The integration of Internet of Things (IoT) technologies into modern healthcare systems has significantly enhanced patient care and real-time monitoring. However, this advancement introduces serious security and privacy concerns, such as data breaches, unauthorized access, and identity leakage, which are especially critical in healthcare environments. These threats are further intensified by the emergence of quantum computing, rendering traditional cryptographic schemes increasingly vulnerable. To address these challenges, we propose secure and efficient lattice-based signcryption for blockchain-enabled IoT healthcare (SELSBH ). The scheme leverages the quantum-resistant hardness of the Learning With Errors (LWE) problem to ensure strong confidentiality, authentication, and data integrity. It also supports user anonymity and unlinkability on the blockchain, while maintaining lightweight operations suitable for resource-constrained IoT devices. The security of SELSBH is formally validated using the Scyther tool and the Random Oracle Model (ROM), demonstrating its resilience against both classical and quantum adversaries. Additionally, we conduct a comprehensive performance evaluation by comparing SELSBH with existing protocols in terms of security features, communication cost, and computational efficiency. The results highlight the advantages of SELSBH in achieving a robust, privacy-preserving, and quantum-secure solution for next-generation e-healthcare systems.

Medium access control (MAC) is crucial for resource allocation in wireless body area networks (WBANs). However, existing MAC protocols often suffer from transmission conflicts and inefficient channel utilization. To address these issues, this paper proposes a Node-Differentiated Resource Scheduling (NDRS) MAC protocol, which dynamically allocates access resources based on node-specific requirements. This protocol employs a superframe structure consisting of a contention-based phase and a contention-free phase for data transmission. A Mamdani fuzzy inference system is utilized to calculate continuous node priorities. These priorities achieve fine-grained differentiation of node importance and thus serve as the foundation for transmission conflict minimization. During the contention-based phase, continuous and differentiated backoff times are assigned to nodes based on their priorities. These backoff times effectively reduce transmission collisions and enhance channel utilization. In the contention-free phase, time slots are preferentially allocated to nodes with higher priority, better channel utilization, and greater transmission reliability. This allocation thereby enhances channel usage efficiency and reduce transmission delays. This protocol is characterized by three key features: precise node prioritization, low transmission collisions, and high channel utilization. Extensive experimental results demonstrate that NDRS outperforms existing protocols in terms of average delay, throughput, packet loss ratio, and average energy consumption.

With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework has shown promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges, such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous FL approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.

The adoption of blockchain technology in healthcare has significantly enhanced data integrity, transparency, and user privacy. However, high storage overhead and resource-intensive operations remain major challenges to its widespread deployment, particularly in large-scale or resource-constrained healthcare environments. To address these challenges, we propose STORChain, a storage-optimized blockchain framework designed for data services in healthcare. The framework introduces the Clustered Merkle Patricia Tree (C-MPT), a novel logical structure that aggregates similar transaction types to maximize storage efficiency while ensuring Proof of Inclusion (PoI). A Selective Transaction Pruning Strategy (STPS) is employed to prioritize and prune essential historical data, improving data access efficiency. Additionally, an incentive-based Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized, integrating a probabilistic election mechanism to promote fairness and node inclusivity. Comprehensive theoretical analysis and practical experiment results indicate that STORChain significantly reduces storage overhead, optimizes data access, and outperforms existing schemes.

Autonomous vehicles (AVs) are transforming transportation systems, necessitating secure digital infrastructures for reliable operation. Vehicular Digital Twin (VDT) networks address AV limitations by enabling synchronized virtual replicas. However, intra-twin communications over public channels expose systems to severe security threats, including impersonation and data tampering. This paper proposes EDTAP-VDT: an Enhanced Distributed Trust Authentication Protocol for VDT networks, which ensures secure identity verification through a threshold cryptographic model anchored in hardware. The protocol employs a hierarchical edge-fog-cloud architecture to balance authentication loads and leverages Trusted Platform Modules (TPMs) to cryptographically bind credentials. Post-quantum secure primitives and a permissioned blockchain with Byzantine consensus ensure long-term security, pseudonymity, and immutable authentication traceability. Attribute-based access control is integrated into the authentication process for fine-grained data sharing. EDTAP-VDT guarantees confidentiality, forward secrecy, and desynchronization resilience while maintaining decentralized control. The protocol is formally validated using the Random Oracle Model, and additional resistance is demonstrated against active and passive attack vectors. Performance evaluation across realistic vehicular settings shows that EDTAP-VDT achieves up to 24% improvement in computational efficiency and up to 22% reduction in communication overhead compared to state-of-the-art alternatives while fulfilling all standard security attributes. The results establish EDTAP-VDT as a future-ready authentication framework for real-time, secure VDT applications in intelligent transportation environments.

This paper introduces a secure federated learning (FL) system aimed at detecting anomalies in the communication of maritime autonomous surface ships (MASS). The model effectively distinguishes between normal and anomalous nano-traffic while safeguarding data privacy among distributed clients. To bolster security, Quantum Key Distribution (QKD) protocols, such as BB84 and E91, are utilized for encrypted key exchange during ship-to-ship and ship-to-ground interactions. The suggested framework demonstrates consistent performance enhancement, achieving individual client accuracies of 0.77, 0.79, and 0.81, with the global model reaching an accuracy of 0.83. Moreover, the global model’s loss significantly reduces from 0.92 to 0.55 across five rounds, indicating successful convergence. These findings reinforce the framework’s efficacy in providing secure, decentralized anomaly detection within maritime networks. Future research will focus on incorporating neuromorphic architectures with FL to improve real-time performance in MASS communication.

The growing demand for large-scale training data in deep learning has promoted the use of open data collection, thereby increasing the risk of data poisoning attacks. Clean-label data poisoning attacks aim to compromise models by injecting malicious samples into the training set, while being constrained to maintain consistency between the visual features of the samples and their assigned labels. Though this constraint enhances attack feasibility in real-world settings, it also introduces significant technical challenges, such as reliance on white-box assumptions and limitations in stealth and effectiveness. To address these limitations, this paper proposes a novel clean-label data poisoning attack scheme based on representation-conditioned data generation (CPRCG). The scheme identifies "natural poisoned data" from open datasets, extracts their deep representations as constraints, and generates numerous new samples using the conditional data generation model MAsked Generative Encoder (MAGE). These samples preserve core similarities while introducing random variations in form and behavior. Experimental results show that samples generated by CPRCG achieve high stealth and diversity, outperforming MetaPoison by 4.1% in centralized learning and approaching the effectiveness of dirty-label attacks in federated learning.

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This article presents multiobjective adaptive learning framework for uncrewed aerial vehicle (UAV)-assisted mobile edge computing (MOALF-UAV-MEC), a novel MOALF-UAV-MEC tailored for dynamic IoT environments. The framework integrates multiobjective reinforcement learning (MORL), model predictive control (MPC), adaptive particle swarm optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted mobile edge computing (MEC), including multiobjective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework’s efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations.

Product design involves the conceptualization and creation of products, where simulation workflows often require dynamic scheduling. Although deep reinforcement learning has shown competitive performance in addressing dynamic scheduling problems, they encounter challenges when dealing with multiple types of dynamic events in workflow scheduling. To this end, the paper introduces a novel meta-reinforcement learning-based dynamic scheduling algorithm (MDSA) to tackle multiple dynamic events. Four types of dynamic events are considered and modeled, including random insertion, loop execution, structural adjustments, and resource uncertainties. The Markov decision process is formulated for the workflow scheduling problem, defining the state, action, reward, and state transition. To capture time-sequential information, a context-aware discretized policy is introduced, and a meta-training algorithm is employed to extract common knowledge across various scheduling scenarios. The effectiveness of the proposed method is demonstrated through a case study in the semiconductor display industry. The simulation environments are constructed using industry-inspired synthetic data, with reference to deployment practices from BOE and task patterns derived from Alibaba cluster traces, to ensure practical relevance while maintaining experimental control. Experimental results indicate that the approach demonstrates strong generalization capabilities, especially in terms of its consistent performance across varying task data distribution and its robustness in adapting to multiple dynamic conditions, thereby enhancing its adaptability in practical applications.

In IoT-enabled smart infrastructure, accurate and real-time road damage detection is crucial for enhancing road safety and optimizing maintenance processes. However, detecting road damage in complex and dynamic environments presents significant challenges, such as varying lighting conditions, diverse damage types, and the need for fast processing to enable real-time decision-making. This study introduces an advanced approach utilizing the YOLOv9s-Fusion model to overcome these challenges. Leveraging the RDD2022 dataset, which comprises 1976 annotated images of road damage from China, we employ comprehensive data preprocessing to create optimal conditions for model training. The YOLOv9s-Fusion model integrates innovative features, including a Transformer-based auxiliary module and enhanced feature extraction layers, specifically designed to detect fine-grained damage patterns accurately. Experimental results demonstrate that the model outperforms existing approaches, achieving notable improvements in mean average precision (mAP) and F1-score. Ablation studies further validate the impact of our modifications, highlighting the model’s robustness in real-time detection across diverse conditions. This IoT-centric approach sets a new standard for autonomous road damage detection, significantly advancing vehicle navigation and smart infrastructure management capabilities.

In recent years, although Wireless Rechargeable Sensor Networks (WRSNs) have broken through the energy bottleneck of traditional sensor networks, the wireless charging process has the risk of sensor location privacy leakage, and the attacker can extrapolate the distance by observing the behavior of the mobile charger (MC), leading to a privacy leakage rate as high as 52%, threatening the network topology integrity and data security. In this paper, we construct a hybrid location privacy attack framework, reveal the attack principle, and build the framework based on least squares and center of mass method, and verify its efficiency. After introducing the evaluation method, simulations and experiments show that the model doubles the localization accuracy, but the number of location privacy leaking nodes increases by 33.3%.

Internet of Things (IoT) technologies have been applied to fault detection in new energy equipment, which is crucial for ensuring the stable operation of energy systems. However, existing approaches typically rely on large amounts of labeled data to train intelligent algorithms, which are difficult to obtain in real-world. To address this challenge, this paper proposes a novel multi-modal few-shot defect recognition framework for new energy equipment, enabling data-efficient defect recognition in real-world scenarios through multi-modal harmony. Unlike previous multi-modal few-shot methods, it eliminates the necessity of pairwise similarity calculations, thereby simplifying the training and inference processes. Our approach trains a shared classifier through text and visual modality features integration. Initially, we extract feature vectors using text and visual encoders and then map them to common feature space. Subsequently, the vectors of these two modalities are used to train a shared classifier, which aids in the simultaneous learning of corresponding visual representations and conceptual information. Furthermore, to enhance interaction between modalities, the feature vectors of the text prompts are used to initialize the classifier weights, thereby promoting cross-modal consistency. Experiments on datasets of wind turbine blades and solar cells show that combining the two modalities improves recognition accuracy by up to 19.13% and 28.52% compared to other multi-modal few-shot methods. Despite its reliance on prompt quality, the approach provides an effective and scalable solution for defect recognition.

Broadcast plays a significant role in the emergency message propagation in Vehicular Ad hoc Networks (VANETs). However, current broadcast relay strategies easily cause serious message loss and larger broadcast costs due to the poor environmental adaptability. In this paper, to handle the above problems, we propose an Adaptive Connectivity-Aware Relay (ACAR) strategy, which has two striking features: multiple network connectivity states and dynamic broadcast relay policies. We design four network connectivity states and their corresponding four broadcast relay policies. In disconnected networks, only the vehicle with the same movement direction as the message propagation direction acts as the relay, so as to relieve the message loss. Moreover, different broadcast periods are allocated for different applications, so as to reduce the broadcast costs. In connected networks, the link quality and transmission distance are adopted to select the relay in the sender-based relay pattern, so as to address the relay invalidation problem. Further, different candidate relays are assigned different broadcast priorities and different broadcast waiting time in the receiver-based relay pattern, so as to address the transmission conflict problem. Simulation results show that ACAR outperforms existing competing schemes in terms of end-to-end delay, broadcast success rate, and packet overhead.

Ensuring the safe transfer of information plays an important role in the development of Industry 4.0. Robust authentication and security frameworks are required to establish confidence among vehicles, provide reliable data flow, and improve the overall safety of vehicle platoons. This is crucial for preventing cyber-attacks that could cause accidents or disrupt the synchronized movement of vehicle platoons. Initially, in this study, a novel privacy-preserving mechanism based on an authentication code and cipher test is suggested. Second, an effective authentication system is proposed for vehicle platoons. Third, a novel tree-based group key-sharing mechanism for the exchange of information between vehicle users is proposed. The proposed scheme also supports the sharing of the same group key for entities in Industry 4.0. Finally, a planar array consisting of four elements was specifically built for use in vehicular ad hoc network (VANET) applications operating inside the Dedicated Short-range Communications (DSRC) (802.11p) band to prove the efficacy in terms of practical implementation. To assess the security level of the suggested authentication scheme, both formal and informal analyses were conducted. Finally, the performance of the suggested protocol is evaluated in terms of computational and communication overheads. Moreover, the designed antenna provides complete impedance bandwidth coverage, good gain, and minimal cross-polarization suppression at the optimum frequency of operation in the C band.

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments.

In the Internet of Vehicles (IoV), data privacy concerns have prompted the adoption of Federated Learning (FL). Efficiency improvements in FL remain a focal area of research, with recent studies exploring model pruning to lessen both computation and communication overhead. However, in the IoV, model pruning presents unique challenges and remains underexplored. Pruning strategy design is critical as it directly impacts each vehicle's learning latency and capacity to participate in FL. Furthermore, FL performance and model pruning are intricately connected. Additionally, the fluctuating number and mobility states of vehicles per round complicate determining the optimal pruning ratio, closely intertwining pruning with vehicle selection. This study introduces Vehicular Federated Learning with Adaptive Model Pruning (VFed-AMP) to tackle these challenges by integrating adaptive pruning with dynamic vehicle selection and resource allocation. We analyze the impact of pruning ratios on learning latency and convergence rate. Then, guided by these findings, a joint optimization problem is formulated to maximize the convergence rate concerning optimal vehicle selection, bandwidth allocation, and pruning ratios. Finally, a low-complexity algorithm for joint adaptive pruning and vehicle scheduling is proposed to address this problem. Through theoretical analysis and system design, VFed-AMP enhances FL efficiency and scalability in the IoV, offering insights into optimizing FL performance through strategic model adjustments. Numerical results on various datasets show VFed-AMP achieves superior training accuracy (e.g., at least 13.4% improvement for BelgiumTS) and significantly reduces training time (e.g., at least up to$1.8\times$for CIFAR-10) compared to traditional FL methods.

Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet’s capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information.

Summary The convergence of reliable and self‐organizing characteristics of Wireless Sensor Networks (WSNs) and the IoT has increased the utilization of WSN in different scenarios such as healthcare, industrial units, battlefield monitoring and so forth, yet has also led to significant security risks in their deployment. So, several researchers are developing efficient authentication frameworks with various security and privacy characteristics for WSNs. Subsequently, we review and examine a recently proposed robust key management protocol for an industrial sensor network system. However, their work is incompetent to proffer expedient security and is susceptible to several security attacks. We demonstrate their vulnerabilities against man‐in‐the‐middle attacks, privileged insider attacks, secret key leakage attacks, user, gateway, and sensor node impersonation attacks, and offline password‐guessing attacks. We further highlight the design flaw of no session key agreement in Itoo et al. Therefore to alleviate the existing security issues, we devise an improved key agreement and mutual authentication framework. Our protocol outperforms Itoo et al.'s drawbacks, as demonstrated by the comprehensive security proof performed using the real‐or‐random (ROR) model and the formal verification accomplished using the Automated Validation of Internet Security Protocols (AVISPA) tool.

The Internet of Robotic Things (IoRT) is growing rapidly with new applications. Co-operatory robotics enables the sharing of information, autonomy, and fail-safe interaction with environment, humans, and other robots. They can also self-maintain, self-aware, and self-heal. To provide reliable and robust online monitoring of the industrial manipulator joint status, this article proposes a new IoRT architecture based on transfer learning (TL) techniques to detect manipulator fault. Robotic manipulator joint status are detected with high accuracy using a hybrid 1-D multichannel convolutional neural network (1D-MCNN), including matrix kernels and recurrent neural network (MCNN-RNN) technique. Moreover, a timestamp mapping method addresses the challenges associated with inconsistencies in sensor data timestamps. Existing data-driven methods struggle with the diverse operating conditions of industrial robots, where load and speed constantly fluctuate. To address this limitation, we propose a novel TL-based MCNN-RNN approach for joint fault diagnosis under varying work conditions. This method leverages the adaptability of TL while incorporating the inherent relations between different failure modes, enhancing the TL process. To demonstrate the performance of the suggested IoRT topology, various experimental scenarios are performed with data acquisition on six degree-of-freedom (DOF) UR16e (universal robot) manipulator. Based on the results, the proposed IoRT architecture can effectively visualize the joint fault status of the manipulator. As a result, TL architecture combined with MCNN-RNN provides an excellent accuracy of 99.03%in detecting faults on manipulator joints, which is significantly higher than traditional convolutional neural network (CNN), deep belief network (DBN), domain adversarial neural network (DANN), and conditional domain-adversarial network (CDAN).

In the ever-evolving landscape of technology, healthcare continuously harnesses its benefits, propelling advancements in medical practices. Within intelligent healthcare, medical robots play a pivotal role, providing integral support to healthcare professionals, streamlining processes, and delivering efficient services. These robots securely transmit patient treatment plans, transferring them to cloud storage and subsequently storing them in blockchain systems. This innovative approach ensures the integrity and accessibility of patient data, introducing novel avenues for seamless interaction with medical information for hospitals and patients’ families. Despite these advantages, the looming privacy risks associated with sensitive patient data transmission pose a compelling challenge, demanding a comprehensive solution. In response to this challenge, we propose a mutual authentication and key agreement protocol designed to optimize healthcare services while prioritizing data security and patient privacy. To validate the robustness of our authentication protocol, we conduct thorough analyses based on both formal and informal models, establishing a foundational framework for evaluating the protocol’s security. Additionally, we perform a comprehensive comparative analysis, assessing the proposed protocol against existing counterparts across various dimensions. This comparative scrutiny reveals the superiority of our protocol in terms of security, as well as its efficiency in communication cost and computational overhead. These findings affirm the efficacy of our proposed solution in navigating the intricate interplay between medical robotics, blockchain, and data security.

Machine learning (ML) has emerged as a promising avenue for enhancing the efficiency and intelligence of channel allocation processes. However, deploying ML algorithms on resource-constrained edge devices poses significant challenges due to their limited computational capabilities and storage capacities. In this study, we propose leveraging tiny ML (TinyML) techniques to address these challenges and optimize channel allocation within long range wide area network (LoRaWAN) deployments. Our key innovation lies in replacing traditional random channel allocation methods with TinyML-based approaches, wherein each edge device autonomously utilizes TinyML to select the most efficient channel prior to each uplink transmission. Furthermore, we conduct comprehensive comparisons between TinyML and conventional channel allocation techniques implemented on edge devices. Through extensive simulations, our results demonstrate that TinyML outperforms existing channel allocation mechanisms in terms of packet success ratio (PSR). Notably, when evaluating TinyML against conventional ML approaches in terms of model size and inference time, TinyML exhibits superior performance without compromising efficiency.

The unmanned aerial vehicle (UAV) array equipped with sensors is widely used for target localization owing to its superior maneuverability. Unfortunately, limited by the current manufacturing technology, sensor arrays usually exhibit inconsistent gain and phase responses across channels, i.e., gain–phase errors, which can seriously affect the target localization accuracy. Herein, we consider that the gain–phase consistency of all array channels is not been precalibrated. For accurate target localization, we develop a system architecture for bistatic multiple-input–multiple-output (MIMO) radar equipped with a UAV array at the receiver part to realize angle estimation. First, the UAV array is controlled to move near the transmitter to receive the transmitted signals directly. Therefore, the gain–phase consistency of the transmitter can be calibrated by using the data after matched filtering and combining the known relative position information of the transmitter and receiver. Second, we control UVAs away from the transmitter to form a bistatic MIMO radar and use the synthetic aperture technique introduced by the UAV array motion to convert the receive array into partially calibrated. Meanwhile, the array manifold matrices with unknown model errors can be obtained by parallel factor decomposition. Finally, the angle estimates, gain–phase errors, and position errors are estimated by the element-wise division of the steering vectors without iteration. Moreover, our method is insensitive to sensor position errors of the original UAV array while determining angles. Simulation results demonstrate that the proposed method can obtain accurate angle estimates under the aforementioned model errors.

The increasing prevalence of malicious activities in Internet of Things (IoT)-enabled healthcare and Internet of Medical Things (IoMT) systems necessitates robust intrusion detection mechanisms. This article introduces a novel approach combining meta-heuristic optimization and machine learning techniques to analyze network traffic for enhanced detection accuracy. Our proposed method utilizes 11 chaotic maps and the K-nearest neighbor (KNN) algorithm to identify malicious activity in IoMT and IoT network systems. Recognizing the significance of feature selection in network traffic intrusion detection, we employ the chaotic grey wolf optimizer (CGWO) to select the most relevant and impactful features for learning strategically. Our approach demonstrates superior performance through comprehensive experiments compared to well-known meta-heuristic algorithms and prior art methods, as evidenced by various evaluation metrics. This research contributes to advancing intrusion detection systems in healthcare IoMT and IoT, offering a reliable and efficient solution to safeguard against evolving cyber threats.

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, pre-deployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy, but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols.

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision-making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 dataset collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors; Faster R-CNN ResNet101 and one-stage detectors; SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mAP of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance.

Direct position determination (DPD) has received much attention in emitter localization, owing to its better accuracy than conventional two-step positioning. Most of the existing DPD algorithms are developed for circular signals (CS) by using uniform linear arrays (ULAs). However, these algorithms may ignore other characters of the signals, e.g., noncircularity. The use of ULAs limits the accuracy of source localization and the number of sources that can be estimated. In this article, a weighted$\ell_{0}$-norm sparse reconstruction algorithm for noncircular signals (NCS) is developed for DPD with a designed sparse array in motion. First, a sparse array configuration named extended nested array (ENA) is devised for NCS, which consists of three subarrays. Theoretical analysis proves that the designed array can obtain higher degrees of freedom (DOFs) effectively, and reduce the mutual coupling effects between antennas. Then, a weighted$\ell_{0}$-norm sparse reconstruction algorithm is developed to improve the accuracy of DPD. Finally, simulation results are provided to demonstrate the superiority of the proposed algorithm with the designed sparse array. Our scheme can provide better localization performance than the state-of-the-art methods.

In the realm of miniature machine learning for Internet of Unmanned Aerial Vehicles (UAVs), the security concerns of machine learning models are obvious, especially when it comes to adversarial attacks. Models can become confused and their performance undermined by the introduction of meticulously crafted distortions. These attacks can even infiltrate a variety of models, bringing greater security risks. To understand how it works and mitigate its effects, our research focuses on scrutinizing the transferability of adversarial attacks in the expanding context of miniature machine learning for UAVs. In this paper, we introduce a formula help measure the transferability of adversarial attacks and explore ways to improve the transferability and effectiveness of adversarial attacks (e.g., a combination of attack techniques), and provide visulizations to vividly illustrate the repercussions of adversarial instances across a spectrum of attack intensities, helping facilitate more intuitive exploration and analysis of the results. For instance, our findings demonstrate that even subtle perturbations directed at specific attributes can lead to a significant decrease in model accuracy. We also evaluates the success rates of various attack algorithms and validates the proposed evaluation methodology for measuring transferability. And the outcomes unveiled in this study make noteworthy strides in fostering a profound comprehension of the transferability of adversarial attacks in the distinct realm of miniature machine learning for UAVs. Robust defense mechanisms, which ensure the impregnability of IoT-enabled UAV systems, can be cultivated by pinpointing the most efficacious attack strategies and evaluating their transferability.

The emerging Internet of Things (IoTs) and cloud technologies spark dramatic growth in efficiency and productivity for the conventional e-health sector. However, the extensive applications of the communication network also expose the sensitive medical data to the unprecedented cyber threats. To protect the data privacy in IoTs-based e-health cloud environments, we propose an adaptively secure data sharing scheme with traceability and equality test (T-ABEET). The T-ABEET not only allows flexible access control to the massive data but also provides the functionality of traitor tracing to identity the users who leak their decryption keys. Meanwhile, through carrying out the equality test, the target ciphertext can be retrieved efficiently without revealing anything about the plaintext. Particularly, distinct from previous traceable ABE works, the tracing cost in our T-ABEET scheme keeps constant even with the increasing number of users. Also, by introducing the multi-authority mechanism, our T-ABEET can avoid the inherent key escrow problem of ABE. Furthermore, our T-ABEET is demonstrated adaptively secure under subgroup decision assumption. Finally, performance comparison reveals that our T-ABEET has superior practicality, efficiency, and security in cloud-enabled e-health systems.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in the ideal environment that ignores the impact of obstacles. This contradicts the practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on the Fresnel diffraction model and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for mobile charger (MC), which largely reduces computation overhead. Afterwards, we re-formalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with an approximation guarantee to solve it. In addition, we present a theoretical analysis and a relevant mathematical proof of our algorithm. Finally, we demonstrate that our scheme outperforms other competing algorithms by 20.5% on average in terms of charging utility through test-bed experiments and extensive simulations.

Critical infrastructure is a collection of physical and cyber systems, which are essentially required to support the day-to-day operations of our daily life. In the critical infrastructure, the computing systems (i.e., Internet of Things (loT) devices) communicate through the Internet. Therefore, most of the time, critical infrastructures are targeted by hackers by launching some cyber-attack, i.e., ransomware. Hence we require some security mechanisms to protect the data and systems of critical infrastructures. This paper proposes a scheme for the detection, analysis and mitigation of ransomware attacks to protect Internet of Things (loT)-enabled critical infrastructure (in short, RADM-ICI). We also practically demonstrated RADM-ICI and computed essential performance parameters, i.e., accuracy and F1-score under different machine learning models. The conducted security analysis of RADM-ICI proved its excellent security for the ransom ware attacks. During the performance comparison of the proposed RADM-ICI and other similar competing existing schemes, it has been observed that the proposed RADM-ICI achieved better accuracy than the other existing competing schemes.

