The rapid expansion of IoT ecosystems introduces severe challenges in scalability, security, and real-time decision-making. Traditional centralized architectures struggle with latency, privacy concerns, and excessive resource consumption, making them unsuitable for modern large-scale IoT deployments. This paper presents a novel Federated Learning-driven Large Language Model (FL-LLM) framework, designed to enhance IoT system intelligence while ensuring data privacy and computational efficiency. The framework integrates Generative IoT (GIoT) models with a Gradient Sensing Federated Strategy (GSFS), dynamically optimizing model updates based on real-time network conditions. By leveraging a hybrid edge-cloud processing architecture, our approach balances intelligence, scalability, and security in distributed IoT environments. Evaluations on the IoT-23 dataset demonstrate that our framework improves model accuracy, reduces response latency, and enhances energy efficiency, outperforming traditional FL techniques (i.e., FedAvg, FedOpt). These findings highlight the potential of integrating LLM-powered federated learning into large-scale IoT ecosystems, paving the way for more secure, scalable, and adaptive IoT management solutions.

Humanoid robots are increasingly capable of operating autonomously in human-centric environments, motivating the emergence of theInternet of Humanoids (IoH)—a paradigm that interconnects humanoid robots into collaborative, intelligent networks. Unlike traditional Internet of Things (IoT) systems that link passive or task-specific devices, IoH treats humanoids as embodied, autonomous, and safety-critical agents with tightly coupled perception, decision-making, communication, and actuation. Leveraging advances in large multimodal models, cloud–edge computing, and next-generation wireless connectivity, IoH enables real-time coordination, collective learning, and scalable fleet deployment across domains such as healthcare, manufacturing, logistics, and disaster response. This paper synthesizes the IoH landscape, proposes a layered architectural reference model to unify heterogeneous subsystems, and critically analyzes challenges in security, privacy, scalability, interoperability, and ethics. We further outline open research directions toward secure, trustworthy, and interoperable humanoid networks at scale.

The vision of sustainable 6G connectivity infrastructure, from space to ground, critically relies on green communication protocols that can efficiently operate within the constraints of wireless power transfer (WPT). Backscatter communication emerges as a cornerstone for such protocols, yet its potential is hindered by the inability to adapt to the dynamic spectrum and energy conditions inherent to WPT-powered networks. This paper presents ChannelDance, an edge-assisted adaptive hopping system for Bluetooth Low Energy (BLE) backscatter, architected specifically for green and sustainable connectivity in WPT-enabled environments. By leveraging real-time excitation channel intelligence from a low-latency edge server, ChannelDance dynamically configures the tag modulation clock, enabling robust and spectrally agile frequency hopping. This agility is paramount for maintaining reliable communication links amidst the interference and intermittent energy supply characteristic of integrated WPT systems. Our prototype demonstrates a median hopping success rate of 93% across 40 channels, a 3.5× goodput gain with channel optimization, and the ability to establish connections with commodity BLE devices under hopping conditions. ChannelDance thus establishes a foundational green communication primitive for future sustainable 6G networks, where seamless coexistence with energy transfer signals is not a feature but a fundamental requirement.

Information security is a non-negligible issue for wireless transmission. Covert communication provides high security by concealing the transmitted signals within environmental noise. However, existing solutions suffer from low transmission efficiency. Ambient backscatter, concealing data within ubiquitous ambient signals, provides a promising way to achieve high-efficiency covert communication. In this paper, we propose CoScatter, an efficient covert transmission system based on OFDM WiFi backscatter. Current studies rely on redundant modulation, resulting in low throughput. This paper is to increase throughput and shorten transmission time, thereby reducing exposure risk. This is the first work to realize single-sample level demodulation, efficiently eliminating the redundancy, increasing the throughput, and reducing the transmission time. We discover that the main obstacles are the additional phase offsets introduced by three independent wireless channels in backscatter systems. Based on this, we design a new backscatter channel equalization procedure to remove the channel influences while preserving all the covert information embedded by the tag, realizing an efficient covert transmission. Evaluation results show that Coscatter achieves a throughput exceeding 15.7 Mbps, which is around 64x of that of RapidRider, and 16x of that of Tscatter. Consequently, the exposure risk of CoScatter is reduced to 1/64 of that of RapidRider and 1/16 of that of Tscatter.

Backscatter is promising to deliver near-zero-power communications for billions of ZigBee devices. However, existing backscatter tags face two practical challenges. Firstly, the deployment depends on dedicated excitors or additional receivers, driving up costs. Secondly, the modulator couples phase modulation with multiple high-frequency shifting clocks, boosting tag power. We propose BumbleBee, a pervasive, universal, and efficient ZigBee backscatter design. It reuses uncontrolled Bluetooth and proprietary FSK devices as excitors and demodulates with a single commodity receiver, slashing deployment cost. The key innovation of BumbleBee lies in overwrite modulation, which embeds tag data through dominant phase shifts, effectively overwriting ambient excitation content. The implementation of overwrite modulation depends on a novel Multi-Phase Shift (MPS) modulator that decouples baseband modulation from frequency shifting control, cutting clock requirements. These techniques benefit not only ZigBee but also other protocols that decode via the sign of phase shifts, as shown by a BLE5 backscatter extension. Our prototype uses commodity BLE4/FSK-based SDR excitors, an off-the-shelf FPGA, and commodity ZigBee/BLE5 receivers. Experiments show that BumbleBee achieves 223.2 kbps for ZigBee backscatter and 890.7 kbps for BLE5, 10x better than FreeRider [1], while MPS reduces power by 3.5x.

Accurate internet traffic prediction is a key challenge in managing next-generation networks such as 6G. This paper presents a novel approach based on Graph Neural Networks (GNNs) for predicting internet traffic in 6G networks. The proposed model integrates Graph Attention Networks (GAT) and Transformer architectures to learn spatial and temporal dependencies in traffic data. A K-Nearest Neighbors (KNN)-based graph construction method is utilized to represent spatial relationships between network cells. The model’s performance is enhanced by leveraging a Genetic Algorithm (GA) for hyperparameter optimization. Experimental results demonstrate the effectiveness of the proposed model in achieving superior prediction accuracy, as evidenced by improvements in RMSE, and MAE compared to baseline models. This work offers a scalable solution for traffic prediction in 6G networks.

5G networks have vitally contributed to meeting quality of service (QoS) requirements for IoT and traditional communication networks. Radio Access Networks in the 5G Infrastructure comprise radio base stations that communicate over wireless radio links. Wireless radio links have been found to be prone to weather changes. This can lead to link failure and interrupt communication. In this paper, we used Graph Attention Networks, a type of Graph Neural Networks on historical weather data and radio link site characteristics to predict radio link failure on a real telecom dataset. We compare our model with commonly used machine learning models such as support vector machines (SVM), logistic regression (LR) and Long Short-term Memory (LSTM). Our model achieves an F1-score of 0.717 performing significantly better than the other models.

Edge Caching for Internet of Vehicles (IoV) is considered as one of the most active academic topics in green computing, which shifts the content caching and computation capacities to Roadside Units (RSU) to alleviate unprecedented network traffic demand. Due to the high mobility of vehicles and limited computing resources of RSU, a strategy that can cache accurate contents under limited cache capacity of RSU is necessary. To cope with these difficulties, we propose a novel proactive caching strategy named Graph Convolutional Network (GCN)-based Recommender System and Deep Reinforcement Learning Caching (GCNRDRL). Moreover, GC-NRDRL is lightweight enough to run on RSUs with modest processing power, making it suitable for cost-sensitive or rural deployments. GCNRDRL dynamically manages cache content at RSUs by the GCN-based recommender to predict vehicles’ content demands and a Proximal Policy Optimization (PPO) [1] based Deep Reinforcement Learning (DRL) agent deployed in RSUs to optimize caching decisions in the dynamic vehicular environment. The DRL agent with GCN [2] preserves the complex relationships between content items generated from the recommender system and the temporal dynamics of vehicles and executes accurate cache replacement actions. Comprehensive experimental results show that GCNRDRL can achieve a 93% higher cache hit ratio and 10% lower latency than the state-of-the-art caching strategies at best.

With the rapid digitalization of healthcare systems, there has been a substantial increase in the generation and sharing of private health data. Safeguarding patient information is essential for maintaining consumer trust and ensuring compliance with legal data protection regulations. Machine learning is critical in healthcare, supporting personalized treatment, early disease detection, predictive analytics, image interpretation, drug discovery, efficient operations, and patient monitoring. It enhances decision-making, accelerates research, reduces errors, and improves patient outcomes. In this paper, we utilize machine learning methodologies, including differential privacy and federated learning, to develop privacy-preserving models that enable healthcare stakeholders to extract insights without compromising individual privacy. Differential privacy introduces noise to data to guarantee statistical privacy, while federated learning enables collaborative model training across decentralized datasets. We explore applying these technologies to Heart Disease Data, demonstrating how they preserve privacy while delivering valuable insights and comprehensive analysis. Our results show that using a federated learning model with differential privacy achieved a test accuracy of 85%, ensuring patient data remained secure and private throughout the process.

Network survivability is a major and critical con-cern in the design and operation of Wavelength Division Multi-plexing (WDM) networks. The vulnerability of these networks to various external (e.g. natural disaster, human accidents) or internal (e.g. equipment aging, power failure) disruptions necessitates effective mechanisms for quick service restoration. To efficiently restore the affected services has been a major research problem for many years. Several solutions such as pre-computed restoration paths or using heuristic algorithms have been proposed. These approaches have limitations in terms of adaptability to unforeseen network topologies and prolonged outages. In this paper, we introduce an approach to improve resilience in WDM networks by integrating Deep Reinforcement Learning (DRL) with Graph Neural Networks (GNN). Our proposed DRL+GNN-based solution leverages the capabilities of DRL in decision-making and the inherent ability of GNNs to generalize over graphs of varying sizes and structures. By considering the current and future state of the network, our solution intelligently selects pre-computed restoration paths that is viable. The results demonstrate the superior performance of our DRL+GNN agent in comparison to existing algorithms across a wide range of failure scenarios and network loading.

The Internet of Medical Things (IoMT) plays a pivotal role in healthcare, connecting a myriad of medical devices and applications for efficient patient care. However, the rising prevalence of cyberattacks targeting healthcare institutions for patient data underscores the critical need for robust security measures. This paper introduces a novel Homogenous Collaborative Machine Learning (HCML)-based model to enhance the security of healthcare-connected devices. Utilizing a Deep Neural Network (DNN) algorithm, our model constructs a tailored security framework by integrating multiple device ’edges’, enhancing the system’s ability to thwart cyber threats. We investigate the impact of incorporating additional edges on the model’s performance, employing various metrics and assessing execution times with the IoT healthcare security dataset. Our findings reveal that, compared to conventional centralized learning methods, our proposed HCML model achieves superior generalization, incremental learning, and performance enhancement while maintaining stringent data privacy. This research contributes significantly to the IoMT field by providing a scalable and robust security solution adaptable to the evolving landscape of cyber threats.

As the automotive industry continues to evolve towards connected and autonomous vehicles, the need for robust and secure communication channels between vehicles and emergency services becomes paramount. Access control mechanisms are implemented to regulate the interaction between vehicles and emergency services, ensuring that only authorized entities can exchange critical information during emergencies. This study investigates access control techniques in vehicle-to-emergency services (V2S) communication, specifically focusing on employing the MQTT for Sensor Networks (MQTT-SN) protocol for transmitting distress signals. This work focuses on implementing strong access control in V2S situations to reduce security vulnerabilities and privacy issues.

Ensuring rapid and secure communication between cars and emergency services is of utmost importance in contemporary transportation networks to enable prompt reaction to urgent circumstances. The reliability and trustworthiness of vehicle-to-emergency services (V2S) communication, ensuring the safety and well-being of vehicle occupants and facilitating rapid emergency assistance when needed. We have proposed a security architecture for V2S communication that employs MQTT-SN protocol, RSA encryption, and SHA-256 hashing algorithm to ensure efficient message exchange, secure data transmission, and message payload integrity verification.

In many scenarios, we need to identify an object and then locate it within high precision (centimeter or millimeter level). RFIDs have played a significant role in this field. While many state-of-the-art systems have shown good performance, they require expensive hardware or extra time. Based on a previous work, GLAC, we present PoM, a 3D localization system within millimeter-level precision using only COTS RFID devices. Inspired by the same idea, PoM also draws power from mobility, and makes two key technical improvements. First, to the best of our knowledge, PoM is the first localization system that simultaneously adopts Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) method. In particular, we employ antenna motion to construct SAR and tag's mobility to construct ISAR. Second, we take actual application scenarios into consideration and apply an extra mechanism so that PoM can gain better performance in special situations. Our simulation experiments show that, in high-speed scenarios and other challenging real-world applications, PoM achieves better performance than the original GLAC system.

