Next-generation networks, including IoT, edge, vehicular, and 6G systems, require cryptographic schemes that not only resist attacks but also withstand channel degradation without disrupting secure data flows. This paper presents a novel adaptive encryption architecture for quantum-resilient networks that integrates real-time attack detection in quantum channels with per frame key management. A lightweight controller has been designed, which tracks the quantum bit error rate (QBER) via a sliding-window hysteresis and buffer thresholds, deciding per frame whether to derive symmetric keys from Quantum Key Distribution (QKD) or from a post-quantum cryptographic fallback, while maintaining a unified AES–GCM AEAD dataplane. Moreover, a deterministic HMAC-based key derivation function (HKDF) schedule binding eliminates key reuse across modes, retransmissions, and re-encapsulations. Experimental evaluation on image payloads shows: (i) timely QBER-spike detection with 9.5-frame latency and zero misses; (ii) full decryptability and tamper detection across both key sources; (iii) symmetric throughput of ≈ 1.1GB/s with negligible adaptation overhead; and (iv) near-ideal ciphertext metrics including entropy 8.02bits/pixel, NPCR ≈ 99.6%, and UACI ≈ 30.7%. The framework achieves secure, continuous encryption by preventing unsafe QKD use and maintaining authenticated operation under quantum-channel degradation.

ÐThis paper presents a split-trust architecture for confidential natural language processing inference that secures preprocessing inside a Trusted Execution Environment and performs classification under CKKS homomorphic encryption. The design addresses the preprocessing exposure gap present in existing encrypted NLP systems, where tokenization and embedding are performed in plaintext prior to encryption. A linear Support Vector Machine is evaluated homomorphically using logarithmic slot-rotation optimization, enabling CPU-only inference without bootstrapping. On 10,000 IMDB test samples, encrypted inference achieves 89.65% accuracy and reproduces plaintext predictions exactly across all samples. Performance analysis across batch sizes of 500 to 2000 samples shows a minimum per-sample latency of 0.01297 seconds, with ciphertext expansion of approximately 1000× relative to plaintext features. Evaluation on SST-2, Movie Reviews, and Amazon Polarity confirms identical plaintext and encrypted predictions under distribution shift. The results demonstrate that confidential NLP inference is practical when secure preprocessing is combined with depth-aware homomorphic evaluation.

As smart cities increasingly rely on digital infrastructure to optimize energy use, electricity consumption data from households and buildings becomes a valuable resource for planning and grid management. However, frequent and detailed collection of such data raises serious privacy concerns, as it may reveal sensitive information about user behavior and daily routines. Traditional cloud-based machine learning solutions often require continuous data transmission, increasing the risk of data leakage or misuse. Tiny machine learning (TinyML) offers a promising alternative by enabling electricity consumption prediction directly on resource-constrained edge devices such as smart meters. By processing data locally, TinyML allows for accurate short-term forecasting without transmitting raw energy usage data to central systems. For instance, a TinyML model can predict the next two hours' electricity usage, such as 4.8 kW, and only send this single value to the Smart Building System, significantly reducing the data footprint and limiting exposure of personal information. To protect even these limited prediction outputs, we integrate homomorphic encryption (HE) into the TinyML framework. This allows encrypted predictions to be transmitted and processed without decryption, preventing leakage of sensitive consumption patterns. We evaluated this approach using the CU-BEMS dataset. The model achieved a root mean squared error (RMSE) of 1.8034 kW, a mean absolute error (MAE) of 1.3163 kW, and a mean absolute percentage error (MAPE) of$\mathbf{1 6. 5 4 \%}$, demonstrating robust predictive accuracy. The quantized model achieved a 70% reduction in size with minimal accuracy loss, making it suitable for TinyML deployment. The combination of edge-based TinyML and HE provides a secure and scalable solution for electricity usage forecasting, enabling privacy-preserving energy management in smart city environments.

Fog computing is paving the way for time-sensitive and energy-efficient IoT applications by placing computation closer to end devices. However, sustainability concerns due to its increasing energy demands and associated carbon emissions stem from the expansion of fog infrastructure. Despite numerous offloading strategies that focus on energy consumption and makespan, green computing and its broader environmental impact are often overlooked. To address this gap, we propose a carbon-aware multi-objective optimisation model that integrates three metaheuristic algorithms - GWO, CSA, and SSA - to balance makespan and energy consumption while minimising$C O_{2}$emissions. In our proposed framework, node suitability is evaluated based on a customised energy model designed to prevent the overuse of non-renewable energy sources and maintain system responsiveness. Our framework advances foglevel scheduling by integrating a hybrid energy model, unlike prior cloud-focused carbon-aware approaches. The optimisation process simultaneously considers three objectives - energy, makespan, and carbon emissions - through a hybrid metaheuristic algorithm. Simulation results using the LEAF simulator indicate that our framework achieves an average improvement in energy consumption, makespan, and$C O_{2}$emissions of approximately$18 \%, 23 \%$, and 20 %, respectively, compared to EEAIOT-EFC and MOHHOSSA. These results show the viability of integrating carbon awareness into fog-cloud task offloading for green computing.

The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and ineffective for large-volume image data, leading to inefficiencies in privacy-preserving distributed learning applications. To address these concerns, this paper proposes a novel Feature-Aware Chaotic Image Encryption scheme that integrates Feature-Aware Pixel Segmentation (FAPS) with Chaotic Chain Permutation and Confusion mechanisms to enhance security while maintaining efficiency. The proposed scheme consists of three stages: (1) FAPS, which extracts and reorganizes pixels based on high and low edge intensity features for correlation disruption; (2) Chaotic Chain Permutation, which employs a logistic chaotic map with SHA256-based dynamically updated keys for block-wise permutation; and (3) Chaotic chain Confusion, which utilises dynamically generated chaotic seed matrices for bitwise XOR operations. Extensive security and performance evaluations demonstrate that the proposed scheme significantly reduces pixel correlation— almost zero, achieves high entropy values close to 8, and resists differential cryptographic attacks. The optimum design of the proposed scheme makes it suitable for real-time deployment in resource-constrained environments.

This paper introduces a robust security framework for IoT networks that leverages hashchain-based authentication and Merkle tree-based data integrity verification to ensure secure and reliable communication. Specifically, during initialization, each node employs a one-way hash function to generate a unique hashchain of a predefined length where the hashchain root serves as the node’s identifier. The network then constructs a Merkle authentication tree using these root hashes. In the operational phase, recipients validate message authenticity by exchanging specific data, verifying the hash sequence order, and reconstructing the Merkle root. The framework ensures node authenticity and resistance against blended Sybil and Flooding attacks in RPL-based IoT networks attacks. Security analysis and a proof-of-concept implementation within the 6LoWPAN/RPL IoT stack, using Contiki OS and TelosB motes, demonstrate the effectiveness of our framework in mitigating such attacks with minimal impact on network performance.

RPL, the standard routing protocol for low-power and lossy networks, offers two modes for handling downward traffic: storing and non-storing. Each has significant limitations - the storing mode struggles with router memory constraints, potentially making destinations unreachable when a router's storage capacity is reached. Conversely, the non-storing mode mitigates this issue by employing source routing, but at the expense of increased network overhead. To address these challenges, this paper introduces the Leaf-Centric Mode (LCM), a novel approach that dramatically reduces storage requirements by enabling nodes to maintain routing information only for leaf nodes within their sub-networks, rather than all nodes. This optimized approach offers key advantages for IoT applications, including reduced storage footprint, improved reliability, lower network overhead, and enhanced overall performance. Through comprehensive experimental evaluation, we demonstrate the practical effectiveness of the LCM and establish its viability for IoT applications.

With the development of software-defined edge networks, network management has become more flexible and realtime. However, this advancement has also led to critical security concerns, especially when detecting attacks efficiently in resourceconstraint environments. Existing solutions often suffer from high computational load, making them unsuitable for the fast, dynamic environments of resource-constrained edge environments. To tackle this issue, we introduce a lightweight attack detection system that combines digital twins with advanced machine learning techniques. Our approach uses a stacked sparse autoencoder (ssAE) for feature extraction and reduction and a hybrid CNNGRU model for accurate attack classification. The simulation results show that our solution significantly outperforms existing models, which are ANOVA-DNN, AE-MLP and CNN-LSTM. It achieves the highest detection accuracy at$\mathbf{9 9. 7 2 \%}$and a suitable low time-cost at 0.215 ms, providing a good balance between accuracy and speed. Moreover, it delivers the lowest computational load compared to others, which makes it ideal for deployment in real-time resource-limited environments.