Smishing is a type of cyberattack that includes delivering a fake messages to normal users in order to get their personal information. Cybersecurity experts are now concerned about this sort of assault because, both the individuals and companies are loosing their money by this attack. This article presents a machine learning base comparison approach using different text analysis features (i.e., Discourse, Linguistic, Psycholinguistic, Persuasion Principle and Statistics based features). Here, different Machine Learning classifiers (i.e., Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine and XGBoost) have been used for smishing detection. The evaluation was performed on a prepared dataset. The dataset includes both smishing and normal messages. According to the comparative experimental results from the implemented classification algorithms, Random Forest classifier gives the best performance with the 99% accuracy.

Encrypted behavior identification is crucial in ensuring network security. Most existing solutions in this area recognize behavior by observing encrypted traffic patterns between users and applications. However, such solutions rely on features such as timing, packet sequence, and packet length, which may be affected by network fluctuations, and thus have weak generalization capabilities. In this paper, we first analyze the impact of noise on the network, such as parameters and network delays during API requests. By combining a noise-based traffic collector with an improved Transformer model, we propose a system-level denoising Transformer method for encrypted traffic behavior identification called SD-Transformer. It is able to filter system noise by utilizing an attention mechanism and targeted noise packet masking. We evaluate the performance of SD-Transformer on three datasets, i.e., ISCX-VPN, USTC-TFC, and our generated noise-containing Web Application Traffic dataset (WEB-APP), and it achieves an accuracy of 95.97%, 93.59%, and 99.82%, respectively. Besides, compared to the state-of-the-art methods, the accuracy is increased to 96.82% (↑16.0%) and 85.41% (↑17.76%) on the WEB-APP dataset under different API parameters and network latency environments, respectively. Additionally, the target mask of the SD-Transformer achieves 96.45% accuracy with an improvement of 11.29% on the WEB-APP dataset with latency.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

Cyber–physical system (CPS) provides a foundation for the Industrial Internet of Things (IIoT) that interconnects all types of devices. The integration of CPS with IIoT generates the large volumes of data forcing the development of artificial intelligence (AI) to extract information more precisely. Nevertheless, the increasing volume/variety of data traffic and the ever-growing number of IIoT devices bring great challenges for the host-centric communication model of the current Internet. In this work, we present a novel information-centric networking (ICN)-based system model in CPS, which enables processing data from IIoT devices closer to the edge as opposed to a content provider. Based on this ICN system model, we propose an edge-assisted authentication scheme in CPS, aiming to protect the system from unauthorized access and reduce workload for resource-constrained devices. The main features of our scheme include a delegation model of security operations and session handshake procedures through edge routers, addressing the rising challenges in managing and securing IIoT devices in the ICN. We formally prove the security of our scheme and conduct performance analysis to show its practicality.

The salient event recognition of soccer matches in the next-generation Internet of Things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an artificial intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players’ performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports’ managerial staff as a financial technology (FinTech) service. In this article, we propose an efficient deep-learning-based framework for multiperson salient soccer event recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: 1) frames preprocessing; 2) frame-level discriminative features extraction; and 3) high-level events recognition in soccer videos. Moreover, we introduce a new soccer video events (SVE) data set containing videos of six salient events of soccer games. To provide a strong baseline, we evaluate our newly created SVE data set using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient event recognition in Nx-IoT environments.

In recent years, voice-interaction-based control systems have attracted considerable attention for industrial control systems implementing Industrial Internet of Things (IIoT) technologies. The development of automated semantic understanding relates to the industrial Internet equipment used to realize remote voice control as well as to its intelligent management and control. In these emerging voice-interaction-enabled industrial central control systems, sorting technologies are considered critical. For complex user questions, the level of satisfaction regarding the answers given by such systems tends to be low. Driven by these challenges and opportunities, the optimization of conventional retrieval-based question answering through deep learning methods has become popular. In this study, we propose three deep semantic sorting models based on deep learning, including a multilayer convolutional matching sorting model for single documents and two interactive pairwise bidirectional encoder representations from transformers (BERT) sorting models for document pairs. Two main network architectures are proposed to model document pairs, named Pairwise-Twin-BERT and Pairwise-Triple-BERT. Experimental results indicate that proposed models performed better than state-of-the-art methods based on text matching in a candidate document sorting task.

Instance segmentation exploits the potential of Internet of Things (IoT) devices to perceive environmental semantic information and achieve complex interactions with the physical world. However, IoT devices usually have limited computing and storage resources and cannot afford the intensive computational costs of instance segmentation networks. This article proposes an edge-assisted instance segmentation method for resource-limited IoT devices; it selectively offloads some computation-intensive tasks from IoT devices to edge servers to accelerate the inference processes of instance segmentation networks. To reduce the communication cost caused by computation offloading, a data compression method is proposed to adaptively adjust the downsampling interval based on an attention mechanism. Considering the susceptibility of the computation offloading scheme to network conditions, an adaptive computation offloading strategy that can jointly optimize the offloading point and the data compression ratio is proposed so that the edge-assisted instance segmentation can meet the preset latency requirements while achieving maximal accuracy under volatile network conditions. Extensive experiments are conducted to verify the feasibility and efficiency of our method. The experimental results show that our method induces less latency than existing instance segmentation methods with a slight drop in accuracy.

The complicated multi-step attacks, such as Advanced Persistent Threats (APTs), have brought considerable threats to cybersecurity because they are naturally varied and complex. Therefore, studying the strategies of adversaries and making predictions are still significant challenges for attack prevention. To address these problems, we proposeDeepAG, a framework utilizing system logs to detect threats and predict the attack paths.DeepAGleverages transformer models to novelly detect APT attack sequences by modeling semantic information of system logs. On the other hand,DeepAGutilizes Long Short-Term Memory (LSTM) network to propose bi-directional prediction for attack paths, which achieves higher performance than traditional BiLSTM. In addition, with previously detected attack sequences and predicted paths,DeepAGconstructs the attack graphs that attackers may follow to compromise the network. Furthermore,DeepAGoffers the mechanisms of Out-Of-Vocabulary (OOV) word processor and online update respectively to adapt new attack patterns that show up during detection and prediction stages. The experiments on open-source data sets show that more than 99% of over 15000 sequences can be detected accurately byDeepAG. Moreover,DeepAGcan improve the baseline by 11.166% of accuracy in terms of prediction.

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool.

In this paper, we investigate the auxiliary vehicle positioning system and localization method for intelligent transportation systems, as a supplement to the Global Navigation Satellite System which is prone to large positioning deviations and even failures in occluded scenes such as urban canyons and tunnels. The time modulated antenna arrays are first introduced into the positioning system, avoiding mutual coupling between antennas and greatly reducing the hardware cost of the vehicle terminal. The auxiliary positioning framework for the smart vehicle is advocated in conjunction with existing radio frequency signals. To take full advantage of the multiple auxiliary sources around the road net, Doppler shifts embedded into the received signals are unearthed, and a smoothed block sparse reconstruction is developed for directly locating the vehicle, providing significant enhancements of degrees of freedom and the localization accuracy. Additionally, the proposed direct localization method is robust to multichannel gain and phase mismatch in practice, and the array perturbations can be estimated and compensated without any calibration source. Extensive simulation results corroborate that the proposed system and method achieves superior localization accuracy (approximately 0.22 m error at SNR of 10 dB), outperforming its state-of-the-art counterparts.

The ubiquitous and fine-grained features of WiFi signals make it promising for realizing contactless authentication. Existing methods, though yielding reasonably good performance in certain cases, are suffering from two major drawbacks: sensitivity to environmental dynamics and over-dependence on certain activities. Thus, the challenge of solving such issues is how to validate human identities under different environments, even with different activities. Toward this goal, in this article, we develop WiTL, a transfer learning–based contactless authentication system, which works by simultaneously detecting unique human features and removing the environment dynamics contained in the signal data under different environments. To correctly detect human features (i.e., human heights used in this article), we design a Height EStimation (HES) algorithm based on Angle of Arrival (AoA). Furthermore, a transfer learning technology combined with the Residual Network (ResNet) and the adversarial network is devised to extract activity features and learn environmental independent representations. Finally, experiments through multi-activities and under multi-scenes are conducted to validate the performance of WiTL. Compared with the state-of-the-art contactless authentication systems, WiTL achieves a great accuracy over 93% and 97% in multi-scenes and multi-activities identity recognition, respectively.

Based on the Internet of Behavior (IoB), mobile crowd-sensing (MCS) utilizes the Internet of Things (IoT) to recruit users by analyzing behavioral patterns. MCS is widely used in numerous large-scale and complex monitoring services, but it cannot provide stable and high-quality services due to nondeterministic user mobility and behaviors, which has a vital impact on recruiting high-quality users. In this article, a stochastic semi-algebraic hybrid system (SSAHS) model is constructed to characterize the user mobility and behaviors of the MCS systems. Based on the definition of probabilistic path and task execution rate, a nondeterministic evaluation mechanism is proposed to measure nondeterministic user mobility and behaviors and to give the probability of the user completing the MCS task under the specified time bound and space conditions. The greater the probability is, the higher the quality of the user. Furthermore, a user recruitment scheme based on a nondeterministic evaluation mechanism (NUR) is developed. The NUR employs historical user data to predict user mobility and behaviors; high-quality users are recruited to quickly upload reliable sensing data. We conduct simulation experiments based on a real-world user trace dataset, Geolife.The results show that compared with competing recruitment strategies, NUR achieves a higher quality of service for the same MCS sensing tasks.

In the edge-based Internet of Things (IoT) era, wireless sensor networks (WSNs) are the prime source for data collection. In such WSNs, mobile edge nodes such as mobile sinks (MSs) are the superior means to collect sensed data by visiting rendezvous points (RPs). However, WSNs are often obstacle-ridden, which creates hurdles to the movement of the MSs. Most of the existing path planning works dealing with obstacles do not address optimal and smooth path construction. In other words, they have not considered a) optimizing the number of RPs and constructing a feasible path and b) smoothing the constructed path by considering sharp edges and convexity of the obstacle perimeter. In this paper, we address all such issues and develop an efficient scheme for determining an optimal number of RPs using a greedy approach to the set-cover problem and optimized path construction, both in polynomial time. Then, we apply the modified BUG2 algorithm to construct an obstacle-free path, which is then smoothed using the concept of the Bezier curve. Extensive simulations show the superiority of our proposed scheme over the existing algorithms in terms of energy consumption, latency, and so on.

Eye defects of visually impaired people bring great inconvenience to daily lives. With the rapid development of computers and artificial intelligence, some assistance tools and wearable devices have provided convenience to a certain extent, but they still possess many shortcomings, such as high prices, limited functionality, poor real-time performance, the inability to be used offline and so on. This paper designs an offline assistance tool specifically for visually impaired people that can help them perceive their surrounding environments by taking photos and reading the descriptions output by an image captioning model. Considering that the photos taken by visually impaired people may be incomplete, an improved image stitching method based on a genetic algorithm is proposed to obtain high-quality stitched images as the inputs of the image captioning model. An improved model pruning algorithm is also designed to compress and accelerate the image captioning model to meet the offline and real-time deployment requirements of portable devices. Experimental results and a practical tool show that the developed system based on an image captioning model and pruning can quickly and accurately describe environments, thus assisting visually impaired people anytime and anywhere.

Vehicular Ad-Hoc Networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. Network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, which is the main challenge for network traffic prediction. This paper proposes a novel algorithm, which combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic prediction. We use DQN to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on two real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data trans-mission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTnk protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

We propose xDIoT, a paradigm for reliable cross-domain communication across separated IoT network domains over some public network such as the Internet. Depending on use-case scenarios, several individual IoT domains, each consisting of heterogeneous end-devices (sensors and actuators) are deployed across geographical regions. When these domains need to communicate with one another they can use an intermediate public network like the Internet. To this end, a standard paradigm is required for such inter-domain communication over public networks to reduce latencies and prevent inconsistencies, which is absent in current deployments. With xDIoT we address this issue to provide a uniform communication paradigm. In xDIoT, each individual domain has an associated gateway access point (AP) acting as the bridge between the intra-domain IoT network and the external public network. These APs perform Domain Information Exchange through JSON format to gain knowledge about each other and use a generalized packet header structure to encapsulate all data flowing between these APs. Through the use of JSON data exchange and the proposed packet header, the APs can perform seamless inter-domain communication. Implementation and analysis show that xDIoT achieves about 10% improvement in total communication latency with 80% improvement in packet processing time at individual gateway APs.

In a healthcare IoT based Fog System, malicious attacks may alter patient’s critical health data, which may lead to severe repercussions. This makes it incumbent to adopt authentication mechanisms for preventing unauthorised access and implement data encryption to enhance the security in the system. This work presents an efficient learning integrated dynamic attribute based encryption mechanism by reducing encryption, decryption and communication costs associated in a Fog system with IoT devices and dynamic attribute updates. The Ciphertext Policy Attribute Based Encryption approach (CP-ABE) has been integrated with a Deep Neural Network model to use learning patterns related to attributes. This in turn reduces the communication cost incurred by the resource limited end devices for dynamic attribute updates. Further, an access control mechanism has been implemented by optimizing the system due to dynamic attributes and by analysing the updates in the access policy defined in CP-ABE. The Deep Neural Network has been trained using existing data sets and experimental results are presented by performing analysis on neural network parameters.

Abstract With the advancement of information technology and the reduction of costs, the application of unmanned aerial vehicle (UAV) has gradually expanded from the military field to the industrial field and civilian field. It brings great convenience to people in surveillance, detection, transportation, emergency rescue etc. However, UAVs usually work in harsh natural environments, and their communication security confronts various challenges. Due to UAVs' limited resources, such as computing capability, storage space, and energy, traditional security protection schemes based on complex cryptographic algorithms are not suitable for UAV systems directly. Therefore, a two‐stage lightweight identity authentication and key agreement protocol for UAV is proposed in this paper. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physical capture attack. In the paper, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, Real‐or‐Random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost, which shows that the proposed protocol has effectiveness and practicality.

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this paper, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. First, the video stream acquired through the vision sensor is processed by a lightweight convolutional neural network (CNN) for the segmentation of important shots. Next, temporal optical flow features are extracted from the informative shots via a residential optical flow CNN. These are concatenated with appearance-invariant features extracted from a Darknet CNN model. Finally, a multilayer long short-term memory network is plugged to generate the final feature map for learning the violence patterns in a sequence of frames. In addition, we contribute to the existing surveillance VD data set by considering its indoor and outdoor scenarios separately for the proposed method's evaluation, achieving a 2% increase in accuracy over surveillance fight data set. Experiments also show encouraging results over the state of the art on other challenging benchmark data sets.

Wireless networks ensure the unmanned aerial vehicles (UAVs) communicate and cooperate with each other, which plays an indispensable role among UAVs. The two crucial challenges in UAV wireless networks are energy saving and security. The current lightweight communication approaches lead to insufficient robustness of the encrypted transmission that is insecure. To address this issue, we propose a secure transmission approach with energy efficiency toward UAVs networks. We design a lightweight symmetric encryption algorithm based on SM4 and the relevant key negotiation and update mechanism to protect the confidentiality of communication contents. Moreover, a modified aggregative BLS signature scheme, together with the Merkle Hash tree (MHT), is introduced to guarantee the integrity and authenticity of data packets in transmission. Furthermore, we propose an online/offline revocable identity-based group signature (OORIBGS) scheme and integrate it into our framework for UAV anonymity, traceability, as well as revocability with small key management cost and high efficiency. We give detailed security analysis and prove that our proposal has the properties of data confidentiality, integrity, and authenticity, as well as identity traceability and anonymity. Moreover, we apply our approach in the UAVs networks and evaluate the runtime and anti-attack performance. The experimental results show that the proposed method can be effectively used in UAVs secure communication.

The machine learning performance usually could be improved by training with massive data. However, requesters can only select a subset of devices with limited training data to execute federated learning (FL) tasks as a result of their limited budgets in today’s IoT scenario. To resolve this pressing issue, we devise a blockchain-enhanced FL market (BFL) to$(i)$make data in computationally bounded devices available for training with social Internet of things,$(ii)$maximize the amount of training data with given budgets for an FL task, and$(iii)$decentralize the FL market with blockchain. To achieve these goals, we firstly propose a trust-enhanced collaborative learning strategy (TCL) and a quality-oriented task allocation algorithm (QTA), where TCL enables training data sharing among trusted devices with social Internet of things, and QTA allocates suitable devices to execute FL tasks while maximizing the training quality with fixed budgets. Then, we devise an encrypted model training scheme (EMT) based on a simple but countervailable differential privacy methodology to prevent attacks from malicious devices. In addition, we also propose a contribution-driven delegated proof of stake (DPoS) consensus mechanism to guarantee the fairness of reward distribution in the block generation process. Finally, extensive evaluations are conducted to verify the proposed BFL could improve the total utility of requesters and average accuracy of FL models significantly.

For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant for the ever-increasing information requirements to promote the quality of citizens’ life. In this article, we design an information prefetching architecture, which investigates a hierarchical data storage and selection strategy, including local to edge and edge to cloud. Building on collected data in the social media system or sensor networks, we specifically focus on analyzing mobile terminals’ behaviors to assure the precision of our prefetching strategy in different kinds of information streaming. To assemble edge agents (EAs) prefetching, we also consider the characteristics of wireless backhaul. This scheme is carried out to optimize the EAs prefetching framework by the independent and joint action modules that are based on the theory of deep reinforcement learning (DRL). It paves a better way of collaborative edge computing (CEC) that can be built by using an independent/joint edge-learning model to help and promote the algorithm efficiency and cost-effectiveness. Furthermore, for hiding the information of data transmission between the cloud and the edge servers during data prefetching, this hierarchical scheme is designed as an implicit index maintained by edge servers. Our results show rationales on the obtainable performance of EAs architectures and their reciprocity with the dynamic change of mobile terminals’ requirements.

Devices in the Internet-of-Things (IoT) are networked and perform massive computations to support various social IoT systems. Applications in social IoT systems often involve complicated computations that are out of the computation capacity of some resource-constrained IoT devices. Thus, how to enable resource-constrained IoT devices to accomplish complex computations efficiently and securely is of significant importance. To address this problem, we develop a secure edge-aided computation scheme for the social IoT systems. We scope the framework of edge-aided computations and identify the security threats in such a system. We define the security requirements that the outsourcing algorithms should meet. Then, we provide two examples of secure outsourcing algorithms (matrix multiplication and modular exponentiation) that meet the given security requirements. The efficiency and security of the proposed algorithms are supported through the theoretical analysis and experimental results.

The Internet of Things (IoT) has a wide range of applications that influence the life of people expeditiously. In recent years, IoT becomes an emerging technology in a number of fields. Different devices with divergent functionality are applied in IoT to work in several domains. These domains include smart home, smart farming, and Industrial Internet of Things (IIoT). Among these territories, the IIoT obtains more attention. In an IIoT environment, a legitimate user can control and access devices remotely. Legitimate users can access real-time data and share confidential information. The information is transmitted via public communication channel, which can be vulnerable to security attacks. In this article, we present a provably secure multifactor authenticated key agreement scheme to offer security regarding transmission of data in IIoT environment. This scheme will support the legitimate user to remotely access the sensing devices. Our presented scheme uses only symmetric cryptographic, bitwise XOR operation, and hash function to be resource-constrained. Our scheme is found to be resource efficient through communication and computation analysis. The performance analysis illustrates that the cost of computation and communication of our scheme is comparatively low as compared to other relevant schemes. The formal and informal security analysis proved that our scheme is secure and efficient as it can withstand several known adversarial attacks. We have used some cryptographic operations like XOR and hashing to provide security and privacy to legitimate entities.

With the tremendous growth of Information and Communications Technology (ICT), Cyber Physical Systems (CPS) have opened the door for many potential applications ranging from smart grids and smart cities to transportation, retail, public safety and networking, healthcare and industrial manufacturing. However, due to communication via public channel occurring among various entities in an industrial CPS (ICPS) with the help of the 5G technology and Software-Defined Networking (SDN), it poses several potential security threats and attacks. To mitigate these issues, we propose a new three-factor user authentication and key agreement scheme (UAKA-5GSICPS) for 5G-enabled SDN based ICPS environment. UAKA-5GSICPS allows an authorized user to access the real-time data directly from some designated Internet of Things (IoT)-based smart devices provided that a successful mutual authentication among them is executed via their controller node in the SDN network. It is shown to be robust against various potential attacks through detailed security analysis including the simulation-based formal security verification. A detailed comparative study with the help of experimental results shows that UAKA-5GSICPS achieves better trade-off among security and functionality features, communication and computation overheads as compared to other existing competing schemes.

In inhospitable environments, connectivity recovery plays a significant role in enabling normal data transmission in wireless sensor networks (WSNs). However, existing approaches lack thorough load-balancing and delay-control functions. In this article, we present a load-balanced connectivity recovery (LBCR) strategy to address these problems. This strategy consists of two relay-segment selection approaches. The first one is the delay-controlled connectivity mechanism, in which mobile relays and static relays are employed to connect isolated segments, and the path length of the two kinds of relays is adjusted based on the requirement of data delivery delay. The second one is the load-balanced connectivity mechanism, in which both the intra-segment and the inter-segment traffic distribution are evaluated to balance the traffic load. For intra-segment load equilibrium, we use the A-Star algorithm to calculate the number of disjoint paths of a relay segment, which helps to evaluate the load sharing ability from a micro perspective. For inter-segment load equilibrium, we compare the traffic load of different paths, which helps to evaluate the load sharing ability from a macroscopic angle. Extensive simulations demonstrate the effectiveness and advantage of our strategy in terms of connectivity cost, data collection delay, and system lifespan.

Vehicular ad hoc Networks have emerged as a viable alternative for enabling user applications on moving vehicles. However, maintaining acceptable levels of Quality of Service and message latency still remains a challenging task. Several solutions have been proposed for improving performance of these networks. Clustering has been considered as one of the important mechanism that structures vehicles into organize groups. However, high deployment overheads and lack of security are the major bottlenecks hindering its deployment. Software defined networking has been emerged as a promising solution on account of its characterstics such as dynamic access control and scalabilty. In view of this, TruClu: a trust based clustering mechanism that creates vehicular clusters for a Software Defined Vehicular Network is proposed. Cluster formation and cluster head selection in TruClu is based on vehicular mobility and trust value that alleviates the drawbacks of traditional clustering and also enabling trust based communication in the network. The performance of TruClu is evaluated through extensive simulations and obtained results indicate the comparable performance of the proposed scheme in terms of standard performance parameters.

With the continuous maturity of Internet of Things (IoT) technology, it has begun to be frequently used in all walks of life to improve people's work efficiency and living standards. The wide use of IoT in the medical field makes it convenient for patients to obtain medical services, and also enables doctors to obtain patients' physical conditions more timely and accurately, so as to formulate more efficient treatment plans. However, when people enjoy the convenience of medical IoT, how to ensure the security of communication and privacy of patients are all problems that cannot be ignored. In order to achieve secure access the network, this paper proposes an anonymous password authenticated key exchange protocol for medical Internet of Things (PAMI), where only a low-entropy password is required to realize the mutual authentication between medical device and telemedicine server, so as to negotiate a high-entropy session key. The security of PAMI is formally proved under the standard model, and the experiment based performance comparison demonstrates that it is more efficient than the existing similar schemes.

With the evolution of Internet Technology, payment methods have undergone drastic changes from entity exchange to Internet banking. Almost every sector has gone through the transformation from conventional technologies to digital technologies. With the arrival of online payment and digital wallet system, making payments has become easier than ever and with the increasing demand for such services, the number of users using instant money transfer systems are growing rapidly. However, the existing online payment system has issues like a single point of failure, transparency, and insider problem. Also, security in such online payments is crucial to mitigate risks and financial inefficiencies. In this paper, a private and permissioned Blockchain-based Payment System for the financial sector in India is proposed. The proposed architecture is based on Istanbul Byzantine Fault Tolerance (IBFT) consensus and it also discusses the integration of banks with the system.

Increased vehicular flow on roads along with proposed deployment of autonomous vehicles has necessitated the need for accurate traffic forecasting so as to achieve effective route guidance, traffic management, public safety and congestion avoidance. Although a number of traffic forecasting algorithms have been proposed but most of these algorithms perform short term traffic predictions. However future vehicular systems also defined as intelligent VANETs will require a hybrid traffic forecasting model that predicts the vehicular traffic for varying values of time. This paper proposes a time varying forecasting model that predicts vehicular flow by utilizing Long Short-Term Memory (LSTM) and Convolutional Neural Network(CNN). The model is based on large-scale, network-wide traffic with spatio-temporal features. The temporal features learned by LSTM and spatial features learned by CNNs from the matrices are further fused with external factors to derive the final forecast. Model has been implemented on the traffic data set of Chandigarh city in India, mapped onto three two-dimensional matrices of time and space. The predicted information is then forwarded by the vehicle to all the other vehicles in their vicinity using vehicular adhoc networks. Experimental results indicate that the proposed model performs significantly better than other state-of-the-art models in terms of accuracy and efficiency.

Medical images have become an integral part of healthcare systems to provide quick and accurate treatment of diseases. The challenge, however, is to store these data, as they are huge in volume. DNA provides an efficient medium for the storage of bulk data. This data can be secured against malicious use using DNA based encryption algorithms. This paper presents a novel DNA based compression-encryption algorithm, specially designed for medical images. A significant part of medical images contains homogeneous pixels, which are even more prominent when decomposed into their bit-planes. This redundancy can be removed while compression, based on which the proposed DNA-based QuadTree Decomposition algorithm has been designed. A sequence of four DNA nucleotides represents the entire image, and these can be used to recover back the original image using the decoding algorithm. To secure the obtained sequences, DNA-based Advanced Encryption Standard (AES) algorithm is applied in Cipher Block Chaining (CBC) mode which encrypts the sequences using the symmetric key and initial vector parameters. The image cannot be recovered from these encrypted sequences unless decrypted using the correct key, and the algorithm for AES is secured against cryptographic attacks.

In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacy-preserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCS-IoTEH is also shown to be better than other existing competing schemes.

An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient’s potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.

