Future wireless networks will play an essential role as the need for performance and feature availability grows. Most of the traffic in future wireless networks is due to increased Internet of things (IoT) devices, making resource optimization critical. Traditional optimization algorithms have limitations due to their high computational complexity, which restricts their use in modern applications. To address this, machine learning algorithms are now the preferred alternative to traditional optimization algorithms due to their improved runtime complexity. We present a comprehensive survey on the use of machine learning for resource optimization in future wireless networks. The use of machine learning is divided into three categories: (i) comprehensive solutions, where machine learning is the primary component of the solution approach; (ii) partial solutions, where machine learning is used alongside a traditional approach for optimization; and (iii) environment-only solutions, where optimization is performed in a machine-learning environment. We have further classified objective functions (e.g., energy, latency, data rate, etc.) within each category based on the pure objective function, variations on the objective function, and objective function tradeoffs with respect to other objective functions. We present objective functions and constraints used in the literature for optimization problem formulation. We provide an overview of frequently used machine learning algorithms for resource optimization, followed by a detailed survey of machine learning works in the literature in the three aforementioned categories. Finally, we discuss future research directions for utilizing machine learning to optimize resource management in future wireless networks.

Unmanned aerial vehicles (UAVs) enable a mobile edge computing (MEC) paradigm with reduced latency by bringing computational resources closer to the network edge. However, UAV-MEC servers have less computation and caching resources than ground base stations (BSs). The management of communication and control resources is crucial to coordinate communication, computing, and caching due to the involvement of aerial networks. Thus, managing joint caching, communication, computing, and control (4C) resources is vital in UAV-assisted MEC networks. To address these challenges, we developed a computational model for efficient resource management to reduce the linear combination of network cost and latency under constrained caching, computing, and offloading. We used binary decision variables for the allocation of computational and offloading resources. The formulated problem is a binary linear programming problem incorporating binary decision variables and linear constraints. We propose an interior point method-based heuristic to obtain a sub-optimal solution with low complexity. Simulation results demonstrate the effectiveness of our proposed approach compared to the branch and bound algorithm.

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.

In recent years, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) has emerged as a novel paradigm for providing computing services to devices in the industrial Internet of Things (IIoT) domain. However, due to the constraints of a single UAV’s battery and processing capabilities, it is unable to fulfill the computational and communication requirements of IIoT devices. We presents a multi-UAV-assisted IIoT system wherein computational services for IIoT devices are collaboratively provided by a terrestrial base station (BS) and UAVs. Considering the impact of energy consumption on the environment, we formulate a long-term weighted system energy consumption minimization problem by jointly optimizing UAVs trajectories, offloading and resource allocation decisions while considering the constraints of task deadlines. Due to the network dynamics and complexity of the optimization problem, we reformulate it as a Markov decision process (MDP) and apply a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach to solve the problem. The simulation results obtained show that our proposed MATD3-based method achieves 6% higher rewards and 30% faster convergence over previous algorithms such as multi-agent deep deterministic policy gradient and the proximal policy optimization.

The Cooperative Intelligent Transportation System (C-ITS) is vital in enhancing road safety, improving traffic efficiency, and increasing user comfort for pedestrians and drivers. However, as vehicle communications evolve, security threats that aim to undermine critical security services, such as data confidentiality, integrity, and privacy, have become crucial issues that must be addressed. Privacy, the freedom from interference or intrusion, is also considered one of the most significant challenges in computer networks and connected vehicles. Privacy in C-ITS can be protected by preventing both the collection of personal information and the tracking of vehicles by malicious users. Tracking is often achieved through the periodic transmission of Cooperative Awareness Messages (CAMs), which contain spatio-temporal information such as position and speed. We investigate key tracking-related critical issues in intelligent connected vehicles. We highlight current security challenges and attacks that lead to the unauthorized tracking of connected cars. We discuss various defense solutions proposed in the literature to address these challenges. We classify these solutions into two groups: general (addressing eavesdropping, Sybil, etc.) and pseudonym change (addressing correlation, swap, silent periods, and mix-zones). Finally, we explore robust future strategies to prevent tracking attacks on the C-ITS.

Distributed Denial-of-Service (DDoS) attacks are among the most insidious cyberattacks targeting any networking system. In the Internet domain, these attacks have demonstrated the capability of bringing down most systems for extended periods. In recent years, researchers have been exploring and simulating DDoS attacks against connected car systems. However, these simulated attacks have primarily employed messages from the Internet domain rather than leveraging the messages exchanged between connected vehicles. This paper presents a novel study that investigates the impact of DDoS attacks employing Cooperative Awareness Messages (CAMs), which serve as fundamental safety messages for establishing awareness in the connected car environment. The results obtained reveal that our attack exerts a significant, silent, and stealthy impact on connected cars. While the system remains operational, the quality of the services it provides (e.g., data collection and vehicle cooperation) is severely compromised. This highlights the vulnerability of connected cars to these attacks and underscores the need for robust mitigation strategies.

The widespread deployment of intelligent Internet of Things (IoT) devices brings tighter latency demands on complex workload patterns such as workflows. In such applications, tremendous dataflows are generated and processed in accordance with specific service chains. Edge computing has proven its feasibility in reducing the traffic in the core network and relieving cloud datacenters of fragmented computational demands. However, the efficient scheduling of workflows in hybrid edge–cloud networks is still challenging for the intelligent IoT paradigm. Existing works make dispatching decisions prior to real execution, making it difficult to cope with the dynamicity of the environment. Consequently, the schedulers are affected both by the scheduling strategy and by the mutual impact of dynamic workloads. We design an intelligent workflow scheduler for use in an edge–cloud network where workloads are generated with continuous steady arrivals. We develop new graph neural network (GNN)-based representations for task embedding and we design a proximal policy optimization (PPO)-based online learning scheduler. We further introduce an intrinsic reward to obtain an instantaneous evaluation of the dispatching decision and correct the scheduling policy on-the-fly. Numerical results validate the feasibility of our proposal as it outperforms existing works with an improved quality of service (QoS) level.

Designing an Emergency Vehicle Management (EVM) system that can provide competent services with the shortest possible delay is challenging. This is primarily due to the highly complex environments in which they are deployed and the diverse scenarios a vehicle could face after deployment. Compared to other types of vehicles, emergency vehicles operate under time-critical circumstances, move at much higher speeds, and require much greater alertness from the driver. Traditionally, emergency vehicles have not relied on dedicated smart/intelligent systems for increased safety and effectiveness. While much research has been carried out to improve the services of commercial and non-commercial vehicles in general, road emergency vehicles have not received the same research attention. The role of emergency vehicles in new environments (e.g., smart cities) will become increasingly important. To address this issue, we review state-of-the-art research results reported to date, focusing on the deployment and use of EVM. We discuss different dimensions of EVM which include route planning, patient information retrieval, accident detection, driver inattention detection, and several assisting tools and technologies. We identify key advancements in Intelligent Transportation Systems that can be leveraged for safe and effective EVM. We identify challenges inhibiting the usage and deployment of such advancements in EVM, and finally we identify future research directions for a more effective EVM that current developments in ITS have not yet addressed. We hope this work will serve as an accurate and updated source of information for the ever-evolving field of smart cities and ITS.

The breakthrough of artificial intelligence (AI) techniques has accelerated their applications in a wide range of industries, such as security protection, transportation, agriculture, and medical care. With the support of edge computing environments, providing latency guaranteed AI as a Service (AIaaS) can accelerate the deployment of data-intensive and computation-intensive AI applications and reduce the investment cost of the customers. However, the deployment architecture and working mechanism design, and performance optimization problems specific for AIaaS with configurable data quality and model complexity have not been studied in existing works. To address the problem, we propose a configurable model deployment architecture (CMDA) for edge AIaaS and present a flexible working mechanism by enabling the joint configuration of data quality ratios (DQRs) and model complexity ratios (MCRs) for the AI tasks. Along with commonly used resource allocation operations, the manager can improve the energy and delay performance of AI services with the desired quality of results (QoRs). We develop an energy-delay minimization problem under the framework of CMDA and propose a polynomial regression based relaxing method to solve the task configuration subproblem. We conduct experiments and simulations on the ImageNet classification and the common objects in context (COCO) object detection tasks using state-of-the-art deep learning models. We present the corresponding result quality tables (RQTs) and QoR regression models to illustrate the proposed method. The results of single task configuration and multi-task configuration and resource allocation on ImageNet classification and COCO object detection tasks demonstrate that the proposed method can achieve over$5\times$HDEC improvement compared with non-optimization schemes, and also show that joint configuration of DQR and MCR can achieve over$1.2\times$HDEC improvement compared with the methods that only configure DQR or MCR.

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Wearables today play a key role in E-Health computing, with investments expected to exceed $70 billion by 2024. With the massive use of apps on wearable devices, it is crucial to improve safety when using wearables, considering that important information about user information is stored on these devices. We present SOMEONE ensemble learning, a set machine learning algorithm for body recognition of wearable devices, which operates on the basis of both PhotoPlethysmoGram (PPG) and ElectroCardioGram (ECG) signals. We consider an individual's PPG and ECG signals, where algorithms process these signals stored on the wearable device to identify the user. The SOMEONE algorithm achieves better results on metrics such as F1 score, accuracy, false acceptance rate (FAR) and false rejection rate (FRR) for human recognition in MIMIC dataset of ECG signals and CapnoBase dataset of PPG signal.