ABSTRACT The focus of this study is to present the detailed process of collecting a dataset of Arabic short‐text in the tourism context and annotating this dataset for the task of sentiment analysis using an automatic zero‐shot labelling technique utilising transformer‐based models. This is benchmarked against a baseline manual annotation approach utilising native Arab human annotators. This study also introduces an approach exploiting both manual/handcrafted and automatically generated annotations of the dataset tweets for the task of sentiment analysis as part of a cross‐domain approach using a model trained on sarcasm labels and vice versa. The total collected corpus size is 2293 tweets; after annotation, these tweets were labelled in a three‐way classification approach as either positive, negative or neutral. We run different experiments to provide benchmark results of Arabic sentiment classification. Comparative results on our dataset show that the highest performing baseline model when utilising manual labels was MARBERT, with an accuracy of up to 87%, which was pre‐trained for Arabic on a massive amount of data. It should be noted that this model enhanced its performance additionally after pre‐training on a dialectical Arabic and modern standard Arabic corpus. On the other hand, zero‐shot automatically generated labels achieved an 84% accuracy rate in predicting sarcasm classes from sentiment labels.

A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model$l_{c}, l_{s}$-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, a Vertical correlation attack ($V_{c0}$) and a Vulnerable sensitive attribute attack ($V_{sa}$) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;“INFORMS”,“YOUTUBE”, and “IMDb” demonstrate that the proposed model outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.

As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to obtain global road information in practical scenarios since there will still be many vehicles on the road without onboard units (OBUs) in the near future. Specifically, although C-V2X vehicles have sensors that can perceive their surroundings and broadcast their perceived information to the C-V2X system, their line-of-sight (LoS) is limited and obscured by the environment, such as other vehicles and terrain. Besides, vehicles without OBUs cannot share their perceived information. These two problems cause extensive areas with unperceived information in the C-V2X system, and whether vehicles are in these areas is unknown. Thus, extending the perceivable range of the limited scenario for C-V2X applications that require global road information is necessary. To this end, this paper pioneers investigating the scenario inpainting task problem in C-V2X. To solve this challenging problem, we propose an effiCient trAfficScenario inpainTing (CAST) solution consisting of a generative architecture and knowledge distillation, simultaneously considering the inpainting precision and computation efficiency. Extensive experiments have been conducted to demonstrate the effectiveness of CAST in terms of Precise Inpaint Rate (PIR), Rough Inpaint Rate (RIR), Lane-Level Inpaint Rate (LLIR), and Inpaint Confidence Error (ICE), paving the way for novel solutions for the inpainting problem in more complex road scenarios.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) technologies into WRSNs.

With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based on popularity at Small Base Stations (SBSs) has emerged as a promising solution. However, accurately predicting the file popularity profile for SBSs remains a key challenge due to variations in content characteristics and user preferences. Moreover, factors such as content size and the length of time slots (that is, the time duration of the update cycle for SBSs) critically impact the performance of caching schemes with limited storage capacity. In this paper, arealism-orientedintelligent caching (RETINA) is proposed to address the problem of content caching with unknown file popularity profiles, considering varying content sizes and time slots lengths. Our simulation results demonstrate that RETINA can significantly enhance the cache hit rate by 4%–12% compared to existing content caching schemes.

Wireless Sensor Networks (WSNs) are used to sense and monitor physical conditions in various services and applications. However, there are a number of challenges in deploying WSNs, especially those pertaining to energy replenishment. Using the current solutions, when a significant number of sensors need to replenish their energy, this would be costly in terms of time, efforts and resources. Thus, this paper aims to solve this problem by efficiently deploying wireless power transfer technologies and scheduling Mobile Charging Vehicles (MCVs) in WRSN. The proposed method deploys multi-criteria decision-making (i.e., Analytical Hierarchy Process (AHP)) to schedule the charging tasks. To the best of our knowledge, this paper is the first to depend solely on AHP in MCVs scheduling. The paper demonstrates the validity of the proposed method by illustrating that the matrices that are created are within the accepted values of consistency ratio. In addition, the paper proposes a method of partitioning the values of our criteria to avoid the problem of different criteria having different measurement units. Unlike existing works, the paper aims to schedule an MCV for charging based on both the distance and residual energy of the sensor. The proposed method exhibits superiority in terms of the average remaining energy available in the system, having the shortest queue length, shorter MCV response time, shorter charging duration, and shorter queue waiting time against the state-of-the-art methods. Our study paves the way for next generation efficient charging and MCV scheduling.

With the widespread use of the Internet of Things (IoT), securing the storage and transmission of multimedia content across IoT devices is a critical concern. Chaos-based Pseudo-Random Number Generators (PRNGs) play an essential role in enhancing the security of image encryption algorithms. This paper introduces a novel 1-dimensional cosine-modulated-polynomial chaotic map to be used as a PRNG in image encryption algorithms. The proposed map utilizes a cosine function to modulate the outcome of a polynomial expression, resulting in complex chaotic behaviour. The designed map acts as a self-modulating system and offers a larger chaotic range, reduced structural complexity, and enhanced chaotic properties, such as aperiodicity, unpredictability, ergodicity, and sensitivity to control parameters and initial conditions, in comparison to the traditional 1-dimensional chaotic maps. An extensive evaluation is performed to gauge the chaotic behaviour of the proposed map, including bifurcation diagrams, chaotic trajectory analysis, fixed point and stability analysis, Lyapunov Exponent, Kolmogorov Entropy and NIST SP800-22 tests demonstrating its effectiveness to be used as a secure PRNG in image encryption algorithms.

Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task offloading. Subsequently, we propose a real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.

It is estimated that by 2050 approximately one in ten individuals globally will experience disabling hearing impairment. In the presence of everyday reverberant noise, a substantial proportion of individual users encounter challenges in speech comprehension. This study introduces a novel application of neuro-fuzzy modeling that synergizes and fuses audio-visual speech enhancement (AV SE) with an initial user preference learning based framework. Specifically, our approach uniquely integrates multimodal AV speech data with innovative SE methods and fuzzy inferencing techniques. This integration is further enriched by incorporating a user-preference learning model that adapts to environmental and user-specific contexts, including signal-to-noise ratios, sound power, and the quality of visual information. The proposed framework facilitates the incorporation of clinical measures such as user cognitive load (or listening effort) with real-world uncertainty to steer the system outputs. We employ an adaptive fuzzy neural network to derive the most effective Sugeno fuzzy inference model, employing particle swarm optimization to ensure optimal SE by considering sound power, ambient noise levels, and visual quality. Experimental results utilize our new benchmark AV multitalker challenge dataset to demonstrate the superiority of our user preference-informed, context-aware AV SE approach in enhancing speech intelligibility and quality in challenging noisy conditions, marking a significant advancement over conventional methods while reducing energy consumption. The conclusion supports the ecological scalability of our approach and its potential for real-world applications, setting a new benchmark in AV SE research, paving the way for future assistive hearing and communication technologies.

LoRa networks represent a promising technology for Internet of Things applications due to their long range, low cost, and energy efficiency. However, their ALOHA-based access method and duty cycle restrictions can limit their scalability and reliability in high-density networks. To address such a challenge, this article proposes the autonomous time-slotted LoRa (ATS-LoRa), protocol that allows LoRaWAN nodes to autonomously determine their optimal transmission parameters without extensive downlink transmissions from the gateway. ATS-LoRa utilizes the geographical coordinates of the nodes and their gateway in a novel way to allow them to determine their appropriate transmission parameters, such as the spreading factor, the channel frequency, and the slot ID. ATS-LoRa performance is evaluated through extensive simulations under different operating conditions showing an average throughput of around 47 times better than the adaptive data rate algorithm of LoRaWAN protocol.

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters the progress of ITS through enabling direct vehicle communication and interaction with infrastructure. However, the penetration rate of V2X vehicles is advancing gradually. Consequently, there will be mixed scenarios on the road, involving both on-board units (OBUs)-equipped and non-equipped vehicles. This results in disparities in communication capabilities, highlighting the need to ensure the efficient and safe operation of vehicles in such mixed scenarios. This paper addresses this challenge by presenting a feasibility analysis and prediction method for lane-changing overtaking maneuvers in mixed scenarios, specifically for vehicles equipped with OBUs. This method assists vehicles in completing overtaking maneuvers by offering a non-binary lane-changing overtaking feasibility index along with corresponding speed guidance. First, vehicle sensors are used to sense the state of surrounding vehicles, addressing any missing sensor data due to occlusions. Moreover, the future driving behavior of the vehicle is taken into account to more accurately predict the future state of the vehicle. Then, a deep reinforcement learning algorithm is deployed to process the hybrid action space to train a lane-changing overtaking model, which also takes into account the influence of the flow of each lane in front of the vehicle, and finally predicts the feasibility of the vehicle performing lane-changing overtaking. Experimental results demonstrate that our method can accurately predict the vehicle’s future state and effectively assist the vehicle in completing lane-changing overtaking maneuvers. This research provides strong support for the integration of ITS and V2X technologies.