The COVID-19 pandemic has adversely affected the lives of millions of people worldwide. With an alarming increase in COVID-19 cases, it is important to detect and diagnose COVID-19 in its early stages to prevent its spread. To diagnose remote patients, the Internet can be useful for accessing data of that patient. But, the Internet has also had issues related to data security, reliability, and privacy. Motivated by these challenges, in this paper, we propose a Blockchain (BC) based COVID-19 detection scheme (BCovX) for fast and reliable diagnosis of COVID-19 using chest X-Ray (CXR) images. For fast and accurate detection of COVID-19 using CXR, BCovX consists of a Convolutional Neural Network (CNN) model, using which a patient can be diagnosed for COVID-19 remotely. CNNs have performed successfully in medical imaging classification. BCovX provides reliable and secure data access and exchange using BC and smart contracts (SC). To solve issues related to data storage and its associated cost, the InterPlanetary File System (IPFS) protocol is used to store medical data. We also present a real-time SC developed in Solidity to govern the transaction between the patient and the doctor. The SC has been compiled and deployed on Remix Integrated Development Environment (IDE). Finally, we have evaluated the performance of BCovX with traditional schemes in terms of storage cost, bandwidth requirements, and accuracy of the CNN model.

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

Virtualization is an essential feature in the IoT-resource-constrained environment due to which the service providers are facing challenges to minimize the energy consumption by IoT devices. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption of IoT devices, which are an integral part of the modern data centers. A lot of research work has focused on efficient management of energy consumption by virtue of virtual machine consolidation. The existing virtualization techniques may not be suitable for this problem due to high computational overhead. As containers have been recently getting much popularity to encapsulate fog services, so they are the best candidate to handle this problem, especially for intelligent IoT devices. Keeping the focus on all these issues, in this article, we propose an energy-efficient container migration scheme by migrating the container from the source host server to the destination host server to meet the container's resource requirement. We used a novel approach to find the best destination host for container placement to solve host overload or underload problems using the best-fit container placement technique. The results obtained on the benchmark data set with respect to various performance evaluation metrics prove the efficacy of the designed scheme in comparison to the other existing state-of-the-art schemes.

Governments of the world have invested a lot of manpower and material resources to combat COVID-19 this year. At this moment, the most efficient way that could stop the epidemic is to leverage the contact tracing system to monitor people's daily contact information and isolate the close contacts of COVID-19. However, the contact tracing data usually contains people's sensitive information that they do not want to share with the contact tracing system and government. Conversely, the contact tracing system could perform better when it obtains more detailed contact tracing data. In this article, we treat the process of collecting contact tracing data from a crowdsourcing perspective in order to motivate users to contribute more contact tracing data and propose the incentive algorithm named CovidCrowd. Different from previous works where they ask users to contribute their data voluntarily, the government offers some reward to users who upload their contact tracing data to reimburse the privacy and data processing cost. We formulate the problem as a Stackelberg game and show there exists a Nash equilibrium for any user given the fixed reward value. Then, CovidCrowd computes the optimal reward value which could maximize the utility of the system. Finally, we conduct a large-scale simulation with thousands of users and evaluation with real-world data set. Both results show that CovidCrowd outperforms the benchmarks, e.g., the user participating level is improved by at least 13.2% for all evaluation scenarios.

The rapid development of the Internet of Things (IoT) has led to the emergence of more and more novel applications in recent years. One of them is the e-health system, which can provide people with high-quality and convenient health care. Meanwhile, it is a key issue and challenge to protect the privacy and security of the user's personal health record. Some cryptographic methods have been proposed such as encrypt user's data before sharing it. However, it is complicated to share the data with multiple parties (doctors, health departments, etc.), due to the fact that data should be encrypted under each recipient's keys. Although several (t, n) threshold secret sharing schemes can share the data only need one encryption operation, there is a limitation that the decryption private key has to be reconstructed by one party. To offset this shortcoming, in this paper, we propose an efficient identity-based distributed decryption scheme for personal health record sharing system. It is convenient to share their data with multiple parties and does not require to reconstruct the decryption private key. We prove that our scheme is secure under chosen-ciphertext attack (CCA). Moreover, we implement our scheme by using the Java pairing-based cryptography (JPBC) library on a laptop and an Android phone. The experimental results show that our system is practical and effective in the electronic personal health record system.

With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,11.This article is an extended version of paper entitled “Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles“ published in IEEE ICC, 20-24 May 2018, Kansas City, USADiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics.

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo Q-learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo Q-learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable because of the large number of prior network traffic information, which needs to consume expensive network resources for sampling. Motivated by that, we propose an effective prediction mechanism using multitask learning (MTL), which is a special paradigm of transfer learning. A deep learning architecture constructed by MTL and long short-term memory is designed. This deep architecture takes advantage of link loads as additional information to improve prediction accuracy. We provide a theoretical analysis for the MTL mechanism. The effectiveness is evaluated by implementing our mechanism on real network.

The papers in this special section focus on pervasive edge computing (PEC)for industrial Internet of Things. With the development of 5G technology and intelligent terminals, computation, communication, and storage capacities of devices are largely improved. Based on that, pervasive edge computing (PEC) becomes possible, where data can be processed on the network edge with the assistant of those intelligent terminals enhanced by 5G technology except the management of any centralized servers, including clouds and remote servers. The papers in this section solicits original research and practical contributions which advance PEC in industrial IoTs (IIoTs), regarding the architecture, technologies, and applications.

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

Mobile charging can provide stable and reliable energy replenishment for wireless rechargeable sensor network (WRSN). However, relatively low charging utility exists in existing solutions. In this paper, we present a utility-based collaborative charging (UBCC) strategy to maximize the charging utility of mobile chargers (MCs) in large-scale WRSNs. Charging MCs and server MCs are employed to jointly achieve our goal by three aspects. First, a path merging scheme is designed to save the traveling paths of MCs. Unlike existing studies with entirely diverse movement trajectories of MCs, the same traveling path is assigned to both the departure charging MCs and the return MCs, which serve different charging areas. Second, an idle-difference alleviating scheme is devised to improve the utilization rate of MCs. Different from current solutions with a large difference of working hours of MCs, each charging MC is assigned the equal charging tasks, resulting in synchronous charging and simultaneous energy replenishment of MCs. Third, an energy-waste averting scheme is designed to maximize the energy utilization of MCs. The energy of each MC is just exhausted until the MC completes its charging tasks and traveling roles. Extensive simulation results demonstrate the advantages of UBCC in the charging cost and charging utility.

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

The mobile edge computing greatly reduces the transmission delay by offloading computing job to the base station, which is closer to the user. It solves the problem of high delay in cloud computing. Since the edge server has limited resources, improving the efficiency of computation offloading becomes a problem that needs to be addressed. Considering the data is reusable, it is a feasible solution to use the cache to improve the efficiency of computation offloading. In order to make the cache better serve the computation offloading process, we must design a reasonable cache replacement strategy. The traditional cache replacement strategy only considers the cache hit ratio, but ignores the cache value. It is not applicable to the edge computing process. In this paper, a new cache replacement strategy is proposed for the cache replacement problem in mobile edge computing, which is more suitable for computation offloading process. First, we define the cache value based on the priority of computing job. Then, we define the problem of maximizing the cache value in mobile edge computing as a multiobjective optimization problem. Finally, in order to find a suitable Pareto solution, we propose a cache replacement strategy based on the ideal point method. The experimental results show that the proposed algorithm results are significantly better than the existing competitive cache replacement methods.

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.

Industrial Internet of Things (IIoT) is a communication environment that consists of various interconnected sensing devices, instruments, and other devices connected together with industrial software tools and applications. The important applications of IIoT include industrial automation, predictive maintenance, smart logistics management, power management, smart package management and smart robotics. However, IIoT may be vulnerable to different types of attacks as the IoT smart devices communicate among each other via insecure communication means. Thus, there is an essential requirement of deployment of secure access control scheme in IIoT environment, which is one of the important security services for securing IIoT. In this paper, we propose a novel access control scheme for fog based IIoT communication, called SAC-FIIoT. We provide the details of network model as well as threat model, which are required to design SAC-FIIoT. The security analysis of SAC-FIIoT shows its resilience against various types of possible attacks. SAC-FIIoT is also compared with other related competing existing schemes and it was found that its performance is better than these competing schemes. Therefore, SAC-FIIoT is suitable for access control in a fog-based IIoT environment.

In this work, we address the problem of efficient utilization of resource-constrained wireless sensor nodes for provisioning Sensors-as-a-Service (Se-aaS) with high quality. In sensor-cloud, the sensor-owners provide their respective sensor nodes to the sensor-cloud service provider (SCSP) on rent. The SCSP utilizes these nodes to create virtual sensors and provisions them as Se-aaS for serving their WSN-dependent applications of the end-users and earns revenue in exchange. To ascertain high quality-of-service (QoS) of Se-aaS while simultaneously ensuring profits for itself and the sensor-owners, the SCSP needs to optimally allocate physical sensor nodes to serve the virtual sensors, while considering their limited capacity and the fair distribution of service load among different sensor-owners. Although a few existing works focused on resource allocation problem in sensor-cloud, none of them considered the possibility of sharing the same physical sensor node among multiple virtual sensors. Hence, in this work, we propose a resource orchestration scheme for sensor-cloud, named SensOrch, which is based on coalition formation game with transferable utility. Using SensOrch, the SCSP ensures the optimal allocation of sensor nodes to form virtual sensors while maintaining high QoS and profitability of Se-aaS. Through simulations, we yield that, using SensOrch, the network lifetime increases by 25.31 - 59.6% along with a simultaneous increase in the profit of the SCSP by 23.64 - 29.49%, compared to the existing schemes. Additionally, SensOrch ensures fair revenue distribution among the sensor-owners.

This paper proposes a novel routing protocol for opportunistic networks called Fuzzy logic-based Q-Learning Routing Protocol (FQLRP), which uses fuzzy based Qlearning for efficient routing. The proposed protocol predicts the next optimal forwarder of a message based on a reward mechanism that considers the node's energy, movement, and buffer space as parameters. Throughout the routing process, the residual energy of each node and the energy distribution of a group of nodes, are both considered in determining a reward function, which in turn helps in deciding the most suitable forwarders of the message towards its destination. Simulation results show that the proposed FQLRP scheme outperforms the Q-Learning based routing and the Epidemic routing protocols, chosen as benchmarks, in terms of delivery rate, average delay and overhead ratio.

Content Centric Networks (CCN) has been evolved as a promising internet architecture that focuses on content centric approach for content requests rather than host centric approach. CCN provide in-network caching and content distribution capability improves Quality-of-Service by reducing intermediatory hop count and server load, which condequently reduces bandwidth requirements. Existing work in CCN emphasis on minimizing content caching operations and maximizing network hit ratio. In this paper, we have investigated the effect of in-network caching based on content provider distance and node centrality parameters over network hit ratio. A novel content placement approach named CPNDD (Content Placement based on Normalized Node Degree and Distance), has been proposed that collectively implement both parameters to intelligently select caching location in the network to maximize gain in hit ratio. The weightage of both parameters has been computed using extensive simulation on abilene network topology. We have compared our scheme with several peer caching algorithms in CCN. Simulation results are obtained for different cache size, exponent value of zipf distribution and number of requests. The results demonstrate that CPNDD increases in-network hit ratio gain upto 40% as comparison to existing algorithms.

Authentication with unlinkability is one of the critical requirements for the security of VANETs. Unlinkability prevents attackers from linking multiple messages to infer vehicular privacy. Pseudonymous authentication schemes are widely adopted to achieve unlinkable authentication. However, they need multiple interactions with a trusted third-party to update pseudonym as well as the attached information. In order to address this issue and provide effective services in distributed systems, we propose a blockchain-based unlinkable authentication protocol called BUA, where Service Manager (SM) of each domain acts as the nodes of consortium blockchain to construct a distributed system. Each SM covers a certain logical area and maintains a sequence of consistent blocks, which hold vehicular registration data. Based on the system, vehicles use homomorphic encryption to self-generate any number of pseudonyms to achieve unlinkability. Pseudonymous validity and ownership can be verified locally by each SM. Performance evaluation results of the proposed scheme show that our protocol provides stronger security with less computation and communication overhead.

The integration of Internet of Things (IoT) and software defined networks is the most suitable network paradigm for development of smart world. IoT based solutions have been developed for fulfilling the gap between Cyber and physical world. There are many issues for realizing the IoT due to its large scale and heterogeneous network structure. Energy efficient node deployment also called green deployment for IoT is one of the major challenging issue. Hence most of the existing deployment methods for WSNs are not workable for IoT. This paper addresses the challenges of deployment schemes to get an energy efficient IoT networks. It presents a homogeneous grid based deployment strategy and a heterogeneous circle packing based deployment strategy. The performance of both approaches are calculated by simulating different deployment scenarios. The network lifetime and energy consumption are calculated. It is found that the circle packing based deployment approach outperforms the other grid based approach in terms of energy efficiency and well suited for the heterogeneous network.

The pervasive and smart healthcare is important for elderly patients which has revolutionized the medical world and caught the attention from industry and academia with the help of portable sensor-enabled devices. Tiny size and resource-constrained nature restricts them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the key challenges to be tackled carefully for ubiquitous healthcare. Energy efficiency, reliability and longer battery cycle are the vital ingredients for wearable devices to empower cost-effective and pervasive medical environment. Thus, this research work has three key contributions. First, a novel transmission power control driven energy efficient algorithm (EEA) is proposed to enhance energy, battery lifetime and reliability while monitoring the health status of elderly patients. Proposed EEA and conventional constant transmission power control (TPC) are evaluated by adopting real-time datasets of static (i.e., wheelchair sitting) and dynamic (i.e., wheelchair moving) body postures of elderly patients. Second, smart healthcare framework is proposed. Third, performance metrics such as, energy drain, battery lifetime and reliability are introduced and calculated by considering average and threshold RSSI and TPC values. Finally, it is observed through experimental analysis that the proposed EEA enhances energy efficiency with acceptable PLR than the constant TPC during data transmission.

Nowadays mobile multiplayer games have been an usual recreation, however energy-limited mobile devices confine the game running time. Noticing the component-based multiplayer game is a loosely coupled application whose components are nearly always required among players in one or more rounds, we study the collaboration of players to develop an energy-efficient offloading scheme. We formulate a 0-1 integer nonlinear programming problem to minimize the overall energy cost on player side under time-delay constraint. The problem is intractable to find the optimal solution, thus we propose a heuristic Two-phase Greedy-based Collaborative Offloading Algorithm (TGCOA) for the objective of cost minimization, while including more energy savings for low-energy players. In each round, we preferentially cache components saving more energy per data size for subsequent rounds, then preferentially offload components saving more energy for current round. Meanwhile, we always assign valid player with highest remaining energy to the uploading tasks. Simulations show that the energy cost ratios of our proposal are significantly down by 1.55% to 99.62% compared to four competing methods under different cache limitations and repeatability factors. Meanwhile, our proposal enables a 6.00% to 14.80% lower energy cost proportion for low-energy players compared to the four methods.

With the popularity of unmanned aerial vehicles (UAVs), more and more valuable data can be collected by UAVs. In order to balance the data usage and communication cost, the data can be preprocessed in UAVs rather than directly transmitting to the data centre in the edge computing paradigm. Users can obtain information of interest by accessing the data centre remotely by authenticating themselves to the data centre using the most pervasive password authentication method. Unfortunately, the data centre becomes the main attack target because it not only stores the data but also maintains the passwords of all the users. Aiming at protecting the data as well as the password in the UAV‐enabled mobile edge computing environment, the authors combine the advantages of gateway‐oriented password authenticated key exchange (PAKE) protocols and two‐server PAKE protocols and put forward an efficient gateway‐oriented two‐server PAKE protocol. The security of the proposed protocol is given in the random oracle model. The performance comparison shows their proposal has comparable efficiency in computation and communication costs. Their protocol provides better protection to the password without sacrificing efficiency. Consequently, their protocol is more suitable for real applications in UAV‐enabled mobile edge computing environment.

With the popularity of edge computing, edge nodes are connected with the Internet of Things (IoT) devices to process and analyze IoT-created data, and feedback corresponding results to users, devices, or data centers. In the edge computing environment, multicast is a typical communication pattern to support data transmitting between edges and devices. It allows the sender to send messages to multiple receivers in one broadcast message. To construct a secure multicast channel, the primary issue is to ensure the privacy and credibility of the transmitted message in the open wireless communication. Then, another essential issue for multicast channels is receiver anonymity, i.e., only the sender knows the receivers' identities. Also, efficiency and provable security are critical in scheme design. In this article, we design a certificateless multimessage and multireceiver signcryption (CLMMSC) scheme by using the elliptic curve cryptography. To facilitate lightweight deployment, we adapt the certificateless mechanism to reduce the system operation and maintenance costs. Then, through security proofs, we demonstrate that the proposed scheme can achieve the expected security properties. The performance analysis shows that the proposed scheme has lower communication costs than previous CLMMSC schemes.

Internet of Things (IoT) has evolved for building smart environments in a distributed system, where the data produced by IoT devices are transmitted through Edge computing devices to streamline the flow of traffic from IoT devices to a distributed network. In such a scenario, the attacker introduces many attacks to the edge before forwarding them to distributed servers. This necessitates intrusion detection systems for such environments to mitigate security attacks. This paper has projected a basis for characterization of intrusive behaviors in a distributed system based on the functional link neural nets response weighted-average and teaching-learning metaheuristic with elitism on weight-space. The proposed technique makes use of teaching-learning metaheuristic optimization to obtain suitable parameters for the functional link neural net. Furthermore, the processing of duplicate parameters is successfully avoided by using mutation operation. In addition to this, in this paper the proposed method is found to be more efficient in terms of computational burden.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

Internet-of-Things (IoT)-based wireless body area networks (WBANs) play an important role in modern medical systems for patient-health monitoring. WBANs have the capability to collect real-time biological information from the patients' body using intelligent sensors and then send the collected information to the remote doctors or medical experts using the Internet. In recent years, numerous anonymous authentication schemes were proposed to provide security in WBANs. However, many of these schemes are not computationally efficient during anonymous authentication. Moreover, the previous schemes did not provide location privacy for both doctors and patients. In order to overcome these limitations, in this article, we propose an efficient and secure anonymous authentication framework with location privacy preservation for IoT-based WBANs. The comprehensive analysis section shows that the proposed scheme overcomes the security weaknesses in the existing schemes and also provides low computation cost during anonymous authentication.

Before the final attack happens, clandestine attackers conduct sequenced stages for being stealthy and elusive. These attacks can leave clues in several different log files. Howeverexisting approaches can only detect the anomalies using single type of log and fail to reveal all of the attack steps through log integration and correlation. Such methods can hardly detect the relationships among events and prevent the attack in advance. Additionally, traditional machine learning or data mining in log analysis has a high overhead in computing which is impractically applied in a real product or system. To address these problems, we present AClog, a multiple log correlated analysis system to construct the attack chain. Inspired by penetration testing and social network analysis, we transfer the attack provenance as an event relationship discover problem. We use different logs to form the steps of the system and regard them as the event sequences before the attack. Then, we leverage Fast Linear SVM and Longest Common Subsequences to find out the regular steps before the attack. Finally, we spot the corresponding log sequences to identify the pre- attackk steps proactively. We apply our approach in the attack prediction of a cloud computing platform and a university network. The results show that the proposed method can effectively and precisely construct the attack steps and identify the corresponding syslogs.

Recommender systems based on collaborative filter- ing technique generate the accurate and reliable predictions for customers by using their preferences about various products. Usage of user ratings has been a prevalent and successful practice for various e-commerce based websites, but launching a new e-commerce site will not benefit from this, as new site has no database of customers. A combined effort by existing companies and newly launched companies for recommendation generation can be beneficial for both companies and customers, if confidential data is protected. In literature, most of the existing techniques for secure prediction generation are based on data distortion and homomorphic encryption techniques, which may cause accuracy loss and high computation cost, respectively. To overcome these issues, this paper proposes a prediction generation scheme for the horizontally distributed data among different companies, which can help new entrants and existing sites, while preserving the privacy of the customer data. Analysis of the proposed scheme is done for parameters: privacy, accuracy, coverage and performance. The proposed scheme is secure, and the accuracy and coverage are improved due to collaboration of multiple parties. Moreover, the computation complexity of the proposed scheme is also low.

A major concern in distributed networks is the ability to provide acceptable levels of security. This is achieved by using encryption and authentication mechanisms that depend on encryption keys. However, given the ever-expanding nature of the network, it is difficult to keep setting up authorities that can aid the key- exchange process. This paper presents a novel solution to the challenge of exchanging keys of a large, distributed network without the need to set up additional authorities. The key-exchange scheme presented takes advantage of features such as packet anonymity, random selection and a multi- path approach for the exchange process. The paper also discusses the effectiveness of the proposed scheme against various threat scenarios.

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance for anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

In this paper, we propose a dynamic edge node selection scheme, named as DENSE, for the Safety- as-a-Service (Safe-aaS) architecture [1]. A Safe- aaS infrastructure provisions customized safety- related decisions remotely to the registered end- users. Depending on the time-criticality of data, the static and mobile sensor nodes sense and transmit data to the edge nodes. The number of edge nodes present within the proximity of a mobile sensor node vary with the change in the locations of the vehicle. Moreover, the distance between the mobile sensor node and the edge nodes, within its proximity, change with the variation in the vehicle's location. Therefore, in such a situation, dynamic selection of the appropriate edge node for processing the time-critical data is necessary. To optimally select the edge node, we use cooperative coalition-based game theoretic approach. Further, we apply Karush-Kuhn-Tucker (KKT) conditions to find the existence of equilibrium. The analytical results of our proposed scheme, DENSE, shows that the average utility increases by 11.33% with respect to the available storage space of the edge nodes. Moreover, the average utility increases by 50.43% with respect to the average number of tasks executed per unit time by the edge node.

Demand side load prediction is one of the most challenging tasks in smart grid environment due to uncertainties between demand and supply. Hence, in order to overcome this issue, this paper presents a scheme based on machine learning and deep learning for energy load forecasting by considering the weather condition of the area. We propose a two-tier Ensemble model, which ensembles the results of machine learning model (Support Vector Machine) and deep learning models (Convolu- tional Neural Network One Dimensional and Long Term Short memory) with a simple neural network to predict the load and demand gap. Then, we train and test the model with the UMass Smart* Dataset - 2017 release by taking the readings of appliances and weather conditions. The experimental results demonstrate that the proposed scheme has a significant improvement over the existing load forecasting methods having short-term and long- term load prediction models with an overall accuracy of 95.6%.

The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q- learning-based approach to deal with the problem of the interface-selection, and propose an approach to compute the reward. Furthermore, a modified Q- learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GEANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

Named data networking(NDN) is a very promising architecture for future network, which can improve the network performance due to its in-network caching feature. However, the pervasive caching is vulnerable against False-Locality Attack (FLA), one kind of cache pollution attack, where attackers repeatedly request a specific set of non-popular contents to replace popular contents. Therefore, the cache hit of legal requests is reduced and the response delay is increased. To mitigate this attack and improve the network performance, we propose a detection scheme based on Convolutional Neural Network (CNN) by fully exploiting the regularity of past requests. The input data of CNN are related to the inherent characteristics of the cached contents including the request ratio, the standard deviation of repeated Interests, the variance of request interval and the change of cache hit ratio. The output of CNN indicates whether FLA has been launched. Simulations through multi-topologies are conducted to validate the performance of our scheme. Compared with other state-of-the-art schemes, it is more effective in detecting FLA with higher detecting ratio, higher cache hit and lower hop count.

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

Optical switching has been investigated for a long time, as a possible effective solution to overcome the limitations of power consumption, footprint and scalability in data center networks (DCNs). However, with the increasing DC traffic and narrow channel spacing between wavelengths for optical interconnects, traditional single-mode and multi-waveguide optical switching solutions have encountered bandwidth bottlenecks. To this end, we propose a 2×2 silicon-based on-chip optical switching architecture compatible with hybrid wavelength-andmode division multiplexing (WDM-MDM), and it is found experimentally that the proposed design increases the bandwidth 8× times with low crosstalk.

Missing values are a common phenomenon in real-world datasets, which decreases the quality and reliability of data mining. Traditional regression-based imputation method estimates missing values through the relationship between attributes inferred by complete records. In order to describe the relationship more appropriately and make better use of present values, a rule-based incomplete data modeling method is proposed to impute missing values in this paper. The method utilizes incomplete records together with complete records for establishing Takagi-Sugeno (TS) models. In this process, the incomplete dataset is divided into several subsets and the linear functions containing only significant variables are built to describe the relationships between attributes in each subset. Experimental results demonstrate that the proposed method can effectively improve the performance of missing value imputation.

This paper looks into the problem of a decentralized data offloading within an edge unmanned aerial vehicle (UAV) swarm to mitigate the complexities of a single UAV continually generating and processing large application-specific data. The mobile edge UAVs considered here are multirotor types having constrained energy and processing power, which makes long-term handling of large data volumes impossible for standalone UAVs. The load mitigation is carried out by offloading data from a source UAV to other swarm members with sufficient energy and processing requirements. In this paper, we focus on selecting the most optimal multihop path through the UAVs concerning available energies and processing resources, which can survive the duration of the data offload between the source and a target UAV. We formulate a multiarmed bandit-based offload path selection scheme, which selects the most energy and processing optimized multihop path between a source and a target UAV. Upon comparison of our scheme against the naive shortest path approach, we observe that our approach results in significant savings of collective network energies, even for long operational durations.

In mobile cloud networks (MCNs), a mobile user is connected with a cloud server through a network gateway, which is responsible for providing the required quality-of-service (QoS) to the users. If a user increases its service demand, the connecting gateway may fail to provide the requested QoS due to the overloaded demand, while the other gateways remain underloaded. Due to the increase in load in one gateway, the sharing of load among all the gateways is one of the prospective solutions for providing QoS-guaranteed services to the mobile users. Additionally, if a user misbehaves, the situation becomes more challenging. In this paper, we address the problem of QoS-guaranteed secured service provisioning in MCNs. We design a utility maximization problem for quality-assured secured load sharing (QuaLShare) in MCN, and determine its optimal solution using auction theory. In QuaLShare, the overloaded gateway detects the misbehaving gateways, and, then, prevents them from participating in the auction process. Theoretically, we characterize both the problem and the solution approaches in an MCN environment. Finally, we investigate the existence of Nash Equilibrium of the proposed scheme. We extend the solution for the case of multiple users, followed by theoretical analysis. Numerical analysis establishes the correctness of the proposed algorithms.