Existing IoT systems suffer from restricted communication distances, high deployment costs, and frequent battery replacements, making them ineffective for managing healthcare data. This paper presents Prometheus, a self-powered wristband for reporting personal health status over long distances and intelligently managing healthcare data. Prometheus backscatters ambient BLE and ZigBee signals for low-power communication while incorporating a multi-source energy harvester to convert ambient RF, light, and heat into electricity. It also features a biochemical sensor array for monitoring sweat biochemical markers. Prototyped on a flexible PCB, Prometheus demonstrates impressive efficiency, consuming only 5.8 mW for sweat sensing, with BLE and ZigBee transmission energies significantly lower than standard electrochemical workstations and commercial alternatives. Our experiments show consistent signal quality at distances up to 20 meters. In summary, Prometheus emerges as a convenient, efficient, and self-powered wristband, promising to provide ubiquitous healthcare data management in our lives.

The rapid increase in the adoption of Electric Vehicles (EVs) and the installation of Charging Stations (CSs) are key components for bidirectional energy transfer between EVs and CSs. However, the traditional techniques of energy trading have issues of trust, scalability, traceability, provenance, and authenticity among energy prosumers. To address these challenges, particularly information imbalances between energy buyers and sellers, we propose TokenGreen, a novel framework that leverages blockchain and Non Fungible Tokens (NFTs) to enable participants to have ownership of energy assets through investments in distributed energy generation, distribution, and clean energy infrastructure, leading to trust and transparency management among the participants. The proposed framework uses Ethereum Virtual Machine (EVM), ERC-721 NFT, Inter Planatery File System (IPFS), and Solidity smart contracts to develop an NFT based energy marketplace. Various smart contracts, contract events, functions, algorithms, have been designed and integrated into the energy marketplace to facilitate the minting, creation, purchase, and resale of NFT tokens, including energy trading. To assess the performance of the proposal, experiments are performed using tools such as Geth, Hyperledger Caliper, and the Ethereum SDK. The obtained results indicate that the average maximum latency for CreateToken reached 12.39s, while BuyToken and ResellToken reached 11.02s. Additionally, the average minimum latency for CreateToken, BuyToken, and ResellToken reached 10.46s, 10.03s, and 9.14s, respectively. On average, memory consumption ranged from 640 to 775 MB, while CPU usage averaged between 30% and 55% for each function. The performance analysis indicate CreateToken has low throughput, while BuyToken shows higher, and ResellToken exhibits the highest throughput due to fewer write operations. TokenGreen demonstrates superior performance compared to the existing state-of-the-art, considering the mentioned parameters.

With increased Internet users, backhaul links are experiencing unprecedented traffic burdens. Meanwhile, edge caching is emerging to reduce the burden. In order to optimize edge caching efficiency, this paper proposes an intelligent caching strategy called “spatial-temporal graph attention network-soft actor-critic” (STGAN-SAC). STGAN-SAC is fully decentralized and makes caching decisions without prior knowledge of the content popularity. In addition, it takes user mobility into account and enables cooperative caching between neighbouring base stations (BSs). Our paper is the first to apply spatial-temporal models to the caching problem, and experimental results have demonstrated their importance in solving this problem. STGAN-SAC achieves at least a 22.4% higher cache hit ratio, 2.1% lower latency and 2.5% lower backhaul link load compared to the state-of-the-art caching policy DDRQN. Moreover, STGAN-SAC achieves at least a 51.4% higher cache hit ratio, 3.7% lower latency and 2.1% lower backhaul link load than the state-of-the-art caching strategy DDGARQN.

Information-Centric Networking (ICN) has recently attracted much attention due to in-network caching and named-based routing. Caching has played a critical role in the era of proliferating network traffic. More importantly, effective caching can improve content delivery and optimize network efficiency. This paper aims to improve cache performance by maximizing cache hit ratio, minimizing content delivery latency, and path stretching in the Software-Defined Networking-ICN (SDN-ICN) context. We first propose a statistical model to generate users' preferences for different contents, which contains many essential ingredients observed in real-world scenarios. Next, we built a Graph Neural Network-Deep Reinforcement Learning (GNN-D RL) agent to generate caching decisions for each node (and at every time step) based on users' content request history. Simulation results show that the proposed caching scheme can reach a 12.3 % higher cache hit ratio, 6.5 % lower average latency time and 6.5 % lower average path stretch compared to the state-of-the-art caching strategy.

Edge computing is an emerging distributed computing paradigm that reduces computation latency and energy consumption by offloading application tasks from user devices to near-edge servers for execution. In order to utilize the computing resources of edge servers and increase efficiency, collaborative edge computing is proposed as a new type of edge computing method where multiple edge servers can work together to solve a task. By dividing a task into interrelated subtasks, each subtask can be processed locally at the device or offloaded to an edge server to minimize processing latency. In the paper, we propose a GNN-DRL-based offloading strategy that considers the edge servers' task attributes and network topology to make an optimal offloading decision for each application subtask. Experiments show that our proposed method performs better than state-of-the-art and baseline strategies in reducing the average latency for each task.

With the increasing number of users connecting to the internet and the number of devices each user owns, the internet is experiencing an unprecedented traffic demand. Providing users with a satisfying surfing experience while consuming minimal transmission costs is critical but also challenging. To cope with these difficulties, edge caching is emerging. Edge caching allows Base Stations (BSs) to cache files, then some of the users' requests can be satisfied by the edge rather than the cloud, where the latter results in higher latency and transmission costs. However, state-of-the-art edge caching strategies either assume file popularity is known in advance or lack of cooperation between neighbouring BSs. This paper proposes a multi-agent spatial-temporal graph attention neural network caching strategy, named “Double Deep Graph Attention Recurrent Q Network” (DDGARQN). The graph attention block can extract spatial dependencies among neighbouring BSs, and the temporal block can capture user preferences dynamics on each Base Station (BS) at each time instant. Comprehensive experimental results show that DDGARQN can achieve a 66% higher cache hit ratio, 6.9% lower latency and 6.5% lower link load than the state-of-the-art caching strategy at best.

Thanks to the Software-Defined Networking (SDN) paradigm, which segregates the control and data layers of traditional networks, large and scalable networks can now be dynamically configured and managed. It is a game-changing networking technology that provides increased flexibility and scalability through centralized management. The Controller Placement Problem (CPP), however, poses a crucial problem in SDN because it directly impacts the efficiency and performance of the network. The CPP attempts to determine the most ideal number of controllers for any network and their corresponding relative positioning. This is to generally minimize communication delays between switches and controllers and maintain network reliability and resilience. In this paper, we present a modified Genetic Algorithm (GA) technique to solve the CPP efficiently. Our approach makes use the GA's capabilities to obtain the best controller placement correlation based on important factors such as network delay, reliability and availability. We further optimize the process by means of certain deduced constraints to allow faster convergence.

The use of advanced communication technologies in vehicles has significantly improved the safety and security of drivers and passengers. However, the effectiveness of these technologies in sending distress signals during emergencies is often limited by various factors. This paper provides a novel design prototype for sending distress signals from vehicles using the MQTT-SN protocol over ZigBee. To the best of our knowledge, the MQTT-SN protocol is not being used in vehicle communication. Here, we proposed a possible solution of using MQTT-SN for sending a distress signal from the vehicle. We also explore the limitations of technologies (e.g., DSRC, Cellular, AEBS and Satellite) used in vehicle communication for sending distress signals and how we can overcome some of them using our design.

The Internet of Things (IoT) connects the physical world to the digital world, and wireless sensor networks (WSNs) play a significant role. There are billions of IoT products in the market. We found that security was not the primary focus of software developers. The first step of designing a secure product is to analyze and note down the security requirements. This research paper proposes a modified approach, incorporating elements from the SREP (Software Requirements Engineering Process) and SQUARE (Security Quality Requirement Engineering), to define security requirements for IoT products. The revised process is applied to determine the security requirements of a Smart Lock system that utilizes the publish/subscribe protocol MQTT-SN (Message Queuing Telemetry Transport for Sensor Networks) communication protocol architecture.

Wireless sensor networks (WSN) are majorly applied in recent times. Sensors are deployed in several areas to collect different kinds of data. Sensor nodes are low power and run out of energy quickly. When a sensor node runs out of energy, depending on the deployed environment, it can be quite difficult to replace it. Therefore, we need to prolong the lifetime of the WSN as long as possible. Genetic algorithm (GA) is a meta-heuristic algorithm that has been applied in many optimization problems. This paper presents a nested GA approach that provides the sink node location, selects cluster heads and computes the inter-cluster heads routing simultaneously significantly improving the network lifetime. We compare our algorithm with four other recent works conducted in the research space, and our method achieves average 15% better performance than the best in normal conditions and 30% better performance in more lossy environments.

Over the past years, Human Activity Recognition (HAR) has shown its great value and has been further developed with the help of deep learning. However, existing HAR systems that use deep learning methods to achieve the ideal accuracy of recognition heavily rely on massive amounts of labeled training samples. Unfortunately, it requires considerable human effort and is unrealistic for real-life applications. In this paper, we propose a novel system, which combines active learning with WiFi-based HAR. The system is capable of building a good activities recognizer in HAR with a limited amount of labeled training samples. We thus call the system ALSensing. To the best of our knowledge, ALSensing is the first system to apply active learning to WiFi-based HAR. We implement ALSensing using commercial WiFi devices and evaluated it with realistic data in several different environments. Our experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% training samples, 58.97% recognition accuracy using 15% training samples and the baseline predicted with the existing method achieves 62.19% recognition accuracy using 100% training samples. When the performance of ALSensing is similar to that of the baseline, the required labeled samples are much less than that of the baseline.

In software-defined networking (SDN), predicting latency (delay) is essential for enhancing performance, power consumption and resource utilization in meeting its significant latency requirements. In this paper, we present a graph-based formulation of Abilene Network and apply a Graph Neural Network (GNN)-based model, Spatial-Temporal Graph Convolutional Network (STGCN), to predict end-to-end packet delay on this formulation. We find this model outperforms the average baseline predictor in predicting packet delay since the STGCN framework captures both spatial and temporal dimensions of the data. We also compare STGCN with other machine learning methods: Random Forest (RF) and Neural Network (NN). In the most complex network traffic condition with high traffic intensity, varying capacities and propagation delay, STGCN is 68.5% and 78.7% better than RF and NN, respectively. This illustrates the feasibility and benefits of a GNN approach in predicting end-to-end delay in software-defined networks.

The growing number of Internet of Things (IoT) applications and connected devices has increased the chance for more cyberattacks against those applications and devices and emphasized the need to protect the IoT networks. Due to the vast network and the anonymity of the internet, it has been challenging to preserve private information and communication. Although most systems implement security devices (i.e. firewalls) to avoid this, the second line of defence, Intrusion Detection Systems (IDSs), are critical in enhancing the system's security level. This paper proposed a model that combines the two machine learning techniques, Federated and Transfer Learning, to build an IDS to secure the IoT networks with less training time and enhanced performance while preserving the user's data privacy. Deep learning algorithms, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), are used to evaluate the performance of the proposed framework on a benchmark dataset, CSE-CIC-IDS2018, and the feasibility of adopting Federated Transfer Learning (FTL) is shown in terms of performance metrics and training and fine-tuning time. The results show that the proposed technique can increase performance and decrease training time compared to the traditional machine learning techniques.

This paper proposes a new adaptive high-fidelity image compression solution to achieve a high compression ratio with the least distortion using a generative adversarial network. This work focuses on preserving the details by compressing the salient regions in the image with a high bit rate to guarantee the generation of high-quality outputs that sustain most of its characteristics. The image background is compressed with a lower bit rate. This work is tested against the Kodak, CLIC, MOTS, and UADTV datasets based on the bit-per-pixel rate where the results prove that our work achieves the highest quality with the lowest rate. To achieve a lower bit rate, the arithmetic coding algorithm is applied to the compression sequence which reduces the rate by 35%. With the achieved low bit rate, our work boosts the rate of image transmission by a factor of more than 2.

Recently, backscatter technology has attracted much interest in wireless communication due to its novel low-cost and battery-free design. Since Bluetooth Low Energy (BLE) was born to be low energy consumption, great efforts have been made in BLE-based backscatter systems, like FreeRider, RBLE. In this paper, we present BonusBlue, a BLE backscatter system that enables high-goodput communication with commercial BLE devices. BonusBlue tag generates BLE packets by modulating data on excitation signals and set up a data connection with BLE receiver using a state machine, thus achieving a high-goodput communication link. We present this state machine design and build a prototype of our tag using an FPGA, and evaluate its performance with BLE devices. Our evaluation shows that the backscatter tag can build a robust data connection link with commodity BLE device in the guidance of our state machine and transmit tag data on this link. Experimental results show that our backscatter system can achieve a goodput of up to 16.9 kbps.

The Internet of Vehicles (IoV) is a set of connected vehicles supported with sensors, communication technologies, and software connected by the Internet as an infrastructure. With the evolution of 5G technology, automation, and artificial intelligence, the IoV is expected to replace traditional transportation systems in the near future. On the other hand, with this evolution, the possibility of new cyberattacks has increased. This paper proposes a security framework in which intrusion detection secures the Intra/Inter-Vehicular communications within the IoV network. The proposed framework uses multi-task trans-fer learning to transfer knowledge gained from two different benchmark datasets. To the best of our knowledge, this is the first work that uses transfer learning to transfer the knowledge between two different benchmark datasets. The performance of the intrusion detection engine is evaluated using two different deep learning algorithms, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), in terms of accuracy, precision, recall and F1-score. In addition to achieving satisfying performance and reduced training/fine-tuning time for the target domains, our analysis illustrates the computational effectiveness of the proposed model by transferring the knowledge from the smaller to the larger dataset.