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely,Collaborated gAmeParallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP-GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because vehicles with time-varying trajectories need to process massive data with high complexity and diversity. In this paper, a VEC-based computation offloading model is developed with consideration of data dependency of tasks. The minimization of the average response time and average energy consumption of the system is defined as a combinatorial optimization problem. To solve this problem, we propose aMobility-aware dependent taskoffloading (MESON) Scheme for urban VEC and develop a DRL-based algorithm to train the offloading strategy. To improve the training efficiency, a vehicle mobility detection algorithm is further designed to detect the communication time between vehicles and RSUs. In this way, MESON can avoid unreasonable decisions by lowering the size of the action space. Moreover, to improve the system stability and the offloading successful rate, we design a task priority determination scheme to prioritize the tasks in the waiting queue. The experimental results show that MESON is superior compared to other task offloading schemes in terms of the average response time, average system energy consumption, and offloading successful rate.

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate theimportanceof nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks’ assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes’ density, and queuing processing.

Vehicle trajectory prediction is a promising technology for improving the performance of Cellular Vehicle-to-Everything (C-V2X) applications by providing future road states. Various vehicle trajectory prediction methods have been proposed to increase the accuracy of the predicted trajectory. Although the existing vehicle trajectory prediction methods can accurately predict the future trajectory under the assumption that data comply with the Independent and Identically Distributed (IID), their performance is seriously degraded in practical implementation due to the ubiquitous distribution shifts in vehicle trajectory data. To improve the universality of the vehicle trajectory prediction method, generalizing the method to an environment that never appeared in the training data, namely, the Domain Generalization (DG) task, should be considered. Thus, we propose a plug-and-play inFORmaTive caUsality-based vehicle trajectory predictioN architecturE (FORTUNE) to improve the DG capability of vehicle trajectory prediction methods. First, a novel structural causal model (SCM) of vehicle trajectory prediction is established to simulate the causality of the data-generating process. Second, we utilize the principle of mutual information to learn the invariant representation of the SCM. Third, an invariant knowledge-transferring module is proposed to increase learning ability without destroying the structure of the original model. The results from simulation experiments demonstrate that the proposed scheme can significantly improve the DG capability of vehicle trajectory prediction methods.

Time-Slotted Medium Access Control protocols bring advantages to the scalability of LoRa networks as an alternative to the ALOHA access method. However, such Time-Slotted protocols require nodes synchronization and schedules dissemination under stringent duty cycles likely resulting in improper performance and limited scalability. One possible solution is to adopt decentralized approaches where nodes autonomously determine their schedules and other transmission parameters. Thus, this paper proposes a novel Time-Slotted MAC protocol, named Autonomous Adaptive Frame Size (AAFS-LoRa) protocol, that allows nodes to individually determine their transmission parameters without extensive downlink transmissions from the gateway. The proposed protocol can conFigure nodes by maintaining information, such as their location and the gateway location. The main contribution of the proposed protocol is the adoption of adaptive frame sizes that are large enough to accommodate nodes with common transmission parameters to mitigate collisions among them. The proposed protocol has been investigated under different operating conditions, and the experiments demonstrates that our protocol can effectively improve the network performance, in terms of latency as well as the capacity.

Due to the increasing privacy demand in data processing, Fully Homomorphic Encryption (FHE) has recently received growing attention for its ability to perform calculations over encrypted data. Since the data can be processed in encrypted form and the output remains encrypted, only an authorized user or a user who holds the key can decrypt the data and understand its meaning. Hence, it is possible to securely outsource data processing to untrustworthy but powerful public computing resources on the edge. However, due to the high computational complexity, FHE-based data processing experiences scalability related concerns. It is currently unclear whether FHE can be used to solve large-scale problems. In this paper, we propose a novel general distributed FHE-based data processing approach as a concrete step towards solving the scalability challenge. The main idea behind our approach is to use slightly more communication overhead for a shorter computing circuit in FHE, hence, reducing the overall complexity. We verify our new model’s efficiency and effectiveness by comparing the distributed approach with the central approach over various FHE schemes (CKKS, BGV, and BFV). This is performed using one of the most popular libraries of FHE ‘‘Microsoft SEAL by performing specific mathematical operations and observing the time consumed. The empirical results demonstrate that the proposed approach results in a significant reduction in time, up to 54% compared to the traditional central approach.

Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabilities. However, the highly dynamic network topology, strict low-delay constraints, and massive data of tasks of VEC pose significant challenges for implementing efficient offloading. Digital Twin-based VEC is emerging as a promising solution that enables real-time monitoring of the state of the VEC network through mapping and interaction between the physical and virtual worlds, thus assisting in making sound offload decisions in the physical world. Thus, this paper proposes an intelligent partial offloading scheme, namely, Digital Twin-Assisted Intelligent Partial Offloading (IGNITE). First, to find the optimal offloading space in advance, we combine the improved clustering algorithm with the Digital Twin (DT) technique, in which unreasonable decisions can be avoided by reducing the size of the decision space. Second, to reduce the overall cost of the system, Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy, allowing for automatic optimization of computational delay and vehicle service price. To improve the efficiency of cooperation between digital and physical spaces, a feedback mechanism is established. It can adjust the parameters of the clustering algorithm based on the final offloading results in this clustering. To the best of our knowledge, this is the first study on DT-assisted vehicle offloading that proposes a feedback mechanism, forming a complete closed loop as prediction-offloading-feedback. Extensive experiments demonstrate that IGNITE has significant advantages in terms of total system computational cost, total computational delay, and offloading success rate compared with its counterparts.

Greedy routing efficiently achieves routing solutions for vehicular networks due to its simplicity and reliability. However, the existing greedy routing algorithms have mainly considered simple routing metrics only, e.g., distance based on the local view of an individual vehicle. This consideration is insufficient for analysing dynamic and complicated vehicular communication scenarios which inevitably degrades the overall routing performance. Software-Defined Vehicular Network (SDVN) and Graph Convolutional Network (GCN) can overcome these limitations. Thus, this paper presents a novel GCN-based greedy routing algorithm (NGGRA) in the hybrid SDVN. The SDVN control plane trains the GCN decision model based on the globally collected data. Vehicles with transmission requirements can adopt this model for inferring and making the routing decisions. The proposed node-importance-based graph convolutional network (NiGCN) model analyses multiple correlated metrics to accurately evaluate the dynamic vehicular network is available at:https://github.com/a824899245/NiGCN. Meanwhile, the SDVN architecture offers a global view for model training and routing computation. Extensive simulation results demonstrate that NiGCN outperforms popular GCN models in training efficiency and accuracy. In addition, NGGRA can improve the packet delivery ratio and substantially reduce delay compared with its counterparts.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Many opportunistic routing (OR) schemes treat network nodes equally, neglecting the fact that the nodes close to the sink undertake more duties than the rest of the network nodes. Therefore, the nodes located at different positions should play different roles during the routing process. Moreover, considering various Quality-of-Service (QoS) requirements, the routing decision in OR is affected by multiple network attributes. The majority of these OR schemes fail to contemplate multiple network attributes while making routing decisions. To address the aforesaid issues, this paper presents a novel protocol that runs in three steps. First, each node defines aRouting Zone (RZ)to route packets toward the sink. Second, the nodes within RZ are prioritized based on the competency value obtained through a novel model that employs Modified Analytic Hierarchy Process (MAHP) and Fuzzy Logic techniques. Finally, one of the forwarders is selected as the final relay node after forwarders coordination. Through extensive experimental simulations, it is confirmed that FLORA achieves better performance compared to its counterparts in terms of energy consumption, overhead packets, waiting times, packet delivery ratio, and network lifetime.