In this paper, we propose a mobility-aware adaptive flow-rule placement scheme, named as Mobi-Flow, with an aim to maximize the overall performance in a software-defined access network (SDAN). The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end-users present in the network, depending on their history location sets, and delivers the predicted locations to the flow-manager. We use the order-k Markov predictor to predict the next possible locations of the end-users. Based on the predicted locations, the flow-manager determines access points (APs) in the network, which can be associated with the users to fulfill the requirements of the latter. We formulate an integer linear programming (ILP) to determine optimal number of APs, so that overall cost associated with flow-rule placement is minimized. Further, we propose a greedy approach to determine optimal number of APs, as solving the ILP is NP-hard. Consequently, the flow-manager implements the flow-rules at APs, so that adequate actions for the incoming requests can be taken in an adaptive manner, without querying the controller. We consider a practical scenario of an IoT environment, in which both static and mobile users are present. Therefore, the proposed scheme, Mobi-Flow, can be integrated atop the SDN controller to support the emerging concept of SDN-based IoT networking. Through extensive simulations, we show that Mobi-Flow is beneficial for minimizing the delay, number of activated APs, control overhead, energy consumption, and cost in the network, compared to the existing schemes-open shortest path first (OSPF), minimum occupied rule capacity (MRC), distributed (non-SDN), and MoRule. Particularly, the proposed scheme is capable of reducing the cost by 39, 38, 65, and 11 percent, compared to OSPF, MRC, distributed, and MoRule, respectively.

In recent years, Industrial Wireless Sensor Network (IWSN) is gaining more popularity due to many applications in industries like fire detection, hazardous gas leakage detection, temperature monitoring, localization of sensors, etc. However, faulty sensors in the network may degrade the performance of the applications. In this paper, a software defined network (SDN) based fault detection method is proposed for IWSN. In this method, SDN plays an important role for controlling the whole system by setting a fault detection algorithm at the cluster heads (CHs). The CH periodically receives the monitoring data from the sensors and follows the fault detection algorithm set by the SDN to detect the faulty sensors in the network. The fault detection algorithm uses a statistical trimean method to detect the faulty sensors. Simulation results show that our proposed method performs better than Ji's fault detection method in terms of detection accuracy (DA) and false alarm rate (FAR). A IWSN prototype is also designed to evaluate the performance of the proposed method.

With the enormous growth of number of nodes in a peer-to-peer (P2P) network, establishing a direct connection between two communicating nodes is a challenging task. It is mandatory to have a strong connection between different nodes for data sharing among P2P networks. Hence, this paper presents a modified data structure, named as Adaptive Skip Graph, a variant of a probabilistic data structure, skip list, which establishes a direct connection between two frequently communicating nodes by changing the membership vector of the caller node once the threshold is crossed. The proposed approach reduces the search time complexity of skip graph (O(log n)) substantially for the scenario where a node 'x' is repeatedly queried by a certain node 'y'. The results obtained using the proposed scheme are validated by experiments performed on a large set of nodes.

Performing virtualization can further improve the infrastructure utilization of Inter-Data-Center Optical Networks (IDCONs). In virtualized IDCONs, a service requestor subscribes one lightpath for transferring the virtual machine to the specific server in the destination DC. However, the infrastructure overload leads to the denial of services. Thus, we design a novel service degradability (well-intentioned service degradation) framework, in order to minimize the number of service requirements declined by overloads, resulting in the maximal utility of the IDCON provider. We mathematically formulate the problem and determine the most appropriate degradation degree based on microeconomic theory. For heuristic algorithms, the availability-aware bandwidth degradability policy is invoked once there is the optical backbone overload; while for the server overload in DCs, the application-specific virtual machine degradability policy is considered. Finally, the simulation results demonstrate the effectiveness of our heuristic algorithms, and the optimal combination of degradation coefficients is achieved by microeconomic theory and LINGO.

Demand response management in smart cities is one of the most challenging tasks to be performed due to the continuous changes in the load profile of the home users. The existing proposals in the literature fail to observe the hidden patterns in the load profile of these users. So, to fill these gaps, the concept of deep learning has been used in this paper for smart energy management in a smart city. The consumption data from smart homes (SHs) is gathered and taken as an input to the deep learning model, convolution neural network (CNN). The CNN model learns the hidden patterns in the data and outputs different load curves. These load curves are then used to train a support vector regression (SVR) model, which predicts the overall load consumption of all SHs in the smart city. This prediction is then compared with the power generation from the grid and consequently the demand response (DR) of the connected SHs is managed so as to minimize the gap between predicted demand and supply. The proposed scheme has been evaluated on the dataset collected from PJM and open energy information with respect to load demand prediction and DR management. The results obtained prove the efficacy of the proposed scheme. The prediction errors, i.e., root mean squared error and mean absolute percentage error are observed less in comparison to the cases when CNN and SVR are used individually.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

In modern era, Internet plays a key role in accessing and fetching web information and web resources from World Wide Web (WWW). The websites act as a medium for retrieving information from the web. Although it increases the data retrieval and users interactions, it also opens the gate for various types of attacks. For example, spams in the websites attract various Internet users. It has been observed from the literature that many authors attempted to detect the web spam using various machine learning techniques. However, none of these techniques used deep learning architecture for detection of hidden patterns. Hence, in this paper, a deep learning algorithm, i.e., Recurrent Neural Networks (RNN), has been used for the classification of spam nodes. We devise here a framework called FS2RNN: Feature Selection Scheme using Recurrent Neural Networks. In this framework, the dataset is preprocessed before applying RNN in which principal component analysis (PCA) is used for dimension reduction on the data set and recursive feature elimination (RFE) is used for feature selection. The accuracy of the proposed framework, when compared before and after preprocessing, is improved by 24.2 %, which is excellent result.

The use of cryptography systems in cyber security domain has become a primary focus to maintain data confidentiality. Several cryptography solutions exist for protecting data against confidentiality attack. Due to the advancement of computing infrastructure, there is always a need for novel security solution to protect data and introduce more complexity to the intruder. In this paper, a novel graph-based crypto-system is proposed to provide data confidentiality during communication between users and devices. The proposed crypto- system uses a set of graphs of order n along with an operation defined over it to form a group algebraic structure. Using this group, plaintext, ciphertext, and secret key are represented as a graph. The encryption and decryption processes are performed over the graphs using the operation defined in the group. It is then demonstrated that the proposed crypto-system is valid, and for a large n value, brute-forcing attempts to derive the key from plaintext or ciphertext is computationally infeasible.

Software-defined networking (SDN) moves the control plane of network devices like switches and routers to the controller. The controller is in charge of managing the whole network through application programming interfaces (APIs). Fault tolerance in the SDN networks can be handled by leveraging multiple controllers. Placing controllers in an SDN network can be seen as facility location problem which is an NP-hard problem. In this paper, we propose a simple heuristic algorithm for controller placement in SDN networks leveraging a learning automaton (LA) approach. The proposed algorithm can place the controllers based on a predefined propagation latency between the controllers and the switches while minimizing the overall propagation latency. We perform several simulations, from the available topologies of ToplogyZoo, and the results show the superiority of the proposed algorithm when compared to competing current state-of-the-art algorithms in terms of propagation latency.

In sensor-cloud framework, the concept of virtual sensor provisioning is applied to serve the end- users, who requests sensing information from the deployed sensor network. In a multi-hop sensor- cloud framework, the information collection from the physical sensors to the virtual sensor needs to activate additional nodes for information forwarding to the Cloud Service Provider (CSP). The existing works mainly consider the activation of these nodes from the same sensor owner (SO) and exhibit higher energy consumption. Although, in a sensor-cloud framework, multiple SOs co-exist naturally, and consequently, the service area of these SOs overlap. In this paper, contrasting to the existing works, we argue that the collaboration between the CSP and SOs can improve dynamic virtual sensor provisioning. We propose a scheme named Intelligent Dynamic Virtual Sensor Provisioning (iDVSP) to enable optimal selection of nodes in a multi-hop path with different SOs. We employ multi-unit single-item combinatorial reverse auction to model the interaction between the CSP and SOs. The auction based scheme facilitates the CSP to dynamically negotiate with the SOs, and ensure cost-effective node selection for virtual sensor provisioning. Simulation based results indicate that the proposed scheme is 46.51% energy-efficient compared to existing literature. Furthermore, we observe that the proposed scheme employ fair policy for node selection from different SOs. Therefore, we can argue that the proposed scheme enforces cooperation between the SOs in the sensor-cloud framework.

Partial charging in wireless rechargeable sensor networks (WRSNs) has been proposed recently, offering an new alternative in dealing with non-schedulable charging tasks. Most previous charging scheduling algorithms assume that a mobile charger must replenish a sensor to its full energy capacity in fulfilling every charging task (non-preemptive tasks). However, this charging manner degrades the charging efficiency and shortens the network lifetime to some extent. On the other hand, a partial-charging model is more effective and flexible. Although some previous works adopted a partial-charging model, they failed to theoretically formalize the schedulability of partial charging scheduling for WRSNs. To the best of our knowledge, this work first proposes a schedulability evaluation mechanism for such partial-charging scheduling. A partial charging scheme (PCS) based on schedulability evaluation for the on-demand charging architecture is given. Next, a simple case is presented for better comprehension and to demonstrate the merits of PCS. Additionally, test-bed experiments are conducted to compare the performance between the proposed scheme and previous schemes by applying different wireless energy transfer standards.

Software Defined Networking (SDN) is envisioned as a novel technology to enable reliable and scalable network management by decoupling the control plane and data plane. As the network scale increases, multiple controllers have been proposed to solve the problems of scalability and reliability caused by single controller. Although some controller placement schemes based on controller replication have been proposed to recover the controller failure in SDNs, few of them can solve the failure with the existing controllers. In this paper, we propose a QoS and cost aware fault tolerant scheme in multi-controller SDNs by exploring a fine-grained trade-off between controller cost and recovery time. With considering the controlle cost, load and communication delay in the failure recovery, we propose a heuristic algorithm to select backup controllers aiming to minimize the average recovery time, meanwhile avoiding the load oscillation in switch migration. Extensive simulations highlight that our scheme can improve the recovery efficiency compared with some other existing approaches.

To solve the bottleneck of a single controller in software-defined networks (SDNs), most of current works based on multiple controllers focus on decreasing propagation delay between the switch and the corresponding controller. However,this kind of methods ignores the synchronization between controllers, which may affect the network performance. Moreover, the controller deployment based on out-band has neglected the association between switches, which is quite costly. In this paper, we propose a community characteristic based strategy to achieve controller placement with optimal latency and balanced controller load. We first divide the network domain according to the relevance between switches, and then we design our controller deployment strategy by reconciling the propagation delay between controllers and the control delay between controllers and switches. We adopt in- band control mode instead of Euclidean distance to compute the distance among network entities. The simulation results show that our strategy can gain lower propagation delay latency and better load balance.

Network traffic analysis is one of the most important tasks in the era of on-demand Cloud Computing. However, increased resilience on computing needs, migration flexibility, and decreased costs, have made the security and privacy issues more challenging in the context of cloud computing. Although, there are several anomaly detection techniques available in literature, but due to the unbalanced nature of data, curse of dimensionality, noise in incoming data, and frequently changing anomalies, most of the existing solutions pose critical challenges in detection of aberrant patterns. Thus, in order to overcome these gaps, a new ensemble based anomaly detection scheme called "Hybrid Classification Model for Anomaly Detection (HyClass)" in cloud environment has been proposed. HyClass operates in two phases: feature selection and classification namely- (i) Boruta algorithm supported by scaling and normalization to identify important set of features and improve the accuracy and efficiency of subsequent classification and (ii) Chaotic Optimization and Differential evolution based Support Vector Machine to reduce the computational complexity by tuning the parameters of kernel function and perform classification with high accuracy. In order to evaluate the proposed anomaly detection model, two case-studies were conducted using real-time dataset from our University network and benchmark Knowledge Discovery and Data Mining (KDD'99) dataset. Experimental results in terms of detection rate, false positive rate and accuracy demonstrate the effectiveness and reliability of the proposed HyClass model.

With an exponential increase in the data generation from various Internet-enabled devices, end user's demand satisfaction with respect to Quality of experience (QoE) has become a prime concern over the past few years. However, to assure QoE to the end users, content delivery networks (CDNs) aim to provide the content close to the user's geographical location so as to decrease network congestion, and latency along with an optimal bandwidth consumption. This paper proposes a popular content storage at the edge nodes/gateways instead of a remote server for increasing the data availability. For efficient cache management at the edge nodes, data is stored using Quotient filters (QFs), where number of QFs considered are determined by the number of categories taken for data segregation. To improve the accuracy and reduce the effort in caching process, one extra bit called timer-based metabit has been used with the QF, which helps to implement least frequently used caching efficiently. It has been experimentally proved that the proposed scheme has an approximate gain of 8.9% in object hit ratio with respect to the existing CDN based techniques. Moreover, the search time complexity of the proposed edge-based CDN is independent of the number of incoming requests.

With the evolution of Internet of Things (IoT), there has been an overwhelming increase in the number of connected devices in recent years. Due to this, generation of massive amounts of data is inevitable from these enormous number of devices in IoT environment, especially in Internet of Vehicles (IoV). In such an environment, there is a need of a paradigm shift from traditional host-centric approach to a more flexible content-centric networking approach. The existing TCP/IP-based congestion control mechanisms can not be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput which most of the existing TCP variants (Tahoe, Reno, NewReno and TCP Vegas) may not be able to provide. So, in this paper, a deep learning- based content centric data dissemination approach for IoV is presented by taking into account the mobility of vehicles and type of content shared among vehicles. The proposed scheme works in three phases: 1) In the first phase, an energy estimation scheme is designed to identify the vehicles which can participate in data dissemination. 2) In the second phase, connection probability of these vehicles is computed to identify stable and reliable connections using Weiner process model. 3) In the last phase, a convolutional neural network (CNN)-based scheme for estimating the social relationship score among vehicle-to-vehicle pairs is designed. CNN is used to identify the ideal vehicle pairs, which can share data to ensure minimum delay and high data availability. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained proves the efficacy of the proposed scheme with respect to performance metrics such as-content disseminated, energy, and social score.

Nowadays, owing to the advantages of high bandwidth and low power consumption, the wired optical network-on-chip (W-ONoC) has emerged as a high-performance on-chip communication solution. However, the W-ONoC suffers from increased latency and limited scalability since the multi-hop data transmission is frequently performed in wired structures. In addition, several problems such as photoelectric conversion and thermal sensitivity pose challenges to the design of WONoCs. In this paper, we develop a novel on-chip communication architecture based on free-space optics (FSO), and leverage a suite of emerging devices. The proposed architecture eliminates the power loss caused by the waveguide and microring resonator previously deployed in W-ONoCs, and the transmission latency is also reduced through simplifying the packet switching. Moreover, compared with the traditional single-layer FSO NoC, our 3D FSO NoC further decreases the number of consumed lasers and power. Extensive simulation results demonstrate the aforementioned advantages of our solution.

Handoff in a cognitive radio networks (CRNs) is a situation that arises whenever a secondary user (SU) has to switch from its current channel to a new target channel in case the primary user (PU) reclaims the current channel or the channel conditions get worst. Before starting the SU transmission, a proactive decision to select the prospective vacant target channel after the PU interruption to resume the unfinished transmission can save substantial sensing time. In addition, the sequence of backup target channels can help reducing the service time of a SU considerably. This paper proposes a proactive decision based handoff scheme for CRNs, in which a non- iterative greedy approach is implemented to proactively determine the optimal target channel sequence without requiring the usual brute force strategy. Simulation results show that the proposed approach outperforms the reactive approach as well as a chosen benchmark scheme in terms of service time and number of handoffs. A comparative performance is also obtained in terms of throughput achieved by the SU under varying PU traffic.

Wireless Sensor Networks (WSNs) have been widely leveraged for military and surveillance applications. Each node in a WSN plays a critical role and it can be targeted for the attackers of the network. Thus, it is required to preserve a certain level of protection for each node while exploiting the nodes for the global goals of the network. The self-protection problem focuses on scenarios that sensor nodes should protect themselves instead of protecting a set of event or objects in order to resist against any types of attacks to the nodes. Choosing a set of proper nodes to maintain self-protection requirements is an NP-Complete problem. In this paper, we devise a distributed learning automaton-based algorithm to select a subset of nodes in the network so that each node is under protection of at least one active node. The pooled simulation results validate the effectiveness of our algorithm in selecting nodes and prove that it acts better than state-of- the-art competing algorithms in term of efficiency by using a small number of nodes to provision the self-protection requirements.

Vehicular Ad Hoc Networks (VANETs) offer many interesting services such as infotainment applications and safety information to vehicles. In order to protect these networks, we should ensure the confidentiality, integrity and authentication for V2I (Vehicles to Infrastructure) communications and V2V (Vehicle to Vehicle) communications. Nevertheless, in VANET environments, mobility of the vehicles is very high, so that a sound security can increase the Quality of Service (QoS) of the protocol. "The Dual Authentication and Key Management Techniques" is a solution that is created to authenticate and secure VANET communication. However, it suffers from some lack of security aspects. In this paper, we devise an improvement to this scheme in order to get a deliberate compromise between security and quality of service.

Design of multipath routing scheme is a challenging task as its topology is very dynamic in mobile adhoc network (MANET) environments. Providing reliability is more important while designing the routing protocol. Hence, multipath routing protocol which is based on Quality of Service parameters is proposed in this paper. The QoS parameters considered are delay, bandwidth and hop count. Different types of traffic such as real- time traffic and non-real-time traffic are considered. In order to improve reliability by reducing number of retransmissions, an additional packet is generated in the case of non-real time traffic and multiple description coding is used in the case of real-time traffic. The proposed algorithm, MRQ is simulated using NS-2 and compared with the existing competing schemes - QAMR and RA-MDC. Simulation results proved that the perfomance of our scheme is better in terms of End-to-End delay, packet delivery ratio, Peak Signal Noise Ratio (PSNR) and retransmissions ratio metrics.

Named Data Network (NDN) can cache data in the router to reduce the data transmission cost and hence can be used in the resource limited Internet of Things systems. However, there are still some problems. Particularly for periodic data, if the cached data can't be updated in real time, the data content will be outdated. However, if each cache node updates the data frequently, it will cost a lot of unnecessary network traffic. In this paper, we improve the traditional NDN routing procedure and propose an active updating strategy for periodic data to further reduce the network traffic. First, we create an Interest Record Table and modify the Content Store to store the periodic information. Then, we modify the router work-flow to allow the router node to actively update the cache data. Finally, we propose an algorithm based on user's access regularity to calculate the update time point. Experimental results indicate that the proposed approach can reduce the network traffic by about 33.8% on average compared to the existing competing methods.

In the context of mobile crowd sensing, incentive mechanism is crucial to recruit mobile users to participate in the sensing task and ensure participants to provide high-quality sensing data. In this paper, we investigate a staged incentive mechanism for mobile crowd sensing. We firstly divide the incentive process into two stages: recruiting stage and sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a time-space correlation algorithm in the interaction and the winners can be screened after the sensing task. Finally, Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our staged incentive mechanism (SIM) can effectively improve participants' motivation and achieve high-quality sensing data from both space dimension and time dimension by extending the motivation from the recruitment stage to the sensing process.

Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the evergrowing demand for service quality of automobiles, the way to provide users with a range of security-related and user-oriented vehicular applications has become significant. This paper concentrates on the design of a service access system in SIoVs, which focuses on a reliability assurance strategy and quality optimization method. First, in lieu of the instability of vehicular devices, a dynamic access service evaluation scheme is investigated, which explores the potential relevance of vehicles by constructing their social relationships. Next, this work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs. At last, a cooperative quality-aware system model is proposed for service access in SIoVs. Simulation results demonstrate the effectiveness of the proposed scheme.

The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

VANETs (Vehicular ad hoc networks) are an important communication to offer safety information and other infotainment applications to vehicles. To secure these networks, it is important to ensure the authentication and confidentiality of communicationV2V (vehicle to vehicle) and V2I (Vehicles to infrastructure). However, in VANET environments, vehicles are moving with a very high speed on the roads, so a strong security can degrade the performance of the protocol. Among the research that presents some schemes to secure VANET communications, we mention the Dual Authentication and Key Management Techniques that suffers from several lack of security. In this paper, we present an enhancement to this solution to obtain a deliberate compromise between quality of service and security. Security analysis proves that our scheme is secure and the result of performance validation is promising.

The design of routing protocols for opportunistic networks (OppNets) generally assume that some cooperation prevail between the nodes. But, in the presence of non-collaborative attacks such as supernova and hypernova, the routing operations become a challenge. This paper proposes a defense mechanism against the misbehavior of supernova and hypernova nodes in an OppNet running Epidemic and ProPHet as underlying routing protocols, respectively. Simulation results are provided, under various opportunistic routing attack scenarios, showing that our proposed mechanism helps defending against such attacks, while yielding an increased average performance in terms of delivery ratio and delay in message delivery by 21% and 19% for Epidemic and ProPHet, respectively.

Opportunistic Networks (OppNets) are a kind of challenged ad hoc networks where frequent topology changes, intermittent connectivity, and no guarantee of end-to-end path prevail. In this context, designing energy-efficient routing protocols for OppNets is quite challenging since most of the node's energy is consumed during node discovery and message transmission. This paper proposes the energy-efficient versions of three existing routing protocols for OppNets, namely, the probability routing protocol using history of encounters and transitivity (Prophet), the Probability-based controlled flooding in opportunistic networks (PRoWait), and the Encounter and Distance based Routing Protocol for Opportunistic Networks (EDR), where the selection of the next best forwarder for a message relies on the energy (or battery power) of the nodes. These energy-aware protocols are referred to as E-Prophet, E-PRoWait and E-EDR schemes. Simulations results show that E-Prophet, E-PRoWait and E-EDR yield a better energy consumption compared to Prophet, ProWait and EDR.

With an exponential increase in the Internet traffic over the network, there are growing concerns of identification of legitimate users which are the bulk sources of Internet traffic generation. However, due to the occurrence of anomalies in the network traffic, normal operations or the functionalities (traffic classification, resource allocation, and service management) of network get affected. Thus, in a given time frame, there is a requirement of anomalies detection in the network. The efficiency of any anomaly detection model mainly depends on the selection of relevant features and the learning algorithms which are used for classification of the network traffic patterns. However, due to curse of dimensionality, imbalance between classes, and variations in the types of anomalies, most of the existing solutions reported in the literature fail to deal with problems that occurs while detecting anomalies in large-scale network data. So, to remove these gaps in the existing solutions, we propose a new hybrid anomaly detection scheme called as Ensemble-based Classification Model for Network Anomaly Detection (EnClass) to detect anomalies in real- world networking datasets. EnClass has three modules as (i) Hoeffding-bound based clustering to identify the optimal subset of features to be taken for classification of network traffic (ii) Eigenvalues computation module to refine the features set for removal of unnecessary attributes and (iii) Very-fast decision tree for network traffic classification. In order to validate the proposed anomaly detection model, experimental evaluation is performed using real-world Knowledge Discovery and Data Mining (KDD'99) dataset with respect to parameters such as-detection rate, false positive rate, and F-score. The comparison with existing approaches clearly demonstrates the effectiveness of the EnClass in terms of detection rate (98.58%), false positive rate (0.42%), and F-score (96.06%).

Security on sensitive data of the patients is essential in e-medical system where, sensors and the servers use the insecure channels like ZigBee and Internet to exchange their information. An adversary may hamper the communications by mounting various attacks as well as he/she may try to get the sensitive data. Therefore, security system is needed so that it can protect the security attacks. This paper provides a new security protocol, referred as SecHealth when the healthcare sensors transmit their data to a local server known as fog server. In this case, after verifying each other (i.e., sensor and fog server), then only the sensors are able to transmit their data to the local fog server. However, security analysis of the proposed protocol states that it can overcome all the possible known attacks.

Internet is an integrated platform where the data is growing at an exponential rate. Since it incorporates numerous business and personal services, we need to protect the data from illegal access or modifications. In literature, a large number of techniques have been proposed to protect the data against the malicious intent of the intruders. However, one of the most important way for monitoring and analyzing network traffic against various attacks is by the deployment of Intrusion detection systems (IDS). This paper presents a novel IDS based on probabilistic data structures named as ProIDS. In the proposed ProIDS, a popular probabilistic data structure (PDS), Bloom filter has been used to store the information about the suspicious nodes. Using Bloom filter, the number of hits on suspicious nodes per unit time has been computed using the modified version of Count min sketch, i.e., MCMS, a PDS. The work also presents a detailed theoretical analysis backed by relevant technical description. Simulation results clearly depict that the proposed system is more reliable and scalable in comparison to the existing Count-min sketch method. The results obtained show that proposed system requires comparatively less computational time and storage in comparison to the existing Count-min sketch method.

The Internet of Things (IoT) is an emerging platform which bridges the real world and cyber world though communication. Any object or thing in the real world are connected and communicated with the user through the IoT. The major research issue involved in the IoT is energy consumption. Many wireless sensor network algorithms are proposed to optimize energy and those are not suitable for the IoT. The architecture of IoT is different from the traditional WSNs. To achieve the energy efficient mechanism, this paper proposed a hierarchical framework for IoT communications. An energy model is developed for each node and associated roles are assigned based on the services. Cluster head selection for energy optimization (CHSEO) algorithm was proposed for electing the optimal cluster head with in the available nodes to reduce the overall network energy. The efficiency of the CHSEO algorithm is tested with different conditions and it is proved that the CHSEO algorithm performs well when compared to the traditions WSN mechanism.

E-Health Systems are transforming the whole healthcare process to become more efficient and less expensive. QoS is usually used as a key criterion for E-Health service. The study shows that QoS of data supply chain are highly related to operating mode of nodes. However, existing optimization methods seldom took this observation into account, which shall decrease the optimal performance. In this paper, we propose an operating mode optimization strategy for nodes of data supply chain environments including E-Health Systems. First, a QoS mathematical model is developed. Then, we put forward the objective function to balance the data freshness, hit rate and cost from the aspects of users and service providers. To this end, we present a novel operating mode selection algorithm. It selects appropriate candidate operating modes for each node when generating composite operating mode of nodes with optimal QoS values. Experimental results indicate that the effectiveness of the proposed approach outperforms the existing methods by at least 20%.