Named data networking (NDN) is an emerging network architecture that has in-network caching functionality. Optimizing caching in NDN can reduce the traffic workload and improve network efficiency. This paper represents a Graph Neural Network (GNN) based caching strategy to improve caching in NDN. Firstly, we utilize the convolutional neural network to extract time-series features for each node. Secondly, we apply GNN to make node-wise content caching probability predictions. Finally, we make cache replacement decisions according to the content caching probability ranking of each node. Experimentation shows that our caching strategy achieves around 30% higher cache hit ratio and 5 milliseconds lower latency than the state-of-the-art caching strategy.

Fog computing can play an important role in Vehicular Ad Hoc Networks (VANETs) in enhancing the quality of fog-based services. The idea of partial reliance on fog computing to support the existing infrastructure has been explored in few research papers. Fog computing holds the promise of significant potential benefits to edge users. The capabilities of fog and its position (i.e., the proximity from edge users) give fog the power to play a vital role in employing the most competent node. In other words, fog can reduce the workload that is required to do by the vehicles (e.g., propagating the event’s details, and evaluating the trust of the sender). In this paper, we deploy fog nodes to gather the trust evaluations from the vehicles, which allow fog nodes to rely on their local vehicles to do certain tasks. Also, fog nodes are used in this work to keep the records of its local vehicles to reduce the need to communicate with the cloud. Also, we proposed a scheme using Task-based Experience Reputation (TER), which reflects the vehicle’s reputation in performing certain tasks. Finally, we shed the light on the issue of two commonly used trust updating methods and, we proposed applying the concept of TER to solve this issue. The proposed model reduces the message transmission overhead and workload on the vehicles compared to experience-based trust models.

Software-Defined Networking (SDN) paradigm provides the ability to handle mobility more efficiently due to its programmability and fine granularity. However, in this emerging setting, the handover procedure still suffers delay due to exchanging and processing handover signaling messages. In this paper, we study the relevancy between an SDN controller’s load and handover delay. We show that an over-loading state can prolong handover delay, so as a countermeasure, reaching that state is mitigated by applying a load balancing mechanism. Our primary metric is the controller’s response time, as it directly affects the completion of any mobility-related procedure. We propose a load balancing management framework that deploys two concepts: network heterogeneity and context-aware vertical mobility. Our proposal is composed of three main aspects. First, we identify candidate users based on their context information. Second, we reduce the frequency of load dissemination between multiple controllers, and hence, reducing processing and communication overhead. Third, after the candidate users are determined, we optimize the decision problem on the selection among heterogeneous candidate networks. Through simulation, our framework has shown as much drop as a 28% drop in response time compared to previous proposals.

Backscatter offers a novel low-cost and low-energy solution for tags to communicate with existing wireless devices. The latest Bluetooth standards (i.e., Bluetooth 4. x and Bluetooth 5) use the Bluetooth Low Energy technology, which is the mainstream of the current Bluetooth market. In this paper, we present multi-channel backscatter with BLE, a BLE backscatter communication system that achieves compatibility with standard BLE devices. Tag information in backscatter communication can be directly obtained from backscatter packets with a single BLE receiver. We present the first multichannel backscatter tag design and build a prototype of our tag using an FPGA and evaluate it with BLE devices. Our evaluation shows that the tag can backscatter BLE signals in a channel-hopping manner, which can be decoded by standard BLE devices. Results demonstrate that our backscatter system can achieve a goodput of up to 2.8 kbps.

Access networks are continuously going through many reformations to make them better suited for various demanding applications and meet new challenging requirements. On one hand, incorporating fog and edge computing has become a necessity for alleviating network congestions and supporting numerous applications that can no longer rely on the resources of a remote cloud. On the other hand, energy-efficiency has grown to be essential for these networks to reduce both their operational costs and carbon footprint but often leads to degradation in their network performance. In this paper, we study the challenges posed by these two imperatives by examining the integration of fog computing with passive optical networks (PONs) under power-conserving frameworks. As most power-conserving frameworks in the literature are centralized-based, we also propose a decentralized-based framework and compare its performance with its centralized counterpart. We study the possible cloudlet placements and the offloading performance in each allocation paradigm to determine which paradigm is able to meet the requirements of next-generation access networks by having better network performance with less energy consumption.

The extensive growth of the Internet of Things (IoT) has impacted diverse applications, including smart homes and cities, Intelligent Transport Systems (ITS) and smart factories. IoT integrates billions of smart devices -predicted to increase from 27 billion in 2017 to 125 billion by 2030- and manages communication between them. This degree of expanded connectivity requires extensive further analysis with respect to security, and the involvement of millions of factors and users increases vulnerability in IoT environments. However, Deep Learning (DL) approaches, which originated from machine learning (ML), have been efficient in many research fields, and current studies show the effectiveness of DL for IoT security applications. In this paper, we present detailed analyses of IoT security requirements and challenges, discuss the specific role of DL and review state-of-art research work in IoT environments using DL approaches. We also performed comparative analysis of DL algorithms such as RNN, LSTM, CNN, DBN and AE. And finally, we identified research issues in the current investigations, and outlined the future directions of DL algorithms in IoT security domains.

Vehicular ad hoc networks (VANETs) have drawn a lot of attention in recent years due to their potential in improving traffic safety applications. Evaluating trust between peers in such networks is an essential component that determines whether a received report from a neighboring vehicle should be accepted or refused. For this purpose, many VANET trust management models have been proposed, differing in their architecture, trust establishment process, and flexibility. However, risk estimation has not been taken into consideration in all of these models. In this paper, we propose a risk-based trust evaluation model that overcomes the information oversampling issue in VANETs. The proposed model provides a decision-making process for vehicles receiving conflicting reports regarding an event's occurrence according to the risk estimation for each required action of both reports. The risk is estimated according to the likelihood of taking an incorrect action and its associated impact. Finally, a decision is made corresponding to the action with the lowest risk. We show that a risk-based decision-making scheme may take different actions than a purely trust-based method. Simulation results show that the risk-based trust model outperforms a purely trust-based model.

Due to speedy increase in IoT devices, bandwidth intensive applications, voice and video streaming services as well as high speed gaming services on the internet, providing QoS-based solutions has become a major issue for service providers and has merited a lot of research from academia. Software defined networking is one of the most current interesting development in the field of research. Also, application of Artificial Intelligence (AI) in SDN for traffic engineering is widely researched. In this work, we present a framework based on SDN and VNF that identify the class of traffic real time and computes the appropriate route to meet the QoS demands of the traffic using a Deep neural network. The simulation results show that our proposed solution performs very well with an accuracy of 99.95% in comparison with its counterpart in the same area.

Incorporating the Software-Defined Networking (SDN) paradigm into mobile networking has its strengths and weaknesses. Aside from the promising benefits that SDN brings to the realm of networking, there are still some challenging issues. For instance, the drastic increase in using real-time demanding applications, combined with mobility, have led to a significant increase in the control traffic and can burden managing controllers. Therefore, we need dynamic adjustments and a redistribution of the load among the controllers to maintain acceptable levels of QoS to be delivered to mobile users. In this work, we propose a load balancing framework that employs vertical handovers to enable load balancing among a set of controllers managing heterogeneous wireless networks. Our main metric is the controller response time, since it affects the completion of any procedure associated with mobile users. Through simulation, our framework has shown as much decrease as a 36% decrease in response time.

Computational offloading in Mobile Edge Computing (MEC) environment can help reduce energy consumption and response time in network, thus improve users' experience. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. In this paper, we propose to leverage the idea of Software-Defined Networking (SDN) and Service Composition (SC) for such problem. We propose a software-defined service composition model and formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user-centric approach. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. The experimental results demonstrate that our approach can obtain better performance than existing methods.

Since electronic health records transmitted in e-health systems are exposed to public networks, it is critical to protect patients' privacy health information. In our work, we provide an efficient and anonymous user authentication protocol to negotiate a session key for secure communications in public networks. Without using complex operations (e.g., scalar multiplication operations, pairing operations), our protocol can enhance performance efficiency. Performance analysis further illustrates that our protocol has a lower communication and computational overhead. Furthermore, a dynamic identity and a masked identity are provided to protect patient anonymity and patient untraceability. Besides, biometric information is hidden in a biohash function and a random number. Thus, all related to patient's privacy information is completely protected in our protocol. Security analysis shows that our protocol could resist impersonation attack, trace attack, off-line password guessing attack, replay attack.

Wireless body area networks (WBANs) that offer various medical applications have received considerable attention in recent years. Due to limited energy of sensors, duty-cycling technique is employed to prolong the network lifetime. However, it results in long delivery delay and suffers from reliability issues. In this paper, we introduce an efficient and reliable sleep scheduling scheme from the perspective of constructing m-fold dominating set (DS), where m is the number of links from a node outside DS to those in DS. The key idea is to activate partial nodes at each frame to form a DS which can guarantee the network reliability such that the other nodes can fall asleep to save energy. Technically, we formulate the sleep scheduling in a WBAN as a problem of constructing minimum weighted m-fold DS, which is proven NP-hard. We first design an H(m + δ)-approximation algorithm, namely global approximation algorithm, by globally picking the optimal node based on a polymatroid function, where H(·) is the Harmonic number and δ is the maximum node degree. Then, we propose a simplified 1 +ln (mδ)-approximation algorithm, referred to as local approximation algorithm, to reduce computational complexity and execution rounds. We further conduct extensive simulations to confirm the superiority of our proposed algorithms.

The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.

Users face location-privacy risks when accessing Location-Based Services (LBSs) in an Opportunistic Mobile Social Networks (OMSNs). In order to protect the original requester's identity and location, we propose a location privacy obfuscation protocols, called Appointment Card Protocol (ACP), utilizing social ties between users. To facilitate the obfuscation operations of queries, we introduce the concept called Appointment Card (AC). The original requesters can send their queries to the LBS directly using the information in the AC, ensuring that the original requester is not detected by the LBS. Also, a path for reply message is kept when the query is sent, to help reduce the time for replying queries. Simulation results show that our protocol preserves location privacy and has a higher query success ratio than its counterparts.

Power conservation in passive optical networks (PONs) has been an active area of research since the cyclic sleep-mode was first proposed for optical network units (ONUs). Many studies have then settled upon locking downstream and upstream transmissions for each ONU in a cyclic fixed slot, thus allowing the ONU to switch to sleep-mode for the rest of the transmission cycle. However, such fixed allocation limits the flexibility and dynamicity of the bandwidth allocation and leads to upstream underutilization. Moreover, to maximize power conservation, the cycle duration used must be long enough to make up for mode-switching overheads, which significantly degrades the network performance in terms of packet delays. In this paper, we develop a novel energy-efficient framework for Ethernet PONs (EPONs). To that end, we propose different upstream allocation schemes to improve the fixed-slot performance while maintaining energy-efficiency at acceptable levels. We also propose a more accurate arrangement for downstream-upstream locking. Moreover, we use a long-reach PON setting, where the long propagation delays impact the network performance posing further challenges to the bandwidth allocation. Numerical results show that, under heavily loaded network conditions, packet delays can be reduced by around 60% at an additional power cost of less than 5%.

Integrating optical access networks with fog computing combines the high capacity of optical fiber with closer-to-the-edge computing and storage capabilities. Such integration is believed to form a highly capable backhaul that will alleviate network congestions, serve local demands with less energy consumption, and live up to the requirements of tomorrow's access networks and application requirements. This integration however requires reexamining the bandwidth allocation in addition to reconsidering the network architecture itself, which was not designed to support direct edge-to-edge communications. Moreover, the growing demands for energy-efficient access networks also add the requirement for sleep-aware bandwidth allocation and a power-conserving framework. In this paper, we study the offloading performance in a long-reach passive optical network (LR-PON) when the underlying bandwidth allocation is either centralized or decentralized. Moreover, we investigate how offloading can be supported in each paradigm when optical network units (ONUs) go through a cyclic sleep-mode to conserve energy. We consider this paper to be one of the first to look into integrating fog with LR-PONs under a power-conserving framework and examine which allocation paradigm would be fit to carry offloaded traffic with better network performance and energy-efficiency within this new setting.

In this paper, we describe a solution that improves the existing Dynamic Resource Allocation for Software-defined Measurements (DREAM) framework. We have enabled prediction capabilities in the framework to generate better counters configurations using previous network traffic information. We have implemented four estimation techniques (EWMA-based Prediction, Polynomial Curve Fitting, KMeans++ Cluster and Pseudo-Linear Extrapolation) that have been tested with simulations running three types of measurement tasks (heavy hitters, hierarchical heavy hitters and traffic change detection) showing that the proposed techniques improve task accuracy and tasks concurrency. For the satisfaction metric, the results are better on average around 30% on constrained switches and 15% on large-capacity switches. For the number of task either dropped or rejected metrics, our estimations techniques improve the performance around 10% compared to the original DREAM implementation on constrained switches while the results are similar for large-capacity switches.