Excessive speed is the leading cause of accidents on highways. The use of fixed road-side surveillance systems has proven ineffective because people take caution around only those locations. With more advanced technologies and exceptional maneuvering capabilities, Unmanned Aerial Vehicles (UAVs) is a promising candidate for real-time monitoring of vehicles on the highways. The dependency of recently proposed UAV-based architectures on terrestrial networks suffers from high deployment costs. To address this shortcoming, we propose a unique closed-loop control architecture for traffic surveillance on highways that increases the surveillance effectiveness and adapts according to varying traffic patterns. The proposed scheme is comprised of Wireless Sensor Network (WSN) and UAV, named Collaborative Highway Surveillance (CHS), that works without human intervention and enables the capture of over-speeding drivers on a highway. The WSN provides routing services to the UAV and dynamically guides UAV to the best hotspot to position itself. If any vehicle exceeds the speed limit allowed, the UAV immediately informs this event to the Mobile Base Station (MBS). However, to conserve the energy resources of the WSN, low-level speed violations are forwarded to the MBS only when the UAV gets closer to the MBS. Performance results obtained with our proposed architecture show an increase in surveillance efficiency with an improved response time compared with cooperative and stand-alone unguided UAV networks.

Fog computing is a promising technology that can provide storage and computational services to future 6G networks. To support the massive Internet-of-Things (IoT) applications in 6G, fog computing will play a vital role. IoT devices and fog nodes have energy limitations and hence, energy-efficient techniques are needed for storage and computation services. We present an overview of massive IoT and 6G-enabling technologies. We discuss different energy-related challenges that arise while using fog computing in 6G-enabled massive IoT. We categorize different energy-efficient fog computing solutions for IoT and describe the recent work done in these categories. Finally, we discuss future opportunities and open challenges in designing energy-efficient techniques for fog computing in the future 6G massive IoT network.

Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future.

In cloud storage, how to search sensitive data efficiently and securely is a challenging problem. The searchable encryption technique provides a secure storage method without loss of data confidentiality and usability. As an important branch of searchable encryption, public-key encryption with keyword search (PEKS) is widely studied by scholars. However, most of the traditional PEKS schemes are vulnerable to the inside keyword guessing attack (IKGA). Resisting the inside keyword guessing attack is likely to become an essential property of all new PEKS schemes. For a long time, mitigating IKGA has been inefficient and difficult, and most existing PEKS schemes fail in achieving their security goals. To address the above problems, we define the notion ofDual-serverPublic-keyAuthenticatedEncryption withKeywordSearch (DPAEKS), which protects against IKGA by leveraging two servers that do not cooperate, and supports the authentication property. Then, we provide a construction of DPAEKS without bilinear pairings. Experimental results obtained using a real-world dataset show that our scheme is highly efficient and provides strong security, making it suitable for deployment in practical applications.

In the context of current smart cities, Cooperative Intelligent Transportation Systems (C-ITS) represent one of the main use case scenarios that aim to improve peoples’ daily lives. Thus, during the last few years, numerous standards have been adopted to regulate such networks. Within a C-ITS, a large number of messages are exchanged continuously in order to ensure that the different applications operate efficiently. However, these networks can be the target of numerous attacks. The sybil attack is among the most dangerous ones. In a sybil attack, an attacker creates multiple identities and then disguises as several fake stations in order to interfere with the normal operations of the system or profit from provided services. We analyze recently proposed sybil detection approaches regarding their compliance with the current C-ITS standards as well as their evaluation methods. We provide several recommendations such as network and attack models as well as an urban and highway datasets that can be considered in future research in sybil attack detection.

Cellular vehicle-to-everything (V2X) communication is emerging as a feasible and cost-effective solution to support applications such as vehicle platooning, blind spot detection, parking assistance, and traffic management. To support these features, an increasing number of sensors are being deployed along the road in the form of roadside objects. However, despite the hype surrounding cellular V2X networks, the practical realization of such networks is still hampered by under-developed physical security solutions. To solve the issue of wireless link security, we propose a deep Q-learning-based strategy to secure V2X links. Since one of the main responsibilities of the base station (BS) is to manage interference in the network, the link security is ensured without compromising the interference level in the network. The formulated problem considers both the power and interference constraints while maximizing the secrecy rate of the vehicles. Subsequently, we develop the reward function of the deep Q-learning network for performing efficient power allocation. The simulation results obtained demonstrate the effectiveness of our proposed learning approach. The results provided here will provide a strong basis for future research efforts in the domain of vehicular communications.

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and ‘security compromise’ are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and ‘number of packets sent’, which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

We investigate how to provide Quality-of-Service (QoS) for diversified video flows. We design a fine-grained video traffic control mechanism that integrates traffic classification with path selection for video flows within the framework of SDN. For the design, we present a category-theoretic ontology log (olog) diagram model, which provides a novel perspective on the interdependency among various system components. For the video traffic classification, we first evaluate various machine learning classifiers in terms of their performance and then chose the most effective one to be the first module. For the path selection, we devise a multi-constrained QoS routing strategy by restructuring a state-of-the-art graph algorithm, combine it with the${k}$-shortest path algorithm, and deploy this strategy as another video traffic control module. We implemented a prototype of the proposed mechanism on the SDN emulator Mininet, and we evaluate its effectiveness using the performance results obtained.

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

Security and privacy will play a pivotal role in the commercialization of Vehicular Ad-hoc NETworks (VANETs). Traditionally, both cryptographic and non-cryptographic approaches have been used by researchers to address security and privacy issues and achieve secure Intelligent Transportation System (ITS) applications. However, some security goals such as trust and reputation, are still hard to achieve through conventional cryptographic approaches. Trust is the degree of certainty with which the received information is accepted and acted upon. Historically trust has been computed for both the content generator and the content itself with former known as entity trust and the latter known as data trust. Both entity and content trust are equally important to support trustworthy communication in VANET. We review, analyze, and compare some of the recently proposed trust establishment and management mechanisms (from 2014 to 2019) in vehicular networks. Furthermore, we also discuss the weaknesses and inadequacies of existing trust establishment and management approaches when deployed in a VANET environment. Finally, we discuss some future challenges that will need to be addressed for trustworthy communications in vehicular networks.

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

The ever-growing trend of making the traditional power grids smarter than before has resulted in their gradual evolution to more sophisticated grids, referred to as Smart Grids (SGs) Cyber-Physical Systems with complex networking technologies. The integration of Information and Communication Technologies with power grids fosters seamless data sharing between different SG entities, which supports effective and smart governance in terms of demand response management, frequency support, and voltage stabilization. Nonetheless, this integration opens up several security and privacy concerns, namely, electricity theft, power loss, battery exhaustion, infrastructure mapping, etc. These issues become even more important with the addition of distributed energy sources, e.g. electric vehicles (EVs), battery energy storage systems, and renewable energy sources, into the SGs. We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security. We leverage the capabilities of SDN to handle the complex interactions between different subsystems of the SG. Furthermore, we also employ BC and smart contracts' properties to secure energy transactions and data communications. We design a secure and efficient mutual authentication protocol based on Elliptic Curve Cryptography (ECC) and BC for privacy preservation during smart energy trading. We also proposed a BC-based smart contract for effective Demand Response Management (DRM) during bidirectional energy transfer between EVs and SG. Finally, we present experimental evaluations to validate the proposed framework's performance. The results obtained demonstrate the improved performance of the proposed scheme compared with current state-of-the-art approaches. The mutual authentication protocol designed is not only secure against major attack vectors (namely, session key security, message integrity, anonymity, forward secrecy, and so on), but it is also cost-efficient in terms of communication and computational costs. Additionally, the SC designed assures power security and maintains an adequate balance between demand and supply.

The Internet of connected Vehicles (IOV) can collect, process, compute and release the information of intelligent transportation systems. IOV is an integrated service system that can support the applications for automatic driving, intelligent transport and information services. As the number of incidents on IOV has been on the rise in the past few years, IOV security is becoming increasingly important in the IOV architecture. One of the most notable risks of IOV faces is intelligent terminal security. The vehicle's intelligent terminal can be used to launch for further attacks on the on-board operating system to penetrate into the internal network of connected vehicle, and consequently threaten the safety of the vehicle. Thus, it is of paramount importance that we protect the security of the intelligent terminal. We propose two intelligent terminal based privacy-preserving multi-modal implicit authentication protocols to protect the security of the intelligent terminal in IOV. The proposed protocols use the password and the vehicle owner's behavior features as the authentication factors to protect the security of the intelligent terminal. Since the vehicle owner's behavior features are sensitive and the privacy information of the user must be protected, we also consider the privacy protection of the behavior features. Our protocols do not reveal any information about the vehicle owner's behavior features to the authentication server and the adversary except the ciphertext size of the feature vector. We analyze the security of our proposed protocol and compare them with other related protocols in terms of computation and communications costs. Our results demonstrate that our proposed protocols yield better security and efficiency.