Wireless Sensor Networks (WSNs) suffer from many resource constraints such as computational, energy, I/O and memory. They are also widely adopted for many applications such as remote monitoring and security applications. These networks highly relies on the limited available resources, therefore, it is crucial to keep the network alive as long as possible. In this paper, we propose a greedy heuristic algorithm to deal with the coverage problem of heterogeneous WSNs in the case when the complete coverage of the network is not needed and the nodes do not have the same sensing and communication features. This is also known as partial coverage. The greedy- based partial coverage (GPC) algorithm can preserve both coverage and connectivity of the nodes in the network. GPC uses the neighbor nodes of the selected nodes in order to preserve the connectivity of the chosen nodes while it uses the overlap between nodes to reach the required coverage ratio. The simulation results show that greedy based solution outperforms recent state-of- art schemes in terms of energy-efficiency.

Nowadays the integration of IoT and SDN has been a research hotspot which attracts significant attention. However, as resources are relatively limited in IoT, direct application of SDN will cause some challenges, one of them is that IoT forwarding nodes cannot store massive complex FlowTables like a traditional OpenFlow switch. To solve the problem, this paper proposes a hash-based distributed storage strategy. Specifically, we present a multi-dimension selection method to decide the optimum distributed storage location. And then a hash space is formed by using the content of data flow in IoT, it is the basis of FlowTables deployment and data forwarding in distributed storage. Moreover, we introduce a new FlowTables search mechanism which is built on the principle of the binary tree. Experimental results demonstrate that our strategy efficiently improves the FlowTables storage capacity with small performance loss in IoT.

With the widespread popularity of low cost sensors, the data collection and processing on real-time from different geographical regions becomes easy. Agriculture is one such areas where sensors can be deployed to get real-time data of different regions. In agriculture field, temperature monitoring, soil moisture monitoring and plants growth monitoring can be obtained using sensor nodes. Obtaining the maximum growth in agricultural domain under optimized resources availability through Precise Irrigation (PI) is called as Precision Agriculture (PA). In this paper, we propose PI-Cloud, which is a sensorcloud based measurement to management (M2M) system, which can be used in an agricultural field to maintain the moisture level of soil above a predefined threshold in real-time. We used a cluster based hierarchical architecture of sensor-cloud, where deployed sensors, called as mobile sensor robot (MSR) can be recharged/replaced as and when required. In order to maintain the real-time monitoring of moisture level of soil, replacement mechanism of energy depleted MSR and cluster heads is also proposed. Management to maintain the PI is done using the control action matrix prepared from sensor cloud. Two novel parameters have been selected as, number of replacements required (NRR) for energy depleted MSR and first replacement analysis (FRA) are used for validation of proposed scheme. The results obtained show that with the availability of energy and recharging of MSR, PI can be done on real-time basis with PICloud based architecture system.

Power conservation mechanisms allow the operator to meet the QoS requirements of its customers while offering mobility service for longer duration. Therefore, it is essential to maximize the battery life of WiMAX equipments. In this paper, we study the mechanisms of power conservation already proposed for the WiMAX network. We then propose an improved mechanism for energy conservation in the IEEE802.16e standard, taking into account the characteristics of the WiMAX network. Finally, we evaluate the various mechanisms of energy conservation in terms of energy consumption, mean delivery time and management throughput. We show through extensive simulations that our proposed mechanism retains more energy while providing a better mean delivery time and decreasing the management throughput.

Since Apple introduced the iBeacons in Worldwide Developers Conference (WWDC) 2013, the iBeacon has been rapidly accepted and generalized in the market. For the deployed iBeacons, it is necessary to monitor their status. In this paper, we design a crowd sensing based monitoring framework which combines the moving and static schemas of participants to monitor the real status of iBeacons. In such a system, the inaccuracy and conflict of the collected signal information, commonly caused by the error rate of participants or the differences of sensing context, have received more and more attention. Estimating the real status of iBeacons according to the uploaded signal information becomes a big challenge for our monitoring system. Towards this end, we propose a context-aware estimation approach in this paper. We first model the effects of sensing context, and then propose an iterative method to infer the error rate of participants and estimate the real status of iBeacons with high precision. Our method is tested via extensive simulations, and verified by our monitoring system which has been applied in the teaching building. The results demonstrate that the proposed estimation approach outperforms recent popular three-estimates algorithm and OtO EM algorithm. At last, we develop the review mechanism, which ensures the efficiency of our monitoring system.

Mobile technology has a major role in every day life. The limitations of the mobile devices cause some serious issues in the performance of an application. The emerging mobile environment needs computational support from the external environment called cloud computing. The mobile devices establish the communication to the cloud through the wireless medium. The property of the mobile devices is not static. So, the link failures occur frequently and this ultimately leads to communication failure. To overcome this issue in the mobile cloud computing (MCC), an ad hoc networking model which uses the available mobile devices within the range is proposed. Virtual Cloud Learning Automata algorithm (VCLA) is proposed for selecting the suitable nodes to create the ad hoc virtual cloud. Some of the nodes are selected as optimal nodes by VCLA on which the computational offloading is performed. The experimental results show the effectiveness of VCLA when compared to the process without LA.

Due to the execution paradigm may be different at different invocation time, users obtain different QoS when interacting with the same Data Supply Chain (DSC). However, existing QoS prediction methods seldom took this observation into consideration, which shall decrease the prediction accuracy. In this paper, we propose a context-based QoS prediction method for data supply chain. First, a QoS mathematical model is developed for considering the mass data transmission across elementary sub-chains. Then, two execution paradigms of data supply chain are discussed. Besides, we explored several special context factors of data supply chain (such as invocation time, data source update period and execution paradigm) which influence QoS. By processing such context information, we can obtain the part of data supply chain which is need to execute when the user query occurs and leverage them to predict QoS. Experimental results indicate that our approach improves the prediction accuracy and efficiency of QoS when compared to previous methods.

Machine-type communication (MTC) provides a potential playground for deploying machine-to-machine (M2M), IP-enabled `things' and wireless sensor networks (WSNs) that support modern, added-value services and applications. 4G/5G technology can facilitate the connectivity and the coverage of the MTC entities and elements by providing M2M-enabled gateways and base stations for carrying traffic streams to/from the backbone network. For example, the latest releases of long-term evolution (LTE) such as LTE-Advanced (LTE-A) are being transformed to support the migration of M2M devices. MTC-oriented technical definitions and requirements are defined to support the emerging M2M proliferation. ETSI describes three types of MTC access methods, namely a) the direct access, b) the gateway access and c) the coordinator access. This work is focused on studying coverage aspects when a gateway access takes place. A deployment planar field is considered where a number of M2M devices are randomly deployed, e.g., a hospital where body sensor networks form a M2M infrastructure. An analytical framework is devised that computes the average number of connected M2M devices when a M2C gateway is randomly placed for supporting connectivity access to the M2M devices. The introduced analytical framework is verified by simulation and numerical results.

More focus of research is going on routing in VANET as there would be frequent path breaks due to its nodes high mobility. It is better to consider multipath routing instead of single path routing to have the uninterrupted transmission in the networks like VANET. This paper shows the design of an optimized multipath routing which is based on learning automata and leapfrog method (LA-MPRLF). Particle Swarm Optimization (PSO) method is utilized to determine the better available paths. Learning automata is used to determine the number of multiple paths that can be used for transmission. Leapfrog algorithm is used to predetermine the path breaks in the network. The projected graphs demonstrate that LA-MPRLF shows improved performance in comparison with legacy systems with respect to the QoS parameters - packet delivery ratio and throughput.

With the development of social network, there are more and more people to share pictures on social platforms. Since the information contained in picture has the different requirements for confidentiality, it makes the selective presentation of secret information to be an urgent problem. Estimating user's privilege of gaining some regions based on his/her attributes is a novel solution. But there are few perfect solutions aiming at the strategy of adaptive calculation for user's privilege in the existing literatures, especially for the scenario in which the real values of some attributes have priorities. In this work, based on the cipher text-policy attribute-based encryption (CP-ABE), we propose an adaptive and safe presenting scheme for the information contained in a picture. This scheme firstly embeds the confidential data outside the secret region, and generates the image mosaic in the secret region; when someone requesting the original version of image, it adaptively calculates the recovery privilege of requestor with the strategy proposed in this paper, then precisely present some regions based on the privilege level of requestor. Moreover, we firstly propose the vote-attribute which facilitates the attribute revocation. The experiments demonstrate that, based on the privilege level, the proposed scheme can safely present the original version of the corresponding image region, and expediently achieve the attribute revocation. Compared with other algorithms, our scheme can restore the original version of image with only 1/2 secret data, and spend little time over the attribute revocation. Besides, the average of peak signal to noise ratio (PSNR) is 4dB more than the algorithms available, and the standard variance of PSNR is less than 0.4.

Stable and reliable wireless communication is one of the critical demands for smart cities to connect people and devices. Although intelligent terminals can be leveraged to deliver and exchange data through Internet, poor network coverage and expensive network access challenge the deployment of network infrastructure. In this paper, we propose a social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities. First, a social-oriented double-auction-based relay selection scheme is investigated to stimulate the relay smartphones to forward packets for others so that the network connectivity can be strengthened. Furthermore, for the sake of achieving high throughput in smartphone-based IoTs, the relay method selection is determined by integrating various kinds of transmission schemes in an optimal fashion to make full use of wireless spectrum resource. Due to its high computational complexity, a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively. Simulation results demonstrate the superiority of our proposed method.

In Long Term Evolution (LTE) Networks, several call admission control mechanisms were proposed. They take into account some network constraints such as service diversity and availability of radio resources. However, the shortcomings of the existing mechanisms led us to propose a new approach called Efficient Bandwidth Call Admission Control (EB_CAC). Our proposition considers the quality of service (QoS) while trying to maximize User Equipment satisfaction levels and optimize the use of bandwidth. The simulation results show that our proposed solution outperforms other existing mechanisms in terms of number of real time users accepted as well as the system throughput.

In this paper, we propose a mobility-aware flow-table implementation scheme with an aim to maximize overall network performance in software-defined IoT. The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end devices present in the network, and delivers info to the flow-manager. Based on predicted locations, the flow-manager implements forwarding rules at access devices (ADs) in the network, so that adequate actions for incoming requests can be taken immediately without asking the controller. We use order-k Markov predictor to predict the next possible locations of the end devices. We consider a practical scenario of an IoT environment, in which both static and mobile devices are present. Extensive simulation results show that the proposed scheme is beneficial for improving network performance in terms of energy consumption and message overhead for flow-table implementation, while predicting the future locations of the devices. We show that the proposed scheme is capable of enhancing the overall network performance approximately by 50%.

Radio Frequency Identification(RFID) is a leading wireless technology with respect to Automatic Identification and Data Capture(AIDC). With its increasing popularity amongst the researchers and industries, it has been successful in paving its way to various domains including supply chain management, healthcare, agriculture, aviation, etc. Potential applications of RFID range from tracking of assets to real-time human monitoring. However, with its wide-scale deployment, RFID systems have become more vulnerable to different kinds of active and passive attacks leading to various issues such as information leakage, identity revelation, spoofing, tracking, etc. Thus, privacy needs to be embedded in such systems so as to maintain highest levels of privacy and authenticity at all times. In order to address these issues, this paper proposes an efficient and lightweight authentication protocol using Elliptical Curve Cryptography(ECC). It is found to be safe as it establishes mutual authentication between the server and tags; while protecting against replay, tracking, eavesdropping, and cloning risks. In addition to this, AVISPA has been used to formally verify the security features of the protocol. The obtained results indicate that it is more preferable for RFID- enabled devices and provides better security than its previous counterparts.

The very high mobility of the nodes in Vehicular Adhoc Networks (VANET) increases the rate at which the handoff occurs. This motivates researchers to investigate the novel strategies for channel allocation such that the handoff becomes transparent. At the same time the utilization of the channels need to be effective in order to enhance the QoS. Hence this paper proposes a channel reservation procedure based on learning automata and node speed to improve the QoS in VANET. Channel reusability technique is incorporated to make the efficient usage of channels. The percentage of dropped calls and handoff latency are used as metrics to evaluate the proposed method.

In medical emergency situations, fair distribution of resources in a multi-tenant scenario is crucial. In such resource-constrained situations, these organizations may behave in a non-cooperative and selfish manner to maximize their individual incentives at the cost of the overall system welfare. Existing research works on dynamic resource allocation, have mostly assumed that the participating agents always behave truthfully, and place bids in accordance with their actual requirements. In practice, this assumption may not always hold true, as organizations have positive incentives for overstating. We design an algorithm, grounded in the theory of distributed mechanism design, to effectively alleviate untruthful demeanor of the organizations. The proposed resource allocation algorithm allows such organizations to maximize their individual incentives only by acting truthfully, whilst the overall system welfare is also maximized. The mechanism designed is resilient to selfish behavior of the organizations, and ensures voluntary participation of the organizations in the auction. It is also incentive compatible in nature, and dictates a truthful incentive-payment scheme.

This paper introduces a new call admission control (CAC) mechanism for Long Term Evolution (LTE) networks supporting multimedia services with different classes of traffic. Our CAC mechanism classifies calls into real time and non-real time users, then estimates the channel quality based upon the received signal strength (RSS) value, and finally identifies the call as either new call (NC) or handoff call (HC) request before performing admission control decision. We also use a simple preemption technique in order to allocate the resources to high priority bearer requests. We show through extensive simulation analysis that our CAC mechanism provides high number of accepted users with higher priorities while providing high system throughput.

Given a wireless network topology, a maximum number of users with specified Quality of service “QoS” requirements, including delay constraints, are allowed to transmit and receive data. Therefore, the best solution is to establish a partition of radio resources that provides a minimum quality for each user in the network. If all users can not be served in the same time, it is important to classify them according to their delay constraints. Also, if a new user requests a new session in the network, some specifications should be considered not only for them but also for the existing users before being involved in the network. The purpose of this paper is to find a solution that maximizes the number of video users by solving the problem of wireless channel partitioning. This solution is based on two main assumptions: calculate the minimum bandwidth required by each video user and interpret the maximum allowed delay constraint guaranteeing QoS requirements. The simulation results show that our proposed method, compared to the fair resource allocation technique, increases significantly the number of served users.

In this paper, we consider the sequential detection problem in a new cognitive radio (CR) scenario when the primary user (PU) works with more than one transmit power levels. The targets of the secondary user (SU) is not only to detect the presence of PU but also to recognize PU's transmit power levels. We formulate a valid sequential detection approach via the modified Neyman Pearson (NP) criterion and then derive the closed-form decision region for each PU's transmit power level. Moreover, the average sample number (ASN), a key metric for any sequential detection method, is analytically derived in closed-form to facilitate the performance evaluation. Finally simulation result is presented to verify the correctness of the proposed studies.

Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing to address reliable and accurate indoor localization. The location of landmark can calibrate the location of user and inertial navigation can optimize the location of landmark in turn. In this work, we define the landmark as certain characteristic structure with Beacon. To tackle the challenges of misjudgment of landmarks and variability in user walking profiles, we have developed algorithms for reliable detection of landmarks and personalization of step length with the aid of crowd sensing. We have built an indoor localization system integrating these modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and easy to deploy. Extensive experiments on users with smartphone devices, with over 37 subjects walking over an aggregate distance of over 20 kilometers were carried out. Evaluation results show that our system can achieve a mean accuracy of 2m initially and 1m with the calibration of landmarks in a 39m × 21m testing area.

In this paper, we explore the underlay cognitive relay networks in the presence of hardware impairment. To avoid interfering with the primary user's communications, the transmission powers of the secondary source and relays are adaptively adjusted by considering the following three practical effects: i) the maximum transmission power constraint at the secondary transmission nodes, ii) the interference power constraint at the primary receiver, and iii) imperfect hardware. By taking the correlations among the received signal-to-interference-plus-noise ratios into account, exact closed form expressions for the outage probabilities are respectively derived for the cases with and without a direct secondary link over Rayleigh fading channels. We further conduct an asymptotic outage probability to evaluate the impact of hardware impairment on diversity order, and show both cases with and without direct secondary link can achieve the full diversity order. Finally, simulation results are presented to verify the correctness of our analytical derivations.

The Multichannel assignment in wireless mesh networks is a challenging problem to be solved efficiently by assigning the channels to communicate among the wireless mesh nodes. An algorithm which solves the control channel contention problem and reduces the ripple factor is proposed. The contention problem is reduced by dividing the channel transmission time period and by using the sequential access control factor to make the probability of accessing the channel for all the nodes to be equal. The proposed method, Reservation and Contention Reduced Channel Access (RCR-CA) improves the channel throughput up to 80% with minimum packet loss rate and end-to-end delay. The simulated analysis shows the performance improvement of the proposed method when compared with the existing models.

Cognitive radio networks play a vital role in solving the problem of underutilization of spectrum by identifying unused licensed radio spectrum. They solve the problem by distributing this unused spectrum among unlicensed users in an intelligent fashion without interfering with existing users. In this paper, using the conflict graph and graph colouring concepts, a Graph Colouring based Dynamic Channel Allocation (GC-DCA) algorithm is proposed that minimizes the network interference when the primary users (PUs) and secondary users (SUs) share the channel simultaneously. The performance of the GC-DCA algorithm is evaluated using the OMNeT++ network simulator under different topologies, in terms of channel utilization, end-to-end delay, packet delivery ratio, and throughput. Numerical results show that the proposed technique yields 40.40% increase in channel utilization when the PU and SU share the channel simultaneously.

Heterogeneity and convergence are two distinctive features for new generation networks like the Long Term Evolution-Advanced (LTE-A) system. LTE-A is now being deployed and is the way forward for high speed cellular services. LTE-A enhancements the four areas of capacity, coverage, inter-cells coordination, and cost. Improvements in these areas are based on using several technologies. Multiple-Input Multiple Output along with Orthogonal Frequency Division Multiple Access (MIMO/OFDMA) are two of the base technologies that are enablers. In addition, self-organizing and optimization (SON) technologies have been also developed to enable automatic configuration, optimization of network operations, including the 802.21 Media Independent Handover protocol (MIH), which is designed to optimize the vertical handover process. In this paper, we show the importance of inter-technologies and inter-entities cooperation, which can exploit heterogeneity as an enabler to improve the system capacity as well as the quality of service (QoS) for users. We present a new cooperative radio resource allocation scheme for LTE-A network to coordinate better the utilization of network's available radio resources. We adopted the MIH framework, in order to facilitate the exchange between heterogeneous network entities to insure self-configuration of radio resource management parameters. We worked on allocating the right PRB to the right user at the right time. We also analyze some existing solutions and evaluate our proposed scheme using simulation analysis. Simulation results illustrate the performance gains brought by the proposed optimization, especially for average throughput of macro-cell users comparing to their initial performance within two-tier LTE-A network.

Abstract Because of the stochastic key pre‐distribution and complicated network topology, designing an energy‐efficient node capture attack algorithm is of great challenge. Although many algorithms have been proposed for node capture attack, previous methods lack of concerning minimizing resource expenditure in modeling attacking behavior. In this paper, we propose a novel way of modeling the node capture attack. First, we transform the problem into a set covering problem with a shortest Hamiltonian cycle problem, which has been shown to be NP‐hard. Consequently, we also develop a heuristic called minimum resource expenditure node capture attack (MREA) to maximize destructiveness while minimizing resource expenditure. Moreover, extensive simulations are conducted to show the performance of MREA. Simulation results show that MREA outperforms other algorithms in reducing the attack rounds and saving resource expenditure. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Client‐server‐based communications provide a facility by which users can get several services from home via the Internet. As the Internet is an insecure channel, it is needed to protect information of communicators. An authentication scheme can fulfill the aforementioned requirements. Recently, Huang et al. presented an elliptic curve cryptosystem‐based password authentication scheme. This work has demonstrated that the scheme of Huang et al. has security weakness against the forgery attack. In addition, this paper also presented that the scheme of Huang et al. has some design drawbacks. Therefore, this paper has focused on excluding the security vulnerabilities of the scheme of Huang et al. by proposing an elliptic curve cryptosystem‐based password authentication scheme using smart card. The security of our scheme is based on the hardness assumption of the one‐way hash functions and elliptic curve discrete logarithm problem. Furthermore, we have demonstrated that our scheme is secured against known attacks. The performance of our scheme is also nearly equal when compared to related competing schemes. Copyright © 2016 John Wiley & Sons, Ltd.

A mobile ad hoc network is a collection of mobiles, autonomous nodes that communicate in a cooperative manner over a wireless channel without any fixed infrastructure, nor built-in security. As such, this type of network is vulnerable to different types of attacks such as blackhole and wormhole attacks. A blackhole attack is a type of attack where the malicious node so-called blackhole node can attract all the data packets by using a forged route reply packet to falsely claim that it has a shortest route to the destination, thereby dropping all the data packets that it receives. In this paper, an improved version of a dynamic source routing DSR protocol so-called detecting blackhole attack based on DSR DBA-DSR is proposed to combat against blackhole attacks in mobile ad hoc networks. Unlike other solutions, which adopt a reactive approach in which blackhole nodes are identified only after the attack has been carried out on the network, our DBA-DSR scheme detects and isolates the blackhole nodes prior to the actual routing process. This is achieved by using fake route request packets. Simulation results are provided, demonstrating the superiority of DBA-DSR over DSR in terms of network throughput, packet delivery ratio, and routing overhead, chosen as performance metrics, when blackhole nodes are present in the network. Copyright © 2013 John Wiley & Sons, Ltd.

In wireless sensor networks (WSNs), sensor observations are spatiotemporally correlated, and that correlation signifies redundancy among the observations. Spatial correlation is primarily employed to estimate the minimum number of event-monitoring nodes. However, an event-monitoring node can intelligently exploit the temporal correlation between its observations to adapt with its dynamic surroundings. This self-adaptation helps resource-constrained nodes to enhance their performance by saving battery power and maintaining the quality of transmitted data. In WSNs, the sensor nodes switch between the active and sleep states to conserve energy. Using temporal correlation, a node can dynamically estimate the appropriate sleep duration, which is an important parameter for a node to adapt with its dynamic surroundings in an energy-efficient manner. In this paper, dynamic Bayesian network and entropy are used to estimate utility of observations. Moreover, a node estimates temporal correlation between its consecutive observations by mutual information. Further, the sensor nodes calculate appropriate sleep duration and control their communications at a particular time instant on the basis of estimated temporal correlation. A reinforcement-learning-based approach is used, in a distributed manner, to calculate the optimum sleep duration. Extensive simulation studies show that the proposed approach performs more efficiently in terms of energy conservation, energy utilization, and data accuracy than the benchmark schemes.

In a smart grid, micro-grids can exchange energy among themselves in order to provide reliable energy service to customers. Therefore, the micro-grids need to exchange their real-time energy status with other micro-grids, which, in turn, maximizes the energy consumption and CO2emissions to them. In this paper, we propose a cloud-based energy forecasting scheme to minimize the energy consumption and CO2emission towards enabling a green smart grid communication technology. Additionally, we device an optimal strategy for the proposed cloud-based energy forecasting scheme to minimize the energy consumption furthermore. Numerical results show the effectiveness of the proposed scheme over without cloud-based approach in terms of message overhead, energy consumption, and CO2emissions of the micro-grids. We see that the proposed scheme can minimize the energy consumption and the CO2emissions involved in the forecasting process significantly, which supports the green architecture of the smart grid communication technology. Additionally, the message overhead for energy forecasting can also be minimized.

In participatory sensing applications, the inaccuracy and conflict of the reported data, commonly caused by the error rate of participants or the differences of observation context, have received more and more attention. Estimating the real status of facilities according to observations becomes a big challenge for participatory sensing. Towards this end, we propose a context-aware estimation approach based on participatory sensing in this paper. We first model the effects of observation context, and then propose an iterative method to infer the error rate of participants and estimate the real status with high precision. Our method is verified using a public facilities monitoring application in our campus, and tested via extensive simulations. The results demonstrate that the proposed method outperforms recent popular three-estimates algorithm and OtO EM algorithm.

Due to the evolutionary nature of computing and communication technologies, smartness and intelligence have become inevitable requirements for all futuristic systems. How to network these futuristic systems and make them deliver their services effectively is a major challenge. This challenge could be achieved by using wireless communication technologies. But, these smart systems need to be interconnected in seamless manner with facilities such as dynamic connection and disconnection, re-configurability, self-configurability, bandwidth optimization, etc. The existing wireless communication provides few of these facilities for the interconnection of generic computing devices such as server computers, desktop computers and laptop computers. A smart system highly depends on the individual real-time data monitoring for their effective performance. Hence, existing wireless communication technologies need to be customized for heterogeneous, event-driven and proactive smart systems. Hence this paper proposes a learning automata based cross layer framework for wireless networks using context awareness which aids in the reduction of energy consumption and better management of resources. The proposed technique has been tested on a simulated wireless multimedia network.

For meteor burst communications (MBC), although per-survivor processing (PSP) based on joint data and channel estimation provides superior performance and robustness for meteor burst communications (MBC), great computational complexity constrains its application in practice. On this occasion, a dimension-down PSP (D- PSP) algorithm and a adaptive state reduction of PSP (ASRP) algorithm are proposed to curve this problem in this paper. On the basis of this, the adaptive state reduction using a dimension-down PSP (ADPSP) is proposed to combine the advantages of D-PSP and ASRP, which reduces the computational time and memory size for exponentially decaying meteor burst channels and makes maximum likelihood sequence detection (MLSD) possible for adaptive data transmission. Simulation results are presented to validate the theoretical analysis. It is shown that deploying the proposed ADPSP algorithm can achieve a good tradeoff between performance and computational complexity dynamically, and provide reliable data transmission for MBC systems with adaptive coding and modulation.

Multi-hop Cellular Network (MCN) is a promising architecture aiming to enhance the performance of the current single hop cellular network. Indeed, it combines the flexibility of Ad Hoc networks and the benefits of the fixed infrastructure. The traffic is relayed through multi-hop communication, which can extend the coverage area and achieve high throughput. Providing security in this type of network is an important issue since the mobile nodes participate in the routing process. Many researchers focus on this topic by providing security for routing protocol and data transmission. In this paper, we propose a secure routing protocol for MCN. In this protocol, we address two challenges: ensuring confidentiality and integrity of routing discovery and establishing a session key to secure the data transmission after route selection. Simulation results show that the proposed protocol is efficient and it ensures a high security level against attacks.