Temporal information is critical for routing computation in the vehicular network. It plays a vital role in the vehicular network. Till now, most existing routing schemes in vehicular networks consider the networks as a sequence of static graphs. We need to find an appropriate method to process temporal information into routing computation. Thus, in this paper, we propose a routing algorithm based on the Hidden Markov Model (HMM) and temporal graph, namely, Prediction-Based Temporal Graph Routing Algorithm (PT-GROUT). This new algorithm considers the vehicular network as a temporal graph, in which each data transmission as an edge has its specific temporal information. To better capture the temporal information, we select Software-Defined Vehicular Network (SDVN) as our network architecture, which is a preferred architecture for processing the temporal graph regarding the vehicular network since all vehicle statuses can be easily managed. To compute the future routing path accurately and efficiently, the future temporal graph is predicted by applying HMM, in which we model the current vehicular network with dynamic programming and greedy strategies. With the temporal information and reasonable setting of HMM, PT-GROUT can better evaluate the vehicular network and discover the evolution of the internal structure of the network. The optimal routing path can be achieved more efficiently. The simulation results demonstrate that PT-GROUT can substantially improve the computation efficiency and reduce packet loss and delivery delay compared with its counterparts.

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a multi-criteria decision making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the analytic hierarchy process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink.

Long-Range (LoRa) has been a major avenue for deploying the Internet of Things (IoT) in large scale environments due to its long-scale connection, energy efficiency, and cost-effectiveness. LoRa networks provide multiple configurable transmission parameters that greatly affect the performance of the overall network. To the best of our knowledge, the optimal combination of these parameters that can allow orthogonal simultaneous transmissions to be successfully decoded by the gateway has not been reported in the literature. Exploiting all the transmission parameters in the physical layer to find optimal combinations between them will inevitably increase the throughput of LoRa without affecting the energy consumption. In this paper, authors propose annulus-based distribution algorithm of LoRa transmission parameters to mitigate well-known and challenging issues, namely the capture effect and the limited scalability. The performance of the proposed algorithm has been compared with the Adaptive Data Rate(ADR) algorithm of LoRaWAN and the simulation results show that the proposed algorithm significantly outperforms the ADR especially in large-scale dense networks. Specifically, the proposed algorithm has improved the network throughput by an average of 59% compared to the legacy LoRaWAN.

The COVID-19 pandemic has recently emerged as a worldwide health emergency that necessitates coordinated international measures. To contain the virus's spread, governments and health organisations raced to develop vaccines that would lower Covid-19 morbidity, relieve pressure on healthcare systems, and allow economies to open. Following the COVID-19 vaccine, the vaccination certificate has been adopted to help the authorities formulate policies by controlling cross-border travelling. To address serious privacy concerns and eliminate the need for third parties to retain the trust and govern user data, in this paper, we leverage blockchain technologies in developing a secure and verifiable vaccination certificate. Our approach has the advantage of utilising a hybrid approach that implements different advanced technologies, such as the self-sovereignty concept, smart contracts and interPlanetary File System (IPFS). We rely on verifiable credentials paired with smart contracts to make decisions about who can access the system and provide on-chain verification and validation of the user and issuer DIDs. The approach was further analysed, with a focus on performance and security. Our analysis shows that our solution satisfies the security requirements for immunisation certificates.

This work is devoted to solving the problem of multicriteria multihop routing in vehicular ad hoc networks (VANETs), aiming at three goals: 1) increasing the end-to-end delivery ratio; 2) reducing the end-to-end latency; and 3) minimizing the network overhead. To this end and beyond the state of the art, heuristic routing for vehicular networks (HERO), which is a distributed routing protocol for urban environments, encapsulating two main components, is proposed. The first component, road-segment selection, aims to prioritize the road segments based on a heuristic function that contains two probability distributions, namely, shortest distance distribution (SDD) and connectivity distribution (CD). The mass function of SDD is the product of three quantities: 1) the perpendicular distance; 2) the dot-production angle; and 3) the segment length. On the other hand, the mass function of CD considers two quantities: 1) the density of vehicles and 2) the interdistance of vehicles on the road segment. The second component, vehicle selection, aims to prioritize the vehicles on the road segment based on four quantities: 1) the relative speed; 2) the movement direction; 3) the available buffer size; and 4) signal fading. The simulation results showed that HERO achieved a promising performance in terms of delivery success ratio, delivery delay, and communication overhead.

The epoch of the Internet of Things (IoT) has come by enabling almost everything to gather and share electronic information. Considering the unreliable factors of public IoT, how to outsource huge amounts of indispensable stream data generated by the nodes to the remote storage (RS) efficiently and securely is one of the most challenging issues. In this article, we propose a secure lightweight stream data outsourcing framework for IoT based on identity and blockchain. Taking advantage of identity-based cryptography and blockchain, for public IoT containing untrusted communication channels, nodes, RS, and even verifiers, we introduce a private mobile network and multiple verifiers to ensure that the stream data are stored intact and updated correctly, without the costs and risks brought by the public-key infrastructures (PKI). Meanwhile, the framework can also achieve privacy-preserving checking, by revealing no data to the other entities besides the RS, even in the blockchains. Our comprehensive analysis and experiments demonstrate that the proposed framework is suitable for lightweight devices and practical for IoT.

Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by the processing and propagation of the routing request and response. Thus, to predict the high-precision trajectory beyond the state of the art, we propose a generative adversarial network (GAN)-based vehicle trajectory prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components: 1) vehicle coordinate transformation for data set preparation; 2) neural network prediction model trained by GAN; and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver’s psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the average accuracy, mean absolute error, and root-mean-squared error.

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi-Sugeno-Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes-road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Abstract Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall.

Lightweight cryptography is a novel diversion from conventional cryptography to minimise its high level of resource requirements, thus it would impeccably fit in the internet-of-things (IoT) environment. The IoT platform is constrained in terms of physical size, internal capacity, other storage allocations like RAM/ROM and data rates. The devices are often battery powered, hence maintenance of the charged energy at least for a few years is essential. However, provision of sufficient security is challenging because the existing cryptographic methods are too heavy to adopt in the IoT. Consequently, an interest arose in the recent past to construct new cryptographic algorithms in a lightweight scale, but the attempts are still struggling to gain robustness against improved IoT threats and hazards.There exists a lack of literature studies to offer overall and up-to-date knowledge on lightweight cryptography. Therefore, this effort is to bridge the areas in the subject by summarising the content we explored during our complete survey recently. This work contains the development of lightweight cryptographic algorithms, its current advancements and futuristic enhancements. In contrast, this covers the history, parametric limitations of the invented methods, research progresses of cryptology as well as cryptanalysis.