The sensing-as-a-service (SaaS) model has been explored to address the challenge of intractability of managing a large number of sensors faced by future smart cities. However, how to effectively share sensor data without compromising confidentiality, privacy protection, and fair trading without third parties is one of critical issues that must be solved in the SaaS in smart cities. While blockchain shows promise in solving these issues, the existing blockchain-based data sharing (BBDS) systems are difficult to apply to SaaS in smart cities because of many unresolved issues such as requiring a customized blockchain, huge storage, communication and computation costs, and dependence on a third party to achieve fair trading. We propose a BBDS system model with its security requirements before we present a concrete construction by combining -protocol, Paillier encryption scheme, and any secure symmetrical encryption and signature schemes. To demonstrate the utility of our proposed BBDS system, we present a security analysis and compare our system with other solutions. We implement the prototype in Remix to analyze the gas cost, and we conduct experiments to evaluate the communication and computation costs of the BBDS system using symmetric encryption (advanced encryption standard (AES)) and a signature scheme (elliptic curve digital signature algorithm (ECDSA)).

Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may consume a lot of bandwidth, and even put the user’s privacy at risk. Although many privacy-preserving data aggregation schemes have been proposed to solve the problem, they still suffer from some security weaknesses or performance deficiency, such as lack of satisfactory data confidentiality and resistance to malicious data mining attack. To address these issues, we propose a novel privacy-preserving data aggregation scheme (called PDAM) for IoT-enabled smart grids, which can support efficient data source authentication and integrity checking, secure dynamic user join and exit. Unlike existing schemes, the PDAM is resilient to the malicious data mining attack launched by internal or external attackers and can achieve perfect data confidentiality against not only a malicious aggregator but also a curious control center for an authorized user. The detailed security and performance analysis show that our proposed PDAM can satisfy several well-known security properties and desirable efficiency for a smart grid system. Moreover, the comparative studies and experiments demonstrate that the PDAM is superior to other recently proposed works in terms of both security and performance.

The vast areas of applications for IoTs in future smart cities, smart transportation systems, and so on represent a thriving surface for several security attacks with economic, environmental and societal impacts. This survey paper presents a review of the security challenges of emerging IoT networks and discusses some of the attacks and their countermeasures based on different domains in IoT networks. Most conventional solutions for IoT networks are adopted from communication networks while noting the particular characteristics of IoT networks such as the nodes quantity, heterogeneity, and the limited resources of the nodes, these conventional security methods are not adequate. One challenge towards utilizing common secret key-based cryptographic methods in large-scale IoTs is the problem of secret key generation, distribution, and storage and protecting these secret keys from physical attacks. Physically unclonable functions (PUFs) can be utilized as a possible hardware remedy for identification and authentication in IoTs. Since PUFs extract the unique hardware characteristics, they potentially offer an affordable and practical solution for secret key generation. However, several barriers limit the PUFs’ applications for key generation purposes. We discuss the advantages of PUF-based key generation methods, and we present a survey of state-of-the-art techniques in this domain. We also present a proof-of-concept PUF-based solution for secret key generation using resistive random-access memories (ReRAM) embedded in IoTs.

The Internet of Vehicles has attracted a lot of attention in the automotive industry and academia recently. We are witnessing rapid advances in vehicular technologies that comprise many components, such as onboard units (OBUs) and sensors. These sensors generate a large amount of data, which can be used to inform and facilitate decision making (e.g., navigating through traffic and obstacles). One particular focus is for automotive manufacturers to enhance the communication capability of vehicles to extend their sensing range. However, the existing short-range wireless access, such as dedicated short-range communication (DSRC), and cellular communication, such as 4G, is not capable of supporting the high volume data generated by different fully connected vehicular settings. Millimeter-wave (mmWave) technology can potentially provide terabit data transfer rates among vehicles. Therefore, we present an in-depth survey of the existing research, published in the last decade, and we describe the applications of mmWave communications in vehicular communications. In particular, we focus on MAC and physical layers and discuss related issues, such as sensing-aware MAC protocol, handover algorithms, link blockage, and beamwidth size adaptation. Finally, we highlight various aspects related to smart transportation applications, and we discuss future research directions and limitations.

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.

If all vehicles are connected together through a wireless communication channel, vehicular ad hoc networks (VANETs) can support a wide range of real-time traffic information services, such as intelligent routing, weather monitoring, emergency call, etc. However, the accuracy and credibility of the transmitted messages among the VANETs are of paramount importance as life may depend on it. In this article we introduce a novel framework called blockchain-assisted privacy-preserving authentication system (BPAS) that provides authentication automatically in VANETs and preserves vehicle privacy at the same time. This design is highly efficient and scalable. It does not require any online registration centre (except for system initialization and vehicle registration), and allows conditional tracing and dynamic revocation of misbehaving vehicles. In this article, we conduct an in-depth security analysis and a comprehensive performance evaluation (which is based on the Hyperledger Fabric platform) for our proposed framework. The results demonstrate that our framework is an efficient solution for the development of a decentralized authentication system in VANETs.

Summary The complexity of cyber‐physical systems (CPSs) opens up several challenges regarding the assurance of high levels of reliability. Therefore, we need to develop a new data quality analysis and new CPS components/subsystems for the detection, isolation, recovery, and prevention of failures and intrusion events. Furthermore, we have to develop new reliability assessment models and metrics in order to properly evaluate CPSs. Agent technologies, through their characteristics such as autonomy, social ability, reactivity, and proactivity, have the potential to address these challenges and requirements. We propose a multi‐agent based solution, namely ReliaCPS, for detecting and preventing the failures of the system components of CPSs. We also use a case study of a CPS for monitoring ambient parameters to evaluate the performance of the proposed approach. Our results indicate improvements in terms of reliability metrics (32.69% for the mean time between failures and mean time to failure, 50% for the mean time to repair and 0.94% for reliability function over time).

Due to its widespread popularity and usage in many applications (smart transport, energy management, e-healthcare, smart ecosystem, and so on), the Internet of Things (IoT) has become popular among end users over the last few years. However, with an exponential increase in the usage of IoT technologies, we have been witnessing an increase in the number of cyber attacks on the IoT environment. An adversary can capture the private key shared between users and devices and can launch various attacks, such as IoT ransomware, Mirai botnet, man-in-the-middle, denial of service, chosen plaintext, and chosen ciphertext. To mitigate these security attacks on the IoT environment, the traditional public key cryptographic primitives are inadequate because of their high computational and communication costs. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a promising technique for secure communication. We discuss the taxonomy of two major problems, namely, the shortest path and the closest path problems with respect to the applicability of lattice-based cryptographic primitives for IoT devices. Moreover, we also discuss various LB-PKC techniques, such as NTRU, learning with errors (LWEs), and ring-LWE (R-LWE) which are often used to solve shortest path and lattice NP-hard problems in a polynomial time. We further classify the R-LWE into three categories, namely identity-based encryption, homomorphic encryption, and secure authentication key exchange. We describe the operations and algorithms adopted in each of these encryption mechanisms. Finally, we discuss the challenges, open issues, and future directions for applying LB-PKC in the IoT environment.

Smart grid has emerged as the next-generation electricity grid with power flow optimization and high power quality. Smart grid technologies have attracted the attention of industry and academia in the last few years. However, the tradeoff between security and efficiency remains a challenge in the practical deployment of the smart grid. Most recently, Li et al. proposed a lightweight message authentication scheme with user anonymity and claimed that their scheme is provably secure. But we found that their scheme fails to achieve mutual authentication and mitigate some typical attacks (e.g., impersonation attack, denial of service attack) in the smart grid environment. To address these drawbacks, we present a new message authentication scheme with reasonable efficiency. Security and performance analysis results show that the proposed scheme can satisfy the security and lightweight requirements of practical implementations and deployments of the smart grid.

Fifth generation mobile communication networks are currently being deployed, thus making Tactile Internet possible. Tactile Internet is the future advancement of the current Internet of Things (IoT) vision wherein haptics, or touch and senses, can be communicated from one geographical place to another, enabling near real-time control and navigation of remote objects. Tactile Internet will have its use cases in several application domains, with the industrial sector being among the most prominent ones. With the Industrial Internet of Things (IIoT), Tactile Internet will be used in healthcare, manufacturing, mining, education, autonomous driving, etc. The acceptable delay in most of these tactile applications will be under one millisecond. Since Tactile Internet communicates haptics and gives visual feedback, quality of service (QoS) becomes an important issue. Similarly, user's satisfaction on the service quality [often measured as quality of experience (QoE)] becomes equally important. To reap the true potential of Tactile Internet, sophisticated and intelligent mechanisms are required between the end-nodes. A middleware such as fog computing can be vital in this context, since it can allocate resources based on the QoS/QoE requirements of each service. In this context, we present a QoE-aware model for dynamic resource allocation for tactile applications in IIoT. We implement the model using Java and discuss the empirical results to elaborate more on the impact of such a model for QoE-aware resource allocation that can be very important in the context of Tactile Internet, especially IIoT. We also discuss some of the most prominent use cases of Tactile IIoT.

Smart energy systems are becoming an important component of smart cities. The wide adoption of existing computing technologies and communication standards by a smart energy system exposes it to the plethora of threats that exist in cyberspace. In this article, we investigate the vulnerabilities and threats associated with smart energy system components, including Internet of Things enabled devices, as well as relevant communication standards, and we discuss countermeasures against adversarial attacks. We found that the existing literature has reported various attacks, including modification, denial-of-service, malware, and message replay, which can cause malfunctioning of different components of a smart energy system. In addition, we propose a framework for securing the physical layer of the smart energy system. The framework is based on advanced key generation, machine learning, and physical layer security techniques, which enhance the security of smart energy systems across different applications.

Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and Internet of Things applications. Computationally intensive artificial intelligence (AI) tasks are well suited to be offloaded to the Cloudlet server, but there is a lack of energy-delay optimization models specifically designed for this edge AI scenario. In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights and computing capacities of virtual machines, at the same time guaranteeing the quality of the results (QoRs). We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks.

The Web has been identified to consist of a large portion of content that cannot be crawled by general-purpose search engines because it is only generated after a valid submission to a search interface. Accessing such content, however, requires the location and identification of search interfaces. Towards the automation of this task, many approaches have been proposed that involve the manual definition of rules for the identification of query interfaces. In this paper, we propose a rule induction approach to automatically construct a set of rules by searching the most promising subspace of all possible rules with a brute-force method and information theoretic criteria. To specify the features for the rules, we initially make a descriptive analysis of Yahoo L11, a specialized dataset containing complex interfaces, which to the best of our knowledge has not been used in previous works. We perform a series of evaluations and present the rules constructed by running the algorithm on a random sample of the Yahoo L11 dataset and another dataset used in similar works. The resulting rules yield high classification accuracy in predicting the functionality of new, previously unseen forms and since humans can easily interpret them, they can be easily ported to any application as-is.

The Internet of Things (IoT) vision is increasingly being realized to facilitate convenient and efficient human living. To conduct effective IoT research using the most appropriate tools and techniques, we discuss recent research trends in the IoT area along with current challenges faced by the IoT research community. Several existing and emerging IoT research areas such as lightweight energy-efficient protocol development, object cognition and intelligence, as well as the critical need for robust security and privacy mechanisms will continue to be significant fields of research for IoT. IoT research can be a challenging process spanning both virtual and physical domains through the use of simulators and testbeds to develop and validate the initial proof-of-concepts and subsequent prototypes. To support researchers in planning IoT research activities, we present a comparative analysis of existing simulation tools categorized based on the scope of coverage of the IoT architecture layers. We compare existing large-scale IoT testbeds that have been adopted by researchers for examining the physical IoT prototypes. Finally, we discuss several open challenges of current IoT simulators and testbeds that need to be addressed by the IoT research community to conduct large-scale, robust and effective IoT simulation, and prototype evaluations.

Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad-hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV). We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.

Rapid advances in wireless communications, hardware/software, and Internet technologies have contributed to an exponential growth in the number of users accessing the Internet using mobile, wearable or other Internet of Things devices. Identity-based signature schemes have been widely applied to enforce user authorization and validate user messages in mobile wireless networks. However, the user’s private key used to generate signatures is prone to leakage because the key is being stored on the mobile device. Several (t, n) threshold secret sharing schemes have been proposed to address the issue. One limitation is that the private keys in most of those schemes have to be recovered on a single device when generating signatures, so that the user who holds the device can sign any message without the participation of other users. To address the recovery limitation, we propose an efficient and secure two-party distributed signing protocol for the identity-based signature scheme in the IEEE P1363 Standard, where two users can generate a valid signature without recovering the whole private key. We formally prove its security under a nonstandard assumption. We also implemented our proposed protocol using the MIRACL Cryptographic software development kit. The experimental results obtained show that the time it takes for two general Android devices to generate a signature is about 709.53 ms.

Rapid technological advances have revolutionized the industrial sector. These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required. Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT). IIoT refers to making industrial processes and entities part of the Internet. Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 4.0. IIoT and Industry 4.0, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel. Hence, a lot of data of diverse nature would be generated. The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud. To achieve this task, middleware support is required between the industrial environment and the cloud/web services. In this context, fog is a potential middleware that can be very useful for different industrial scenarios. Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry. Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud. We present an architectural overview of IIoT and Industry 4.0. We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT. We also present a few interesting prospective use cases of IIoT. Finally, we discuss some emerging research challenges related to IIoT.

The smart grid is considered to be the next-generation power system because of its reliability, efficiency, and cost-effectiveness. In recent years, the smart grid technology has attracted a lot of attention from both academia and industry. Advances in smart grid technologies are enabling more data to be collected and analyzed in real-time for many kinds of smart grid applications. As the amount of data increases, the traditional smart grid data management system cannot provide sufficient storage and processing capacities. To address these challenges, cloud computing is being introduced into the power system and the cloud-based smart grid data management system has been proposed to better support smart grid applications. In this cloud-based system, the data are stored and analyzed by the remote cloud server according to the requirements of smart grid applications. However, the loss of physical control over the smart grid data makes it a significant challenge in ensuring the integrity of the data. Many provable data possession schemes have been proposed in the past few years. However, most of them suffer from serious security weaknesses or poor performance. We propose an efficient certificateless provable data possession (CL-PDP) scheme for cloud-based smart grid applications. Security analysis shows that the proposed scheme is provably secure in a robust security model and can satisfy several security requirements. Performance analysis demonstrates that the proposed scheme results in lower computation costs as compared to two recently proposed CL-PDP schemes.

Industrial Internet of Things (IIoT) integrates various types of intelligent terminals, mobile devices, and communication technologies to enable the upgrade of traditional industries to intelligent industries. IIoT relies on the powerful data processing capabilities of cloud computing to reduce the cost of various on-demand services as per the requirements of users. However, the privacy and confidentiality of the outsourced data should be protected in this environment because the data are typically “handled” by a third-party service provider. An encryption technique can guarantee the confidentiality of the data but it limits data retrieval due to its innate “all-or-nothing” decryption feature. To apply encryption to privacy-preserving data retrieval, many public key encryption techniques with keyword search systems have been proposed in the literature. However, most of the existing schemes are vulnerable to inside keyword guessing attack (IKGA), which is caused by a small keyword space. To address this problem, we propose a certificateless public key authenticated encryption with keyword search scheme, which is provably secure against IKGA. A performance analysis of the proposed scheme demonstrates that it is more secure and effective compared with other certificateless public key encryption with keyword search schemes.

High reliability is an important characteristic of Cyber-Physical Systems (CPS). New CPS components/subsystems have to be developed for the detection, isolation, recovery and prevention of failures and external attacks. To address this challenge, we propose a multi-agent-based subsystem architecture that can detect and prevent CPS hardware components' failures. The architecture comprises of several types of agents with well-defined behaviors which cooperate to fulfil the goal of the CPS subsystem. We evaluate the performance of the proposed approach using reliability metrics for a CPS aimed at monitoring ambient and environmental parameters.

Recent advances of Internet and microelectronics technologies have led to the concept of smart grid which has been a widespread concern for industry, governments, and academia. The openness of communications in the smart grid environment makes the system vulnerable to different types of attacks. The implementation of secure communication and the protection of consumers’ privacy have become challenging issues. The data aggregation scheme is an important technique for preserving consumers’ privacy because it can stop the leakage of a specific consumer’s data. To satisfy the security requirements of practical applications, a lot of data aggregation schemes were presented over the last several years. However, most of them suffer from security weaknesses or have poor performances. To reduce computation cost and achieve better security, we construct a lightweight data aggregation scheme against internal attackers in the smart grid environment using Elliptic Curve Cryptography (ECC). Security analysis of our proposed approach shows that it is provably secure and can provide confidentiality, authentication, and integrity. Performance analysis of the proposed scheme demonstrates that both computation and communication costs of the proposed scheme are much lower than the three previous schemes. As a result of these aforementioned benefits, the proposed lightweight data aggregation scheme is more practical for deployment in the smart grid environment.

Abstract Attribution of cybercrimes is significant in limiting the rate of crime as well as in preparing the required level of response. Motivated by this significance, we introduce a level‐based approach for achieving attribution. In our proposed approach, attribution consists of three steps: (1) identification of the cyberweapon used; (2) determination of the origin of the attack; and (3) identification of the actual attacker. We conduct an in‐depth analysis of recently proposed attribution techniques. Our analysis reveals that indirect methods of attribution are particularly effective when attributing cybercrimes; many of them remain unattributed. We also discuss some of the legal issues pertaining to attribution, and we argue that well‐defined international laws for cyberspace along with strong cooperation among governments are needed to track down and punish cybercriminals. Copyright © 2016 John Wiley & Sons, Ltd.

Cyber criminals are covertly attacking critical infrastructures, and botnets are a common component of those attacks. In recent years, botnets have been shifting their focus from broad-based attacks to more targeted ones such as attacking financial institutions, especially banks. The primary reason for this shift towards financial institutions is that, where the money is. We present an empirical study of the components, features and operations of some of the most widely deployed HTTP-based financial botnets (such as Zeus, SpyEye, ICE 1X, Citadel, Carberp, Tinba, Bugat and Shylock). Our study provides critical insights into the design of these botnets and should help the security community to generate intelligence and develop more robust security solutions to defend against cyber attacks by these botnets. In addition, our comparative analysis of insidious techniques pertaining to Command and Control (C&C) communication, system exploitation and data exfiltration also provides an effective and a holistic view of the capabilities of HTTP-based financial botnets. This study also highlights the evolution of various HTTP-based financial botnets over a period of time. Finally, we discuss security solutions that can help mitigate some of the techniques used by HTTP-based financial botnets.