The problem of detecting malware in mobile devices is becoming increasingly important. While most of the mobile devices run on very limited resources, having anti-viruses installed on-board is not very practical, especially in IoT devices. Even if such tools exist, malware could hide or manipulate their fingerprint, making them not easy to detect. Thus, having effective countermeasures for after malware intrusion is paramount. In this work, we utilize deep learning ability to learn multiple levels of representations from raw data to classify power consumption signals obtained from smartphones. The objective is to build a framework that can intelligently tell if the smartphone has a malware or not by only monitoring its power consumption. Validation tests confirm that the proposed framework show that information contained in the measured power consumption of smartphones can in principle be used to identify malware existence and further can tell how active malware is with very high accuracy.

Users face location-privacy risks when accessing Location-Based Services (LBSs) in an Opportunistic Mobile Social Networks (OMSNs). In order to protect the original requester's identity and location, we propose a location- privacy obfuscation protocol Multi-Hop Location-Privacy Protection (MHLPP) protocol that utilizes social ties between users. To increase chances of completing obfuscation operations, users detect and make contacts with one-hop or multi-hop neighbor friends in social networks. Encrypted obfuscation queries avoid users learning important information except for the original requester who generates queries and trusted users, especially the original requester's identity and location. Simulation results show that our protocol can give a higher query success ratio compared to its existing counterpart.

The future power network will be designed to accommodate and integrate all types of distributed renewable energy resources, storage units, and flexible demand response loads in the existing conventional grid. Also it should be able to perform intelligent energy management utilizing advancement in computation and communication to cater the needs of ever growing energy demand in secure manner. This leads the transition of conventional power grid into smart grid. However, performance of the smart grid utilizing automated, intelligent, and integrated functional blocks with widely interconnected distributed energy resources is dependent on advanced communication network, sensors, computing, and information technologies. There is a need for reliable and efficient communication network and computing infrastructure for the robust, affordable, and secure supply of electric power through smart grid operation. This Special Section has accepted altogether seven research manuscripts depending on the novelties and problems that they address. The paper are briefly summarized here.

The demand and supply problems are encountered widely in almost all fields. While the users hope to get their demand satisfied always, the suppliers do not want to produce superfluous supplies. One way to tackle this problem is to have a good prediction on the future demand. Many forecasting algorithms have been proposed by using time series and extrapolation techniques. However, most of the algorithms are based on the complex theories and consume potentially large amount computing resources. In this paper, we propose a new algorithm to predict the demand by applying classical learning automata techniques. The algorithm uses more concise strategies and consumes considerably less computing resources.

A software-defined network paradigm has been recently proposed as a solution to tackle several issues in legacy networking, specifically, in cellular networks. In cellular networks such as LTE, SDN has been incorporated into different parts, especially into the Evolved Packet Core (EPC). The current SDN-based centralized EPC controller architecture for the cellular network displays weaknesses on handover efficiency and scalability. In this paper, we present a distributed approach for implementing EPC controller architecture that utilizes functional entities to handle handover procedures asynchronously in order to improve system efficiency and scalability. Moreover, we carried out a comparative analysis of current centralized EPC controller architecture and our proposed distributed EPC controller architecture. The simulation results quantify the advantages of the distributed EPC controller architecture over centralized EPC controller architecture in terms of handover latency and average throughput per user. We demonstrate that the proposed architecture offers better performance on handover and data transmission in general.

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.

Today's cloud datacenters host wide variety of applications which generate diverse mix of internal datacenter traffic. In a cloud datacenter environment 90% of the traffic flows, though they constitute only 10% of the data carried around, are short flows with sizes up to a maximum of 1MB. The rest 10% constitute long flows with sizes in the range of 1MB to 1GB. Throughput matters for long flows whereas short flows are latency sensitive. Datacenter Transmission Control Protocol (DCTCP) is a transport layer protocol that is widely deployed at datacenters nowadays. DCTCP aims to reduce the latency for short flows by keeping the queue occupancy at the datacenter switches under control while ensuring throughput requirements are met for long flows. But, DCTCP congestion control algorithm treats short flows and long flows equally. We demonstrate that treating them differently, by reducing the congestion window for short flows at a lower rate compared to long flows at the onset of congestion, we could improve the flow completion time for short flows by up to 25%, thereby reducing their latency up to 25%. We have implemented a modified version of DCTCP for cloud datacenters, based on the DCTCP patch available for Linux, which achieves better flow completion time for short flows while ensuring that throughput of long flows are not affected.

In this paper, we focus on the cyber-physical system consisting of interdependent physical-resource and computational-resource networks, e.g., smart power grids, automated traffic control system, and wireless sensor and actuator networks, where the physical-resource and computational-resource network are connected and mutually dependent. The failure in physical-resource network might cause failures in computational-resource network, and vice versa. A small failure in either of them could trigger cascade of failures within the entire system. We aim to investigate the issue of cascading failures occur in such system. We propose a typical and practical model by introducing the interdependent complex network. The interdependence between two networks is practically defined as follows: Each node in the computational-resource network has only one support link from the physical-resource network, while each node in physical-resource network is connected to multiple computational nodes. We study the effect of cascading failures using percolation theory and present detailed mathematical analysis of failure propagation in the system. We analyze the robustness of our model caused by random attacks or failures by calculating the size of functioning parts in both networks. Our mathematical analysis proves that there exists a threshold for the proportion of faulty nodes, above which the system collapses. Using extensive simulations, we determine the critical values for different system parameters. Our simulation also shows that, when the proportion of faulty nodes approaching critical value, the size of functioning parts meets a second-order transition. An important observation is that the size of physical-resource and computational-resource networks, and the ratio between their sizes do not affect the system robustness.

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.

Radio Frequency Identification technology (RFID) is widely used in many applications, such as asset monitoring, e-passport and electronic payment, and is becoming one of the most effective solutions in cyber physical system. Since the identification alone does not provide any guarantee that tag corresponds to genuine identity, authentication of tag information is needed in most RFID systems. Meanwhile, as the number of tags is rapidly growing in recent years, per-tag based methods suffer from severely low efficiency and thus give way to probabilistic batch authentication. Most previous methods, however, share a common drawback from statistical perspective: they fail to explore correlation information, i.e., they do not comprehensively utilize all the information in authentication data structures. In addition, those schemes are not scalable well when multiple tag sets need to be verified simultaneously. In this paper, we propose a fast and efficient batch authentication scheme, Wise Counting (WIC), for large-scale RFID systems. We are the first to formally introduce the general batch authentication problem with multiple tag sets and give counterfeits estimation scheme with high efficiency. By employing a novel hierarchical authentication structure, we show that WIC is able to fast and efficiently authenticate both a single tag set and multiple tag sets in an easy, intuitive way. Through detailed theoretical analysis and extensive simulations, we validate the design of WIC and demonstrate its large superiority over state-of-the art approaches.

Efficient authentication in the vehicular networks has been studied by several researchers in the past. However, there are still several issues to be addressed like high communication/ computation overhead, security problems, etc. In this paper, we propose an Efficient Authentication Protocol (EAP) for secure vehicular communication. This protocol employs smart cards based on users(vehicles)' passwords and identities to provide strong user authentication, and uses dynamic login identities to provide the anonymity of authentication. Performance analysis shows that this protocol does not only provide high efficient authentication, but also can resist attacks like offline password guessing attack, smart card loss attack, impersonation attack and so on.

Software-defined network (SDN) is the next generation of networking architecture that is dynamic, manageable, cost-effective, and adaptable, making it ideal for the high-bandwidth, dynamic nature of today’s applications. In SDN, network management is facilitated through software rather than low-level device configurations. However, the centralized control plane introduced by SDN imposes a great challenge for the network security. In this paper, we present a secure SDN structure, in which each device is managed by multiple controllers, not just a single as in a traditional manner, with the dynamic and isolated instance provided by the cloud. It can resist Byzantine attacks on controllers and the communication links between controllers and SDN switches. Furthermore, we study a controller minimization problem with security requirement and propose a cost-efficient controller assignment algorithm with a constant approximation ratio. From the experiment result, the secure SDN structure has little impact on the network latency, provide better security than general distributed controller, and the proposed algorithm performs higher efficiency than random assignment.

Developing and maintaining context-aware efficient systems in heterogeneous environments is a challenging task. In our research work, we enable context awareness in users, devices, and applications to enable context-based systems in Ambient Networks. We achieve this by proposing a context dissemination system which propagates the fast evolving context information from its sources (e.g., context sensors) to various interested information sinks (e.g., context sensitive clients). The proposed system implements a context aware overlay architecture composed of multi-level overlay networks. This overlay architecture acts as a base (middleware) for the development and maintenance of the application-layer context-specific dissemination protocol (the CSON-D protocol). The protocol's multi-level overlay structure and its intelligence, personalization, and fault tolerance features exhibit adaptive behavior with minimized casting functionality. Our conceptual model is reinforced by means of experimental evaluations. In general, the cost-based overhead for multi-level overlay formation and maintenance is minimal.

Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an “entrance” to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40--50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.

We propose a new decentralized access control scheme for secure data storage in clouds that supports anonymous authentication. In the proposed scheme, the cloud verifies the authenticity of the series without knowing the user's identity before storing data. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme prevents replay attacks and supports creation, modification, and reading data stored in the cloud. We also address user revocation. Moreover, our authentication and access control scheme is decentralized and robust, unlike other access control schemes designed for clouds which are centralized. The communication, computation, and storage overheads are comparable to centralized approaches.

Current geocasting algorithms for VANETs are being designed to enable either private or reliable communications, but not both. Existing algorithms preserve privacy by minimizing the information used for routing, and sacrifice message delivery success. On the other hand, reliable protocols often store node information that can be used to compromise a vehicle's privacy. We propose a secure, privacy-preserving geocasting protocol for VANETs that uses direction-based dissemination and ensures confidentiality. Privacy is achieved via unlinkable pseudonymous channels, and encryption and authentication with a public key technique. To reduce message duplication, we apply dynamic traffic restriction and probabilistic forwarding techniques, which depend on message rate and cumulative payload, as well as the value of the angle of spreading of the direction-based scheme. Our analysis shows that due to dynamic traffic restriction, node density does not meaningfully affect reliability, while the angle of spreading does have a significant influence.

An Unattended Wireless Sensor Network (UWSN) is a type of sensor network where a trusted sink visits each node periodically to collect the data. Due to the offline nature of this network, every node has to secure its data until the next visit of the sink which makes the network susceptible of attacks focusing on the data collected. In this work, we focus on the data authentication in the presence of a mobile adversary aiming to modify the data without being detected. We propose a data authentication scheme which uses inexpensive cryptographic primitives and few message exchanges. The proposed scheme is analyzed both mathematically and using simulations proving that the proposed scheme is better than the previous schemes in terms of security and communication overhead.

The articles in this special section focus on new applications that are supported by cloud computing.

We address pairwise and (for the first time) triple key establishment problems in wireless sensor networks (WSN). Several types of combinatorial designs have already been applied in key establishment. A BIBD(v, b, r, k, λ) (or t - (v, b, r, k, λ) design) can be mapped to a sensor network, where v represents the size of the key pool, b represents the maximum number of nodes that the network can support, and k represents the size of the key chain. Any pair (or t-subset) of keys occurs together uniquely in exactly λ nodes; λ = 2 and λ = 3 are used to establish unique pairwise or triple keys. We use several known constructions of designs with λ = 2, to predistribute keys in sensors. We also describe a new construction of a design called strong Steiner trade and use it for pairwise key establishment. To the best of our knowledge, this is the first paper on application of trades to key distribution. Our scheme is highly resilient against node capture attacks (achieved by key refreshing) and is applicable for mobile sensor networks (as key distribution is independent on the connectivity graph), while preserving low storage, computation and communication requirements. We introduce a novel concept of triple key distribution, in which three nodes share common keys, and discuss its application in secure forwarding, detecting malicious nodes and key management in clustered sensor networks. We present a polynomial-based and a combinatorial approach (using trades) for triple key distribution. We also extend our construction to simultaneously provide pairwise and triple key distribution scheme, and apply it to secure data aggregation.

Delay tolerant networks (DTN) are characterized by a lack of continuous end-to-end connections due to node mobility, constrained power sources, and limited data storage space of some or all of its nodes. To overcome the frequent disconnections, DTN nodes are required to store data packets for long periods of time until they come near other nodes. Moreover, to increase the delivery probability, they spread multiple copies of the same packet on the network so that one of them reaches the destination. Given the limited storage and energy resources of many DTN nodes, there is a tradeoff between maximizing delivery and minimizing storage and energy consumption. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the presence of a global observer that can collect information about all the nodes in the network. Next, we propose a new protocol based on social grouping among the nodes to maximize data delivery while minimizing network overhead by efficiently spreading the packet copies in the network. We compare the new protocol with the optimal results and the existing well-known routing protocols using real life simulations. Results show that the proposed protocol achieves higher delivery ratio and less average delay compared to other protocols with significant reduction in network overhead.