There are more and more systems using mobile devices to perform sensing tasks, but these increase the risk of leakage of personal privacy and data. Data hiding is one of the important ways for information security. Even though many data hiding algorithms have worked on providing more hiding capacity or higher PSNR, there are few algorithms that can control PSNR effectively while ensuring hiding capacity. In this paper, with controllable PSNR based on LSBs substitution- PSNR-Controllable Data Hiding (PCDH), we first propose a novel encoding plan for data hiding. In PCDH, we use the remainder algorithm to calculate the hidden information, and hide the secret information in the last x LSBs of every pixel. Theoretical proof shows that this method can control the variation of stego image from cover image, and control PSNR by adjusting parameters in the remainder calculation. Then, we design the encoding and decoding algorithms with low computation complexity. Experimental results show that PCDH can control the PSNR in a given range while ensuring high hiding capacity. In addition, it can resist well some steganalysis. Compared to other algorithms, PCDH achieves better tradeoff among PSNR, hiding capacity, and computation complexity.

In this paper, we propose an adaptive call admission control scheme based on higher order Markov chains to effectively handle the call blocking probability in 3GPP LTE femtocell networks supporting multimedia services with different classes of traffic and diverse bandwidth requirements. The accuracy of the approximate analytic model is validated by simulation analysis. The numerical results show that the proposed scheme is able to attain negligible call blocking probability without sacrificing resource utilization.

Wireless Mesh Networks (WMNs) are continuously overwhelmed with various kinds of security threats. Amongst these threats is Denial of Service (DoS) which represents a huge umbrella of powerful attacks. It is very essential to understand the complexities of these attacks and counter-mechanisms existed in the literature. The best antidote to defend against these attacks would be to resolve the problem at its root by identifying the source of the attacks. The traceback technique realizes such a forensic analysis of the internet traffic. In this paper, we explain our novel approach of IP traceback based on marking approach and that used the Chinese remainder theorem to conceive the communication protocol in WMN IEEE 802.11s environments. We evaluated the performance and the efficiency of our proposed scheme based on some collected evaluation metrics.

A Multi-Channel Cognitive MAC Protocol for adhoc cognitive networks that uses a distributed learning reinforcement scheme is proposed in this paper. The proposed protocol learns the Primary User (PU) traffic characteristics and then selects the best channel to transmit. The scheme, which addresses overlay cognitive networks,avoids collision with the PU nodes and manages to exceed the performance of the less adaptive statistical channel selection schemes in normal and especially bursty traffic environments. The simulation analysis results have shown that the performance of our proposed scheme outperforms that of the CREAM-MAC scheme.

In many developing countries, agriculture is one of the primary livelihoods of common people. Agriculture requires various types of technologies for improving crop yields. The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation. In this work, we propose a hardware prototype using Wireless Sensor Network (WSN) for intruder detection in an agricultural field. The proposed system is named Agricultural Intrusion Detection (AID). AID helps to generate alarms in the farmer's house and at the same time transmits a text message to the farmer's cell phone when an intruder enters into the field. In order to implement the proposed scheme, we design and deploy Advanced Virtual RISC (AVR) micro-controller-based wireless sensor boards over an outdoor environment and evaluate the performance.

Increased population in an area degrades the performance of Wireless Body Area Networks (WBANs) in terms of throughput and packet delivery latency. WBANs by nature do not get fair amount of resources (bandwidth, time and spectrum). In this work, we consider the WBANs with varying traffic load in an area. In order to minimize the computational complexity of the algorithms executed the WBANs, the latter form different groups named Relational Patient Group (RPG), based on the disease types and the syndromes of the patients who are equipped with WBANs. RPG minimizes the computational complexity but does not minimize the traffic load. To minimize the traffic load the WBANs in the RPG form optimal grouping based on the optimal decision making process, named as Virtual Patient Group (VPG). We have formulated the proposed scheme mathematically and evaluated through a series of simulations. Results show that the proposed scheme provides significant improvement in the traffic load and the packet drop probability.

Mobile ad hoc networks (MANETs) are infrastructureless. As such, they are subject to various types of security attacks if malicious nodes are present in the network. One of such attacks is the wormhole attack. In an earlier work, a scheme (called Cell-based Open Tunnel Avoidance (COTA)) was proposed to address this problem, which consisted in a mechanism for detecting and classifying the wormhole attacks in the network. In this paper, the COTA mechanism is implemented on the location aided routing protocol (LAR1), leading to the so-called COTA-LAR1 scheme. Simulation results are provided, showing that the COTA-LAR1 scheme is an improved secured routing scheme against wormhole attacks in MANETs, in terms of packet delivery ratio, throughput, and end-to-end delay, chosen as performance metrics.

Currently, Network Mobility (NEMO) Basic Support protocol enables the attachment of mobile networks to different points in the Internet. It permits session continuity for all nodes in the mobile network to be reachable as the network moves. While this standard is based on the MobileIPv6 standard, it inherits these disadvantages such as security vulnerabilities. To manage the problems of NEMO, many schemes combine it with a network-based approach such as Proxy Mobile IPv6 (PMIPv6). Despite the fact that this latter expedites the real deployment of IP mobility management; it suffers from lack of security. Therefore, we propose an Efficient and Secure Mutual Authentication Mechanism during initial attachment in NEMO-based Proxy Mobile IPv6 Networks called EMA-NEMO based PMIPv6 in order to provide mutual authentication between a mobile router and diameter server during initial attachment of the mobile router to a PMIPv6 domain. Moreover, we evaluate the performance of our scheme using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software which has proved that authentication goals are achieved.

Summary Password‐based two‐party authenticated key exchange (2PAKE) protocol enables two or more entities, who only share a low‐entropy password between them, to authenticate each other and establish a high‐entropy secret session key. Recently, Zhenget al.proposed a password‐based 2PAKE protocol based on bilinear pairings and claimed that their protocol is secure against the known security attacks. However, in this paper, we indicate that the protocol of Zhenget al.is insecure against the off‐line password guessing attack, which is a serious threat to such protocols. Consequently, we show that an attacker who obtained the users' password by applying the off‐line password guessing attack can easily obtain the secret session key. In addition, the protocol of Zhenget al.does not provide the forward secrecy of the session key. As a remedy, we also improve the protocol of Zhenget al.and prove the security of our enhanced protocol in the random oracle model. The simulation result shows that the execution time of our 2PAKE protocol is less compared with other existing protocols. Copyright © 2015 John Wiley & Sons, Ltd.

The evolution of Internet of Things (IoT) leads to the emergence of Internet of Vehicles (IoV). In IoV, nodes/vehicles are connected with one another to form a vehicular ad hoc network (VANET). But, due to constant topological changes, database repository (centralized/distributed) in IoV is of spatio-temporal nature, as it contains traffic-related data, which is dependent on time and location from a large number of inter-connected vehicles. The nature of collected data varies in size, volume, and dimensions with the passage of time, which requires large storage and computation time for processing. So, one of the biggest challenges in IoV is to process this large volume of data and later on deliver to its destination with the help of a set of the intermediate/relay nodes. The intermediate/relay nodes may act either in cooperative or non-cooperative mode for processing the spatio-temporal data. This paper analyze this problem using Bayesian coalition game (BCG) and learning automata (LA). The LA stationed on the vehicles are assumed as the players in the game. For each action performed by an automaton, it may get a reward or a penalty from the environment using which each automaton updates its action probability vector for all the actions to be taken in future. A detailed comparison has been provided by analyzing the cooperative and noncooperative nature of the players in the game. The existence of Nash equilibrium (NE) with respect to the probabilistic belief of the strategies of the other players in the coalition game is also analyzed.

Abstract In the literature, several pairing‐based blind signature schemes have been put forwarded using identity‐based cryptography. However, the private key escrow problem of these schemes makes them unsuitable in practical environments because the private key generator computes signer private key using signer public identity. Therefore, an untrusted/semitrusted private key generator may perform malicious activity on behalf of the signer. We took the advantage of certificateless public key cryptography and constructed a robust and efficient certificateless blind signature (CL‐BS) scheme using bilinear pairing. Furthermore, the probabilistic map‐to‐point hash function, which requires more computation time than the execution time of an elliptic curve scalar point multiplication, is circumvented in our scheme. We found that our scheme is unlinkable and provably secure against the adaptive chosen message and identity adversaries. From the perspective of computational costs, our scheme is more efficient and secure than other related schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In order to separate a host's identity from its location on the Internet, the Host Identity Protocol (HIP) was developed by the Internet Engineering Task Force as a mobility management solution. HIP provides a solid basis to enable secured mobility and multihoming features. Several extensions and proposals have been introduced in recent publications to improve the micro‐mobility features of HIP. Moreover, many other publications have dealt with the efficiency of Network Mobility (NEMO) management with HIP. However, the HIP‐based micro‐mobility management solutions adapted to NEMO scenario do not cover all security aspects requirements and still suffer from security flaws. Therefore, in this paper, a number of potential threats in the typical HIP with Rendez Vous Server are identified. A new secure and efficient scheme for network mobility management is also proposed to overcome the outlined ones. The proposed solution ensures strong authentication between network entities, reduces Denial of Service attacks, secures against Domain Name Server spoofing, reply, and eavesdropping attacks, and ensures end‐to‐end confidentiality and integrity protection. To analyze the security properties of the proposed scheme, we have performed automated formal specification and evaluation with the help of both the Automated Validation of Internet Security Protocols and Applications and the Security Protocol Animator, which have proved that authentication and confidentiality goals are achieved. Hence, the scheme is effective when an intruder is present. Copyright © 2014 John Wiley & Sons, Ltd.

Recently, the IEEE 802.15.6 Task Group introduced a new wireless communication standard that provides a suitable framework specifically to support the requirements of wireless body area networks (WBANs). The standardization dictates the physical (PHY) layer and medium access control (MAC) layer protocols for WBAN-based communications. Unlike the pre-existing wireless communication standards, IEEE 802.15.6 standardization supports short-range, extremely low power wireless communication with high quality of service and support for high data rates upto 10 Mbps in the vicinity of living tissues. In this work, we construct a discrete-time Markov chain (DTMC) that efficiently depicts the states of an IEEE 802.15.6 CSMA/CA-based WBAN. Following this, we put forward a thorough analysis of the standard in terms of reliability, throughput, average delay, and power consumption. The work concerns non-ideal channel characteristics and a saturated network traffic regime. The major shortcoming of the existing literature on Markov chain-based analysis of IEEE 802.15.6 is that the authors did not take into consideration the time spent by a node awaiting the acknowledgement frame after transmission of a packet, until time-out occurs. Also, most of the work assume that ideal channel characteristics persist for the network which is hardly the case in practice. This work remains distinctive as we take into account the waiting time of a node after it transmits a packet while constructing the DTMC. Based on the DTMC, we perform a user priority (UP)-wise analysis, and justify the importance of the standard from a medical perspective.

In a smart grid, smart meters are expected to be the key technology to support bi-directional information exchange between service providers and end-users. The on-growing demand of smart meters (residential customers and plug-in hybrid electric vehicles) would result in huge energy consumption, while communicating with the entities in the smart grid. Therefore, green wireless communication technologies are expected to help in reducing their impact on environment. Therefore, it is important to design energy-efficient schemes that can reduce CO2emissions and cost-effective energy management in the smart grid. In this paper, an energy-efficient smart metering scheme is proposed - an effort towards minimizing the energy consumption by the smart meters for green smart grid communication. We incorporate the use of coalition game to form multiple coalitions among smart meters to communicate with the service provider. We show that there exists a stable condition of the coalitions for which the payoff values of the smart meters are maximized. The simulation results show that using the proposed approach, energy consumption by the smart meters can be reduced, which, in turn, would enable green wireless communication in the smart grid.

This paper presents a priority-based MAC-frame payload tuning mechanism with reduced energy consumption for healthcare systems that use Wireless Body Area Networks (WBANs). A fundamental problem in WBANs is to prioritize the physiological sensors depending on several health and external criteria. The challenge is to design a dynamic decision making model that can optimize the energy consumption of each physiological sensor. To address this problem we employ the concept of Fuzzy Inference System (FIS) in order to calculate Criticality Index (CI), which signifies the severity or the priority of the physiological data sensed by each sensor. Considering the obtained CI value we proceed with designing a Markov Decision Process (MDP) based dynamic decision making model in order to tune MAC-frame payload by optimizing the energy consumption of each sensor node. We achieve around 25% decrease in the overall energy consumption using our proposed mechanism.

Call admission control mechanisms play an important role in heterogeneous wireless networks where different radio access technologies will coexist to grant Quality of Service (QoS) in a network. They are used to decide whether or not an incoming service request will be accepted according to an admission constraint as well as determining in which radio access technology among the available it will be connected. In this paper, we propose an adaptive bandwidth management and call admission control scheme for heterogeneous wireless networks. The proposed scheme takes into account the separation between the incoming traffic for each class and prioritizes handoff calls over the new calls. The objectives of the proposed adaptive CAC approach are to guarantee QoS requirements of all accepted calls, reduce new call blocking probability and handoff call dropping probability and maintain efficient resource utilization.

Power management has been advanced on a crucial factor in the design of modern access networks. Furthermore, the proliferation of optical networking in the last mile led major Telecom unions, such as the International Telecommunication Union (ITU), to emerge energy consumption as a critical objective of the next generation passive optical networks (NG-PONs). In particular, the standardization of the 10-gigabit-capable PON (XG-PON) entails well-defined specifications towards power management and energy reduction, especially regarding the power control of optical terminal devices such as the optical network units (ONUs). In this way, the optical line terminator (OLT) along with ONUs are able to cooperate with each other in order to succeed energy reduction, by applying doze or cyclic sleep periods to idle ONUs. However, the sleep period determination remains a quite challenging research area. In this study, we endeavor to provide XG-PON networks with an effective forecasting mechanism that is capable of estimating the time duration of the forthcoming sleep session. To this end, we apply the exponential smoothing technique to best estimate the sleep duration based on the monitoring time series observations. The obtained evaluation results sound quite promising, since the proposed model accomplishes to advance the trade-off between the energy reduction and network efficiency.

The standardization of the IEEE 802.15.6 protocol for wireless body area networks (WBANs) dictates the physical layer and medium access control layer standards from the communication perspective. The standard supports short-range, extremely low power wireless communication with high quality of service and data rates upto 10 Mbps in the vicinity of any living tissue. In this paper, we develop a discrete-time Markov model for the accurate analysis of reliability and throughput of an IEEE 802.15.6 CSMA/CA-based WBAN under saturation condition. Existing literature on Markov chain-based analysis of IEEE 802.15.6, however, do not take into consideration the time a node spends waiting for the immediate acknowledgement frame after transmission of a packet, until time-out occurs. In this work, we take into consideration the waiting time for a node after its transmission, and accordingly modified the structure of the discrete-time Markov chain (DTMC). We also show that as the payload length increases, the reliability of a node decreases; whereas its throughput sharply increases.

Workload and resource management are two essential functions provided at the service level of the distributed systems infrastructure. To improve the global throughput of these software environments, workloads have to be evenly scheduled among the available resources. To realize this goal, several load balancing strategies and algorithms have been proposed. Most o f t h e s e strategies were developed assuming homogeneous set of sites linked with homogeneous and fast networks. However, for computational grids, we must address some new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a decentralized cluster-based algorithm which achieves dynamic load balancing in the cloud architecture. The proposed algorithm presents the following main features: (i) it supports heterogeneity, (ii) scalability, (iii) low network congestion and (iv) absence of any bottleneck node due to its decentralized nature. Simulation results using CloudSim show the performance analysis of the algorithm for patronizing our claims about the load balancing achieved in the system.

This paper proposes a new approach for optimizing the handover decision and the target network selection process in 3GPP LTE-Advanced network. We aim to manage load state between LTE-Advanced cells, as well as between different existing Radio Access Technologies (RATs). In fact, a well-balanced load ensures the prevention of a congestion state. With Fourth generation networks, like LTE_A, the access layer includes all existing RATs. All of them are controlled by a unique core network. To managewith more flexibility and different interworked RATs, we adopt the MIH 802.21 protocol. Our proposed scheme offers to the Radio Resources Management (RRM) entity a dynamic and autonomous aspect in order to maintain the network performance. Finally, the performance of the proposed scheme is evaluated using simulation analysis.

Honeypots are systems aimed at deceiving threat agents. In most of the cases the latter are cyber attackers with financial motivations, and malicious software with the ability to launch automated attacks. Honeypots are usually deployed as either production systems or as research units to study the methods employed by attackers. In this paper we present the results of two distinct research honeypots. The first acted as a malware collector, a device usually deployed in order to capture self-propagating malware and monitor their activity. The second acted as a decoy server, dropping but logging every malicious connection attempt. Both of these systems have remained online for a lengthy period of time to study the aforementioned malicious activity. During this assessment it was shown that human attackers and malicious software are constantly attacking servers, trying to break into systems or spread across networks. It was also shown that the usage of honeypots for malware monitoring and attack logging can be very effective and provide valuable data. Lastly, we present an open source visualization tool which was developed to help security professionals and researchers during the analysis and conclusion drawing phases, for use with one of the systems fielded in our study.

Routing issues in the existing Smart Grid (SG) literature are focused on Home Area Networks (HANs), Neighborhood Area Networks (NANs), or Wide Area Networks (WANs). Among these, routing in NANs is the most challenging as it entails construction and maintenance of backhaul having various Mesh Routers (MRs). Wireless networks are generally used for communication between backhaul and centralized controller for power distribution. This triggers increased chances of congestion due to scarce resources of available bandwidth and number of channels. Keeping in view of the same, in this paper, we propose a new Efficient Routing Scheme (ERS) as a Bayesian Coalition Game (BCG). The solution strategy integrates the concepts of Learning Automata (LA) in NANs. LA are assumed to be the players in the game, which are deployed at the MRs in NANs. Coalition among the players of the game is scaffolded upon the concepts of Bayesian Networks. Each player in the game is allowed to move from one coalition to another depending upon the payoff function. Corresponding to each move of the player in the game, its action may be rewarded or penalized from the environment. Based upon reward/penalty from the environment, each player updates its action probability vector. The proposed scheme is evaluated with respect to various performance evaluation metrics such as load utilization factor, user satisfaction levels, delay and probability of transmission.

The vital trend inside wireless networks is the heterogeneity of access technology. Vertical handover management is one of key challenges in such environment to ensure seamless mobility and service continuity especially in the case of network mobility. To deal with some of these challenges, numerous efforts were made in the context of standards and scientific research to perform the vertical handover process such as the IEEE 802.21 Media Independent Handover framework and the Network Mobility Basic Support Protocol. However, these optimizations stay insufficient to solve all issues. This paper proposes new mobility management framework in the case of Network Mobility handover. The proposed scheme is based on the principal concepts of IEEE 802.21 for context information gathering and optimized handover decision making. Also, a Modified P-NEMO for the handover execution phase is suggested to overcome the dependence of standardizations on the NEMO application deployment and the infrastructure network. Detailed simulations show that our proposed framework provides less complexity and better performance for enhancing vertical handover.

Heterogeneous Wireless Network aims to provide seamless services for mobile stations or mobile routers while roaming across different mobile communication networks. Designing secure and efficient authentication protocols to enable fast handover is one of key challenges in such heterogeneous environment. In this paper, we propose a new authentication protocol called SMM-NEMO (Secure Macro Mobility protocol for NEtwork MObility) which is a deliberate compromise between security and quality of service. It provides lower handover latency and protects against many attacks. The proposed protocol has been modeled and verified using the AVISPA (Automated Validation of Internet Security Protocols and Applications) and SPAN for AVISPA software and it is found to be a safe and efficient scheme.

The success of mobile communication shows the interest of a mobility support of the whole network. Hence, by extending existing host mobility solutions, Network Mobility (NEMO) protocol was developed by the Internet Engineering Task Force (IETF) to enable the movement of mobile nodes and networks while maintaining connectivity to their network or the Internet in case of mobility events. The emergence of NEMO is facing more and more problems linked to security threats. Thus, in order to achieve seamless handover in NEMO, we propose in this paper a Secure Micro Mobility for Network Mobility (SMM-NEMO) using AAA (Authentication, Authorization and Accounting) mechanism. This helps to efficiently authenticate and securely generate session keys. It enables the integration of authentication schemes with the mobility protocol Fast Handover for Hierarchical Mobile IPv6 (F-HMIPv6). This proposed protocol has been modeled and verified using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software which has proved its security when an intruder is present.

ABSTRACT With the rapid evolution of the Internet, security has become a major area of concern and, consequently, an interesting research area. Different applications transmit sensitive information over the Internet, which creates increased chances for attackers to look into every piece of data, unless it is secured using secure socket layer (SSL) certificate. However, the present SSL certificates too face challenges because of various attacks, and these certificates need to be verified before transmitting information. In this paper, we show how the concepts of learning automata (LA) can be used to verify SSL certificates. The proposed LA‐based system can detect safe or unsafe SSL certificates. The LA reward/penalty scheme is used to build the trust value for SSL certificates. Copyright © 2013 John Wiley & Sons, Ltd.

In recent years, the dependence of people on electronic systems (e-systems) has increased tremendously.Examples of e-systems widely used in everyday life include stand-alone computers, wired and wireless networks, cellular telephony networks, corporate Web sites, electronic service, e-commerce and e-payment systems, and e-government systems.Because of the aforementioned spread of e-systems, many corporations and businesses rely heavily on the effective, proper, and secure operation of them.In most cases, such systems are used for storing, transmitting, and exchanging sensitive and confidential information, unauthorized access of which entails loss of money and credibility as well as the release of confidential information to competitors or enemies.This security is a crucial factor for these systems; however, its implementation must be cost-efficient so as to enable their widespread use.The aim of this special issue of the Journal of Security and Communication Networks is to highlight recent research in the broad area of e-systems security.We hope that this issue will be a useful reference for current and future trends in the very active area of wireless sensor networking.We received 10 papers from all over the world.Each paper was reviewed by at least two qualified reviewers.We have accepted six papers; thus, the acceptance rate for this issue is 60%.The articles in this special issue are organized as follows.The first article is entitled "A Hybrid NFC-Bluetooth Secure Protocol for Credit Transfer Among Mobile Phones" and is authored by

ABSTRACT In mobile ad hoc networks (MANETs), nodes are mobile in nature, but at the same time, they are assumed to rely on each other to relay their traffic even in case the wireless transmission medium is out of range. This requirement poses a serious challenge when malicious nodes are present in the MANET and may contribute to the routing operations, either by tampering the data packets or dropping them. This paper addresses this particular type of wormhole attacks, by introducing an enhancement (the so‐called E‐HSAM) to a recently proposed ad hoc on‐demand distance vector‐based protocol for preventing against such attacks in MANETs (the so‐called highly secured approach against attacks on MANETs (HSAM)). Our contributions are twofold: (i) a simulation study of the HSAM protocol is provided for the first time, and (ii) the Advanced Encryption Standard (AES) is introduced in the route selection phase of E‐HSAM (yielding our so‐called E‐HSAM‐AES scheme) to strengthen the integrity of the data while securing the potential routes chosen for data transfer from source to destination nodes. Simulation results are presented, showing the superiority of E‐HSAM‐AES over E‐HSAM and HSAM in terms of packet delivery ratio and broken link detected during data transmission, chosen as performance metrics. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile cloud computing (MCC) improves the computational capabilities of resource-constrained mobile devices. On the other hand, the mobile users demand a certain level of quality-of-service (QoS) provisioning while they use services from the cloud, even if the interfacing gateway changes due to the mobility of the users. In this paper, we identify, formulate, and address the problem of QoS-guaranteed bandwidth shifting and redistribution among the interfacing gateways for maximizing their utility. Due to node mobility, bandwidth shifting is required for providing QoS-guarantee to the mobile nodes. However, shifting alone is not always sufficient for maintaining QoS-guarantee because of varying spectral efficiency across the associated channels, coupled with the corresponding protocol overhead involved with the computation of utility. We formulate bandwidth redistribution as a utility maximization problem, and solve it using a modified descending bid auction. In the proposed scheme, named as AQUM, each gateway aggregates the demands of all the connecting mobile nodes and makes a bid for the required amount of bandwidth. We investigate the existence of Nash equilibrium (NE) in the proposed solution. Theoretically, we deduce the maximum and minimum selling prices of bandwidth, and prove the convergence of AQUM. Simulation results establish the correctness of the proposed algorithm.

This paper presents a Learning Automata (LA)-based QoS (LAQ) framework capable of addressing some of the challenges and demands of various cloud applications. The proposed LAQ framework ensures that the computing resources are used in an efficient manner and are not over- or under-utilized by the consumer applications. Service provisioning can only be guaranteed by continuously monitoring the resource and quantifying various QoS metrics, so that services can be delivered in an on-demand basis with certain levels of guarantee. The proposed framework helps in ensuring guarantees with these metrics in order to provide QoS-enabled cloud services. The performance of the proposed system is evaluated with and without LA, and it is shown that the LA-based solution improves the performance of the system in terms of response time and speed up.

Different wireless technologies have been standardized and commercialized, but none of these technologies is considered the best because each one has its own advantages and disadvantages. For example, Wireless Local Area Network (WLAN) offers bit rates surpassing those of Third generation (3G), but is found lacking with respect to roaming, mobility support and lack of sufficient security measures and architecture beyond basic radio access. Hence, inter-working between heterogeneous wireless networks is important to combine their best features and achieve the optimal in wireless communications. Security in inter-working is a major challenge due to the difference of security architectures between these wireless technologies. Therefore, several authentication protocols have been proposed to improve security such as Extensible Authentication Protocol for 3rd generation Authentication and Key Agreement (EAP-AKA) protocol which is used in 3G mobile networks to authenticate to non-cellular networks such as WLANs. In this paper, we analyze threats and attacks of this standard and we propose a re-authentication protocol from WLAN to LTE (ReP WLAN-LTE). This proposed solution allows a mobile connected to a WLAN to migrate to a LTE network. Moreover, it provides perfect forward secrecy to guarantee stronger security, mutual authentication, and resistance to replay attack. Through extensive simulation experiments, we found out that our proposed protocol presents optimal values for handoff latency and satisfactory loss rates, which meet the need for the quality of services.