Rapid advances in wireless communication technologies have paved the way for a wide range of mobile devices to become increasingly ubiquitous and popular. Mobile devices enable anytime, anywhere access to the Internet. The fast growth of many types of mobile services used by various users has made the traditional single-server architecture inefficient in terms of its functional requirements. To ensure the availability of various mobile services, there is a need to deploy multi-server architectures. To ensure the security of various mobile service applications, the anonymous mobile user authentication (AMUA) protocol without online registration using the self-certified public key cryptography (SCPKC) for multi-server architectures was proposed in the past. However, most of the past AMUA solutions suffer from malicious attacks or have unacceptable computation and communication costs. To address these drawbacks, we propose a new AMUA protocol that uses the SCPKC for multi-server architectures. In contrast to the existing AMUA protocols, our proposed AMUA protocol incurs lower computation and communication costs. By comparing with two of the latest AMUA protocols, the computation and the communication costs of our protocol are at least 74.93% and 37.43% lower than them, respectively. Moreover, the security analysis of our AMUA protocol demonstrates that it satisfies the security requirements in practical applications and is provably secure in the novel security model. By maintaining security at various levels, our AMUA protocol is more practical for various mobile applications.

Abstract The information‐centric networking concept was proposed to fulfill the scalability and efficiency requirements of the content‐centric Internet in the future. Among the multiple information‐centric networking proposals, Named Data Networking (NDN) is one of the most important representatives. NDN uses a hierarchical name to identify the data after which the on‐path cache can be deployed to improve the efficiency of data retrieval. However, with the development of mobile Internet, how to extend NDN in the mobile environment to enable efficient and scalable mobility management remains a challenge. We propose a distributed mobility management scheme for both the mobile receiver and the mobile publisher in NDN. Our proposed approach is based on the basic NDN naming and routing principles to select the branching node of the previous and new access locations of the mobile terminal after which the on‐path routing states are dynamically adjusted accordingly. Then we propose a novel analytical model to analyze the performance of the proposed scheme. The results demonstrate that the proposed scheme inherits the scalability and efficiency of NDN in the mobile Internet. Copyright © 2015 John Wiley & Sons, Ltd.

Advances in information and communication technologies have led to the emergence of Internet of Things (IoT). In the healthcare environment, the use of IoT technologies brings convenience to physicians and patients as they can be applied to various medical areas (such as constant real-time monitoring, patient information management, medical emergency management, blood information management, and health management). The radio-frequency identification (RFID) technology is one of the core technologies of IoT deployments in the healthcare environment. To satisfy the various security requirements of RFID technology in IoT, many RFID authentication schemes have been proposed in the past decade. Recently, elliptic curve cryptography (ECC)-based RFID authentication schemes have attracted a lot of attention and have been used in the healthcare environment. In this paper, we discuss the security requirements of RFID authentication schemes, and in particular, we present a review of ECC-based RFID authentication schemes in terms of performance and security. Although most of them cannot satisfy all security requirements and have satisfactory performance, we found that there are three recently proposed ECC-based authentication schemes suitable for the healthcare environment in terms of their performance and security.

By broadcasting messages about traffic status to vehicles wirelessly, a vehicular ad hoc network (VANET) can improve traffic safety and efficiency. To guarantee secure communication in VANETs, security and privacy issues must be addressed before their deployment. The conditional privacy-preserving authentication (CPPA) scheme is suitable for solving security and privacy-preserving problems in VANETs, because it supports both mutual authentication and privacy protection simultaneously. Many identity-based CPPA schemes for VANETs using bilinear pairings have been proposed over the last few years to enhance security or to improve performance. However, it is well known that the bilinear pairing operation is one of the most complex operations in modern cryptography. To achieve better performance and reduce computational complexity of information processing in VANET, the design of a CPPA scheme for the VANET environment that does not use bilinear paring becomes a challenge. To address this challenge, we propose a CPPA scheme for VANETs that does not use bilinear paring and we demonstrate that it could supports both the mutual authentication and the privacy protection simultaneously. Our proposed CPPA scheme retains most of the benefits obtained with the previously proposed CPPA schemes. Moreover, the proposed CPPA scheme yields a better performance in terms of computation cost and communication cost making it be suitable for use by the VANET safety-related applications.

In the last few years, significant efforts have been devoted to developing intelligent and sustainable transportation to address pollution problems and fuel shortages. Transportation agencies in various countries, along with several standardization organizations, have proposed different types of energy sources (such as hydrogen, biodiesel, electric, and hybrid technologies) as alternatives to fossil fuel to achieve a more ecofriendly and sustainable environment. However, to achieve this goal, there are significant challenges that still need to be addressed. We present a survey on sustainable transportation systems that aim to reduce pollution and greenhouse gas emissions. We describe the architectural components of a future sustainable means of transportation, and we review current solutions, projects, and standardization efforts related to green transportation with particular focus on electric vehicles. We also highlight the main issues that still need to be addressed to achieve a green transportation management system. To address these issues, we present an integrated architecture for sustainable transportation management systems.

In the past few years, cloud computing has emerged as a new reliable, scalable and flexible virtual computing environment (VCE). In this new VCE, users can use the available resources as a service by paying for that service according to the time for which these resources are used. It remains a significant challenge to achieve quality of service (QoS) in a VCE with the available resources. The main goal is to schedule the available resources so that the overall QoS delivered by the VCE can be improved. Resources are assumed to be located both at local and global sites. We propose a three-step scheme: resource selection, scheduling of users requests with shared resources and a new Resource Allocation and Adaptive Job Scheduling algorithm, which improves the QoS delivered by the cloud. For job scheduling, we define a new weight metric that is used to efficiently schedule jobs competing for available resources. Our proposed strategy increases the reliability of resource availability for a job and reduces the job completion time, which in turn increases the QoS delivered to end-users. We evaluate our proposed scheme using well-known heuristics. The results obtained show that our proposed scheme considerably reduces the job execution time, and increases the reliability of resource availability for job execution and throughput.

Vehicular information network and Internet of Things (IoT) technologies have been receiving a lot of attention in recent years. As one of the most important and promising IoT areas, a vehicular information network aims to implement a myriad of applications related to vehicles, traffic information, drivers, passengers, and pedestrians. However, intervehicular communication (IVC) in a vehicular information network is still based on the TCP/IP protocol stack which is not efficient and scalable. To address the efficiency and scalability issues of the IVC, we leverage the named data networking (NDN) paradigm where the end user only cares about the needed content and pays no attention to the actual location of the content. The NDN model is highly suitable for the IVC scenario with its hierarchical content naming scheme and flexible content retrieval and caching support. We design a novel vehicular information network architecture based on the basic communication principle of NDN. Our proposed architecture aims to improve content naming, addressing, data aggregation, and mobility for IVC in the vehicular information network. In addition, the key parameter settings of the proposed schemes are analyzed in order to help guide their actual deployment.

As current server capacity and network bandwidth become increasingly overloaded by the rapid growth of high quality emerging multimedia services such as mobile online gaming, social networking or IPTV, a critical factor of success of these multimedia services becomes the end-user perception of quality while them using the service. As a result, user-centered approaches that consider quality of experience (QoE) constitute the current design trend for network systems of content providers and network operators. A content distribution network (CDN) that replicates the content from original servers to the replicated servers close to end users is actually an effective solution to improve network quality. We propose a QoE-based server selection algorithm in the context of a CDN architecture. Using realistic characteristics of the server selection process, we formalize our selection model as a sequential decision problem solved by the multi-armed bandit (MAB) paradigm. By using realistic experiments, we demonstrate that our approach yields significant improvements in term of user perception compared to traditional methods (such as Fastest, Closest and Round Robin).

The traditional electrical grid has become inadequate in meeting the needs and demands of electricity users in the 21st century. To address this challenge, smart grid technologies have emerged, which promise more efficient production and usage of electricity through bidirectional interactions between the consumer and the utility provider. This two-way interaction allows electricity to be generated in real time based on the actual needs of the consumers. However, this two-way interaction also raises concerns related to the privacy and the personal habits of consumers. To protect sensitive energy usage information of consumers, we propose a virtual ring architecture that can provide a privacy protection solution using symmetric or asymmetric encryptions of customers' requests belonging to the same group. We compare the efficiency of our proposed approach with two recently proposed smart grid privacy approaches namely, one based on blind signature and other based on a homomorphic encryption solution. We show that our approach maintains the privacy of customers while reducing the performance overhead of cryptographic computations by more than a factor of 2 when compared with the aforementioned past solutions. We further demonstrate that our smart grid privacy solution is simple, scalable, cost-effective, and incurs minimal computational processing overheads.

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexical features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online deception using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods proposed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities.

Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user’s satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a non-intrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks’ impairments, videos’ characteristics, and users’ perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user’s perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

SUMMARY We propose a routing scheme called energy‐efficient beaconless geographic routing with energy supply (EBGRES) for wireless sensor networks. EBGRES provides loop‐free, fully stateless, energy‐efficient source‐to‐sink routing with minimal communication overhead without the help of prior neighborhood knowledge. It locally determines the duty‐cycle of each node, based on an estimated energy budget for each period, which includes the currently available energy, the predicted energy consumption and the energy expected from the harvesting device. In EBGRES, each node sends out the data packet first rather than a control message. By sending a data packet first, EBGRES performs the neighbor selection only among those neighbors that successfully received the data packet. EBGRES uses a three‐way (DATA/ACK/SELECT) handshake and a timer‐assignment function, the Discrete Dynamic Forwarding Delay (DDFD). We investigate the lower and upper bounds on hop count and the upper bound on energy consumption under EBGRES for source‐to‐sink routing. We further demonstrate the expected total energy consumption along a route toward the sink with the proposed EBGRES approach including a lower bound on energy consumption when the node density increases. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small‐sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust‐based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

This article proposes a state-dependent routing algorithm based on a global optimization cost function whose parameters are learned from the real-time state of the network with no a priori model. The proposed approach samples, estimates, and builds the model of pertinent and important aspects of the network environment such as type of traffic, QoS policies, resources, etc. It is based on the trial/error paradigm combined with swarm-adaptive approaches. The global system uses a model that combines both a stochastic planned prenavigation for the exploration phase with a deterministic approach for the backward phase. We conducted a performance analysis of the proposed algorithm using OPNET based on several topologies such as the Nippon telephone and telegraph network. The simulation results obtained demonstrate substantial performance improvements over traditional routing approaches as well as the benefits of learning approaches for networks with dynamically changing traffic.