In spite of the dramatic growth in the number of smartphones in recent years, the challenge of limited energy capacity of these devices has not been solved satisfactorily. However, in the era of cloud computing, the limitation on energy capacity can be eased off in an efficient way by offloading heavy tasks to the cloud. It is important for smartphone and cloud computing developers to have insights into the energy cost of smartphone applications before implementing the offloading techniques. In this paper, we evaluate the energy cost of multimedia applications on smartphones that are connected to Multimedia Cloud Computing (MCC). We have conducted an extensive set of experiments to measure the energy costs to investigate whether or not smartphones save energy by using MCC services. In other words, we investigate the feasibility of MCC to provide the Energy-as-a-Service (EaaS). Specifically, we compared the energy costs for uploading and downloading a video file to and from MCC with the energy costs of encoding the same video file on a smartphone. The aforementioned comparison was performed by using HTTP and FTP Internet protocols with 3G and WiFi network interfaces. All the experiments were conducted on an Android based HTC Nexus One smartphone. Our results show that MCC provides the smartphones with many multimedia functionalities and saves smartphone energy from 30% to 70%.

In this paper, we propose a new privacy preserving authenticated access control scheme for securing data in clouds. In the proposed scheme, the cloud verifies the authenticity of the user without knowing the user's identity before storing information. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme prevents replay attacks and supports creation, modification, and reading data stored in the cloud. Moreover, our authentication and access control scheme is decentralized and robust, unlike other access control schemes designed for clouds which are centralized. The communication, computation, and storage overheads are comparable to centralized approaches.

The identification and processing of the risk sources that prevail in a sensor-monitored area is crucial to guarantee the uninterrupted and efficient operation of the surveillance system. In particular, a time-varying schematic depiction of the risk associated with each object will allow the human expert to draw meaningful conclusions about the system dynamics. In this paper, we introduce an online clustering algorithm for risk visualization in a Territorial Security environment. The clustering machinery leans upon shadowed sets due to their robustness and interpretability. The proposed algorithm is able to process data arriving in real time as it only memorizes a small subset of them. It is strong to noisy and abnormal samples and represents each cluster as a shadowed set. Experiments conducted in a simulated Critical Infrastructure Protection scenario confirm the feasibility and robustness of the proposed technique.

Power-aware routing and controlled mobility schemes are two commonly used mechanisms for improving communications in a wireless sensor network. While the former actively consider the transmission costs when selecting the next hop on the route, the latter instruct mobile relay nodes (either sensors or actuators) to pursue more promising locations so as to optimize end-to-end transmission power. Rarely, if ever, the two methodologies are exploited together for achieving relevant energy savings and prolonging network lifetime. In this paper, we introduce a hybrid routing-mobility model for the optimization of network communications. First, we find a multi-hop path between a source and its destination in an energy-efficient fashion and then we move all hop nodes in an uninterrupted, straight manner to some predefined spots with optimal energy-saving properties, fully preserving the path connectivity as they move. Such synergetic approach allowed us to: (1) seamlessly guarantee message delivery regardless of the network density (average number of neighbors per node), (2) easily incorporate any power-related optimization criterion to the routing protocol and (3) even target scenarios where both end nodes are actually disconnected from each other. Results gathered from extensive simulations argue for the introduction of the proposed hybrid framework.

In a mobile Unattended Wireless Sensor Network (UWSN), a trusted sink visits each sensor node periodically to collect data. Data has to be secured until the next visit of the sink. Securing the data from an adversary in UWSN with mobile nodes is a challenging task.We present two non-cryptographic algorithms (DS-PADV and DS-RADV) to ensure data survivability in mobile UWSN. The DS-PADV protects against proactive adversary which compromises nodes before identifying its target. DS-RADV makes the network secure against reactive adversary which compromises nodes after identifying the target. We analyze memory overheads and communication costs both mathematically and using simulations. In existing schemes, sensors remain static between visits from the sink, whereas in our scheme sensors can move between successive visits from the sink. We show that our approaches perform better than known schemes in terms of communication overheads.

Mobile robots nowadays can assist wireless sensor networks (WSNs) in many jeopardizing scenarios that unexpectedly arise during their operational lifetime. We focus on an emerging kind of cooperative networking system in which a small team of robotic agents lies at a base station. Their mission is to service an already-deployed WSN by periodically replacing all damaged sensors in the field with passive, spare ones so as to preserve the existing network coverage. This novel application scenario is here baptized as “multiple-carrier coverage repair” (MC2R) and modeled as a new generalization of the vehicle routing problem. A hybrid metaheuristic algorithm is put forward to derive nearly-optimal sensor replacement trajectories for the robotic fleet in a short running time. The composite scheme relies on a swarm of artificial fireflies in which each individual follows the exploratory principles featured by Harmony Search. Infeasible candidate solutions are gradually driven into feasibility under the influence of a weak Pareto dominance relationship. A repair heuristic is finally applied to yield a full-blown solution. To the best of our knowledge, our scheme is the first one in literature that tackles MC2R instances. Empirical results indicate that promising solutions can be achieved in a limited time span.

We propose to apply the concept of Cost-to-Progress Ratio (CPR) in greedy routing for load reduction and balancing. The load of a node is the percentage of time it is occupied by forwarding traffic or inability to forward due to interference. The resultant routing protocol, named CPR-routing, is a localized parameterless approach, optimizing the ratio of nodal load and geographic progress. Through extensive simulation, we evaluate it in comparison with an existing parameter-based localized solution, α-routing. Our simulation results indicate that CPR-routing outperforms α-routing in per node load, success rate, and average hop count.

Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) offers high data rate capabilities to mobile users, and network operators are trying to deliver a true mobile broadband experience over LTE networks. Mobile TV and Video on Demand (VoD) are expected to be the main revenue generators in the near future, and efficient video streaming over wireless is the key to achieve this goal. In this paper, we propose a Scalable Video Coding (SVC) based video streaming scheme with dynamic adaptations and a scheduling scheme based on channel quality. Cross layer signaling between Medium Access Control (MAC) and Real Time Transport (RTP) protocols is used to achieve the channel dependent adaptation in video server. Channel Quality Indicator (CQI) feedbacks from User Equipments (UE) are used for dynamic adaptations. An adaptive Guaranteed Bit Rate (GBR) selection scheme based on CQI feedbacks is also presented, and this scheme improves the coverage of the cell. Simulation results indicate improved video quality for more number of users with reduced bit rate video traffic. Approximately 13% video quality gain is observed for users at the cell edge using this adaptation scheme.

Mobile TV and Video on Demand (VoD) streaming services represents an important service which will be provided by Fourth Generation (4G) cellular networks. Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) is one of the most successful 4G technologies used by most of the 4G operators for delivering mobile broadband. In the past, cellular systems have mostly focused on unicast transmission systems. But, with the adoption of new services which are intended to be delivered to a broad range of users, multicast and broadcast systems are becoming widely popular. Enhanced Multimedia Broadcast/Multicast Service (EMBMS) is defined in 3GPP specification to support download delivery and streaming delivery to group users in LTE mobile networks. In 3GPP Release 8 specification, the EMBMS transmission is classified into single-cell transmission and MBSFN (Multicast Broadcast Single Frequency Network) transmission. H.264 was the recommended video codec for Universal Mobile Telecommunication System (UMTS) MBMS service. However, the Scalable Video Coding (SVC) extension of H.264 allows efficient temporal, spatial and quality scalabilities. In this paper, we propose a video streaming method with SVC for MBSFN networks with adaptive modulation and coding (AMC) and frequency scheduling based on distribution of users in different channel quality regions. Through simulations we demonstrate that spectrum savings in the order of 72 to 82% is achievable in different user distribution scenarios with our proposed scheme. These savings in spectrum can be used for serving other MBSFN, single cell MBMS or unicast bearers, and it can also be used for increasing the video quality of the same MBSFN bearer.

Accessing video content on mobile devices has become widespread in recent years. The wireless network has to become content aware in order to offer enhanced quality of video service through efficient utilisation of the wireless spectrum. This paper proposes a scheme for end-to-end video transmission over Long Term Evolution (LTE) network. The network elements are made aware of the critical video frames that are transmitted through the network with the help of a field in Internet Protocol (IP) header which reduces the processing time. A new mode of operation called Hybrid Mode (HM) for Radio Link Control (RLC) of evolved NodeB (eNodeB) has been defined to ensure the reliable delivery of critical video frames which in turn increase the video quality. The simulation results with the proposed scheme show a 7% increase in received video quality in comparison to the legacy Unacknowledged Mode (UM) of RLC. The proposed scheme also removes the major jitter and provides a reduction in end-to-end delay by 99% when compared with the Acknowledged Mode (AM).

We propose a novel localized Integrated Location Service and Routing (ILSR) scheme, based on the geographic routing protocol GFG, for data communications from sensors to a mobile sink in wireless sensor networks. The objective is to enable each sensor to maintain a slow-varying routing next hop to the sink rather than the precise knowledge of quick-varying sink position. In ILSR, sink updates location to neighboring sensors after or before a link breaks and whenever a link creation is observed. Location update relies on flooding, restricted within necessary area, where sensors experience (next hop) change in GFG routing to the sink. Dedicated location update message is additionally routed to selected nodes for prevention of routing failure. Considering both unpredictable and predictable (controllable) sink mobility, we present two versions. We prove that both of them guarantee delivery in a connected network modeled as unit disk graph. ILSR is the first localized protocol that has this property. We further propose to reduce message cost, without jeopardizing this property, by dynamically controlling the level of location update. A few add-on techniques are as well suggested to enhance the algorithm performance. We compare ILSR with an existing competing algorithm through simulation. It is observed that ILSR generates routes close to shortest paths at dramatically lower (90% lower) message cost.

Due to the heterogeneity involved in smart interconnected devices, cellular applications, and surrounding (GPS-aware) environments there is a need to develop a realistic approach to track mobile assets. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Our aim is to develop an efficient and improved geographical asset tracking solution and conserve valuable mobile resources by dynamically adapting the tracking scheme by means of context-aware personalized route learning techniques. We intend to perform this tracking by proactively monitoring the context information in a distributed, efficient, and scalable fashion. Context profiles, which indicate the characteristics of a route based on environmental conditions, are utilized to dynamically represent the values of the asset's properties. We designed and implemented an adaptive learning based scheme that makes an optimized judgment of data transmission. This manuscript is complemented with theoretical and practical evaluations that prove that significant costs can be saved and operational efficiency can be achieved.

We consider the fault identification problem, also known as the system-level self-diagnosis, in multiprocessor and multicomputer systems using the comparison approach. In this diagnosis model, a set of tasks is assigned to pairs of nodes and their outcomes are compared by neighboring nodes. Given that comparisons are performed by the nodes themselves, faulty nodes can incorrectly claim that fault-free nodes are faulty or that faulty ones are fault-free. The collections of all agreements and disagreements, i.e., the comparison outcomes, among the nodes are used to identify the set of permanently faulty nodes. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the diagnosis algorithm at the beginning of the diagnosis phase, i.e., partial syndromes, remains an outstanding research issue. In this paper, we introduce a novel diagnosis approach using neural networks to solve this fault identification problem using partial syndromes. Results from a thorough simulation study demonstrate the effectiveness of the neural-network-based self-diagnosis algorithm for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have then conducted extensive simulations using partial syndromes and nondiagnosable systems. Simulations showed that the neural-network-based diagnosis approach provided good results making it a viable addition or alternative to existing diagnosis algorithms.

The dynamic composition of networked appliances, or virtual devices, enables users to generate complex, strong, and specific systems. Current MANET-based composition schemes use service discovery mechanisms that depend on periodic service advertising by controlled broadcast, resulting in the unnecessary depletion of node resources. The assumption that, once generated, a virtual device is to remain static is false; the device should gracefully degrade and upgrade along with the conditions in the user's environment, particularly the network's current performance requirements. Presently, schemes for infrastructure-less virtual device composition and management do not consider this adaptation. We present a distributed constraint satisfaction problem (distCSP) for virtual device composition in MANETs that addresses these issues together with simulations that show its effectiveness and efficiency.

The efficient identification of hardware and software faults in parallel and distributed systems still remains a serious challenge in today's most prolific decentralized environments. System-level fault diagnosis is concerned with the detection of all faulty nodes in a set of interconnected units. This is accomplished by thoroughly examining the collection of outcomes of all tests carried out by the nodes under a particular test model. Such task has non-polynomial complexity and can be posed as a combinatorial optimization problem, whose optimal solution has been sought through bio-inspired methods like genetic algorithms, ant colonies and artificial immune systems. In this paper, we employ a swarm of artificial fireflies to quickly and reliably navigate across the search space of all feasible sets of faulty units under the invalidation and comparison test models. Our approach uses a binary encoding of the potential solutions (fireflies), an adaptive light absorption coefficient to accelerate the search and problem-specific knowledge to handle infeasible solutions. The empirical analysis confirms that the proposed algorithm outperforms existing techniques in terms of convergence speed and memory requirements, thus becoming a viable approach for real-time fault diagnosis in large-size systems.

