over SCTP during the slow start phase is less than that for HTTP over TCP by 11%.

We have developed an exact model and algorithm for the delay-constrained minimum cost loop problem (DC-MCLP) of finding broadcast loops from a source node. While the traditional minimum cost loop problem (MCLP) deals with only the traffic capacity constraint served by a port of source node, the DC-MCLP deals with the mean network delay and traffic capacity constraints simultaneously. The DC-MCLP consists of finding a set of minimum cost loops to link end-user nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-MCLP, the objective function is to minimise the total link cost. We have formulated the DC-MCLP and proposed an exact algorithm for its solution. The proposed algorithm is composed of two phases: in the first phase, it generates feasible paths to satisfy the traffic capacity constraint; in the second phase it finds the exact loop topology through matching and allocating optimal link capacity to satisfy the mean delay constraint. In addition, we have derived several properties including the memory and time complexity of the proposed algorithm. Performance evaluation shows that the proposed algorithm has good efficiency for networks with less than thirty nodes and light traffic. Our proposed algorithm can be applied to find the broadcast loops for real-time multimedia traffic.

The delay-constrained capacitated minimum spanning tree (DC-CMST) problem of finding several broadcast trees from a source node is discussed. While the traditional CMST problem deals with only the traffic capacity constraint served by a port of the source node, and delay-constrained minimum spanning tree (DCMST) considers only the maximum end–end delay constraint, the DC-CMST problem deals with both the mean network delay and traffic capacity constraints. The DC-CMST problem consists of finding a set of minimum cost spanning trees to link end-nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-CMST problem, the objective function is to minimise the total link cost. A dynamic programming-based three-phase algorithm that solves the DC-CMST problem is proposed. In the first phase, the algorithm generates feasible solutions to satisfy the traffic capacity constraint. It finds the CMSTs in the second phase, and allocates the optimal link capacities to satisfy the mean delay constraint in the third phase. Performance evaluation shows that the proposed algorithm has good efficiency for any network with less than 30 nodes and light traffic. The proposed algorithm can be applied to any network regardless of its configuration, and used for the topological design of local networks and for efficient routing algorithms capable of constructing least cost broadcast trees.

IETF has developed Mobile IP to support mobility of IP hosts at the network layer. The national aeronautics and space administration has implemented mobile IP to handle handovers in space networks. Due to a number of limitations of Mobile IP, such as high handover latency, packet loss rate, and conflict with existing network security solutions, a new IP- diversity based mobility management scheme, called SIGMA, has been developed through collaborative efforts of NASA and University of Oklahoma. In this paper, we illustrate the performance of SIGMA for managing handovers in space networks. We show by simulation that SIGMA extends network connectivity from space to ground, and ensures smooth handover between spacecrafts for different space network scenarios.

IP mobility can be handled at different layers of the protocol stack. Mobile IP has been developed to handle mobility of Internet hosts at the network layer. As an alternative solution, a number of transport layer mobility protocols have been proposed. However, the location management schemes used in these transport layer solutions are not suitable for frequent mobile handovers due to user's high mobility. In this paper, we propose HiSlGMA, a hierarchical location management scheme for transport layer mobility schemes. We used an analytical model to evaluate HiSlGMA using signaling cost as the performance measure, followed by comparison of the signalling cost of HiSlGMA and hierarchical mobile IPv6 (an enhancement of Mobile IP) and existing transport layer mobility solutions.

HTTP (Hyper Text Transfer Protocol) and TCP (Transmission Control Protocol) are usually used to retrieve objects in the Internet. Stream Control Transmission Protocol (SCTP) has attractive multi-streaming feature, which allows for independent delivery among streams, thus can avoid the head-of-line blocking experienced by TCP and reduce the mean response time of users. We present an analytical model and algorithm to estimate the mean response time for HTTP over SCTP and compare with that of HTTP over TCP in the wireless environment. We validate the accuracy of our model using experiments. It is shown that mean response time for HTTP over SCTP is less than that for HTTP over TCP by 20% on average.

Paging is a well established technique to reduce signalling cost in mobile devices. Different proposals in literature have shown the implementation and improvement of Mobile IP with paging. Here we propose P-SIGMA, an improved and signalling cost-effective version of SIGMA, an IP diversity based end-to-end mobility scheme. We show that with all the benefits of end-to-end mobility management scheme like SIGMA, P-SIGMA, as a mobility management scheme, is more efficient in terms of l3ocation management and is more cost effective in terms of signalling load.