In the emerging environment of the Internet of things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we propose a fully distributed model for federated RFID data streams. This model combines two techniques, namely, tilted time frame and histogram to represent the patterns of object flows. Our model is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose several algorithms that use a statistically minimum number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.

As Wireless Mesh Networks (WMNs) are being increasingly deployed, there is an increasing demand for new quality assessment mechanisms that allow service operators to evaluate and optimize the utilization of network resources, while ensuring a good quality level on multimedia applications as perceived by end-users. However, existing real-time assessment schemes for WMNs are not capable of capturing the actual quality of received multimedia content with regard to user perception. Therefore, it is not possible to assure the user experience of content services. To address this problem, this paper introduces the Hybrid Quality of Experience (HyQoE) Prediction, which is a quality estimator specially designed to assess real-time multimedia applications. HyQoE is designed based on the framework of the widely used Pseudo-Subjective Quality Assessment (PSQA) Tool which exploits Random Neural Network (RNN). Crucial extension work has been implemented to achieve our objectives. A performance evaluation verifies the effectiveness and advantages of HyQoE in predicting users' perception of multimedia content in WMNs over existing subjective and hybrid methods.

Migration to an electronically controlled electrical grid to transmit, distribute, and deliver power to consumers has helped enhance the reliability and efficiency of conventional electricity systems. At the same time, this digitally enabled technology called the Smart Grid has brought new challenges to businesses and consumers alike. A key component of such a grid is the smart-metering technology, which is used to collect energy consumption data from homes and transmitting it back to power distributors. A crucial concern is the privacy related to the collection and use of energy consumption data. We present an analysis of Smart Grid privacy issues and discuss recently proposed solutions that can protect the privacy of Smart Grid users.

We propose a new technique to relocate a set of redundant sensor nodes from their initial deployment area called ROI (region of interest) to a new location outside the ROI called COI (center of interest) where a new event happened (e.g., to measure the soil contamination level in the case of a chemical spill disaster). Relocating mobile sensor nodes from their initial deployment area ROI requires covering that area at a given center of interest while maintaining connectivity with the initial region of interest. Our proposed technique to relocate redundant mobile sensors nodes towards a new target position is a four-phase solution that includes the clustering phase and the cell head election, the evaluation of the number of nodes to be relocated, the propagation of the COI position, and finally the election of the redundant nodes and their relocation. A performance evaluation of our novel relocation approach shows that the consumed energy and the relocation time increase as the distance separating the initial ROI from the COI increases regardless of the size or the density of the initial deployment area.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the lifestyle of users.The multimedia era is allowing users to create, distribute, and access content in ubiquitous way and cost-effectively, while providers explore new ways to increase their revenues.The efficient delivery of real-time multimedia services over emerging diverse and heterogeneous systems is a challenging research goal.The interoperability of applications, transport and network protocols as well as the demand for improved Quality of Experience (QoE) create new challenges and opportunities for further research on novel communication protocols, architectures, and methods to efficiently support current and future multimedia networking systems.In 2010, the Third International Future Multimedia Networking (FMN 2010) workshop was organized in Kraków, Poland and achieved a success attracting numerous submissions from various countries.FMN 2010 produced a high quality peer reviewed technical programme with an acceptance rate at around 24% and addressed important aspects of future multimedia systems with a focus on QoE.Authors of selected papers from FMN 2010 were invited to submit extended versions of their papers in order to be considered for inclusion in this Special issue on Future Multimedia.

Recent advances in various wireless communication technologies and the emergence of computationally rich vehicles are pushing vehicular ad hoc network (VANET) research to the forefront in academia and industry. A lot of research results have been published in various areas (such as routing, broadcasting, security and others) of VANET in the last decade covering both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) scenarios. One specific area of VANET that still faces significant challenges is the design of reliable and robust media access control (MAC) protocols for V2V communications. The authors present a survey of V2V MAC methods (including various VANET standards) that have been proposed for VANETs over the last few years. The authors also focus on the benefits and limitations of the proposed MAC techniques as well as their ease of implementation in practice and future deployment. In addition some of the challenges that still need to be addressed to enable the implementation of highly efficient and high performance MAC protocols for V2V communications are discussed. Finally, some innovative solutions that can be developed to address these challenges are proposed.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the life style of users.The multimedia era is allowing users to create, distribute, and access content in ubiquitous way and cost-effectively, while providers explore new ways to increase their revenues.The efficient delivery of real-time multimedia services over emerging diverse and heterogeneous systems is a challenging research goal.The interoperability of applications, transport and network protocols as well as the demand for improved Quality of Service (QoS), Quality of Experience (QoE), and seamless mobility create new challenges and opportunities for further research on novel communication protocols, architectures, and methods to efficiently support current and future multimedia networking systems.In 2009, the Second International Future Multimedia Networking (FMN 2009) workshop was organized in Coimbra, Portugal and achieved exceptional success attracting 64 submissions from 33 countries.FMN 2009 produced a high quality peer reviewed technical programme with an acceptance rate at around 25% and addressed important aspects of future multimedia systems with a focus on Autonomic Content Networks.Authors of selected papers from FMN 2009 were invited to submit extended versions of their papers in order to be considered for inclusion in this Special issue on Future Multimedia.These papers were peer-reviewed again and then further revised by the authors.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the life style of users. User demands for multimedia access anywhere, anytime from any device are creating new challenges for research communities from both academia and industry. It is expected that video-based services alone will account for 50 percent of all consumer network traffic in 2012 and we will continue to witness the explosive growth in users sharing multimedia content over the Internet. In this context, new network, application, and user-based approaches must be created to deal with such complex multimedia systems. This paper presents some of the recent advances in multimedia networking focusing primarily on areas that have been receiving attention recently and are expected to continue to generate further interests in coming years. These areas include Quality of Experience (QoE) and various related standardization issues, Content Distribution Networks (CDNs), multimedia communications, mobile Multimedia. This paper also briefly highlights some of the major challenges that still need to be addressed to enable the support and delivery of multimedia services anywhere, anytime over highly heterogeneous infrastructures and user terminal devices.

Abstract The Internet has become a universal communication network tool. It has evolved from a platform that supports best‐effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end‐to‐end QoS with recently proposed DoS/DDoS solutions. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Delivering video streaming over wireless Internet is becoming increasingly popular. However, most of the research studies focused on the modeling analysis of system performance such as saturation throughput and channel utilization. Perceived quality of video streaming cannot be assessed solely based on the results of analytical models. In this paper, we propose a model to assess the perceived quality of MPEG‐4 video streaming over IEEE 802.11 distribution coordination function (DCF)‐based wireless local area networks. The analysis of our proposed model considers not only effects of losses such as collision loss from channel access competition but also wireless loss caused by wireless interferences. Moreover, the impact of the loss of specific MPEG‐4 video frames is also taken into account in the performance analysis. The model was validated by comparing our performance results with results obtained from simulation and analytical models. The results show that our proposed model is able to predict the perceived quality of MPEG‐4 video streaming over DCF‐based WLAN more accurately than other models. Copyright © 2010 John Wiley & Sons, Ltd.

Vehicular ad hoc networks (VANETs) have attracted a lot of attention over the last few years. They have become a fundamental component of many intelligent transportation systems and VANETs are being used to improve road safety and enable a wide variety of value-added services. Many forms of attacks against VANETs have emerged recently that attempt to compromise the security of such networks. Such security attacks on VANETs may lead to catastrophic results such as the loss of lives or loss of revenue for those value-added services. Therefore making VANETs secure has become a key objective for VANET designers. To develop and deploy secure VANET infrastructures remains a significant challenge. The authors discuss some of the main security threats and attacks that can be exploited in VANETs and present the corresponding security solutions that can be implemented to thwart those attacks.

Purpose The purpose of this paper is to address some weaknesses in the handling of current multi-factor authentication, suggests some criteria for overcoming these weaknesses and presents a simple proof of concept authentication system. Design/methodology/approach First, this paper evaluates some of the underlying practices and assumptions in multi-factor authentication systems. Next, the paper assesses the implications of these when compared to a quantitative authentication risk management approach. Based upon these implications this paper next note the requirements for an improved system and detail some related research areas that meet these requirements. Finally, this paper discussed how a system that meets these requirements through the application of that research could provide benefits and outlined a simple points-based authentication system. Findings The paper proposes that many of the weaknesses in authentication confidence management could be effectively mitigated through the deployment of a factor independent multi-modal fusion quantitative authentication-based system. This paper details a simple point-based approach that does this and discuss how addressing the problems in handling authentication confidence could further optimise risk management in multi-factor authentication systems. Practical implications This paper's suggestions for optimising multi-factor authentication have many implications within medium to high-security commercial and government applications. Correct authentication risk handling enables decisions regarding risk and authentication to be made more accurately. Originality/value This implications of the issues discussed in this paper have relevance to anyone who deploys or uses any medium to high-security authentication system. As the bottom end of the medium to high-security range includes online banking, there are implications for a wide range of stakeholders.