In this paper, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when AR, ARX or a previously proposed Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

In this paper, we propose a new routing protocol for home networks, called dependable routing protocol (DRP) that adapts to the changes in local topology within home networks environments. DRP is based on an effective selection of paths through which a packet must pass to reach the home unit. The selection, in such dynamic home networks, is made using dependable routing, i.e, in a way that maximizes the routes quality between the network nodes and the home unit while minimizing the Failure of Service (FoS). To minimize FoS, DRP maintains requirements on both the tolerable end-to-end delay (for time-sensitive routing) and the bit error rate (for reliable routing) within the network. To achieve this, we formulate the routing dependability problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the route quality as well as the delay and bit error rate of a route in a home network scenario are derived. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the home network performance when compared to one prominent routing protocol: the Minimum Total Transmission Power Routing scheme, MTPR.

Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) offers high data rate capabilities to mobile users, and, operators are trying to deliver a true mobile broadband experience over LTE networks. Mobile TV and Video on Demand (VoD) are expected to be the main revenue generators in the near future and efficient video streaming over wireless is the key to enabling this. In this paper, we are proposing an efficient adaptation scheme for Scalable Video Coding (SVC) transport over LTE networks and investigate the benefits of this scheme for video streaming over LTE networks. Video streaming over LTE networks is analyzed using a 3GPP compliant LTE simulator using H.264 and SVC video traces. Analysis is done using real time use cases of mobile video streaming. Different parameters like throughput, packet loss ratio, delay, and jitter are compared with H.264 single layer video for unicast and multicast scenarios using different kinds of scalabilities. Results show that considerable packet loss reduction and throughput savings (18 to 30%) with acceptable video quality are achieved with proposed scheme based on SVC compared to H.264. Advantages of proposed scheme for LTE networks are evident from the simulation results.

We address pairwise and (for the first time) triple key establishment problems in wireless sensor networks (WSN). We use combinatorial designs to establish pairwise keys between nodes in a WSN. A BIBD(v; b; r; k; λ) (or t - (v; b; r; k; λ)) design can be mapped to a sensor network, where v represents the size of the key pool, b represents the maximum number of nodes that the network can support, k represents the size of the key chain. Any pair (or t-subset) of keys occurs together uniquely in exactly λ nodes. λ = 2 and λ = 3 are used to establish unique pairwise or triple keys. Our pairwise key distribution is the first one that is fully secure (none of the links among uncompromised nodes is affected) and applicable for mobile sensor networks (as key distribution is independent on the connectivity graph), while preserving low storage, computation and communication requirements. We also use combinatorial trades to establish pairwise keys. This is the first time that trades are being applied to key management. We describe a new construction of Strong Steiner Trades. We introduce a novel concept of triple key distribution, in which a common key is established between three nodes. This allows secure passive monitoring of forwarding progress in routing tasks. We present a polynomial-based approach and a combinatorial approach (using trades) for triple key distribution.

In mission-critical activities, each user is allowed to access some specific, but not all, data gathered by wireless sensor networks. Yu et al recently proposed a centralized fine grained data access control mechanism for sensor networks, which exploits a cryptographic primitive called attribute based encryption (ABE). There is only one trusted authority to distribute keys to the sensor nodes and the users. Compromising the single authority can undermine the whole network. We propose a fully distributed access control method, which has several authorities instead of one. Each sensor has a set of attributes and each user has an access structure of attributes. A message from a sensor is encrypted such that only a user with matching set of attributes can decrypt. Compared to, our schemes need simpler access structure which make secret key distribution more computation efficient, when user rights are modified. We prove that our scheme can tolerate compromising all but one distribution centers, which independently distribute their contributions to a single user key. Our scheme do not increase the computation and communication costs of the sensors, making it highly desirable for fine grained access control.

In contention-based wireless networks, packet collisions are considered the main source of data loss. Retransmission of the lost packet is done several times until an acknowledgment of successful reception (ACK) is received or the maximum number of retries is reached. The retransmission delay is drawn randomly from an interval, called the Backoff Interval. A good choice the backoff interval reduces the number of collisions and, therefore, increases the throughput and decreases the energy consumed in retransmissions. In this paper, we propose a backoff scheme based on fuzzy logic. The new scheme depends on locally measured data to estimate the backoff interval which supports the distributed nature of the vehicular networks. We present several versions of the Fuzzy Backoff scheme and compare them with other known schemes: the binary exponential backoff (BEB), and an optimal scheme which requires the knowledge of the total number of nodes in the network. We used throughput, fairness, and energy consumption as performance measures for evaluation. Results show an improvement of the fuzzy-based schemes compared to the BEB, and approaching the optimal results.

We propose a localized Delay-bounded and Energy-efficient Data Aggregation scheme (DEDA) for wireless sensor and actor networks that are modeled as undirected graphs (URG). The scheme is based on a novel concept of Desired Hop Progress (DHP) and designed for low-traffic, request-driven network scenarios, where delay is proportional to hop count [10]. It builds a local minimal spanning tree (LMST) sub-graph of the network with links weighted by transmission powers. Using edges from LMST, it constructs a shortest path (thus energy-efficient) tree rooted at actor (sink) for data aggregation. The tree is used as is if it generates acceptable delay. Otherwise, it is adjusted by replacing LMST sub-paths with URG edges. The adjustment is done locally, according to the DHP value at each node, with hop count reduction corresponding to the delay allowance per hop (ratio of current LMST delay over maximal allowed one). Through extensive simulation, we show that DEDA may save 25–75% energy per node on average and extend up to 150% network life, depending on network conditions, in comparison with the only existing competing localized solution [11].

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wire less Sensor Networks (WSNs), especially when the time synchronization cannot be well guaranteed and consumes much extra energy. This paper presents a localized and on demand scheme ADC to adaptively adjust duty cycle based on quorum systems. ADC takes advantages of TDMA and CSMA and guarantees that (1) each node can fairly access channel based on its demand; (2) channel utilization can be increased by reducing competition for channel access among neighboring nodes; (3) every node has at least one rendezvous active time slot with each of its neighboring nodes even under asynchronization. The latency bound of data aggregation is analyzed under ADC to show that ADC can bound the latency under both synchronization and asynchronization. We conduct extensive experiments in TinyOS on a real test-bed with TelosB nodes to evaluate the performance of ADC. Comparing with B-MAC, ADC substantially reduces the contention for channel access and energy consumption, and improves network throughput.

Vehicular networks ensure that the information received from any vehicle is promptly and correctly propagated to nearby vehicles, to prevent accidents. A crucial point is how to trust the information transmitted, when the neighboring vehicles are rapidly changing and moving in and out of range. Current trust management schemes for vehicular networks establish trust by voting on the decision received by several nodes, which might not be required for practical scenarios. It might just be enough to check the validity of incoming information. Due to the ephemeral nature of vehicular networks, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to vehicular ad hoc networks (VANET). We point out several limitations of trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling and gives incorrect results. We propose an algorithm to overcome this problem in VANET. This is the first paper which discusses the concept of cascading effect and oversampling effects to ad hoc networks.

We propose a new model for data storage and access in clouds. Our scheme avoids storing multiple encrypted copies of same data. In our framework for secure data storage, cloud stores encrypted data (without being able to decrypt them). The main novelty of our model is addition of key distribution centers (KDCs). We propose DACC (Distributed Access Control in Clouds) algorithm, where one or more KDCs distribute keys to data owners and users. KDC may provide access to particular fields in all records. Thus, a single key replaces separate keys from owners. Owners and users are assigned certain set of attributes. Owner encrypts the data with the attributes it has and stores them in the cloud. The users with matching set of attributes can retrieve the data from the cloud. We apply attribute-based encryption based on bilinear pairings on elliptic curves. The scheme is collusion secure; two users cannot together decode any data that none of them has individual right to access. DACC also supports revocation of users, without redistributing keys to all the users of cloud services. We show that our approach results in lower communication, computation and storage overheads, compared to existing models and schemes.

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is a very important problem with wide range of implications, including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. It is therefore more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alerts with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. After misbehavior is detected, we do not revoke all the secret credentials of misbehaving nodes, as done in most schemes. Instead, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.

Context based dynamic adaptation of autonomous services and applications require the acquisition of an array of contextual information. Context dissemination mechanisms may result in network flooding, unrestricted access to private context information, and the inability of consumers to limit or personalize received context. Adapting context information disseminated to users is linked to direct contextual requests made by users. This paper describes policy- and ontology-based, context-aware system architecture. A Context Level Agreements is proposed to help deliver contextual information to users according to their needs. The paper illustrates use of the negotiation protocol through design and implementation of context-aware system architecture capable of acquiring, modeling, reasoning and disseminating context through ontologies.

Abstract Vehicular networks are envisioned for large scale deployment, and standardization bodies, car manufacturers, and academic researchers are solving a variety of related challenges. After a brief description of intelligent transportation system (ITS) architectures and the main already‐established low‐level standards, this tutorial elaborates on four particular aspects of vehicular networks, which are (i) the potential for a large set of innovative applications, (ii) a review of the main modeling approaches used for both roads and traffic, and finally two important communication primitives, that are (iii) data disseminationviabroadcasting/geocasting, and (iv) routing in both highway and urban environments. A particular emphasis is on recent protocols that realistically consider the inherently complex nature of vehicular mobility, such as intermittent connectivity, speed variability, and the impact of intersections. Copyright © 2009 John Wiley & Sons, Ltd.

ABSTRACT In data aggregation, sensor measurements from the whole sensory field or a sub‐field are collected as a single report at an actor by using aggregate functions such as sum, average, maximum, minimum, count, deviation, and so on. We propose a localized delay‐bounded and energy‐efficient data aggregation (DEDA) protocol for request‐driven wireless sensor networks with IEEE 802.11 carrier sense multiple access with collision avoidance run at media access control layer. This protocol uses a novel two‐stage delay model, which measures end‐to‐end delay by using either hop count or degree sum along a routing path depending on traffic intensity. It models the network as a unit disk graph (UDG) and constructs a localized minimal spanning tree (LMST) sub‐graph. Using only edges from LMST, it builds a shortest‐path (thus energy‐efficient) tree rooted at the actor for data aggregation. The tree is used without modification if it generates acceptable delay, compared with a given delay bound. Otherwise, it is adjusted by replacing LMST sub‐paths with UDG edges. The adjustment is done locally on the fly, according to the desired progress value computed at each node. We further propose to integrate DEDA with a localized sensor activity scheduling algorithm and a localized connected dominating set algorithm, yielding two DEDA variants, to improve its energy efficiency and delay reliability. Through an extensive set of simulation, we evaluate the performance of DEDA with various network parameters. Our simulation results indicate that DEDA far outperforms the only existing competing protocol. Copyright © 2011 John Wiley & Sons, Ltd.

The efficient diagnosis of hardware and software faults in parallel and distributed systems remains a challenge in today's most prolific decentralized environments. System-level fault diagnosis is concerned with the identification of all faulty components among a set of hundreds (or even thousands) of interconnected units, usually by thoroughly examining a collection of test outcomes carried out by the nodes under a specific test model. This task has non-polynomial complexity and can be posed as a combinatorial optimization problem. Here, we apply a binary version of the Particle Swarm Optimization meta-heuristic approach to solve the system-level fault diagnosis problem (BPSO-FD) under the invalidation and comparison diagnosis models. Our method is computationally simpler than those already published in literature and, according to our empirical results, BPSO-FD quickly and reliably identifies the true ensemble of faulty units and scales well for large parallel and distributed systems.

We introduce a novel combinatorial optimization problem: the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD), characterized by the fact that the demand of any delivery customer can be met by a relatively large number of pickup customers. While all delivery spots are to be visited, only profitable pickup locations will be included in the tour so as to minimize its cost. The motivation for 1-TSP-SELPD stems from the carrier-based coverage repair problem in wireless sensor and robot networks, wherein a mobile robot replaces damaged sensors with spare ones. The ant colony optimization (ACO) meta-heuristic elegantly solves this problem within reasonable time and space constraints. Six ACO heuristic functions are put forward and a recently proposed exploration strategy is exploited to accelerate convergence in dense networks. Results gathered from extensive simulations confirm that our ACO-based model outperforms existing competitive approaches.

Presently, smart interconnected heterogeneous devices with built-in GPS, Wi-Fi, and Bluetooth capabilities are more affordable, resulting in numerous novel mobile applications. To adapt applications based on the environment they are executed in, we perform context management by proactively monitoring the (network- and location- based) context information available in such devices and applications. As this requires continuous monitoring, the common fixed data flow based technique for forwarding contexts from multiple context sensors is not energy efficient. In this paper, we propose the design and implementation of an adaptive context monitoring scheme. Alongside context awareness, we employ overlay network, agent, and policy theories. We utilize learning and personalization characteristics to make an optimized judgment of context information in an efficient and scalable fashion. This paper is complemented with protocol evaluations to validate scalability claims based on real logs.