The throughput degradation of transport control protocol (TCP) over lossy links due to the coexistence of congestion losses and link corruption losses is very similar to the degradation of processor performance due to control hazards in CPU design. First, two types of loss events in networks with lossy links can be considered as two possibilities of a branching result (correct speculation vs. incorrect speculation) in a CPU. Secondly, both problems result in performance degradations in their application environments, i.e., penalties (in clock cycles) in a processor, and throughput degradation (in bit per second) in TCP networks. This has motivated us to apply speculative techniques (i.e., speculating on the outcome of branch predictions), used to overcome control dependencies in a processor, to TCP improvements when lossy links are involved in TCP connections. The objective of this paper is to propose a protocol-level speculation based TCP modification to improve its throughput over lossy links. Simulation results show that, compared to other prior research, our proposed algorithm significantly improves TCP throughput in a network with satellite links.

Stream control transmission protocol (SCTP) is a new transport layer protocol which is being standardized by IETF. Multi-homing, one of the most attractive features of SCTP, makes it competitive in high-availability and mobile environments. In this paper, we propose an analytical model for evaluating the performance of SCTP multi-homing. The proposed model has been validated against and found to agree well with simulation results.

In our earlier study, we proposed SIGMA, a seamless IP diversity based generalized mobility architecture. SIGMA utilizes IP diversity to achieve a seamless handover of a mobile host, and is designed to solve many of the drawbacks of mobile IP. In this paper, we compare the handover latency of SIGMA and recent MIPv6 enhancements, namely, FMIPv6, HMIPv6, and FHMIPv6. Various parameters are considered such as layer 2 handover/setup latency, IP address resolution latency, layer 2 beacon period, and mobile host moving speed. Our results show that SIGMA handover latency is insensitive to layer 2 setup latency, IP address resolution latency and beacon periods. Moreover, SIGMA is able to seamlessly handle relatively high speed movement of mobile host

Domain name system (DNS) can be deployed in the network as a location manager (LM) for mobility management. The suitability of domain name system (DNS) as an LM can be measured by how successfully it can serve to locate a mobile host. In this paper, we developed an analytical model to measure the performance of DNS as LM for mobility management techniques with IP diversity support based on success rate which takes into account the radius of the subnet, the residence time of MH in that subnet, latency in the network and the overlapping distance of two neighboring subnets. Our analysis shows that for a reasonable overlapping distance, DNS can serve as an LM with very high success rate even under some high network latency.

The Internet Engineering Task Force has developed Mobile IP to handle the mobility of Internet hosts at the network layer. Mobile IP, however, suffers from a number of drawbacks such as high handover latency, packet loss, and conflict with network security solutions. We describe and evaluate the performance of SIGMA (seamless IP diversity based generalized mobility architecture). SIGMA utilizes IP diversity to achieve the seamless handover of a mobile host, and is designed to solve many of the drawbacks of Mobile IP. Various aspects of the performance of SIGMA and Mobile IPv6 enhancements have been compared. Criteria for performance evaluation include handover latency, packet loss, throughput, and network friendliness.

Retransmission ambiguity, arising from delay spikes in a wireless mobile environment, results in poor TCP performance. Eifel improves the performance of TCP by using the timestamp option, which requires additional header bytes, resulting in increased overhead in bandwidth constrained wireless networks. Moreover, the destination needs to support the timestamp option. In this paper, we propose a new algorithm, called DualRTT, which increases the performance of TCP in the presence of delay spikes, without requiring any additional header bytes. It requires changes only at the sender, and hence is easier to deploy in the existing Internet infrastructure. It also does not require the destination to support the TCP timestamp option. Results show that DualRTT increases the performance of TCP, and also achieves a higher transport layer efficiency than previous algorithms.

This study deals with the delay-constrained minimum cost loop problem (DC-MCLP) of finding several loops from a source node. The DC-MCLP consists of finding a set of minimum cost loops to link end-nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-MCLP, the objective function is to minimize the total link cost. This paper proposes a dynamic programming based three phase algorithm that solves the DC-MCLP. In the first phase, the algorithm generates feasible solutions to satisfy the traffic capacity constraint. It finds the capacitated minimum loops in the second phase, and allocates the optimal link capacities to satisfy the mean delay constraint in the third phase. Performance evaluation shows that the proposed algorithm has good efficiency for network with less than thirty nodes and light traffic. Our proposed algorithm can be applied to any network regardless of its configuration, and used for the topological design of FDDI and SONET.

Mobility of Internet hosts allows computing nodes to move between subnets. Mobility can be handled at different layers of the protocol stack, with network and transport layer mobility being the most widely studied. Transport layer mobility can overcome many of the limitations of network layer schemes like mobile IP. Various approaches have been proposed to implement mobility in the transport layer. In this paper, we discuss a number of transport layer mobility schemes, classify them according to their approach, and compare them based on a number of evaluation criteria.