With the rapid development of hardware and software technologies, an increasing number of mobile devices, eg, smartphones and tablets get connected to the Internet, which results in a large proportion of traffic attributed to mobile devices.1 The mobile users have therefore increasingly high demands to access the Internet with guaranteed Quality-of-Experience.2, 3 Software-Defined Networking (SDN)4, 5 is an emerging network architecture where network control is decoupled from data forwarding and provides a powerful tool for fine-grained network management. The SDN-based network virtualization techniques have been widely used by Internet Service Providers to facilitate the service delivery with such performance guarantees, where network routers and switches are dynamically coordinated under the SDN APIs in wired networks.6 With the rapid advancement of wireless network technologies, many last mile connections go for wireless. However, the SDN-based wireless network virtualization is not straightforward. Many research challenges still need to be resolved before practical large-scale deployment. The accepted eight papers in this special issue are devoted to addressing the state-of-the-art technologies related to SDN, wireless network, network virtualization, security issues, and performance improvements. These papers can be organized under the following key themes: trust model and management, SDN security, and network performance optimization. The contributions of these papers are outlined below. There are two accepted papers aiming at establishing frameworks for trust models, management, and methodologies. The paper “A Trust Management Framework for Software-Defined Network Applications” proposed a trust management framework for SDN applications.7 It evaluates the applications' trust values according to their impact on the network performance. With the prototype system based on a floodlight controller, the framework is shown to be accurate and effective. Different from the above work, the paper “Graph Encryption for All-path Queries”8 aimed at providing the ability to store sensitive graph data to untrusted servers. A searchable symmetric encryption scheme is proposed to support all-path queries. The proposed scheme is proved to be able to adaptively semantically secure in the semihost settings. The scheme can be widely applied in the network virtualisation as graph data structures are commonly used to represent the topology of substrate and virtual networks. Security is one of the most important topics in SDNs. There is one accepted paper in this issue focusing on this topic. The paper “A Comprehensive Survey of Security Threats and Their Mitigation Techniques for Next-Generation SDN Controllers”9 provided a comprehensive survey of the security threats and corresponding mitigation techniques for SDN controllers. In this survey, a detailed classification for various security attacks on the control plane has been provided. Besides, the latest corresponding mitigation techniques are also presented and summarized. Based on the survey, future directions on a number of security issues such as policy violation and malware injection are discussed. DDoS attack is one of the most popular and threatening attacks for SDNs. Two accepted works are focused on this specific topic and have made contributions from different aspects. In the paper “An Intelligent Trust Model for Hybrid DDoS Detection in Software Defined Networks”,10 a trust evaluation and management model for hybrid DDoS detection in SDNs is proposed, where the extreme learning machine is applied. The proposed model can monitor the trust values of the OpenFlow switches in real time and thus can respond to different types of DDoS attacks. The model provides a more efficient alternative for DDoS detection in SDNs. The paper “Machine Learning Algorithms to Detect DDoS Attacks in SDN”11 managed to exploit different kinds of machine learning algorithms to avoid three kinds of DDoS attacks (controller attack, flow table attack, and bandwidth attack). Compared to the above work on trust model, this paper deals with the problem in a different way and considers more kinds of attacks. SDN and NFV have attracted increasing research attentions in recent years. The various network resources, traffic patterns, user demands, and protocol policies have significant impact on the network performance, which makes the performance optimization a challenging problem. The paper “The Optimization of Virtual Resource Allocation in Cloud Computing Based on RBPSO”12 considered the task requirements from different users to manage virtual machines. A novel algorithm with resampled binary particle swarm optimization is proposed. Compared with the existing works, the efficiency of the proposed algorithm is improved, the population diversity is maintained, and redundant calculations are reduced. The paper “Traffic Modeling and Performance Evaluation of SDN-Based NB-IoT Access Network”13 specifically considered the impact of network traffic in the SDN-based Narrowband Internet of Things (NB-IoT) network. SDN-enabled NB-IoT is discussed in this paper. Besides, the network performance is evaluated and modeled using various network parameters in the NB-IoT networks. The analysis and simulation results can be used in the SDN controller to dynamically allocate resources and make network management decisions to satisfy different performance requirements of NB-IoT applications. The paper “LBBESA: An Efficient Software-Defined Networking Load-Balancing Scheme Based on Elevator Scheduling Algorithm”14 dealt with the problem of load balancing in SDNs, based on the elevator scheduling algorithm. According to the real-time measurement of the server loads, the regional elevator allocation is applied to coordinate the connection of the clients' requests. The throughput and scalability are improved. The articles presented in this special issue have provided insights in fields related to SDN, wireless networks, network virtualization, and network security, including the trust models and methodologies, SDN DDoS attacks, and performance optimization for various SDN-enabled networks. We wish the readers can benefit from the insights of these papers and contribute to these rapidly growing areas. We would like to express our deep thanks to the Editor-in-Chief, Professor Geoffrey Fox, for providing us with the opportunity to host this special issue in Concurrency and Computation: Practice and Experience. We also thank all the authors who submitted their papers. Last but not least, we thank the thoughtful work of the many reviewers who have provided invaluable evaluations and recommendations.

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the routing flexibility. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without interrupting the network services. Through extensive performance evaluation studies and simulations, the results demonstrate that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade.

This article extends the promising software-defined networking technology to wireless sensor networks to achieve two goals: 1) reducing the information exchange between the control and data planes, and 2) counterbalancing between the sender's waiting-time and the duplicate packets. To this end and beyond the state-of-the-art, this work proposes an SDN-based architecture, namely MINI-SDN, that separates the control and data planes. Moreover, based on MINI-SDN, we propose MINI-FLOW, a communication protocol that orchestrates the computation of flows and data routing between the two planes. MINI-FLOW supports uplink, downlink and intra-link flows. Uplink flows are computed based on a heuristic function that combines four values, the hops to the sink, the Received Signal Strength (RSS), the direction towards the sink, and the remaining energy. As for the downlink flows, two heuristic algorithms are proposed, Optimized Reverse Downlink (ORD) and Location-based Downlink(LD). ORD employs the reverse direction of the uplink while LD instantiates the flows based on a heuristic function that combines three values, the distance to the end node, the remaining energy and RSS value. Intra-link flows employ a combination of uplink/downlink flows. The experimental results show that the proposed architecture and communication protocol perform and scale well with both network size and density, considering the joint problem of routing and load balancing.

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.

In Software Defined Vehicular Networks (SDVNs), most existing studies of routing consider the vehicular network as a static graph and compute the flow table based on static information. However, a static graph could only contain partial network data. Routing computation based on the static graph could be inefficient because vehicular networks are temporal graphs. Thus, in this paper, we propose a novel routing algorithm based on the Markov model and the temporal graph. Unlike conventional routing algorithms, the proposed algorithm adopts the concept of the temporal graph where every edge has its specific temporal information. We apply the Markov model to predict the future routing of the network and adopt prediction data to get the optimal routing by running the temporal graph optimal path algorithm. A benefit of our proposal is, the proposed algorithm searched on the temporal graph of SDVNs can avoid generating additional routing overhead. Besides, based on the information of the vehicular network which is collected from the data plane, the controller can enhance the Markov model as time flows. By applying the above mechanisms, the flow table (route) could be calculated more precisely to enable efficient vehicular communication. The simulation experiments demonstrate the superiority of the proposed algorithm over its counterparts in high-density vehicular networks.

Software-Defined Vehicular Networks (SDVNs) technology has been attracting significant attention as it can make Vehicular Ad Hoc Network (VANET) more efficient and intelligent. SDVN provides a flexible architecture which can decouple the network management from data transmission. Compared to centralized SDVN, hybrid SDVN is even more flexible and has less overhead. This hybrid technology can eliminate the burden on the central controller by moving regional routing tasks from the central controller to local controllers or vehicular nodes. In the literature, different routing protocols have been reported for SDVNs. However, these existing routing protocols lack flexibility and adaptive approaches to deal with changing and dynamic traffic conditions. Thus, this paper proposes a new software-defined routing method, namely, Novel Adaptive Routing and Switching Scheme (NARSS), deployed in the controller. This adaptive method can dynamically select routing schemes for a specific traffic scenario. To achieve this, this paper firstly presents a method for collecting road network information to describe traffic condition where the method extracts the feature data used to generate the routing scheme switching model. Secondly, we train the feature data through an artificial neural network with high training speed and accuracy. Finally, we use the model as a basis for establishing the NARSS and deploy it in the controller. Simulation results show that the proposed scheme outperforms the single traditional routing protocol in terms of both packet delivery ratio and end-to-end delay.

In this paper, we introduce Drizzle, a new algorithm for maintaining routing information in the low-power and lossy networks. The aim is to address the limitations of the currently standardized routing maintenance (i.e., Trickle algorithm) in such networks. Unlike Trickle, Drizzle has an adaptive suppression mechanism that assigns the nodes different transmission probabilities based on their transmission history so to boost the fairness in the network. In addition, Drizzle removes the listen-only period presented in Trickle intervals leading to faster convergence time. Furthermore, a new scheme for setting the redundancy counter has been introduced with the goal to mitigate the negative side effect of the short-listen problem presented when removing the listen-only period and boost further the fairness in the network. The performance of the proposed algorithm is validated through extensive simulation experiments under different scenarios and operation conditions. In particular, Drizzle is compared to four routing maintenance algorithms in terms of control-plane overhead, power consumption, convergence time, and packet delivery ratio (PDR) under uniform and random distributions and with lossless and lossy links. The results indicated that Drizzle reduces the control-plane overhead, power consumption and the convergence time by up to 76%, 20%, and 34%, respectively, while maintaining approximately the same PDR rates.

Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions.

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is originally designed for disseminating code updates through a wireless sensor network. Thus, when it comes to routing maintenance in LLNs, Trickle suffers from some issues related to power, convergence time, network overhead and load-distribution. In this paper, a new algorithm for maintaining the network topology in LLNs is developed motivated by Trickle weaknesses, namely, Drizzle algorithm. Unlike Trickle, Drizzle uses an adaptive suppression mechanism that permits the nodes to have different transmission probabilities consistent with their transmission history. Another distinctive feature of Drizzle in comparison with Trickle, is the absence of the listen-only period from Drizzle's intervals, thus, leading to faster convergence time. Furthermore, a new policy for setting the redundancy coefficient has been used to mitigate the negative effect of the short-listen problem presented when removing the listen-only period and to further boost the fairness in the network. Our extensive simulation experiments confirm the superiority of the proposed algorithm over Trickle under different operating conditions.