Providing end-to-end support for Quality of Service (QoS) guarantees is a central and critical issue in heterogeneous networks. In this paper, we address the problem of assigning delay budgets to each network node along the routing path so that the end-to-end delay requirements of the supported applications are met. In order to meet the QoS requirements of multimedia applications, different scheduling algorithms are used. Each scheduling service is characterized by a mandatory set of QoS parameters, which is tailored to best describe the guarantees required by the applications. In this paper, we propose a new method for distributing end-to-end delay in the heterogeneous networks. We show that our scheme can achieve better delay distribution. Further, we evaluate the performance of our proposed scheme using simulation analysis.

Due to various operational constraints on the unmanned autonomous vehicle (AUxV) networks operating in an adversarial environment, a fault-tolerant routing scheme is an imperative need. Looking at the risk involved in their applications such as search and rescue, threat surveillance, chemical and biohazard sampling, even a fault of minor nature in the system software/hardware may result in destructive consequences. The AUxV network member nodes vary in architecture, capability, application and power of their internal systems. In such a case it is important that the fault-tolerant scheme should take into consideration the kind of heterogeneity involved and should be able to perform in such a scenario as well. Therefore, to address these issues, in this paper we propose a cross-layer and learning automata (LA) based fault-tolerant routing algorithm for AUxVs, named as ULARC (Unmanned Vehicle Network with LA based Routing using Cross Layer Design). We use the theory of LA for the selection of optimal path for routing between source and destination. In this paper, we also focus on making our proposed strategy an energy-efficient one by using a cross-layer architecture for sleep scheduling of nodes. Further, we have devised an α-based scheduling scheme which further adds to the energy efficiency of our protocol by reducing the overhead on the network.

As the world's economic activities are expanding, the energy comes to the fore to the question of the sustainable growth in all technological areas, including wireless mobile networking. Energy-aware routing schemes for wireless networks have spurred a great deal of recent research towards achieving this goal. Recently, an energy-aware routing protocol for MANETs was proposed by us, in which the energy load among nodes is balanced so that a minimum energy level is maintained and the resulting network lifetime is increased. In this paper, an Ant Colony Optimization (ACO) inspired approach to EEAODR (so-called ACO-EEAODR) is proposed. To the best of our knowledge, no attempts have been made so far in this direction. The obtained simulation results show that the ACO-EEAODR outperforms the EEAODR scheme in terms of energy consumed and network lifetime performance metrics.

Opportunistic networks (Oppnets) are a kind of wireless networks that provide the opportunity to have social interaction and obtain data that can be used for message passing decision. The increase observed in the number of people with PDAs and other handset devices equipped with wireless technologies makes the forwarding paradigm and Oppnets scenarios more interesting and challenging. The main challenge in Oppnets is to take efficient routing decisions on securing the delivery of messages to the destination. Cooperation and trust between nodes in the network saves them from malicious attacks. The trust of a node is a basic value that symbolizes the magnitude of its social responsibility in the network, which include helping groups of nodes in message delivery, saving these nodes from malicious attacks, just to name a few. This paper focuses on blackhole attack against the PRoPHET routing protocol for Oppnets. A Trust-based Security Protocol (TSP) is proposed to secure Oppnets against blackhole attacks. Simulation results are provided to support the effectiveness of our proposed TSP approach, in the sense that considerable control is observed in the number of dropped packets, number of messages captured bythe malicious nodes (so-called malicious count) and overhead ratio.

ABSTRACT Wireless sensor networks are often deployed in hostile and unattended environment that are very prone to node capture attack. In node capture attack, information such as key, data on captured nodes, can all be extracted by the adversary. Node capture attack in wireless sensor networks suffers from low efficiency and high resource expenditure. To enhance the efficiency of node capture attack, we propose here a high efficiency node capture attack algorithm that is based on route minimum key set, namely greedy node captured based on route minimum key set (GNRMK). To obtain the route minimum key set, the sensor network is mapped as a flow network. The route minimum key set can be calculated by the maximum flow of the flow network. Then, an overlapping value is assigned to each node on the basis of route minimum key set. The node with maximum overlapping value will be captured in every round of attack. Simulation results indicate that, compared with other node capture attack schemes, GNRMK can compromise the network by capturing fewer nodes. Moreover, the fraction of traffic compromised is much higher. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Wireless mesh networks are one of the most important improvements in the world of wireless technologies. In fact, they offer many advantages to ensure a free mobility and a self‐configuration of the various network equipment, a better quality of services, as well as an extensible domain and area. In spite of all of these contributions, mesh technology still suffers from some problems such as security especially in handoff phases. In this study, we propose an authentication scheme that is designed to reduce the authentication delay during a wireless mesh network handoff process. The proposed mechanism takes into account the mobility of clients as well as the router movements. This work takes advantage of some existing techniques such as Blom key predistribution scheme and Du key process generation method. Our simulation results prove the effectiveness of our proposed scheme. Our performance evaluation results shows that the suggested solution outperforms existing schemes in improving handoff latency values (<15 ms) and in the ability of satisfying the requirements of security as well as the quality of services in terms of having low loss rate (less than 1%) and having a low blocking rate. Copyright © 2012 John Wiley & Sons, Ltd.

Owing to the features of erratic speeds and varying topography and requirements of minimum delay and high application reliability in terms of data delivery and security, existing MANET routing protocols prove to be inefficient in VANETs. In acknowledgment to the requirement for new VANET protocols addressing issues of routing, data dissemination, data sharing and security, this paper proposes a novel geographic routing technique called GROOV, which takes into account varying topographies and densities of highways as well as cities. To increase reliability, GROOV calculates transmission feasibility for each node, based on link quality (average acceleration), range weight (weightage to relative positions of nodes) and direction, instead of traditional greedy forwarding, in the selection of the next relay node. Taking volatility of critical city intersection scenarios into account, GROOV calculates new node coordinates of vehicles at intersections to make best route selections at intersections and thus, routes the data packet through the path directed at the intended recipient. This prevents the occurrence of a routing loop, thereby, decreasing delay and increasing packet delivery ratio. Simulation results show that GROOV achieves a high level of routing performance in terms of packet delivery ratio, end-to-end delay and average number of hops in both city straight road/highway and city intersection scenarios.

In this paper, we propose a community detection scheme in an integrated Internet of Things (IoT) and Social Network (SN) architecture. The paper takes a graph mining approach to solve the problem in complex network of IoT and SN. A number of pieces of research literature exist on community detection in SNs; however, no work specifically on integrated IoT and SN architecture addresses this issue. The existing community detection approaches have not considered things into account. We propose the scheme, Community Detection in an Integrated IoT and SN (CDIISN) in which we divide the nodes/actors in complex networks into basic nodes and IoT nodes, and execute the community detection algorithm. We consider two nodes to be in a community, only if the nodes are at most one hop apart and have at least two mutual friends. The smallest community in our case is a subgraph with a cycle of length four. In our approach, a node can be part of multiple communities, and it works well for weighted graphs. Once communities are extracted, we use an access control scheme, based on which access to nodes is provided. This approach of community detection in an integrated environment would find tremendous use in the future, because in the case of any search operation performed by any node, the results obtained intra-community are more relevant than inter-community.

Radio over fiber (RoF) technology is considered as an energy and cost-effective solution to cover the users' rapidly increasing demands for bandwidth and mobility. However, integration of a wireless and an optical network into a hybrid one needs the design of new protocols. In this work, a novel MAC protocol based on the MultiPoint Control Protocol (MPCP) is proposed. The network's decision center receives feedback from the mobile clients via MPCP's GATE/REPORT mechanism so as to efficiently allocate the bandwidth and the wavelength resources in a dynamic manner. The proposed MAC protocol adapts its operation according to the actual client traffic demands. Simulation results reveal the superior performance of the proposed protocol compared to other similar competing proposals reported in the literature.

It is a known fact today that VANETs [1] are going to play an increasingly important role in vehicular communication in the future. The impact of these networks especially to solve the problem of traffic optimization and control is significant. Traffic Optimization through Information Dissemination (TOID) is an algorithm based on a mathematical model in which vehicles moving on the road can exchange information among themselves to report the average velocity and density of the roads within a certain region such that the driver has a fair idea of the traffic conditions in the region and which road to take depending upon his discretion. The algorithm ensures that the information reaches the network in a timely manner and the overheads are a minimum. The overheads are further reduced by optimizing the input parameters related to the average velocity and density in the network. The algorithm also reports the estimated density and estimated velocity in the network with a fair degree of accuracy. The effectiveness of the algorithm has been substantiated through extensive simulations using QualNet [2]. The algorithm is ideal for traffic problems in developing countries as it works for vehicular ad hoc networks that are completely infrastructure-less.

Mobile Worldwide Interoperability for Microwave Access (WiMAX) constitutes an attractive trademark for supporting wireless access to Intranets and the Internet. Scheduling and mapping processes are of paramount importance since they dramatically affect the network performance. This work endeavors to provide a robust mapping scheme for the downlink sub-frame with respect to various Quality of Service (QoS) constraints. Each downlink request creates rectangular regions, called Horizons, and the mapping technique applies a persistent probing of the Horizons recorded. The evaluation experiments conducted indicate that the scheme designed beneficially affects the system performance.

Securing the routing of message in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes an enhanced trust-based multipath Dynamic Source Routing (DSR) protocol (so-called ETB-MDSR) to securely transmit messages in MANETs. Our method consists in a combination of soft-encryption, novel trust management strategy, and multipath DSR routing. Simulation results are presented to validate our proposal, showing that our ETB-MDSR scheme outperforms a recently proposed Trust-Based Multipath DSR message scheme (TB-MDSR), in terms of route selection time.

Internet of Things (IOT) is a wireless ad-hoc network of everyday objects collaborating and cooperating with one other in order to accomplish some shared objectives. The envisioned high degrees of association of humans with IOT nodes require equally high degrees of reliability of the network. In order to render this reliability to IOT networks, it is necessary to make them tolerant to faults. In this paper, we propose mixed cross-layered and learning automata (LA)-based fault-tolerant routing protocol for IOTs, which assures successful delivery of packets even in the presence of faults between a pair of source and destination nodes. As this work concerns IOT, the algorithm designed should be highly scalable and should be able to deliver high degrees of performance in a heterogeneous environment. The LA and cross-layer concepts adopted in the proposed approach endow this flexibility to the algorithm so that the same standard can be used across the network. It dynamically adopts itself to the changing environment and, hence, chooses the optimal action. Since energy is a major concern in IOTs, the algorithm performs energy-aware fault-tolerant routing. To save on energy, all the nodes lying in the unused path are put to sleep. Again this sleep scheduling is dynamic and adaptive. The simulation results of the proposed strategy shows an increase in the overall energy-efficiency of the network and decrease in overhead, as compared to the existing protocols we have considered as benchmarks in this study.

Underwater sensor networks (UWSNs) can be deployed for sensing the environment in oceanographic columns and other water bodies in which they are deployed. The peculiar characteristic of the underwater medium, coupled with the queer nature of the sound waves in water, poses an enigmatic problem for UWSN researchers. In this study, the authors focus on the problem of UWSN jamming, which is a popular type of denial-of-service attack. The existing jamming detection solutions for sensor networks are primarily targeted towards the terrestrial ones. In this work, the authors study the unique characteristics of jamming in UWSN, and propose a protocol, known as underwater jamming detection protocol (UWJDP), to detect and mitigate jamming in underwater environments. The results show that if the packet delivery ratio (PDR) is less than or equal to 0.8, the authors have the maximum probability of detecting jamming. The jamming detection ratio is around 2–11% more for the said PDR.

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

In recent years, multimedia network services have moved from a single service to rich services which integrate multiservice capabilities integration. If all service requests require the user to send the request by himself, this will result to a huge control function load and complex service collaboration. In order to address service interaction and reuse the service capability, 3GPP proposes a Service Capability Interaction Manager, which can provide service capabilities invocation and service interaction management between Application Servers (ASs) and Serving-Call Session Control Function (S-CSCF). However, its architecture may cause joint and cooperation problems between the different service providers. In this paper, we propose a scalable service capability interaction framework called ServiceChord that can process multiple service capabilities with different ASs and reduce the call set-up delay while communicating with the S-CSCF. The Chord DHT technique is used to improve the framework, leading to a reduction of message redundancy on the S-CSCF while achieving an efficient service capability interaction, and providing scalability for IMS services and ASs.

This paper looks into the problems faced by Underwater Sensor Networks (UWSNs), with regard to energy optimization and efficient data delivery. A new algorithm is proposed which uses the properties of a "Parabola" in order to transmit data packets from source to destination. The scheme is called the "Parabola-based Routing"(PBR) algorithm. The algorithm is adaptive, and transmits packets in hop-by-hop fashion. Each node in the scenario is aware of its location in the form of its Cartesian coordinates. These coordinates help the nodes in the formation of the parabola for packet sending, as well as in knowing the location of other nodes. The best suited node is selected every time so as to maintain low probability of data transmission failure and optimum energy consumption thereby, using greedy approach every time in selecting the node for packet transmission. In order, to maintain the packet delivery ratio, a hop-by-hop "acknowledgement process" has also been devised. We have validated the efficiency of our algorithm through simulation analysis. The simulation results indicate a better performance of PBR over DSR in both mobile and static scenarios.

Mobile Ad Hoc Networks (MANETs) offer a dynamic environment in which data exchange can occur without the need of a centralized server or human authority, providing that nodes cooperate among each other for routing. In such an environment, the protection of data en route to its destination is still a challenging issue in the presence of malevolent nodes. This paper proposes a message security approach in MANETs that uses a trust-based multipath AOMDV routing combined with soft-encryption, yielding our so-called T-AOMDV scheme. Simulation results using ns2 demonstrate that our scheme is much more secured than traditional multipath routing algorithms and a recently proposed message security scheme for MANETs (our so-called Trust-based Multipath Routing scheme (T-DSR)), chosen as benchmark. The performance criteria used are route selection time and trust compromise.

Securing the transfer of data in mobile ad hoc networks (MANETs) is still a challenging issue. This paper proposes a method for providing message security in MANETs when nodes cooperate in routing. Our approach combines a trust-based multipath routing scheme and a real-time recurrent neural network-based (RRNN) cipher (yielding our so-called TR-RRNN scheme) to deal with the issues underlying message confidentiality, integrity, and access control. Simulation experiments using QualNet were conducted, showing that the proposed scheme is much more secured compared to the traditional multi-path routing algorithms and a recently proposed message security scheme for MANETs (our so- called Original Trust-based Multi-path Routing scheme (OTMR)). The route selection time and trust compromise are used as the performance criteria.

Abstract The universal description, discovery, and integration (UDDI) OASIS standard, was designed for storing, publishing, and advertising information about XML web services. Commonly used in service oriented architecture (SOA) infrastructures, the security of UDDI is often overlooked. Embedded within the specification are “optional” security measures that are commonly not implemented or enforced. In this paper we describe the UDDI security model, potential security related concerns, and mitigation strategies. Preliminary performance evaluation results are presented to show the merits of our proposed scheme. Finally, an example of the registry with additional security constraints is analyzed and evaluated for performance. Copyright © 2011 John Wiley & Sons, Ltd.

Most of the work on geocasting has been done for mobile ad-hoc networks and vehicular ad-hoc networks. This paper presents a novel Geocast technique with the hole detection in the geocast region for underwater environment. The proposed model named as Routing and Multicast Tree based Geocasting (RMTG) has been designed for underwater sensor networks. Here we propose a theoretical model for underwater geocasting RMTG that uses greedy forwarding and previous hop handshaking to route the packets towards the geocast region. This technique further disseminates the data within the geocast region by creating a multicast shortest path tree. The technique also proposes a method of hole detection inside the geocast region and a virtual area based routing around the boundaries of the geocast region to reach the geocast region from opposite side. This model provides an efficient geocast technique in terms of node mobility handling, lesser memory utilization, lesser calculations and a better end to end latency. In addition to this the proposed model does not use flooding inside the geocast region that is used in most of the geocasting techniques and also for guaranteed data delivery inside the geocast region, the proposed model does not use the planarization graph concepts.

The past few years have observed vehicular networks becoming increasingly important in order to exchange information between stationary or moving vehicles. The information exchanged can range from warning messages in case of accidents that have occurred on the road and traffic jams to guiding the vehicles about the most efficient path to reach their destination. The biggest challenge, a vehicular network routing protocol faces, is providing a stable packet delivery mechanism. The proposed PMH protocol aims at providing a high packet delivery ratio by selecting each next node (vehicle) in the path from the source to the destination based on a parameter called the Disconnection Probability Metric (DPM). The DPM mainly represents the link breakage probability between nodes and the node with the least DPM is selected as the next node. Moreover, the PMH protocol also calculates the predicted link lifetime based on which it initiates the route selection algorithm before the weakest link actually breaks. This mechanism further enhances the usefulness of the algorithm as there is lesser control overhead to overcome link breakage.

IEEE 802.16 (WiMAX) constitutes one of the most promising broadband access technologies for high-capacity and high-distance wireless access networks, supporting user mobility. The contribution of this paper is twofold. Firstly, a novel mapping scheme for IEEE 802.16 Mobile standard is introduced, applying horizon mapping. Secondly, an efficient adaptive prediction-based scheme is devised, which is able to adjust the downlink sub-frame capacity, accordingly to the traffic load, since the standard allows the downlink-to-uplink subframe ratio to be changeable from 3:1 to 1:1. The novel adaptive horizon burst mapping (AHBM) scheme is evaluated by simulation experiments, which indicate that the proposed scheme operates effectively and efficiently, by reducing the number of unserved users, the number of unserved traffic requests, and the portion of wasted bandwidth.

The cache replacement mechanism in cooperative caching has a significant bearing in determining the caches performance. Valid data items still get evicted from the cache space when new item is to be cached but there is no space available to hold it. The existence of data items in caches indicates some degree of interest on the data item. Salvaging the evicted valid data item could improve the overall caching performance. In this paper, we propose an efficient cooperative caching scheme known as CacheRescue for wireless mesh networks. The CacheRescue scheme caches data in the mesh routers expandable storage space to hold valid but evicted data items. We have used simulation to evaluate the performance of CacheRescue scheme. The simulation results show that our proposed approach improves the caching performance when compared to other existing and previously proposed caching solutions.

Inter-vehicular communication involves the exchange of data between two mobile devices in an ad hoc network. Since the devices are not stationary and the topology is wide, the passage of messages between source and destination nodes involves various intermediate nodes that act as links between the two. The more the number of nodes involved in a network at a time, the more is the power consumed by them, thereby adding to the average power consumption of the network and the transmission time. The authors aim to develop an efficient routing protocol, which finds the minimum possible path length between a source and a destination involving minimum nodes to transmit data. Information regarding the angular position of the nodes is exploited in selecting the most suitable node for transmission, thereby achieving proper network connectivity among nodes with minimum power consumption. The proposed protocol has been compared with dynamic source routing (DSR) and DSR with stale route removed (DSR-SRR). The results achieved by implementing the proposed protocol establish the fact that our protocol is better than DSR and DSR-SRR in terms of the following: (i) average power consumption during transmission, (ii) throughput of transmission and (iii) number of control packets used. The proposed protocol proves to work relatively efficiently even under dense traffic conditions.

In this paper, we present a learning-automata-like The reason why the mechanism is not a pure LA, but rather why it yet mimics one, will be clarified in the body of this paper. (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL Random Early Detection (LALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes.

The six papers in this special issue represent both the theoretical and application flavors of adaptive and learning systems.

The Gnutella protocol of peer-to-peer (P2P) networks has undergone several changes since its inception in the beginning of this century. However, despite the large number of revisions to the original version of the protocol, Gnutella suffers from serious problems of dead searches, complexity in study of network topology and network overloading. In this paper, we report the development of a new P2P simulator, PeerNS, which was built to study different problems of P2P networks and Gnutella, including those mentioned above. PeerNS works on actual P2P network statistics and, hence, it is very close to the real scenario. Moreover, we also discuss the implementation and the integration issues involved in using PeerNS to simulate our crawling-based algorithm, which could minimize the number of dead searches in the network and enhance the availability of information across the network.

IEEE 802.11 wireless local area networks provide the ability to build a cost efficient network infrastructure that is flexible and mobile. This technology tried to provide mechanisms by which to secure the network, however, it failed to do so. The security mechanisms evolved from wired equivalent privacy (WEP) to Wi-Fi protected access (WPA), which introduced more enhancements to both encryption and authentication. Although WPA has improved significantly the security of the wireless LANs, it still has some weaknesses. We are going to explore a new security scheme that attempts to fix a hole in the disassociation mechanism of the WPA protocol. This vulnerability allows an attacker to shutdown the entire network even for those users that are authorized within the network.

This paper presents a technique called as Virtual Back off Algorithm (VBA), based on the sequencing technique for efficient media access control. The proposed method minimizes the number of collisions as well as reduces delays during back off periods. We present an analytical study on MAC layer issues that are very important while accessing channel over wireless networks. The VBA method uses fair distributed mechanisms to access channel. We introduce a counter at each node to maintain the discipline of the nodes. The performance of the proposed method is evaluated under various conditions and results are very promising.

In this paper, we present an adaptive learning (specifically, learning automata) Like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as learning automata like random early detection (LALRED), is founded on the principles of operations of the existing random early detection (RED) congestion avoidance mechanisms, augmented with a LAL philosophy. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED, which was chosen as the benchmark for performance comparison purposes.

One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. In this work, we address the problem of congestion in the nodes of a WSN using Learning Automata (LA)-based adaptive learning approach. Our primary objective, using this approach, is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as Learning Automata-Based Congestion Avoidance Algorithm in Sensor Networks (LACAS), can counter the congestion problem in WSNs effectively. The results obtained through the experiments with respect to important performance criteria showed that the proposed algorithm is capable of successfully avoiding congestion in typical WSNs requiring a reliable congestion control mechanism.

Quality of Service (QoS) support has become a key factor in designing Media Access Control (MAC) protocols. This paper introduces a novel scheduling scheme which supports priority based QoS in Wavelength Division Multiplexing (WDM) star networks. The proposed Interval-based Prioritized Orderly Scheduling Strategy (IPOSS) employs a collision-free scheduling approach and handles variable-length data packets. In practice, it is designed to handle real-time traffic, on the basis that each node may generate high- and low-priority packets with high-priority packets being scheduled prior to low-priority ones. Moreover, the proposed scheme differentiates the packets' schedule order, by prioritizing the long-length over the short-length packets. The performance of IPOSS is evaluated under Bernoulli traffic and simulation results indicate that the novel scheme achieves a significantly high throughput-delay performance for real-time traffic, without sacrificing the performance for non-real-time traffic.

Optical burst switching (OBS) is one promising method for data transfer in photonic networks based on a WDM (Wavelength Division Multiplexing) technology. In the OBS scheme, the wavelength is exclusively reserved along the source and destination nodes, when the burst data is generated at the source. Then, efficient data transfer is expected. However, its performance is heavily dependent on the number of links that the lightpath goes through. TCP-based applications account for a majority of data traffic in the Internet; thus understanding and improving the performance of TCP over OBS networks is critical. In this paper, we present a new parallel wavelength reservation method for optical burst switching (OBS) networks based on adapting the set of potential wavelengths with the number of hops in the path. The uniqueness of this work when compared to existing works is that buffering resources, which consist of Optical Delay Lines (ODLs), are considered in the reservation mechanism. The consequence of this is that the time made by the segments in the various buffers across the selected path is taken into consideration. Various simulation experiments have been conducted to evaluate the performance of the scheme. It is found that our approach has better performance than previous related works reported in the literature.

Several protocols have been proposed to improve data accessibility in MANETs. Some of these proposals have adopted the cooperative caching scheme, allowing multiple mobile hosts within a neighborhood to cache and share data items in their local caches. Cross-layer optimization has not been fully exploited to further improve the performance of cooperative caching in these proposals. In this paper, we propose a cluster-based cooperative caching scheme which uses a cross-layer design approach and prefetching to further improve the performance of cooperative caching scheme. The cross-layer information is maintained in a separate data structure and is shared among network protocol layers. The performance evaluation was carried out in the NS-2 simulation environment and the results show that the proposed approach improves system performance.

It is possible for vehicles moving on a highway to communicate with each other, if they are equipped with wireless interfaces. These vehicles, equipped with wireless connectivity, are referred to as nodes in a Vehicular Ad Hoc Network (VANET). The more the number of nodes involved in a network at a time, the more is the power consumed by them, thereby adding to the average power consumption of the network and the transmission time. In this paper, we propose an efficient routing protocol, named as Efficient Angular Routing (EAR), which finds the minimum possible path length between a source and a destination involving minimum nodes to transmit data. Information regarding the angular position of the nodes is exploited in selecting the most suitable node for transmission, thereby, achieving proper network connectivity among nodes with minimum power consumption. The proposed protocol has been compared with Dynamic Source Routing (DSR) and DSR-with stale route removed (DSR-SRR). The results achieved establish the fact that the proposed protocol, EAR, outperforms DSR and DSR-SRR in terms of the average power consumption during transmission and the number of control packets used. The proposed protocol proves to work relatively better even under dense traffic conditions.

Routing in ad-hoc networks can consume considerable amount of battery power. However, as the nodes in these networks have limited power, routing is very much energy-constrained. Continuous drainage of energy degrades battery performance as well. If a battery is allowed to intermittently remain in an idle state, it recovers some of its lost charge due to the charge recovery effect, which, in turn, results in prolonged battery life. In this paper, we use the ideas of naturally occurring ants' foraging behavior and based on those ideas we design an energy-aware routing protocol, which not only incorporates the effect of power consumption in routing a packet, but also exploits the multi-path transmission properties of ant swarms and, hence, increases the battery life of a node. The efficiency of the protocol with respect to some of the existing ones has been established through simulations.