Networks in which nodes are intermittently connected, and have limited storage space and power, are termed Delay Tolerant Networks (DTN). To overcome these conditions, DTN routing protocols require nodes to store data packets for long periods of time until they contact with each other. In addition, they spread multiple copies of the same packet in the network to increase the probability of one of them reaching the destination. Long-term storage and multiple transmissions require large buffer space and non-restricted power availability which is hard to exist in DTN. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the knowledge of present and future nodes contact and buffer space. After that, we analyze the previously developed heuristic protocols, and we propose a new protocol based on social relations between the nodes to avoid redundant copying of packets. Simulation results show that the proposed protocol significantly reduces energy consumption and provides better delivery ratio compared to other protocols.

In this paper, we propose a new intersection-based geographical routing protocol, called delay tolerant routing protocol (DTRP) that adapts to the changes in the local topology within city environments. DTRP is based on an effective selection of road intersections through which a packet must pass to reach the gateway to the Internet. The selection, in such delay tolerant VANETs, is made in a way that maximizes the connectivity probability of the route between mobile nodes and the gateway while maintaining a threshold for the end-to-end delay and the hop count within the network. To achieve this, we formulate the QoS routing problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the connectivity probability as well as the delay and hop count of a route in a two-way road scenario are derived. Then, we propose a genetic algorithm to solve the optimization problem. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the VANETs performance when compared with several prominent routing protocols, such as GPSR, GPCR and OLSR.

We propose a novel localized carrier-based sensor placement algorithm, named Back-Tracking Deployment (BTD). Mobile robots (carriers) carry static sensors and drop them at visited empty vertices of a virtual square, triangular or hexagonal grid in a bounded 2D environment. A single robot will move forward along the virtual grid in open directions with respect to a pre-defined order of preference until a dead end is reached. Then it back tracks to the nearest sensor adjacent to an empty vertex on its backward path. The robot resumes regular forward moving and sensor dropping from there. To save movement steps, the back tracking is performed along a locally identified shortcut. We extend the algorithm to support multiple robots, which move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise it will take over the back-track path of another robot, by consulting with neighboring sensors. We prove that BTD terminates in finite time and produces full coverage when no sensor failures occur. We also describe an approach to handle sensor faults. Through extensive simulation we show that BTD far outperforms the only competing algorithm LRV in robot moves and robot messages.

This paper proposes a new solution to the problem of self-deploying a network of wireless mobile robots with simultaneous consideration to several criteria, that are, the fault-tolerance (biconnectivity) of the resulting network, its coverage, its diameter, and the quantity of movement required to complete the deployment. These criteria have already been addressed individually in previous works, but we propose here an elegant solution to address all of them at once. Our approach is based on combining two complementary sets of virtual forces: spring forces, whose properties are well known to provide optimal coverage at reasonable movement cost, and angular forces, a new type of force proposed here whose effect is to rotate two angularly consecutive neighbors toward one another when the corresponding angle is larger than 60° (even if these nodes are not direct neighbors). Angular forces have the global effect of biconnecting the network and reducing its diameter, while not affecting the benefits obtained by spring forces on coverage. In this paper we give a detailed description of the combination of both types of forces. We also provide an implementation relying only on position exchanges within two hops. Simulations results are finally presented to evaluate our solution with respect to the four considered criteria (coverage, biconnectivity, quantity of movements, and diameter), and compare it with prior approaches.

In wireless sensor networks (WSN), stochastic node dropping and unpredictable node failure greatly impair coverage, creating sensing holes, while locally redundant sensors exist. If sensors are all equipped with locomotion, they will be able to relocate themselves to improve coverage. But this approach increases the complexity of hardware design for sensors as well as deployment budget. In this paper, we consider a small group of mobile robots to serve WSN. We propose an algorithm, named Randomized Robot-assisted Relocation of Static Sensors (R3S2), for coverage repair and a grid-based variant, called G-R3S2. By these algorithms, mobile robots move within the network to collect redundant sensors and deliver them to reported sensing hole positions. In R3S2, robots move completely at random and relocate encountered redundant sensors. In G-R3S2, the robots random movement is restricted on a virtual grid, and the robots continually move to the next least recently visited grid point so as to increase the chance of discovering redundant sensors and sensing holes. Through extensive simulation, we show their effectiveness and practicality and evaluate their performance. The simulation results indicate in particular that G-R3S2 outperforms R3S2 across all measured metrics.

In sparse mobile networks, nodes are connected at discrete periods of time. This disconnection may last for long periods in suburban and rural areas. In addition, mobile nodes are energy and buffer sensitive, such as in mobile sensor networks. The limited power and storage resources, combined with the intermittent connection have created a challenging environment for inter-node networking. This type of networks is often referred to as Delay Tolerant networks (DTN). Routing protocols developed for DTN focused on minimizing the end-to-end delay as a means of maximizing number of delivered packets. Therefore, they tend to spread many copies of the same packet into the network, assuming the availability of sufficient storage space and power. A key factor to help maintain a clean environment, is the reduction of energy consumption which can be achieved by decreasing number of transmissions in the network. In this paper, we formulate a mathematical model for optimal routing in DTN to minimize number of transmissions. In addition, we study and analyze the DTN heuristic routing protocols. After that, we propose an eco-friendly routing protocol, EFR-DTN, that efficiently uses simple information provided from the network to deliver packets with higher delivery ratio and minimum energy consumption than the other protocols. Simulation results show the outperformance of the proposed protocol under different buffer capacities, traffic loads, packet TTL values, and number of nodes in the network.

The proliferation of networked appliances or dedicated function consumer devices with embedded processors and network connection capabilities is driving the virtual device concept, whereby such appliances can be controlled, monitored, managed, and extended beyond what they were initially designed to do. The dynamic and ad hoc nature of the discovery and composition of such devices and the services they provide inevitably leads to capability differences, whereby the input of a service B is not compatible with the output of a service A; A and B needing to be composed. Moreover, current schemes for service composition in MANETs do not consider capability differences. We present a distributed constraint satisfaction problem for capability reconciliation in MANETs, and through simulation show its effectiveness and efficiency.

We describe a novel Distributed Storage protocol in Disruption (Delay) Tolerant Networks (DTN). Since DTNs can not guarantee the connectivity of the network all the time, distributed data storage and look up has to be performed in a store-and-forward way. In this work, we define local distributed location regions which are called cells to facilitate the data storage and look up process. Nodes in a cell have high probability of moving within their cells. Our protocol resorts to storing data items in cells which have hierarchical structure to reduce routing information storage at nodes. Multiple copies of a data item may be stored at nodes to counter the adverse impact of the nature of DTNs. The cells are relatively stable regions and as a result, data exchange overheads among nodes are reduced. Through experimentation, we show that the proposed distributed storage protocol achieves higher successful data storage ratios with lower delays and limited data item exchange requirements than other protocols in the literature.

Artificial immune systems (AIS) have been widely applied to many fields such as data analysis, multimodal function optimization, error detection, etc. In this paper, we show how AIS can be used for system-level fault diagnosis. Experimental results from a thorough simulation study and theoretical analysis demonstrate the effectiveness of the AIS-based diagnosis approach for different small and large systems in both the worst and average cases, making it a viable addition to the existing diagnosis algorithms.

This paper describes a new line segment detection and extraction algorithm for computer vision, image segmentation, and shape recognition applications. This is an important pre processing step in detecting, recognizing and classifying military hardware in images. This algorithm uses a compilation of different image processing steps such as normalization, Gaussian smooth, thresholding, and Laplace edge detection to extract edge contours from colour input images. Contours of each connected component are divided into short segments, which are classified by their orientation into nine discrete categories. Straight lines are recognized as the minimal number of such consecutive short segments with the same direction. This solution gives us a surprisingly more accurate, faster and simpler answer with fewer parameters than the widely used Hough Transform algorithm for detecting lines segments among any orientation and location inside images. Its easy implementation, simplicity, speed, the ability to divide an edge into straight line segments using the actual morphology of objects, inclusion of endpoint information, and the use of the OpenCV library are key features and advantages of this solution procedure. The algorithm was tested on several simple shape images as well as real pictures giving more accuracy than the actual procedures based in Hough Transform. This line detection algorithm is robust to image transformations such as rotation, scaling and translation, and to the selection of parameter values.

In contention-based wireless networks, collisions between data packets can be reduced by introducing a random delay before each transmission. Backoff schemes are those that provide the backoff interval from which the random delay is drawn. In this paper, we propose a new scheme which calculates the backoff interval dynamically according to the network conditions. The network conditions are measured locally by each node, which supports the distributed nature of the vehicular networks. The measures are used by a fuzzy inference system to calculate the backoff interval. We compare the proposed scheme with other known schemes: the binary exponential backoff (BEB), the sensing backoff algorithm (SBA) and an optimal scheme which requires the knowledge of the number of nodes in the network (Genie). The evaluation measures are the throughput and fairness. Results show an improvement of the fuzzy-based schemes compared to the BEB and SBA, especially for large number of nodes in the network.

Dissemination of data related to dynamic and timely traffic / road condition, and any unexpected events can significantly improve the quality of driving with respect to time, distance, and safety. Although extending region of data dissemination in the network constructed among vehicles informs more vehicles on road network, it increases communication cost and imposes delay on the network. The imposed delay has an undesirable impact on vehicle safety applications which require very low message latencies. This research investigates how communication cost and additional travel cost are affected while the region of dissemination is increased. Moreover, this research aims at studying the effective region for data dissemination in an ad-hoc network among vehicles. The simulation results indicate that extending the region of data dissemination increases the communication cost and decreases the additional travel cost. Also, there is an optimal region for data dissemination so that effective propagation of data to a certain area reduces the additional travel cost.

The dynamic composition of systems of networked appliances, or virtual devices, enables users to generate complex strong specific systems. Current prominent MANET-based composition schemes make use of service discovery mechanisms dependent on periodic service advertising by controlled broadcast, resulting in the unnecessary depletion of node resources (i.e. battery and processing power). Moreover, current schemes do not allow for the protection of private or security sensitive information in the process of composition. We present a distributed constraint satisfaction problem for virtual device composition in MANETs addressing these issues, and through simulation show its high efficiency and QoS-awareness.

The recent advancements in technologies such as cellular networks, wireless sensor networks (WSN), radio-frequency identification (RFID), and global positioning system (GPS) have lead us to develop a realistic approach to tracking mobile assets. Tracking and managing the dynamic location of mobile assets is critical for many organizations with mobile resources. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Thus our main research goal is to develop efficient and improved asset tracking solutions and consume valuable mobile resources. In addition, we also adapt their routes by means of a novel and efficient geographical tracking approach that performs route adaptation. We focus on tracking GPS-enabled mobile devices mounted on the asset by understanding the behavior of a mobile device for reporting GPS data in various demographics. This paper is complemented with result evaluations based on a simulation environment with real logs.

The dynamic composition of systems of networked appliances, or virtual devices, in MANETs, enables users to generate, on-the-fly, complex strong specific systems. Current work in the development of service composition architectures in MANETs has yet to address QoS metrics for enhanced composition from a virtual device perspective. In this paper, we present an extension to a prominent dynamic broker-based distributed service composition protocol, embedding in a distributed manner, a QoS model providing compositions that form the best possible virtual device at the time of need. Simulation results show that our protocol extension provides a high increase in QoS at a low cost in terms of increased amounts of messages and composition time.

One of the main challenging problems in distributed systems is the comparison-based self-diagnosis which aims at identifying the set faulty of nodes (or units) based on the matching and mismatching among the system's nodes. The comparison-based self-diagnosis approach assigns tasks to the nodes that need to be diagnosed. The outcomes from each pair of units performing the same task are compared, and based on such comparison their fault status is identified. In this work, we consider symmetric t-comparison-based diagnosable systems in which at most t nodes can fail permanently at the same time. We introduce a novel backpropagation neural network-based (BPNN) approach to implement a new fault identification algorithm. The BPNN diagnosis algorithm uses the comparison approach to collect the agreements and disagreements among the nodes, and then uses these comparison outcomes to identify which nodes are faulty and which ones are fault-free. The BPNN-based diagnosis algorithm needs first to undergo an extensive training phase using various faulty situations. Results from a thorough simulation study demonstrate the effectiveness of the BPNN-based self diagnosis algorithm for randomly generated diagnosable systems, making it a viable addition to existing diagnosis algorithms. The novel approach is shown to scale very well for large systems.

We describe a Fast Delivery Guaranteed Face Routing (FDGF) in ad hoc wireless networks. Since it is expensive for wireless nodes to get the whole network topology information, geometric routing decisions should be made locally by nodes using location information of neighboring nodes which are at most k hops away. In this paper, we first define k-local algorithm and obtain two local geometric graphs, i.e., k-Local Delauany Triangulation Graph (k-LDTG) and k-Local Minimum Weight Spanning Tree (k-LMWST), which are more efficient than existing definitions. We present problems with existing face routing protocols and propose FDGF to counter possible long delivery delay with high probability. The performance of face routing differs on different planar graphs. We compare face routing characteristics of four different underlying routing graphs which include k-LDTG, Gabriel Graph (GG), Relative Neighbor Graph (RNG) and k-LMWST. Due to different attributes of these graphs, the message delivery delay, routing hop and minimum energy consumption differ greatly. Through experimentation in the NS-2 simulator, we have shown that the proposed face routing protocol achieves 100% delivery ratio on Unit Disk Graph (UDG) when source to destination connection path exists. Face routing on k-LDTG is fast with less relay hops and face routing on k-LMWST is energy efficient. RNG achieves desirable minimum energy consumption attribute, better than all the others when the propagation model is free space and close to k-LMWST when the propagation model is Two Ray Ground.