Wireless body area networks (WBANs) facilitate efficient and cost-effective e-health care and well-being applications. The WBAN has unique challenges and features compared to other wireless sensor networks. In addition to battery power consumption, the vulnerability and the unpredicted channel behaviour of the MAC layer make channel access a serious problem. Time Division Multiple Access (TDMA) Medium Access Control (MAC) protocols can help in achieving a reliable and energy efficient WBAN. However, traditional TDMA techniques adopted by WBAN legacy standards, such as IEEE 802.15.4 and the IEEE 802.15.6 do not consider sufficiently the channel status or the reliability of nodes. Thus, this paper presents two new TDMA based techniques to improve WBAN reliability and energy efficiency. Both techniques synchronise nodes adaptively whilst tackling their channel link status. The proposed techniques are evaluated within various traffic rates and time slot lengths. In addition, their performance is compared with the IEEE 802.15.4 and IEEE 802.15.6 MAC. The results confirm that the proposed techniques have the potential to improve WBAN reliability and energy efficiency.

We introduce in this paper two main approaches, Triangulateration (TL) and Angle-Difference-of-Arrival (ADoA) for indoor localization and mapping using single-anchor and millimeter wave (MMW) propagation characteristics. Then, we perform context inference through obstacle localization. To do so, we first include and estimate the positions of virtual anchor nodes (VANs), known as mirrors of the real anchor with respect to obstacle. Then, it is followed by estimating the obstacle position and its dimensions. We assess the performance of each technique via cumulative distribution function (CDF) for the location estimation root mean square error (RMSE). Simulations confirm that localization of the receiver relying on a single anchor and the localization of obstacles in MMW achieves a few centimeters accuracy.

Clustering mechanisms in WSNs are among the most widely recommended approaches to sustaining a network throughout its lifetime. Despite a number of research activities associated with clustering in WSNs, some aspects of clustering have not yet been adequately investigated. Unlike existing solutions, this paper proposes a new weighting based approach to electing CHs, considering the node's context; i.e. its transmission range, its degree, remaining energy and centrality from its neighbours. The novelty of our algorithm relates to the selection of a set of nodes with the highest weight as CH candidates, in order to compete for final CH. This will guarantee the selection of optimal cluster heads among sensor nodes. Moreover, this novel approach eliminates the re-clustering process for the entire network in each round, by rotating the CHs inside the created clusters in the first set up phase. The simulation experiments demonstrate that the new approach outperforms its counterparts (HEED, LCP, EEUC and DLCP) with respect to the network's lifetime.

Abstract With the resource‐constrained nature of mobile devices and the resource‐abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource intensive tasks from mobile devices to the cloud. Although these schemes address similar questions within the same domain of mobile cloud application (MCA) optimisation, evaluation is tailored to the scheme and also solely mobile focused, thus making it difficult to clearly compare with other existing counterparts. In this work, we first analyse the existing/commonly adopted evaluation technique, then with the aim to fill the above gap, we propose the behaviour‐driven full‐tier green evaluation approach, which adopts the behaviour‐driven concept for evaluating MCA performance and energy usage—ie, green metrics. To automate the evaluation process, we also present and evaluate the effectiveness of a resultant application program interface and tool driven by the behaviour‐driven full‐tier green evaluation approach. The application program interface is based on Android and has been validated with Elastic Compute Cloud instance. Experiments show that Beftigre is capable of providing a more distinctive, comparable, and reliable green test results for MCAs.

An examination of existing studies in the area of Routing Protocol for Low-Power and Lossy Networks (RPL) implementation in wireless sensor networks (WSNs) reveals a consistent approach taken of optimal node distribution. This in order to best evaluate networking metrics such as Packet Delivery Ratio (PDR), latency and energy consumption. The tests detailed in this paper differ from previous work, in that there is no concerted effort to ensure the appropriate density of the network topologies. The intention being to `strain' the limits of the transmission ranges. Using the Cooja simulator, we take the approach of utilising nodes in less-than perfect, real-world scenarios. In this way the main factor at play is the ability to retain nodes as part of the Destination Oriented Directed Acyclic Graph (DODAG) build in environments with `strained' transmission ranges. In this regard we compare Objective Function Zero (OF0) Hop-count with The Minimum Rank with Hysteresis Objective Function (MRHOF) Energy and expected transmission count (ETX) metrics. In utilising the energy metric, a novel approach, we prove that it is ineffective in this scenario. Resultantly, the ETX metric outperforms Hop-count, producing results that improve over time, adjusting to `strained' environments to include more motes in the DODAG build as time passes. In conclusion, we propose future work to develop an extension to Cooja to utilise the ETX metric with an Energy constraint. This in order to better evaluate the use of node energy levels as part of a DODAG build in `strained' WSN implementations in the future.

Constrained Low-power and Lossy networks (LLNs) represent the building block for the ever-growing Internet of Things (IoT) that deploy the Routing Protocol for Low Power and Lossy networks (RPL) as a key routing standard. RPL, along with other routing protocols, relies on Trickle algorithm as a mechanism for controlling and maintaining the routing traffic frequency. The efficiency of Trickle has been approved in terms of power consumption and scalability. However, different settings of Trickle parameters affect differently the routing behavior, energy consumption and network convergence time. In particular, a network could be only configured to have either lower convergence time with high power consumption or vice versa using Trickle. Thus, motivated by this observation, the paper presents Trickle-Plus as an extended version of the Trickle algorithm. Trickle-Plus increases the elasticity of the protocol, enabling the network configuration within guaranteed optimality for both convergence time and energy consumption. Our simulation experiments confirm the gains of the proposed Trickle-Plus under different operating conditions.

In Wireless Sensor Networks (WSNs), clustering techniques are usually used as a key effective solution to reduce energy consumption and prolong the network lifetime.Despite many works on clustering in WSNs, this issue is still, however, in its infancy as most existing solutions suffer from long and iterative clustering cycles.In an attempt to fill in this gap, we propose a new cluster-based protocol, referred to as Load-balancing Cluster Based Protocol (LCP) that introduces a new inter-cluster approach to increase network lifetime.This new protocol rotates continuously the election of the Cluster Head (CH) election in each cluster, and selects the node with the highest residual energy in each round.Extensive simulation experiments show that our proposed approach effectively balances energy consumer among all sensor nodes and increases network lifetime compared to other clustering protocols.

In Wireless Sensor Networks (WSNs), energy conservation is one of the main concerns challenging the cutting-edge standards and protocols.Most existing studies focus on the design of WSN energy efficient algorithms and standards.The standard IEEE 802.15.4 has emerged for WSNs in which the legacy operations are based on the principle that the power-operated battery is ideal and linear.However, the diffusion principle in batteries shows the nonlinear process when it releases a charge.Hence, we can prolong the network lifetime by designing optimized algorithms that reflect the battery characteristics.Within this context, this paper proposes a cross-layer algorithm to improve the performance of beacon enabled IEEE 802.15.4 network by allowing a Personal Area Network Coordinator (PANc) to tune its MAC behavior adaptively according to both the current remaining battery capacity and the network status.The performance of the new algorithm has been examined and compared against that of the legacy IEEE 802.15.4 MAC algorithm through extensive simulation experiments.The results show that the new technique reduces significantly the energy consumption and the average end-to-end delay.

Intelligent Transportation Systems (ITSs) have been instrumental in reshaping transportation towards safer roads, seamless logistics, and digital business-oriented services under the umbrella of smart city platforms. Undoubtedly, ITS applications will demand stable routing protocols that not only focus on Inter-Vehicle Communications but also on providing a fast, reliable and secure interface to the infrastructure. In this paper, we propose a novel stable infrastructure-based routing protocol for urban VANETs. It enables vehicles proactively to maintain fresh routes towards Road-Side Units (RSUs) while reactively discovering routes to nearby vehicles. It builds routes from highly stable connected intersections using a selection policy which uses a new intersection stability metric. Simulation experiments performed with accurate mobility and propagation models have confirmed the efficiency of the new protocol and its adaptability to continuously changing network status in the urban environment.

In Wireless Sensor Networks (WSNs), clustering techniques are among the effective solutions to reduce energy consumption and prolong the network lifetime. During the iterative re-clustering process, extra energy and time are consumed especially at the setup phase of every round. To address these issues, we propose a new cluster-based protocol namely, Dynamic Load-balancing Cluster Based Protocol (DLCP). Our protocol introduces a new inter-cluster approach to increase the network lifetime, rotates continuously the election of the Cluster Head (CH) in each cluster, and selects the node with the highest residual energy in each round. When the energy of CHs falls below a fixed threshold (TCH), a new clustering process is created. Extensive simulation experiments show that our proposed approach balances effectively the energy consumption among all sensor nodes and increases the network lifetime compared to other clustering protocols.