Mobile IP has been developed to handle mobility of Internet hosts at the network layer. Mobile IP (MIP), however, suffers from a number of drawbacks such as requirement of infrastructure change, high handover latency, high packet loss rate, and conflict with network security solutions. In this paper, we describe and evaluate the performance of SIGMA, a Seamless IP diversity-based Generalized Mobility Architecture. SIGMA utilizes multihoming to achieve seamless handover of mobile hosts, and is designed to solve many of the drawbacks of MIP, including requirement for changes in infrastructure. We first evaluate the signaling cost of SIGMA and compare with that of hierarchical Mobile IPv6 (an enhancement of Mobile IP) by analytical modeling, followed by comparison of handover performance of SIGMA and Mobile IPv6 enhancements. Criteria for performance evaluation include handover latency, packet loss, throughput, and network friendliness. Our results indicate that in most cases SIGMA has a lower signaling cost than Hierarchical Mobile IPv6. Moreover, for a typical network configuration, SIGMA has a higher handover performance over Mobile IP. Copyright © 2005 John Wiley & Sons, Ltd.

Wireless sensor networks is an emerging paradigm of computing and networking where a node may be selfpowered, and have sensing, computing, and communication capabilities. They have been proposed for use in a wide variety of applications. The objective of this article is to describe a wireless sensor network for monitoring of the health of aircraft engines. We describe the architecture of the wireless sensor network along with how it fits into the general area of wireless sensor networks.

In this paper, we study the impact of multistreaming on the performance of SCTP over satellite networks. We first show that multistreaming results in higher goodput than single streams when the receiver buffer is constrained as in the case of wireless handheld devices. We then demonstrate that the multistreaming feature of SCTP results in reduced buffer requirements at the receiver in the presence of losses in the satellite network. The above advantages makes SCTP an attractive transport protocol for wireless handheld devices.

TCP has been found to perform poorly in the presence of spurious timeouts (ST) caused by delay spikes which are especially more frequent in today's wireless mobile networks than in traditional wired network. Because STs are generally considered to represent a transient state in wired networks, previous research did not consider the effects of ST on the steady state performance of TCP. In this paper, we propose an analytical model for the sending rate and throughput of TCP Reno as a function of packet error rate and characteristics of spurious timeouts. The proposed model has been validated against simulation results and has been found to be more accurate than previous models in the presence of spurious timeouts.

Eifel has been proposed as a solution to enhance the performance of TCP during delay spikes in a wireless mobile environment. This paper evaluates and compares the performance of SCTP, TCP, and Eifel during delay spikes. We have shown that although Eifel performs better than TCP Reno and SCTP when there are no packet losses, the opposite is true when packets are lost in the presence of delay spikes. Our results also show that a higher link bandwidth does not always increase the data throughput of SCTP, TCP Reno, and Eifel.

Recent demand for real time applications has given rise to a need for quality of service (QoS) in the Internet. Differentiated Services is one such effort currently pursued by IETF. Previous researchers found unfairness in the DiffServ network. To solve the unfairness problem, we propose a new TSW based three color marker (ItswTCM) which achieves proportional fair sharing of excess bandwidth among aggregates. We have compared the fairness of our proposed ItswTCM marker with srTCM, trTCM, and tswTCM. Results show that our proposed marker performs better than the other three schemes for low to middle network provision level (20%-70%); we believe this is the region where all well provisioned networks will operate. Results also show that our proposed marker is not as sensitive to the number of flows as previous markers. We point out that yellow packets play a significant role in achieving proportional fair sharing of excess bandwidth among aggregates. We conclude that in order to achieve proportional fair sharing of excess bandwidth, it is important to inject the right amount of yellow packets into the network.

Studies have shown that the IETF recommended active queue management scheme, random early detection (RED) suffers from low throughput, large delay/jitter, and induces instability in networks. To improve the throughput and delay of the RED gateways, double slope RED (DSRED) was proposed and was shown to outperform RED in a homogeneous TCP/IP network. The objective of this paper is to evaluate DSRED in a heterogeneous network environment. Simulation results have shown that, in a heterogeneous network, DSRED results in better performance than RED in terms of throughput and queuing delay.