Network Address Translation (NAT) alleviates the shortage of IPv4 addresses but incurs peer-to-peer communication, application functionality and packet integrity problems. To date, no approach has yet been proposed to solve these three problems. By exploiting mobile agent and active networking technologies, we propose a Programmable Network Address Translation (PNAT) implementation that enables peer-to-peer communication while maintaining application functionality and packet integrity. For peer-to-peer communication, our proposed PNAT approach works for various NAT types (including the Symmetric NAT) with simple APIs supported by our proposed NAT design. For application functionality, the PNAT uses the mobile code to update protocol information in packet payloads according to different application needs. For packet integrity, the PNAT allows applications to delay their data encryption until NAT begins to translate addresses and ports in packet headers. To validate our proposed PNAT approach, we implemented the PNAT design on Windows 2000, and we present an empirical performance evaluation of the implemented design.

Welcome to this special issue of the Wiley's Wireless Communications and Mobile Computing Journal. This special issue is devoted to the topic of the latest research and development in the field of wireless communications and networking. With the explosive growth of the ever-increasing users' demands for broadband services, there are a number of emerging wireless technologies, including cognitive radio, wireless sensor networks, WiMAX/LTE, adaptive communications, etc. These technologies have led to significant innovations that enable systems in providing more bandwidth with convenient and inexpensive deployment and mobility. This issue called for papers in various aspects of recent wireless communications and mobile networking. In this special issue, we selected 11 papers to show the recent advances. The papers cover both topical and innovative areas. A detailed overview of the selected works is given below. In 'A Software-Defined Radio Based Cognitive Radio Demonstration over FM Band', R. Zhou et al. present a software-defined radio (SDR) based cognitive radio (CR) implementation and demonstration over the frequency. The authors have proposed, implemented, and demonstrated a frequency hopping scheme over multiple spectrum holes to support multiple secondary users while attaining the minimum interference among the users. In 'A Sensing-Based Cognitive Coexistence Method for Interfering Infrastructure and Ad-Hoc Systems', S. Geirhofer et al. present a novel cognitive coexistence framework, which enables an infrastructure system to reduce interference to ad hoc or peer-to-peer communication links in close proximity. They study how the centralized resource allocation can accommodate the ad hoc links based on sensing and predicting their interference patterns. Results illustrate that utilizing the superior flexibility of the infrastructure links can effectively mitigate interference. In 'Medium Access Control Protocols in Cognitive Radio Networks', J. Xiang et al. make a comprehensive survey of the state-of-the-art MAC and categorize the MAC protocols on the basis of spectrum sharing mechanisms, i.e., overlay and underlay modes. In each mode, they discuss the protocols in both centralized and distributed manners. Finally, they summarize the schemes and identify several open research issues in the realization of cost-efficient MAC protocols. In 'Constructing Secured Cognitive Wireless Networks: Experiences and Challenges', C. Li et al. conduct a high-level survey to review and reflect the state-of-the-art work on the security issues in cognitive radio networks. They focus on analyzing the security system at the macroscopic level, where both protection and detection are considered to be the most essential parts to ensure security in the whole network. Furthermore, they investigate special characteristics of cognitive radio network at different protocol layers, including physical layer, link layer, network layer, transport layer, and application layer. In 'Adaptive Antenna Selection at Mobile Stations for SDMA in WiMAX Networks', Wang et al. present a new antenna selection protocol associated with low signaling and implementation complexity overhead. The protocol has been developed for next-generation IEEE 802.16 mobile stations operating in space division multiple access (SDMA) mode. The authors demonstrate through simulation and analysis that the proposed protocol performs better than traditional IEEE 802.16 terminals that do not have support for antenna selection. The robustness of the protocol is further demonstrated by its capability to adapt different channel conditions. In 'Per-User Service Model for Opportunistic Scheduling Scheme over Fading Channels', Dianati et al. investigated opportunistic scheduling for data transmissions from a single base station to several users. They demonstrate that the received service by a single user can be modeled analytically as a finite-state Markov process under saturated conditions when user queues are heavily loaded. The simulation results obtained closely match those predicted by the proposed analytical model demonstrating the efficacy and accuracy of the model. In 'TinyNET—A Tiny Network framework for TinyOS: Description, Implementation and Experimentation', Castellani et al. present the architecture of an interesting modular network framework that is aimed at speeding up the development and integration of wireless sensor network applications and protocols in TinyOS. By exploiting the framework interfaces, components can be rapidly developed, debugging is made much easier, and software maintenance is simplified because of the in-built modularity. The authors also present a proof-of-concept scenario that explores the proposed framework's basic functionalities and the associated overheads it incurs. The results demonstrate that TinyNET provides significant benefits with no additional complexity and memory costs. In 'Towards Utility-optimal Random Access without Message Passing', Y. Yi et al. study a generalization of CAMA algorithm. In the continuous-time model, a proof is presented of the convergence of these adaptive CSMA algorithms to be arbitrarily close to utility optimality, without assuming that the network dynamics converge to an equilibrium in between consecutive CSMA parameter updates. In the more realistic, slotted-time model, the impact of collisions on the utility achieved is characterized, and the tradeoff between optimality and short-term fairness is quantified. In 'Cross-layer design of joint routing and rate control in ad hoc wireless networks', Ju-Lan et al. present an adaptive modulation coding scheme in multi-hop wireless network in which each node independently selects its cross-layer parameter vector for each packet that it forwards. Furthermore, to enable throughput-effective network operations, a cross-layer scheme is presented under which each node configures its parameter vector by using the link transport capacity measure that it computes as a key metric. The results demonstrate the efficiency of the proposed schemes employing the link transport capacity measure. In 'PC-MAC: Pico Cellular MAC Protocol for Motorway Vehicular Multimedia Communication (extended version)', Bilal et al. presented a detailed analysis of real vehicular traffic data taken from inductive loops on a motorway, and developed their own vehicular simulator to study real-time vehicular traffic scenarios in which a main BS is responsible for multimedia communication. The pico cellular medium access control (PC-MAC) protocol is introduced as a novel MAC protocol combining CSMA and S-TDMA for multimedia communication with optimal utilization for a motorway environment. This proposed protocol was integrated in the simulator to allow managing the access to the base station. In 'Behavior of Clock-Sampling Mutual Network Synchronization in Wireless Sensor Networks: Convergence and Security', MacNeil et al. presented some simulation results that indicate the potential of clock sampling mutual network synchronization algorithm (CS-MNS) to achieve high clock synchronization accuracy in mobile multi-hop wireless networks. It is shown through analysis that in the absence of offset errors the network clocks converge. Finally, a method for adding external reference synchronization that is compatible with a security discussion, given in the paper, is proposed. Finally, we would like to express our gratitude to the Editor-in-Chief, Dr. Mohsen Guizani for his advice, patience, and encouragements since the beginning until the final stage. We thank all anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also thank all authors who have submitted their papers for consideration for this issue. A special thank goes to Jennifer Chichester Hillier, who made a great effort on the production of this issue within a very tight schedule. We hope you will enjoy reading the great selection of papers in this issue.

Abstract We present a novel single sign‐on (SSO) scheme known as secure distributed SSO (SeDSSO). SeDSSO provides secure fault‐tolerant authentication using threshold key encryption with a distributed authentication service. The authentication service consists of n total authentication servers utilizing a (t, n) threshold encryption scheme, where t distinct server‐signed messages are required to generate a message signed by the service. SeDSSO provides secure portable identities by defining a two‐factor identity that uses both a username/password and a unique USB device. The combination of a distributed authentication service and two‐factor identities allows SeDSSO to securely authenticate users in any environment. Copyright © 2008 John Wiley & Sons, Ltd.

As one of the fundamental issues in wireless sensor networks (WSNs), the sensor localization problem has recently received extensive attention. In this work, we investigate this problem from a novel perspective by treating it as a functional dual of target tracking. In traditional tracking problems, static location-aware sensors track and predict the position and/or velocity of a moving target. As a dual, we utilize a moving location assistant (LA) (with a global positioning system (GPS) or a predefined moving path) to help location-unaware sensors to accurately discover their positions. We call our proposed system Landscape. In Landscape, an LA (an aircraft, for example) periodically broadcasts its current location (we call it a beacon) while it moves around or through a sensor field. Each sensor collects the location beacons, measures the distance between itself and the LA based on the received signal strength (RSS), and individually calculates their locations via an Unscented Kalman Filter (UKF)-based algorithm. Landscape has several features that are favorable to WSNs, such as high scalability, no intersensor communication overhead, moderate computation cost, robustness to range errors and network connectivity, etc. Extensive simulations demonstrate that Landscape is an efficient sensor positioning scheme for outdoor sensor networks.