The IEEE 802.15.4 standard is designed for low-power and low-rate wireless Personal Area Networks. Although energy is a key parameter in shaping the communication and interaction methods of wireless sensor nodes, the specification of the MAC layer doesn't embed enough information for energy and mobility support. For instance, using the IEEE 802.15.4 beacon frames, a node will be able just to discover whether other peers are powered by mains or are they fully or reduced functional devices. However, sensors nodes are able to gain power by different means including: rechargeable batteries, moving sensor charger, ambient or renewable energy, or a combination of these methods. To address these issues, we propose an extension of the IEEE 802.15.4 frame formats to support mobility and embed new energy powering modes and levels. We discuss how the MAC layer extensions could be used to optimize device discovery and routing processes in IEEE 802.15.4 based wireless sensor networks.

In VANETs, there are many applications that use broadcast communication as a fundamental operational tool, in disseminating information of interest to other road users under the umbrella of both safety and entertainment applications. Recently, the probabilistic broadcasting scheme is suggested as an efficient broadcast approach. Although a number of probabilistic schemes found in the literature, they still suffer from a high level of rebroadcast redundancy, which often leads to the Broadcast Storm Problem (BSP). Thus, in this paper a new efficient probabilistic broadcast scheme is developed, to target both achieving a high delivery ratio and reducing broadcast redundancy. Using simulation experiment, we compared the performance of the proposed scheme against the recent well known probabilistic schemes and our results confirm the superiority of our scheme over existing schemes in terms of key performance metrics, namely Reachability (RE) and Saved Rebroadcast (SR).

Wireless community networks (WCNs) have emerged as a cost-effective ubiquitous broadband connectivity solution, offering a wide range of services in a given geographical area. QoS-aware multicast over WCNs is among the most challenging issues and has attracted a lot of attention in recent times. The existing multicast schemes in WCNs suffer in terms of several key performance metrics, such as, latency, jitter and throughput, particularly in large-scale networks. Consequently, these schemes cannot accommodate the desired performance levels, especially when dealing with high-bandwidth applications that require efficient gateway-based management. To fill in this gap, a new strategy for supporting QoS-aware multicast in large-scale WCNs is proposed in this paper. Specifically, a new Gateway based Multi-hop Routing algorithm (GMR) is firstly proposed to enhance the routing management capability of the network. Built upon GMR, a new Multicast Gateway Multi-hop Routing algorithm (MGMR) is devised to cope with high-bandwidth applications in WCNs. The MGMR is the first of its kind that considers both the capability of gateway-based management and the requirements of high-bandwidth applications. Extensive simulation experiments and performance results demonstrate the superiority of both GMR and MGMR when compared to other methods under various operating conditions.

Intelligent Transportation Systems have been instrumental in reshaping transportation towards safer roads, seamless logistics, and digital business-oriented services under the umbrella of smart city platforms. Broadcasting transmission is an essential operational technique that serves a broad range of applications which demand different restrictive QoS provisioning levels. Although broadcast communication has been investigated widely in highway vehicular networks, it is undoubtedly still a challenge in the urban environment due to the obstacles. In this paper, we propose the Road-Topology based Broadcast Protocol (RTBP) is proposed, a distance and contention-based forwarding scheme suitable for both urban and highway vehicular environments. RTBP aims at assigning the highest forwarding priority to a car, called a mobile repeater, having the greatest capability to send the packet in multiple directions. Realistic experimental environments are used to test the performance against well-known protocols. The results show that RTBP can reduce latency, increase reachability and save system resources.

Advances in

Wireless technologies have been facilitating potential digital inclusion opportunities and a wide range of community partnership platforms. Wireless Mesh Networking (WMN) plays a key role in the next generation wireless and mobile networks. Supporting QoS-aware communications to enable a rich portfolio of real-time and QoS sensitive applications is foreseen to be vital for the success of the next generation WMNs. Unfortunately, existing standards supporting instant group communications in WMNs are not perfectly equipped to cater to this task as these standards come with an inherent complexity and suffer from innate problems with respect to QoS provisioning. Thus, in this study, we propose a new multicast algorithm, namely, Multicast Gateway Centralized Multi-hop Routing algorithm (MGCMR) to facilitate the instant/real time communication applications. The MGCMR is the first that considers both the requirements of instant applications and the capability of gateway based management in WMNs. Our experiments confirm the superiority of our proposed MGCMR against its counterparts.

Special issue of trust, security, and privacy for emerging applications in computer and information systemsSatisfying the requirements of users on trust, security, and privacy in an efficient way is one of the key elements to almost all emerging applications in computer and information systems.However, traditional security technologies and measures may not satisfy these user requirements in open, dynamic, heterogeneous, and distributed computing environments.This necessitates adopting some emerging technologies, such as pervasive computing, peer-to-peer computing, grid computing, cloud computing, virtualization, and mobile and wireless technologies, to preserve trust, security, and privacy while users enjoy more scalable and comprehensive services.The main objectives of this special issue (SI) are to make available the state-of-the-art research results in design, development, and applications of trust, security, privacy, and related issues, such as technical, social, and cultural implications for emerging applications; to facilitate the recognition of leading contributions in the field; and to provide the best communication medium for the international research community.Following the Call for Papers, this special issue (SI) attracted 28 submissions.The final decision for the inclusion in this SI was strictly based on the outcome of the rigorous peer-review process to ensure that the papers published in the Journal are of top quality.Following the peer-review process, only seven papers have been accepted for inclusion in this SI.The authors of the selected papers presented at the CIT 2010 conference included at least 30% new and significant material for this SI.The selected seven papers span a range of important topics including efficient signcryption, finegrained reputation systems in the grid environment, fuzzy trust mechanisms for distributed networks, many-core graphics processing units, cryptography authentication protocols, trusted peer-to-peer mobile social networks, and efficient radio frequency identification authentication protocol.The contributions of these papers are outlined as follows.Efficient signcryption in the standard model [1]: An efficient signcryption scheme is proposed.Compared with Tan's scheme, it has |G| + |p| (320) bits shorter ciphertext, 2|p| À |G|(160) bits shorter private key, and |G| + |p| (1184) bits shorter public key.The scheme only needs a public key/private key pair, whereas in Tan's scheme, a user must hold two public key/private key pairs at a time if it acts as both sender and receiver at the same time.A QoS-based fine-grained reputation system in the grid environment [2]: The authors present an efficient reputation system, where economic elements are considered to make the reputation system more sensitive in the commercial grid environments.Moreover, the weighted combination of interorganizational trust, direct trust, and recommended trust makes the reputation system more robust against collusion attacks.LFTM, linguistic fuzzy trust mechanism for distributed networks [3]: An adaptation of a bio-inspired trust model to deal with linguistic fuzzy labels is proposed.The new model keeps the accuracy of the underlying bio-trust model and the level of client satisfaction while enhancing the interpretability of the model and thus making it closer to the final user.Tsunami: massively parallel homomorphic hashing on many-core GPUs [4]: CHu et al. focus on exploiting the widely available many-core graphics processing units.The authors present a massively parallel solution, named Tsunami, to achieve a significant improvement over the existing results.Dual cryptography authentication protocol and its security analysis for radio frequency identification systems [5]: Ning et al. introduce a dual cryptography authentication protocol and prove that the protocol owns tag anonymity and forward security, and it also has the capability to resist major attacks, such as replay, reader forgery, and tag forgery.

A new analytical model is proposed to investigate the delay and throughput in cognitive mesh networks. The validity of the model is demonstrated via extensive simulation experiments. The model is then used to evaluate the effects of the number of licensed channels and channel utilisation on the network performance.

The cognitive radio (CR) is an emerging technique for increasing the utilisation of communication resources by allowing the unlicensed users to employ the under-utilised spectrum. In this paper, a new analytical performance model is developed to evaluate the QoS of multi-hop CR networks. After validating its accuracy through extensive simulation experiments, the analytical model is adopted as a cost-effective tool to investigate the effects of the primary users' activities on the network performance. Moreover, the model can be used to study the strategy of employing under-utilised spectra so as to maximise the overall resource utilisation and network performance.