In this paper, we address some of the problems such as dead searches, complexity in the study of network topology and network overloading that are associated with Gnutella and Gnutella-like peer-to-peer (P2P) networks. We use advanced heuristic parameters with information shuffling as a solution for them. We propose an advancement of Gnutella using the above-mentioned schemes. At a panoramic level, our work is founded on the following concepts: (a) Crawling the P2P networks to shuffle information, so that the knowledge is distributed over the whole network, and (b) Bringing the information within searchable hops of each network. These have been verified on a self-built P2P simulator, named PeerNS, which works on actual P2P network statistics and is, hence, very close to the actual scenario. The results obtained through simulation affirm that the nodes with extremely large number of dead searches benefit the most and are observed to have a sharp decrease in their dead search count after crawling a small part of the overall network.

The fault-prone nodes in mobile ad-hoc networks (MANETs) degrade the performance of any routing protocol. Using greedy routing mechanisms that tend to choose a single path every time, may cause major data losses, if there is a breakdown of such a path in a fault-prone environment. On the other hand, using all the available paths causes an undesirable amount of overhead on the system. Designing an effective and efficient fault-tolerant routing protocol is inherently hard, since the problem is NP-complete, due to the unavailability of precise path information in adversarial environments. To address the challenges of effective fault-tolerant routing, we present a fault- tolerant routing algorithm (FTAR), based on the ideas of how swarms of natural ants operate. The algorithm is divided into various stages namely initialization, path selection, pheromone deposition, confidence calculation, evaporation and negative reinforcement. Simulation results show that FTAR achieves high packet delivery ratio and throughput as compared to some of the key protocols which do not do fault-tolerance at all. Most importantly, FTAR beats the best fault-tolerant MANET routing algorithm known currently, with respect to the amount of routing overhead incurred, which is an important consideration.

A mobile ad hoc network (MANET) is an infrastructure-less system having no designated access points or routers and it has a dynamic topology. MANETs follow a distributed architecture, in which each node can move randomly in an area of operation. MANETs are vulnerable to various attacks. Security services in these kinds of networks are more complex than in traditional networks. In this paper, we implement a new RSA-threshold cryptography-based scheme for MANETs using verifiable secret sharing (VSS) scheme (Feldman, 1987). Threshold cryptography (TC) provides a promise of securing these networks. The proposed scheme is based on the Chinese remainder theorem (CRT) under the consideration of Asmuth-Bloom secret sharing scheme (Kaya and Selcuk, 2008). To the best of our knowledge, such a work does not exist in MANETs. The proposed scheme is efficient in terms of computational security.

One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. Congestion affects the continuous flow of data, loss of information, delay in the arrival of data to the destination and unwanted consumption of significant amount of the very limited amount of energy in the nodes. Obviously, in healthcare WSN applications, particularly in the ones that cater to medical emergencies or in the ones that closely monitor critically ailing patients, it is desirable in the first place to avoid congestion from occurring and even if it occurs, to reduce the loss of data due to congestion. In this work, we address the problem of congestion in the nodes of healthcare WSN using a learning automata (LA)-based approach. Our primary objective in using this approach is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as learning automata-based congestion avoidance algorithm in sensor networks (LACAS), can counter the congestion problem in healthcare WSNs effectively. An important feature of LACAS is that it intelligently' learns' from the past and improves its performance significantly as time progresses. Our proposed LA based model was evaluated using simulations representing healthcare WSNs. The results obtained through the experiments with respect to performance criteria having important implications in the healthcare domain, for example, the number of collisions, the energy consumption at the nodes, the network throughput, the number of unicast packets delivered, the number of packets delivered to each node, the signals received and forwarded to the medium access control (MAC) layer, and the change in energy consumption with variation in transmission range, have shown that the proposed algorithm is capable of successfully avoiding congestion in typical healthcare WSNs requiring a reliable congestion control mechanism.

Abstract In wireless sensor networks (WSN), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSN is a challenging task. In this paper, we propose a protocol, quality‐based distance vector routing (QDV), for securing WSN using concepts based on Ant colony optimization ACO [1]. Two fundamental parameters—quality‐of‐service (QoS) and reputation [2]—are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affects its quality‐of‐security QSec [2], thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve “equilibrium” where the node is able to guarantee that its neighbors are secure. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we address the problem of intrusion detection in wireless sensor networks (WSNs) using a learning automata (LA)‐based approach. We are not aware of any LA‐based intrusion detection systems (IDSs) for WSN. Additionally, the S‐model approach that we have taken to solve the problem, wherein the feedback of the environment to the automaton can not only be completely favorable or completely unfavorable, but also be any continuous value within these extremities, makes it one of the attractive solution approaches in LA. We have rigorously evaluated the performance of our proposed solution by performing a variety of experiments and have found our solution approach to be promising. Copyright © 2008 John Wiley & Sons, Ltd.

Recent development in wireless communication networks has enabled the large-scale deployment of low-cost, energy efficient, and multi-purpose wireless sensor networks. A lot of real-world applications have been already deployed and many of them will be based on wireless sensor networks. These applications include geographical monitoring, medical care, manufacturing, transportation, military operations, environmental monitoring, industrial machine monitoring, and surveillance systems. A Wireless Sensor Network is a wireless network which consists of spatially scattered independent devices by the help of sensors to considerately observe the conditions of an environment. A sensor device is typically of small size and consists of the processing subsystem running on the device's CPU, the sensor subsystem, and the communication subsystem. A sensor network is a collection of a number of sensors that utilize wireless transmission in order to establish communication and form an ad-hoc wireless network. Inside such a network the task of each sensor node is dual; the node should: (a) collect data regarding a phenomenon that is being observed (e.g. seismic activity, humidity, fire detection etc.) and (b) assist in routing of data captured by other nodes to a special node, called the sink, which is accessible via the system manager over a backbone network such as the Internet or a satellite link. The typical issues that differentiate a wireless sensor network from the more general case of an adhoc one are attributed to the operating environment of a sensor network and can be summarized as follows: (a) sensor network nodes have fewer capabilities for energy storage, computation, and communication, (b) sensor networks are mostly stationary; however topology changes do occur due to the frequent node failures in a wireless sensor network as opposed to mobility of nodes in ad hoc networks, (c) low-energy consumption may be an even more critical criterion than performance, as in most cases it is impossible to refuel the depleted batteries of sensors. Due to the nature of wireless communication in sensor networks, many security challenges are faced, including eavesdropping, man-in-the-middle, spoofing, and DDoS. The concern for security in a wireless sensor network can be even bigger than that in a conventional ad hoc wireless network, as in many cases, the afore-mentioned computational and energy consumption restrictions pose barriers in the implementation of existing security solutions in a wireless sensor network. Thus, advances in the design and prototype of security mechanisms in wireless sensor network systems for protecting the confidentiality, availability, and integrity are crucial for the success of sensor applications. The aim of this special issue of the Journal of Security and Communication Networks is to highlight some of the most important challenges in the area of security in wireless sensor networks and to present possible solutions. We hope that this issue will be a useful reference for current and future trends in this very active and crucial area of wireless sensor networks. We received 12 papers from all over the world. Each paper was reviewed by at least three qualified reviewers. We have accepted eight papers in this special issue. Papers in this special issue are organized as follows. The first article is entitled “LAID: A Learning Automata-Based Scheme for Intrusion Detection in Wireless Sensor Networks” and is authored by Misra, Abraham, Obaidat, and Krishna. The paper uses learning automata, a machine learning method, to address the problem of detecting intrusion in a wireless sensor network. The second paper, “Chaotic Communication Improves Authentication: Protecting WSNs Against Injection Attacks,” authored by Martinovic, Gollan, and Schmitt, presents the idea of using parameters of the wireless physical layer to form a mechanism, via which, nodes can authenticate themselves to the wireless sensor network. The third paper is “FBT: An Efficient Traceback Scheme in Hierarchical Wireless Sensor Network” and is authored by Cheng, Chen, and Liao. It proposes a traceback method via which the attacking path can be reconstructed in order to identify the attacking source of a DoS/DDoS attack to a hierarchical wireless sensor network. The fourth paper is entitled “An Effective Defensive Node against Jamming Attacks in Sensor Networks” and is authored by Mpitziopoulos and Gavalas. The authors describe the design specifications of a prototype node that effectively defends, via use of a hybrid FHSS-DSSS approach, against the possible jamming attacks a wireless sensor network may encounter. The fifth paper entitled “The Marvin Message Authentication Code and the LetterSoup Authenticated Encryption Scheme” is authored by Simplicio, D'Aquino Barbuda, Barreto, Carvalho, and Margi. The paper presents an authentication function based on a new parallelizable message authentication code and also discusses a related authenticated encryption scheme. The sixth paper is “Confidentiality and Integrity for Data Aggregation in WSN Using Peer Monitoring” and is authored by Di Pietro, Michiardi, and Molva. In this paper the authors present a new data aggregation mechanism in a sensor network that can achieve confidentiality and integrity, and detect bogus data injections. Moreover, it has significant resilience to failures of the network's nodes. The seventh paper is entitled “Privacy-preserving Robust Data Aggregation in Wireless Sensor Networks” and is authored by Conti, Zhang, Roy, Di Pietro, Jajodia, and Mancini. In this paper the authors propose a solution to protecting a node's privacy from issues being raised due to the use of data aggregation in a wireless sensor network. The Special Issue concludes with an article entitled “An Ant Colony Optimization Approach for Reputation and Quality-of-Service-Based Security in Wireless Sensor Networks,” which is authored by Dhurandher, Misra, Obaidat, and Gupta. The authors address the problem of secure routing from the perspective of having nodes abiding to QoS parameters, thus preventing malicious nodes from being part of network routes. The guest editors would like to thank all authors and reviewers for their valuable contributions to this special issue. We would also like to thank Prof. Hsiao-Hwa Chen, Editor-in-Chief of Security and Communication Networks Journal, for hosting this issue. Thanks are also due to all editorial assistants of the journal. We hope that this issue will become a useful reference and fuel for more research efforts in this important and fascinating research area of wireless sensor networking.

Time synchronization is a key issue in wireless and satellite networks; time-stamping collected data, tasks scheduling or efficient communications are just some applications. From all the existing techniques to achieve synchronization, those that work at the MAC layer and can precisely timestamp sync messages are the most accurate. However, working with standard protocols, usually prevents the user from accessing lower layers and consequently reduces accuracy. Receiver-receiver schema improves time-stamping performance because it eliminates the biggest non-deterministic error at the sender side; the medium access time. Nevertheless, utilization of these methods in multi- hop networks usually requires an extra amount of traffic. In this paper we present a method which allows accurate synchronization of large multi-hop networks such as satellite networks working at the application layer while keeping the message exchange to the minimum. Through an exhaustive experimentation, we show the protocol's performance and analyze the factors that influence synchronization accuracy the most.

In wireless sensor networks (WSNs), message security is an important concern. The protection of integrity and confidentiality of information and the protection from unauthorized access are important issues. However, due to factors such as resource limitations, absence of centralized access points, open wireless medium and small size of the sensor nodes, the implementation of security in WSNs is a challenging task. In this paper, we propose a protocol, quality-based distance vector routing (QDV), for securing WSNs using concepts based on ant colony optimization (ACO). Two fundamental parameters: quality-of-service (QoS) and reputation are used. The high value of reputation of a node signifies that the node is trusted and is more reliable for data communication purposes. As a node shows signs of misbehavior, its reputation decreases, which, in turn, affect its quality-of-security (QSec), thereby disabling the malicious nodes from gaining access to the network. By incorporating these two factors, we are able to distinguish the nodes present in the network. We, then, present a method to achieve "equilibrium" where the node is able to guarantee that its neighbors are secure.

In this paper, we address the problem of intrusion detection in wireless sensor networks (WSNs) using a learning automata (LA)-based approach. We are not aware of any LA-based intrusion detection systems (IDSs) solutions for WSNs. Additionally, the S-model approach that we have taken to solve the problem, where in the feedback of the environment to the automaton can not only be completely favourable or completely unfavourable, but also be any continuous value within these extremities, makes it one of the attractive solution approaches in LA. We have rigorously evaluated the performance of our proposed solution by performing a variety of experiments and have found our solution approach to be promising.

Optical label switching (OLS) has been proposed as a promising technology for providing fast switching capabilities, resource provisioning and quality of service (QoS) support. In this paper, we address the issue of providing congestion avoidance in OLS networks inn order to guarantee efficient resource utilization and QoS requirements, and allow the network to operate safely. We develop a novel congestion avoidance protocol through the use of a core node architecture and dynamic contention resolution. The proposed scheme handles signaling tasks, admission control, resource provision and QoS needs. Finally, simulation analysis is used to validate the proposed technique.

Optical Burst Switching (OBS) technology offers a promising solution for the next generation Internet backbone. One of the main aspects in the deployment of OBS services is the development of an optical traffic/performance monitoring scheme allowing the provision of user-specified quality of service (QoS). In this paper, we develop a performance monitoring scheme for an OBS network architecture suitable for contention resolution and QoS provisioning. It mainly addresses congestion control and QoS monitoring. Simulation experiments are also performed to validate the proposed scheme and analyze its performances.

Among the promising solutions for next generation Internet backbones, one can consider the optical burst switching (OBS) technology. One of the main aspects in the design of optical burst-switched networks is the development of a burst admission control protocol suitable for QoS provisioning. In this paper, we develop a method to address the call admission control (CAC) in OBS networks that is QoS-oriented. For this, an analytic model is developed for formulating the burst admission control problem. A QoS-constraints based burst admission control protocol is developed. Finally, simulation experiments are performed to validate the proposed schemes.

Devising new methods for watermarking with high robustness capabilities is still a challenging research problem. Most of the already proposed schemes suffer from some drawbacks. These proposed algorithms are robust for some range of attacks, but not most of them. As an example, they can not sustain in front of rotation or cropping. This work is a continuation of our works reported in [1-2]. The paper combines both techniques in [1] and [2] to propose a novel watermarking method for data hiding in media signal operating in the frequency domain using content based image segmentation with security enhancing technique. Such watermarking methods present additional advantages over the published watermarking schemes in terms of detection and recovery from geometric attacks, and with better security characteristics.

This paper presents an approach for wireless intrusion detection based on signal analysis with wavelet transform. The main function of this approach is performed by an entity called radio supervisor, which can be implemented in any node of the wireless network. Radio supervisor is also able to detect distributed attacks by cooperating with other radio supervisors via a signal-based correlation scheme that we provide for the circumstance. Finally, functions and features of the proposed approach are studied through simulations.

Satisfying quality of service (QoS) is one of the main goals of the wireless fourth generation (4G), which will integrate tightly a multitude of different heterogeneous networks including cellular networks (second generation, third generation, wireless local area networks, bluetooth, etc). In this paper, we develop an information-theoretic framework for optimal location updating and paging for wireless 4G network, which integrate tightly many different access networks. Then, we adopt the LZW compression algorithm as the basis of our location management schemes. Simulation results demonstrate that our proposed schemes decrease the signaling cost.

To support the growing demand for transmission, optical label switching (OLS) technology seems to be attractive due to its ability to allow fast switching and quality of service (QoS) support. Planning is a major issue in optical networks, since switch design and data transmission costs are essential criteria in OLS networks. In this paper, we use a novel OLS node architecture to build planning and dimensioning of OLS network based on a set of QoS parameters. We address the issue of providing the differentiated services to IP traffic through the use of optical buffering and link capabilities. We also formulate dimensioning and optimization problems using a conservation law and queuing model. Finally, we evaluate the performance of the proposed model through simulation.

Optical burst switching (OBS) technology is a promising solution for the next generation Internet backbone. However, call admission control (CAC) and QoS support constitute critical issues for this technology. In this paper, we propose a novel QoS-Oriented scheme for burst admission control in OBS networks. An analytic model is developed to estimate the provided QoS for a given traffic type. We also develop a performance evaluation study to validate the proposed scheme and evaluate its impacts on the efficiency of network resource utilization.

Mobile wireless sensor networking is characterized by a set of challenging issues including sensor and target mobility management, sensing continuity, target tracking and sensor density management. We address in this paper various issues related to the localization and tracking of targets. Our approach provides novel schemes for effective location, robustness and sensor density management.

With the increasing popularity of wireless networks and mobile computing, data broadcasting has emerged as an efficient way of delivering data to mobile clients having a high degree of commonality in their demand patterns. This paper proposes a push system that continuously adapts to the demand pattern of the client population in order to reflect the overall popularity of each data item. The adaptation is accomplished using a simple feedback from the clients. We propose that the simple feedback is sent only from clients whose distance from the server does not incur a significant timing overhead for the acknowledgment of an item. Simulation results are presented which reveal satisfactory performance in highspeed environments with a-priori unknown and dynamic client demands.

The ease of reproducing digital data in their exact original form is likely to encourage copyright violation, data misappropriation and abuse. Watermarking security enhancement is highly required for multimedia copyright applications. This work enhances the security of watermarking algorithm without affecting the robustness of the watermark by implementing the wavelet filter parameterization (WPF). The experimental results show that the watermarking algorithm based WPF robustness can enhance the security of watermarking.

The emergence of wireless ad-hoc networks is considered extremely attractive in terms of new applications' enabler. The integration of reliable sensors in nodes of wireless ad-hoc networks has posed various interesting challenges to the community of researchers and engineers. We focus in this paper on two fundamental issues: supporting high mobility of sensors and guaranteeing continuity of sensing, while providing for the correlation of collected data. We propose an efficient scheme for the management of data, sensor mobility, and sensing security

In many data broadcasting applications clients are grouped into several groups, each one located at a different region, with the members of each group having similar demands. This paper proposes a mechanism that exploits locality of demand in order to increase the performance of wireless data dissemination systems. It trades the received energy per bit redundancy at distances smaller than the radius of the service area for an increased bit rate and thus transmission speed for items demanded by clients at such distances. The bit rate for an item transmission is dynamically determined from the distance between the server's antenna to the group of clients that demand this item via a simple feedback from the clients. Additionally, a simple mechanism is introduced that protects performance around the geographical area of interest from degradation caused by clients that are located elsewhere and demand the same information items with clients inside that area

Current algorithms for distributed applications, such as the wireless GRID, for wireless ad hoc networks (WAHN) contain a few mechanisms for providing robust/tolerant network operation in the face of security attacks launched on the network by intruders. One approach to address thus issue is to design these applications for WAHNs in a way that they can handle intruder-induced malicious faults. However, this presents several drawbacks, including the considerable investment that it can induce. We present a new approach for building intrusion tolerant WAHN. The approach relies on extending the capabilities of existing applications to handle intruders without modifying their structure. We describe a new network mechanism for resource allocation using capabilities for detecting and recovering from intruder induced malicious faults. We also present a wireless router component that allows these mechanisms to be added to existing wireless nodes.

An adaptive MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol is compared via simulation to TDMA and IEEE 802.11 and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

This paper presents a method for dynamically setting 802.11 wireless LAN waveforms and transmission power levels based on the wireless channel's signal to noise ratio. Our method, known as the signal-to-noise ratio-waveform power adaptation (SNR-WPA), changes the power in discrete steps matched to each of the 802.11 data rate-waveform steps. By matching the power to the spreading symbol rate, our technique maximizes the network throughput while minimizing the MAC layer contention. Unlike the other power adaptation methods, this method does not increase the wireless LAN (WLAN) station's overall effective operational range and does not change the minimum-spanning tree used to calculate routing. We found through experimentation that the power adaptation in SNR-WPA yields up to a 30% increase in throughput in a mobile wireless LAN network.

A carrier-sense-assisted learning automata-based MAC protocol for wireless LANs, capable of operating efficiently under bursty traffic and unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback.

A self-adaptive neural-based MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol is compared via simulation to TDMA and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

In this paper, we present a scheme for dynamic resource management using heterogeneous traffic descriptors. A simple priority class scheme to support basic QoS at the DWDM layer in optical networks is devised. Unlike existing mechanisms that depend on buffer management and scheduling algorithms, our mechanism does not require any electronic buffering in the intermediate DWDM network nodes, which is greatly desired. The proposed scheme assigns different delay times to service classes in order to isolate higher priority classes from lower priority classes. Finally, we develop a traffic model and an allocation wavelength model, and analyze the blocking probability of each class.

We propose a simplified and accurate path loss equation for calculating the free space path loss for 802.11 WLAN line-of-sight links with antennas between 1 and 2.5 meters in height. We validated the accuracy of our WLAN propagation model with empirical measurements using several wireless LAN systems. We compared our model with other models including Lee's (1986), free space's, Hata's (1980) and Cost231 models and found that our model is superior for predicting the propagation performance of radios with very low antenna heights. This result is very useful for modeling and simulation of wireless LANs and mobile ad-hoc radio networks.

A TDMA-based randomly addressed potting protocol (TRAP) is proposed. TRAP employs a variable-length TDMA-based contention stage with the length based on the number of active stations. Simulation results are presented that reveal the superiority of TRAP against the RAP protocol in cases of medium and high offered loads. Furthermore the implementation of TRAP is much simpler than that of CDMA-based versions of RAP, since no extra hardware is needed for the orthogonal reception of the random addresses.

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learning automata have made a significant impact on all areas of engineering. They can be applied to a broad range of modeling and control problems, which are characterized by nonlinearity and a high degree of uncertainty. Learning automata have some key features, which make them applicable to a broad range of applications: they combine rapid and accurate convergence with a low computational complexity. Learning is defined as any permanent change in behavior as a result of past experience, and a learning system should therefore have the ability to improve its behavior with time, toward a final goal. In a purely mathematical context, the goal of a learning system is the optimization of a function not known explicitly [2]. Thirty years ago, Tsypkin [3] introduced a method to reduce the problem to the determination of an optimal set of parameters and then applied stochastic hill-climbing techniques. Tsetlin [4] started the work on learning automata during the same period. An alternative approach to applying stochastic hillclimbing techniques, introduced by Narendra and Viswanathan [5], is to regard the problem as one of finding an optimal action out of a set of allowable actions and to achieve this using stochastic automata. The difference between the two approaches is that the former updates the parameter space at each iteration while the latter updates the probability space. The stochastic automaton attempts a solution of the problem without any information on the optimal action. One action is selected at random, the response from the environment is observed, action probabilities are updated based on that response,

Although new high-bandwidth network technologies are being introduced and widely deployed, asynchronous transfer mode (ATM) is still considered one of the most important network technologies currently in use. A number of ATM switch architectures have been proposed in the literature. However, industry has shown that is better to use the well-known shared-medium technique in the design of these ATM switches. In this paper, four variations of a new distributed scheme are proposed for the arbitration of a shared bus of an ATM switch. These schemes are based on learning automata. By taking advantage of the bursty nature of ATM traffic, the new arbitration scheme demonstrates superb performance compared to the time division multiple access (TDMA) scheme.

Due to its fixed assignment nature, the well-known time division multiple access (TDMA) protocol suffers from poor performance when the offered traffic is bursty. In this paper, an adaptive TDMA protocol, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which is granted permission to transmit at each time slot is selected by means of learning automata (LA). The choice probability of the selected station is updated by taking into account the network feedback information. The system which consists of the LA and the network is analyzed and it is proven that the choice probability of each station asymptotically tends to be proportional to the probability that this station is not idle. Although there is no centralized control of the stations and the traffic characteristics are unknown and time-variable, each station tends to take a fraction of the bandwidth proportional to its needs. Furthermore, extensive simulation results are presented, which indicate that the proposed protocol achieves a significantly higher performance than other well-known TDMA protocols when operating under bursty traffic conditions.

An adaptive protocol for WDM passive star networks, which is capable of operating efficiently under bursty, and correlated traffic, is introduced. According to the proposed protocol, the stations which are granted permission to transmit at each time slot, are selected by taking into account the network feedback information. Although the traffic parameters are unknown and time-variable, the bandwidth of each wavelength is allocated to the stations according to their needs. In this way, the number of idle slots is reduced, resulting in a significant increase of the network throughput.

This paper presents a new QoS management scheme for wireless ATM networks using the large deviation concepts. We consider an ATM network, and assume that all switches are modeled by stochastic servers fed by sets of parallel buffers. The dynamics of such systems evolve in discrete-time using an on demand processor sharing (ODPS) discipline that we introduce. The arrival process in each buffer is an arbitrary stochastic process. Using an extension of the large deviation theory, we developed predictive techniques that allow the management of QoS in the presence of mobiles. Our approach is based on the determination of overflow probabilities. The proposed scheme has important applications for QoS management in wireless ATM networks.

The design of efficient mobile communication systems is considered a challenging task. A major task activity related to these systems addresses the call admission control and resource management. In this paper, we consider these issues in wireless ATM networks integrating the code division multiple access (CDMA) technology. An approach for dynamic resource management is proposed using heterogeneous traffic descriptors as well as the concept of signal-to-interference rate, computed at the base station receivers. An adaptive monitoring scheme that is based on an estimation algorithm and driven by a measurement of the interference and predicted traffic parameters of the admitted connections is established. The dynamic control that we propose at the user network interface, UNI, provides information about the instantaneous bit rate of a source allowing more effective flow control and achieves a good match in terms of predicting the congestion of any switch. It is able to police implicit resource management, which is used for both continuous bit rate (CBR) and variable bit rate (VBR) traffics as well as explicit resource management that is used for available bit rate (ABR) traffics.

An intelligent network (IN) is characterized by the distribution of network intelligence and capabilities wherever required within the telecommunications network. IN is also an architectural concept that can be applied to a variety of telecommunication networks including the public switched networks, mobile networks, and the integrated services digital networks (ISDNs). In this paper, we analyze and evaluate the performance of wireless ATM network equipped with intelligent services. The mathematical analysis uses the concept of virtual resource, while the simulation uses the object-oriented (O-O) scheme.

The growing fields of wireless networks and asynchronous transfer mode (ATM) are merging to form wireless ATM networks. This paper addresses dynamic bandwidth allocation, connection admission procedures, routing, and location management strategies in wireless ATM. More precisely, we investigate the issue of extending the Private Network to Network Interface, PNNI, protocol to support mobility. PNNI-based hierarchical routing, hand-off location management and routing schemes are proposed to integrate wireless capabilities. These schemes provide a reduction in the connection disruption time during a connection handoff session and a predictable resource need of the mobile during its connection. This substantially reduces the overhead due to end-to-end re-transmissions invoked at higher layer. An analytical model is developed to illustrate the hand-off algorithm.

This paper presents a method for network synthesis and optimum design in telecommunication and computer communication systems. Our method uses two concepts: the dynamic buffer size queue and the equivalent product form network. While the first concept helps in reducing the complexity of the network by hiding local parts in the network, the latter provides a mathematical analysis that can be helpful in the design of the network.