The Internet was initially designed for non real-time data communications and hence does not provide any quality of service (QoS). The next generation Internet will be characterized by high speed and QoS guarantees. The aim of this paper is to develop a prioritized early packet discard (PEPD) scheme for ATM switches to provide service differentiation and QoS guarantee to end applications running over the next generation Internet. The proposed PEPD scheme differs from previous schemes by taking into account the priority of packets generated from different applications. We develop a Markov chain model for the proposed scheme and verify the model with simulation. Numerical results show that the results from the model and computer simulation are in close agreement. Our PEPD scheme provides service differentiation to end-to-end applications.

Random early detection (RED), an active queue management scheme, has been proposed by the Internet Engineering Task Force (IETF) to improve the throughput of TCP/IP based networks in the next generation routers. RED has a number of problems such as low throughput, large delay/jitter and inducing instability in networks. Previous enhancements to RED attempted to improve the performance of RED by modifying the parameters of RED. However such modifications result in limited improvement. We propose a new active queue management scheme which achieves a higher throughput than RED and retains all the advantages of RED. Results show that our proposed scheme results in better performance than RED in terms of throughput and delay.

The Internet Engineering Task Force (IETF) has defined the integrated services model for supporting real-time services. Guaranteed service is one of these services with firm delay and bandwidth guarantees. Before setting up a guaranteed service flow, traffic parameters of the source have to be specified in terms of token bucket traffic descriptors. The source must conform to these descriptors by shaping its traffic. In this paper, we study the effects of a token bucket traffic shaper at the source on the transmission characteristics of Motion Picture Experts Group (MPEG) video streams over the guaranteed service. For different types of MPEG video sequences, the effects of the shaper parameters on the transmission performance are studied. We develop an analytical model of the traffic shaper, and also carry out numerical simulation of the transmission performance to verify the analytical model. Our study also provides a technique to analyze stored MPEG video sequences to determine the token bucket traffic descriptors that have to be specified during flow set up.

Multimedia is an emerging service which integrates voice, video and data in the same service. With the progress made in high speed large capacity multimedia servers, high speed networks, cost effective QoS, acceptable service category and cost effective set top boxes, it is currently possible to carry multimedia over high speed networks cost effectively and efficiently. This paper surveys the progress made and the future of efficiently carrying multimedia over ATM networks.

A new and accurate Markov chain model for evaluating the performance of multistage ATM switching fabrics with 2/spl times/2 finite output-buffered switching elements is proposed. By comparing the results obtained from the proposed model, existing models, and simulations, it has been shown that the proposed model is much more accurate than existing models in the presence of a non-uniform traffic in the switch. The results from existing models are unsatisfactory in the presence of an increased blocking in the switch arising due to a non-uniform traffic in the switch. On the contrary, the proposed model is very robust even under severe blocking inside the switch.

Multistage networks are strong candidates for implementation of ATM switching fabrics in broadband ISDN networks. To prevent internal loss of data, buffers are often used inside the switching elements of the fabric. The objective of the paper is to develop models to evaluate the throughput and packet delay, in the presence of a general traffic pattern, of an ATM switching fabric using multiple buffers at the outputs of the switching elements. The models are based on Markov chains, and use several simplifying assumptions to make the model tractable. One of the models take into account of the blocked packets and the history of blocking. The models produce accurate results in the case of a general traffic pattern.>

Performance evaluation of multiple-bus multiprocessor systems is usually carried out under the assumption of uniform memory reference model. Hot spots arising in multiprocessor systems due to the use of shared variable, synchronization primitives etc., give rise to non-uniform memory reference patterns. The objective of this paper is to study the performance of multiple bus multiprocessor system in the presence of hot spots. Analytic expressions for the average memory bandwidth and probability of acceptance of prioritized processors have been derived. The results are validated by simulation results.

A new multiresolution coarse-to-fine search algorithm for efficient computation of the Hough transform is proposed. The algorithm uses multiresolution images and parameter arrays. Logarithmic range reduction is proposed to achieve faster convergence. Discretization errors are taken into consideration when accumulating the parameter array. This permits the use of a very simple peak detection algorithm. Comparative results using three peak detection methods are presented. Tests on synthetic and real-world images show that the parameters converge rapidly toward the true value. The errors in rho and theta , as well as the computation time, are much lower than those obtained by other methods. Since the multiresolution Hough transform (MHT) uses a simple peak detection algorithm, the computation time will be significantly lower than other algorithms if the time for peak detection is also taken into account. The algorithm can be generalized for patterns with any number of parameters.>

The authors extend the work done by L.M. Ni et al. (see ibid., vol.C-31, p.1017-22 (1982)) on microprocessor-based office image processing systems. Ni et al. did not distinguish between the compute-bound and I/O-bound state of the machine in the case where image input time is greater than the output time. The authors clearly show the above distinction and present the correct expressions for such cases.>