The increasing number of collective communication-based services with a mass interest and the parallel increasing demand for service quality are paving the way toward end-to-end QoS guarantees. Although many multicast algorithms in interconnection networks have been widely reported in the literature, most of them handle the multicast communication within limited performance metrics, i.e., either delay/latency or throughput. In contrast, this study investigates the multicast communication within a group of QoS constrains, namely latency, jitter, throughput, and additional traffic caused. In this paper, we present the Qualified Groups (QGs) as a novel path-based multicast algorithm for interconnection networks. To the best of our knowledge, the QG is the first multicast algorithm that considers the multicast latency at both the network and node levels across different traffic scenarios in interconnection networks. Our analysis shows that the proposed multicast algorithm exhibits superior performance characteristics over other well-known path-based multicast algorithms under different operating conditions. In addition, our results show that the QG can significantly improve the parallelism of the multicast communication.

Broadcast is a common operation used in Mobile Ad hoc Networks (MANETs) for many services, such as, routdiscovery and sending an information messages. The direct method to perform broadcast is simple flooding, which itcan dramatically affect the performance of MANET. Recently, a probabilistic approach to flooding has beenproposed as one of most important suggested solutions to solve the broadcast storm problem, which leads to thecollision, contention and duplicated messages. This paper proposed new probabilistic method to improve theperformance of existing on-demand routing protocol by reduced the RREQ overhead during rout discoveryoperation. The simulation results show that the combination of AODV and a suitable probabilistic rout discoverycan reduce the average end- to- end delay as well as overhead and still achieving low normalized routing load,comparing with AODV which used fixed probability and blind flooding

The increasing demand for the coverage of high-speed wireless local area networks (WLANs) is driving the installation of a very large number of access points. Wireless mesh networks (WMNs) have emerged as a promising technology in next generation networks to provide economical and scalable broadband access to wireless interconnection of multiple access points which manage individual WLANs in order to extend the coverage of conventional single WLANs. Due to various types of applications with particular purposes running at different WLANs, the traffic generated by stations in different WLANs possess a high degree of heterogeneity. To the best of our knowledge, there is hardly any analytical model reported in the current literature to handle heterogeneous network traffic in the integrated WMNs and WLANs. To fill this gap, we develop a new analytical model to investigate the Quality-of-Service (QoS) performance metrics in WMNs interconnecting multiple WLANs with heterogeneous stations. The Poisson process is employed to model the traffic of non-bursty data applications and the Markov modulated Poisson process (MMPP) is used to model the traffic of bursty multimedia applications. Extensive simulation experiments are conducted to validate the accuracy of the analytical model.

Broadcasting in MANETs is a fundamental data dissemination mechanism, with important applications, e.g., route query process in many routing protocols, address resolution and diffusing information to the whole network. Broadcasting in MANETs has traditionally been based on flooding, which overwhelm the network with large number of rebroadcast packets. Fixed counter-based flooding has been one of the earliest suggested approaches to overcome blind-flooding or the ldquobroadcast storm problemrdquo. As the topological characteristics of mobile networks varies instantly, the need of an adapted counter-based broadcast emerge. This research argues that neighbouring information could be used to better estimate the counter-based threshold value at a given node. Additionally results of extensive simulation experiments performed in order to determine the minimum and maximum number of neighbours for a given node is shown. This is done based on locally available information and without requiring any assistance of distance measurements or exact location determination devices.

Wireless mesh networks have been attracting significant attention due to its promising technology. It is becoming a major avenue for the fourth generation of wireless mobility. Communication in large-scale wireless networks can create bottlenecks for scalable implementations of computationally intensive applications. A class of crucially important communication patterns that have already received considerable attention in this regard are group communication operations, since these inevitably place a high demand on network bandwidth and have a consequent impact on algorithm execution times. Multicast communication has been among the most primitive group capabilities of any message passing networks. It is central to many important distributed applications in Science and Engineering and fundamental to the implementation of higher-level communication operations such as gossip, gather, and barrier synchronisation. Existing solutions offered for providing multicast communications in WMN have severe restriction in terms of almost all performance characteristics. Consequently, there is a need for the design and analysis of new efficient multicast communication schemes for this promising network technology. Hence, the aim of this study is to tackle the challenges posed by the continuously growing need for delivering efficient multicast communication over WMN. In particular, this study presents a new load balancing aware multicast algorithm with the aim of enhancing the QoS in the multicast communication over WMNs.

IP multicast is an efficient method for distributing multimedia content to a large number of receivers while saving the network bandwidth and reducing processing overhead on the source side. However, current IP multicast deployment figure is still far away behind the expectations of both internet service and content providers due to several deployment complexities. In particular, multicast routers cannot trigger automatically a unidirectional or a bidirectional multicast routing protocol unless they are manually configured to use a specific routing protocol for a predetermined pool of IP multicast addresses. To limit flooding multicast content, each multicast group is identified by a specific scope. With such scope, the access control rights of senders and receivers access are not properly defined. To ease multicast router dynamic auto-configuration and access control check, we propose a new integrated multicast addressing and access control method1. In our new method, we define a new expanded scope filed for IPv6, which enables a plurality of hierarchical distribution scopes to be embedded simultaneously within the multicast address. In addition, in the new scope format, the access rights are encoded for each distribution scope with respect to sending and receiving permissions.

Wireless mesh networks (WMNs) are emerging as a key technology for the next-generation wireless networks owing to its attractive properties, such as dynamic self-organisation, quick deployment, easy maintenance, low cost, and high scalability. In this paper, we develop an analytical model to investigate the end-to-end delay in a random-access two-tier (i.e., the backhaul tier and access tier) WMN. In the backhaul tier, mesh routers are uniformly distributed in a grid placement. The access tier is formed by a series of wireless local area networks. After validating the accuracy of the analytical model through simulation experiments, we use the model to tune the system parameters in order to minimise the end-to-end delay.

Multicast communication has been a major avenue for many studies in interconnection networks. However, such a communication pattern has been studied under either limited operating conditions or within the context of deterministic routing algorithms. This paper investigates the impact of routing algorithms, both deterministic and adaptive routing on the multicast communication over interconnection network. In particular, we focus on the provision of efficient multicast communication algorithms in interconnection networks. Using detailed simulation experiments, different multicast algorithms have been compared for a range of system sizes, traffic loads, and destination nodes. For the case of multicast latency, our proposed algorithms exhibit the best performance when the traffic load is high and the start-up overhead does not dominates the propagation overhead. The results also highlight the impact of adaptive routing on both latency and throughput when designing efficient multicast algorithms. Thus, these results demonstrate significant potential to be applied to current and future generation interconnection networks.

The efficient and high throughput multicast communication in interconnection networks is known as a fundamental but hard problem. In the literature, most of the related works handle multicast communication within limited operating conditions and low throughput. Recently, a new multicast scheme, known as qualified groups QG, was proposed in [2] and has shown promising performance characteristics. However, this scheme has been examined only, so far, under deterministic routing and symmetric networks. In order to examine the QG in more realistic and different scenarios, this paper makes two major contributions. Firstly, the QG is generalised here to handling multicast communication in symmetric, asymmetric and different network sizes. Secondly, unlike most existing multicast algorithms the present study proposes a new adaptive multicast algorithm that maintains good performance levels for various system sizes. Our experiments show that the QG exhibits significant improvement in both throughput and multicast latency.

Many broadcast algorithms have been proposed for the mesh over the past decade. However, most of these algorithms do not exhibit good scalability properties as the network size increases. As a consequence, most existing broadcast algorithms cannot support real-world parallel applications that require large-scale system sizes due to their high computational demands. Motivated by these observations, this study proposes a new adaptive broadcast algorithm for the mesh. The unique feature of our algorithm is that it handles broadcast operations with a fixed number of message passing steps irrespective of the network size. Our algorithm is based on the coded path routing, which has been proposed in (Al-Dubai and Ould-Khaous, 2001). Results from extensive comparative analysis reveal that the proposed algorithm exhibits superior performance characteristics over those of the well-known Recursive Doubling and Extending Dominating Node algorithms.

Most existing broadcast algorithms proposed for the mesh do not scale well with the network size. Furthermore, they have been mainly based on deterministic routing, which cannot exploit the alternative paths provided by mesh topology to reduce communication latency. Motivated by these observations, this paper introduces a new adaptive broadcast algorithm for the mesh. The unique feature of our algorithm is its ability to handle broadcast operations with only two message-passing steps irrespective of the network size. Results from extensive comparative analysis reveal that the proposed algorithm exhibits superior performance characteristics over those of the well-known recursive doubling and extending dominating node algorithms.