A vehicular ad hoc network (VANET) is a network of intelligent vehicles that communicate with other vehicles in the network. The main objective of VANET is to provide comfort and safety for passengers. In addition, various transaction based services, such as information about gas prices, restaurant menu, and discount sale, can be provided to drivers. In order to make these services available, there is a need for an efficient service discovery protocol. In this paper, we propose a new protocol called Address Based Service Resolution Protocol (ABSRP) to discover services in vehicular ad-hoc networks. As most of the transaction based services are provided by roadside units, we exploit their presence to perform service discovery. We utilize the unique address assigned to each service provider in order to discover a route to that service provider. Our technique proactively distributes the service provider's address along with its servicing capabilities to other roadside units within a particular area. Each roadside unit will then utilize this information to service the request placed by the vehicles. If the service provider (destination node) is not reachable over the vehicular network, we propose to use a backbone network to service requests. Our approach is independent of the network layer routing protocol. We have evaluated the performance of our approach by using the Qualnet simulation tool.

Artificial immune systems (AIS) have been widely used in many fields such as data analysis, multimodal function optimization, error detection, etc. In this paper, we introduce a novel artificial immune systems approach for diagnosing faults in a network of processors under the PMC model. We investigate how AIS can be used for system-level fault diagnosis. Our theoretical analysis and experimental results demonstrate the effectiveness of the AIS-based diagnosis approach for small and large class of networks in both the worst and average cases, making it a viable alternative to traditional fault diagnosis approaches.

Recently proposed beaconless georouting algorithms are fully reactive, with nodes forwarding packets without prior knowledge of their neighbors. However, existing approaches for recovery from local minima can either not guarantee delivery or they require the exchange of complete neighborhood information. We describe two general methods that enable completely reactive face routing with guaranteed delivery. The Beaconless Forwarder Planarization (BFP) scheme finds correct edges of a local planar subgraph at the forwarder node without hearing from all neighbors. Face routing then continues properly. Angular Relaying determines directly the next hop of a face traversal. Both schemes are based on the Select and Protest principle. Neighbors respond according to a delay function, if they do not violate the condition for a planar subgraph construction. Protest messages are used to remove falsely selected neighbors that are not in the planar subgraph. We show that a correct beaconless planar subgraph construction is not possible without protests. We also show the impact of the chosen planar subgraph construction on the message complexity. This leads to the definition of the Circlunar Neighborhood Graph (CNG), a new proximity graph, that enables BFP with a bounded number of messages in the worst case, which is not possible when using the Gabriel graph (GG). The CNG is sparser than the GG, but this does not lead to a performance degradation. Simulation results show similar message complexities in the average case when using CNG and GG. Angular Relaying uses a delay function that is based on the angular distance to the previous hop. Simulation results show that in comparison to BFP more protests are used, but overall message complexity can be further reduced.

Ad hoc, actuator and sensor wireless networks normally have critical connectivity properties before becoming fault intolerant. Existing algorithms for testing k-connectivity are centralized. In this article, we introduce localized algorithms for testing A-connectivity. In localized protocols, each node makes its own decision based on the information available in its local neighborhood. In the first proposed local neighbor detection (LND) algorithm, each node verifies whether or not itself and each of its p-hop neighbors have at least k neighbors. In the second local critical node detection (LCND) protocol, it also tests if the subgraph of its p-hop neighbours of a given node is k-connected. The third local subgraph connectivity detection (LSCD) protocol is based on communications between neighboring nodes to exchange the local decisions starting from k=l. All nodes declare themselves locally 1-connected. For k=2,3,..., iteratively, local decisions are propagated to p-hop neighbors. If node A is (k-1,)-connected, all its p-hop neighbors are (k-1)-connected, and the graph consisting of p-hop neighbors of A (excluding A) is (k-1,)-connected, then node A declares its neighborhood as k-connected. The experiments are carried with two ways of uniform generation of connected unit disk graphs. They show low percentage of false 'alarms', ability to locate critical areas in k-disconnected networks, and increased accuracy with increased local knowledge.

Although much is known about the nature of testing structures for t-diagnosable systems, the problem of efficiently identifying the set of faulty units of a system in which the fault situation is known to be diagnosable remains an outstanding research issue. In this paper, we propose and evaluate an approach, based on swarm intelligence, to identify the set of faulty units in diagnosable systems. We consider t-diagnosable systems under the PMC model, where each node is capable of testing a particular subset of the other nodes in the system. We show that the ant-colony- based fault diagnosis algorithm is efficient, in that, it is able to diagnose a faulty situation in very short periods of time even if the number of faults is around the bound t, and with very few number of ants. The simulation results show that the new adaptive fault identification approach constitutes an addition to existing diagnosis algorithms.

We assume that sensors are aware of the positions of neighbors within a specific knowledge range, which is smaller than their maximum transmission range. We propose the GRoVar protocol (Geographic Routing with Variable transmission range) that extends the well-known GFG protocol [2], a combination of greedy forwarding and recovery, by applying variable transmission range and beaconless routing techniques. In our protocol, each node locally selects the best forwarding neighbor within its knowledge range, using power or other metric. If no neighbor is closer to the destination, the current node may incrementally increase its transmission range to find suitable candidates for the next hop, with the help of request messages. Face routing is applied when no forwarding neighbor is found after sending requests with the maximum transmission range. It also applies range increases until recovery is possible. We investigated different possibilities: a linear increase, doubling the range in each iteration or directly jumping to the maximum range possible. The comparison of energy usage for data transfer from source to sink shows that a significant saving in energy consumption can be achieved using the proposed method.

Peer-to-peer systems can share the computing resources and services by directly communicating within a widely distributed network. It is important that these systems can efficiently locate, in as few hops as possible, the node storing the desired data in a large system. Thus, it is worth consuming some extra storage to obtain better routing performance. In this paper, we propose redundant strategies to improve the routing performance and data availability on Chord and De Bruijn topologies. Hybrid-Chord combines multiple chord rings and successors, and Redundant D2B maintains successors, to improve the routing performance. The proposed systems can reduce the number of lookup hops significantly (by as much as 50%) compared to the original ones, and have better fault tolerance capabilities, with a small storage overhead.

The problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model (GCM) is considered. GCM assumes that a set of jobs is assigned to pairs of units and that the outcomes are compared by the units themselves (self-diagnosis). Based on the set of comparison outcomes (agreements and disagreements among the units), the set of up to t faulty nodes is identified (t-diagnosable systems). This paper proposes an artificial-immune-based algorithm to solve the fault identification problem. The immune diagnosis algorithm correctly identifies the set of faulty units, and it has been evaluated using randomly generated t-diagnosable systems. Simulation results indicate that the proposed approach is a viable alternative to solve the GCM-based diagnosis problem.

Broadcasting is a data communication task in which one processor sends the same message to all other processors. Routing is a task where a source processor sends a message to a destination processor. A faulty node is in an error state and cannot participate in the activities or the communication in a given network. In this paper, we consider the family of mesh networks, which include the mesh connected computer (MCC), k-dimensional mesh, torus, and k-ary n-cube. Our goal is to design routing and broadcasting algorithms which will use local knowledge of faults, no additional resources, will work for an arbitrary number and structure of faults, will guarantee delivery to all nodes connected to the source, and will remain optimal in a fault free mesh. We did not find any solution in literature to satisfy these desirable properties. Our routing and broadcasting schemes for MCCs and tori, and our broadcasting algorithm for the all-port model on any faulty mesh network satisfy all of these properties. For routing and broadcasting in a one-port model in higher dimensions, a condition on fault structure needs to be met. We propose a new broadcasting algorithm which guarantees delivery to all processors connected to the source in the all-port model of faulty meshes. We then describe a routing algorithm that guarantees delivery in faulty MCCs and tori, the connectivity of the source and destination being the only obvious requirement. The algorithm can be extended to faulty k-D meshes and k-ary n-cubes, where the delivery will be guaranteed if healthy nodes in every 2-D submesh (sub-tori) remain connected. We then describe broadcasting algorithms for the one-port model, which again guarantee delivery to all connected processors in two-dimensional cases, and guarantee delivery in k-dimensional cases if healthy processors in every 2-D submesh (sub-tori) remain connected.

Software testing is expensive and time consuming. Given the different input parameters with multiple possible values for each parameter, performing exhaustive testing which tests all possible combinations is practically impossible. Generating an optimal test set which will effectively test the software system is therefore desired. Pair-wise testing is known for its effectiveness in different types of software testing. Pair-wise testing requires that for a given numbers of input parameters to the system, each possible combination of values for any pair of parameters be covered by at least one test case. Pair-wise testing is known for its effectiveness in different types of software testing. The problem of generating a minimum size test set for pair-wise testing is NP-complete. This paper presents new techniques for reducing the number of test cases for pair-wise testing. The paper shows an algorithm to generate test cases for 2-valued parameters and how orthogonal arrays and ordered designs may be used for deriving test cases for parameters with more than two values. Moreover, using mixed-level or asymmetric orthogonal array as tool, we present test set generation strategy for parameters with different number of values. A comparison of empirical results with previous test generation strategies "AETG" and "IPO" shows that the number of test cases generated with the proposed methodology is never higher and in some cases significantly lower than using AETG or IPO

Existing routing and broadcasting protocols for ad hoc networks assume an ideal physical layer model. We apply the log-normal shadow fading model to represent a realistic physical layer and use the probability p(x) for receiving a packet successfully as a function of distance x between two nodes. We define the transmission radius R as the distance at which p(R)=0.5. We propose a medium access control layer protocol, where receiver node acknowledges packet to sender node u times, where u*p(x)/spl ap/1. We derived an approximation for p(x) to reduce computation time. It can be used as the weight in the optimal shortest hop count routing scheme. We then study the optimal packet forwarding distance to minimize the hop count, and show that it is approximately 0.73R (for power attenuation degree 2). A hop count optimal, greedy, localized routing algorithm [referred as ideal hop count routing (IHCR)] for ad hoc wireless networks is then presented. We present another algorithm called expected progress routing with acknowledgment (referred as aEPR) for ad hoc wireless networks. Two variants of aEPR algorithm, namely, aEPR-1 and aEPR-u are also presented. Next, we propose projection progress scheme, and its two variants, 1-Projection and u-Projection. Iterative versions of aEPR and projection progress attempt to improve their performance. We then propose tR-greedy routing scheme, where packet is forwarded to neighbor closest to destination, among neighbors that are within distance tR. All described schemes are implemented, and their performances are evaluated and compared.

Existing routing and broadcasting protocols for ad hoc networks assume an ideal physical layer model. We apply the log normal shadow fading model to represent a realistic physical layer and propose a MAC layer protocol to produce the optimal shortest hop count routing scheme. We then study the optimal packet forwarding distance to minimize the hop count. A hop count optimal, greedy, localized routing algorithm (referred as ideal hop count routing (IHCR)) is then presented. We also present another algorithm called expected progress routing with acknowledgements (referred as aEPR) and then propose a projection progress scheme. In conclusion, we propose a tR-greedy routing scheme, where the packet is forwarded to the neighbor closest to the destination, among neighbors that are within distance tR. All described schemes are implemented, and their performances are evaluated and compared.

Test generation for sequential circuits has been a difficult task. This is due to the large search space to be considered in test pattern generation. In this paper the detection of permanent faults in sequential circuits by random testing is analyzed utilizing the circuit partitioning approach together with a continuous parameter Markov model. Given a large sequential circuit, it is partitioned into several smaller partitions using either series or parallel decomposition. For each partition with certain stuck faults specified, the original state table and its error version are derived from an analysis of the partition under fault-free and faulty conditions, respectively. A random testing strategy that uses a three-state Markov model is used for detecting permanent stuck faults. Experimentation on various sequential circuits has shown that a significant saving in testing or test generation time can be achieved if we partition the circuit and then test each of its components as opposed to testing the circuit in its original form.>

General-purpose distributed systems comprised of computing nodes with different characteristics and connected by high-speed communication networks are very popular these days. The development of a dependable distributed system, however, necessitates the use of various techniques including fault tolerance to avert occurrences of failures or system malfunction. The ad hoc techniques of adding redundancy to improve reliability are not always suitable in these circumstances because of excessive design cost. Redundancies have to be allocated at various hardware and software levels in order to optimize their utilization in the system. This paper considers the design of general-purpose fault-tolerant distributed systems based on a layered approach. The benefits of the layered approach in the process of allocation of redundancy and fault tolerance at various system levels are presented and analyzed in the paper.