Recently, there has been increasing interest in applying MPLS to IP-based wireless access networks [1]. During a handover on Mobile MPLS-based networks, a registration update message is sent from the Foreign Agent (FA) to the Home Agent (HA). This update message results in a handover delay because of the distance the registration update message must travel from the FA to the HA. To decrease frequent registration handover delays, a micro-mobility approach over MPLS-based networks (also known as Hierarchical Mobile MPLS (H-MPLS)) [2] method was proposed to overcome the limitations of the Mobile MPLS protocol. Studies have shown that with H-MPLS, the Mobile Node (MN) experiences considerably lower handover delays when compared with the delay incurred with conventional mobile MPLS [2], [3]. In this paper we propose to extend the use of H-MPLS by introducing Intermediate Label Information Base (I-LIB) on a Label Switching Router (LSR), which uses the MPLS forwarding and localized signaling. Our simulations demonstrate that with an I-LIB, the end-to-end delay is reduced as a result of a decrease in the handover latency during the establishment of a new Label Switched Path (LSP). With increasing usage of wireless devices supporting delay sensitive traffic (such as voice and video over IP), it is essential to provide lower end-to-end by minimizing handover delays since large delays cause Quality of Service (QoS) degradations.

In this work, we consider the sensor localization problem from a novel perspective by treating it as a functional dual of target tracking. In traditional tracking problems, static location-aware sensors track and predict the position/speed of a moving target. As a dual, we utilize a moving location-assistant (LA) (with global positioning system (GPS) or pre-defined moving path) to help location-unaware sensors to accurately discover their positions. We call our proposed system Landscape. In Landscape, an LA (an aircraft, for example) periodically broadcasts its current location while it moves around or through a sensor field. Each sensor collects the location beacons, measures the distance between itself and the LA based on received signal strength (RSS), and individually calculates their locations via an unscented Kalman filter (UKF) based algorithm. Our contributions are at least twofold. (1) Landscape is a distributed scheme, it does not rely on measured distances among neighbors (as used by most current proposals), which makes it robust to topology and density; Landscape involves zero sensor-to-sensor communication overhead, and is highly scalable to network size. (2) By introducing UKF in sensor localization problem, we reap multiple benefits: our UKF-based algorithm nicely exploits the constraints increasingly added by the beacons; it elegantly solves the nonlinear problem with low computation cost and complexity; and most importantly, it efficiently reduces the effects of measurement errors, making Landscape robust to ranging errors. Extensive simulations and evaluations against the state-of-the-art systems show that Landscape is a high-performance sensor positioning scheme for outdoor sensor networks.

The emergence of the session initiation protocol (SIP) promises simple and efficient handling of multimedia sessions among multiple users. We present the design and implementation of a VoIP system based on SIP. Our SIP-based VoIP system provides a wide range of services multipoint call, multiple audio encoding formats, and complex functionalities such as call muting and call-hold facilities, and personal mobility support.

Jini provides a plug-and-play network architecture suitable for network configurations used in dynamic computing environments. The advent of the Java 2 platform Enterprise Edition (J2EE) enables a component-based approach to designing, developing, assembling and deploying multi-tier enterprise applications and services. We demonstrate by design and implementation how Jini and J2EE technologies can be used together to provide flexible, dynamic access to enterprise services in mobile computing environments such as those involving mobile handheld devices. We have designed and implemented a Jini-J2EE bridge architecture, which enables wireless handheld devices spontaneous access to J2EE services by exploiting the plug-and-play features of Jini. We present an empirical performance evaluation of the Jini-J2EE bridge implementation. Our results show that the Jini-J2EE bridge introduces negligible overheads on the end-to-end performance of user applications and demonstrate that the bridge enables a viable, cost-effective approach in providing flexible access to enterprise services by handheld devices used in mobile computing environments.

In recent years, we have seen remarkable growth of the Internet and IP-based networks, which currently carry most of the end-user traffic. This traffic is being generated by a wide range of applications, often involving different media types such as audio and video. Another significant development recently has been in the area of mobile computing where wireless networks are frequently being used as extensions to wired Internet. As a result, there is increasing demand to run end-user applications seamlessly over both wired and wireless networks with minimal interruption. We present and discuss the design and implementation of two architectures each of which provides support for terminal and personal mobility. The first architecture combines the session initiation protocol (SIP) with mobile IP and the second architecture exploits a pure SIP-based solution to achieve both personal and terminal mobility. We demonstrate the effectiveness of these architectures using a voice over IP (VoIP) application. Based on our performance results, we found that a pure SIP-based solution yields better performance out of the two architectures implemented and tested.

Currently deployed IEEE 802.11 WLANs work mostly with distributed coordination function (DCF) mode at the MAC layer, which does not provide QoS support. The upcoming IEEE standard 802.11e achieves service differentiation by assigning different channel access parameters (CAPs) to different traffic classes at the MAC layer. However, such relative differentiation does not yield QoS guarantee. In practice, appropriately selecting CAPs a priori is difficult. Time-varying traffic loads also make the use of fixed CAPs inefficient for both QoS support and channel utilization. We propose a novel architecture called HARMONICA, in which the access point dynamically selects the best CAPs for each traffic class to optimally match their QoS requirements. We present and discuss a simple admission control mechanism used by HARMONICA to avoid congestion. Our simulation results demonstrate that under an interference-free environment, HARMONICA can guarantee the QoS for all traffic classes while simultaneously achieving quasi-optimal channel utilization.

To achieve innovative network architectures capable of delivering high-speed data transfers to end users, considerable efforts have been invested in minimizing or eliminating the bottlenecks that exist in high-speed network environments. These bottlenecks exist primarily at two levels, namely, network data transmission to the end system and data delivery within the end system to the user. For wired networks, improvements in fiber optic technologies have shifted the bottleneck from the underlying physical network to the end system. However, wireless networks still face obstacles at both levels to achieving high, end-to-end performance data delivery, particularly at gigabit per second rates. We first present current wireless communication technologies aimed at delivering gigabit per second transmission rates to end systems. We then investigate the bottleneck at the end system by exploring experimentally the performance benefits of a network interface architecture designed to enable high-performance, low-latency applications using minimal host resources. We compare the performance of our network interface architecture with the traditional one, using commodity PCs connected by gigabit per second local area networks running protocols such as TCP/IP and UDP/IP. We argue that such a network interface architecture can eliminate the bottlenecks prevalent in current end systems and, consequently, enables users to reap the full benefits of the high-speed networks available today.

Abstract Currently deployed IEEE 802.11 WLANs work mostly with distributed coordination function (DCF) mode at the media access control (MAC) layer, which does not provide quality of service (QoS) support. The upcoming IEEE standard 802.11e achieves service differentiation by assigning different channel access parameters (CAPs) to different traffic classes at the MAC layer. However, such relative differentiation does not yield QoS guarantee. In practice, appropriately selecting CAPs a priori is difficult. Time‐varying traffic loads also make the use of fixed CAPs, inefficient for both QoS support and channel utilization. In this work, we propose a novel architecture called HARMONICA, in which the access point (AP) dynamically selects the best CAPs for each traffic class to optimally match their QoS requirements. We present and discuss a simple admission control (AC) mechanism used by HARMONICA to avoid congestion. Our simulation results demonstrate that under an interference‐free environment, HARMONICA can guarantee the QoS for all traffic classes while simultaneously achieving quasi‐optimal channel utilization. Copyright © 2004 John Wiley & Sons, Ltd.

Most desktop applications execute on commodity operating systems and networks that support end-user requirements on a best effort basis. As a result, the end-user cannot indicate to the underlying system the priority of networked applications when they are executed concurrently. Consequently, the performance of these applications is degraded non-deterministically based on the availability of resources and furthermore the degradation is not in the order and to the extent the end-user would want. This unpredictable degradation frequently affects the usefulness of a user session when multiple networked multimedia applications are executed simultaneously. We designed and implemented a prioritization services that allows an end-user to prioritize applications according to the requirements of an individual session. We report on our experiences during the implementation and our solutions to the difficulties encountered. Our performance analysis demonstrates effective prioritization when multiple applications are executed.

STREAMS-based protocol stacks have become integral components of many desktop operating systems. We explore the UNIX STREAMS architecture in providing priority to networked applications at the end system. We modified the underlying network device driver (a STREAMS-based Ethernet device driver) to enable demultiplexing of packets based on their priorities. We explored the benefits of our approach by conducting several performance tests. Using the modified Ethernet driver combined with the priority STREAMS offers, our test results yield a 550% improvement over the traditional approach where no priority is used in packet delivery to end user applications.

In recent years, we have witnessed the emergence of different types of multimedia applications, particularly those involving continuous media such as digital audio and video. Improvements in hardware, software and networking technologies are enabling a wide deployment of these applications, each with its own Quality of Service (QoS) requirements. Along with these technological advances, Java has emerged as a powerful platform for the development of many desktop applications. In this paper, we first present a review of popular QoS-aware standard networking APIs available to developers on both UNIX and Windows systems. We then discuss the design and implementation extensions we made to the Java architecture to support QoS over native Asynchronous Transfer Mode (ATM) networks. Our implementation offers Java application developers the ability to specify their QoS requirements over ATM networks and simultaneously takes advantage of platform independence provided by Java. In addition, we discuss practical performance experiences obtained with our design in supporting digital video applications running over ATM networks.

Describes the design and implementation of a multimedia architecture for UNIX in an ATM environment. An ATM card has been designed for the EISA bus and provides host connectivity to an ATM network. Applications that use standard protocols such as TCP/IP are supported, as well as other applications that require a raw AAL5 service. To support continuous media applications such as digital video/audio, a kernel module has been designed and incorporated into the UNIX operating system. The kernel module provides an in-kernel data path which allows digital video/audio streaming directly between an ATM network adapter and a display device.