Distributed fog computing environments demand efficient resource management to support real-time Internet of Things (IoT) applications. This paper proposes a fully distributed load-balancing framework based on multi-agent reinforcement learning (MARL), where independent agents learn to manage heterogeneous fog resources without centralized control or inter-agent coordination. The agents jointly optimize a global objective that minimizes workload waiting delay (reduces overall fog queue accumulation) while ensuring fair resource utilization. We evaluated agents’ dynamic adaptation to unpredictable load bursts through transfer learning in simulated fog environments with heterogeneous, unbalanced, and geographically distributed fog nodes. Compared to centralized RL, learning localized policies within smaller collaboration regions allows our distributed agents to achieve superior performance (up to 70.1% reduction in average waiting delay), reduces state-action space, accelerates convergence (6× faster), and scales efficiently as the network grows. In addition, we analyze the impact of realistic interval-based state observation (using a protocol like Gossip) to evaluate the trade-off between performance and practical deployment constraints. Compared to the unrealistic assumption of real-time state availability before every decision, a Gossip interval of 3 seconds in our simulations reduces the state observation overhead by a factor of 9.5×, ensuring the solution is viable for real-world deployment.

The introduction of several new unlicensed spectrum bands in Beyond 5G (B5G) and HetNet enables unprecedented data rates, ultra-low latency, and the capacity to support vast numbers of connected devices. However, to fully realize the potential of unlicensed spectrum bands, efficient management is essential. Traditionally, managing unlicensed bands has focused on controlling contention and interference, often using protocols like LTE-U and Listen Before Talk (LBT). Yet, because unlicensed spectrum is shared, achieving fair usage across users remains a challenge. In this paper, we present a decentralized approach for managing unlicensed spectrum in B5G HetNets. We propose a population-based spectrum sharing with soft-threshold variable to reduce hoarding and to improve fairness. The resource sharing logic is implemented through smart contract to facilitate fair and efficient spectrum sharing among coexisting base stations. Simulations comparing different methods, including fairness-enabled and inter-cell LBT approaches, reveal that our solution reduces both spectrum hoarding and rejection rates compared to solutions that do not use fair-sharing.

The fog-computing paradigm provides low-latency processing and storage between Internet of Things (IoT) applications and cloud data centers. Normal IoT activity may produce infrequent yet substantial spikes in user traffic, which would require a large static fog infrastructure to service. Instead, we consider the viability of having a smaller static fog infrastructure, and supplementing additional traffic spikes with fog-enabled uncrewed aerial vehicles (fog-UAVs). This article formulates the optimal design and dimensioning of fog-UAVs and fog-UAV charging/deployment stations as a probabilistic location set covering problem (PLSCP). We model the fog-UAV PLSCP as a mixed-integer linear program (MILP), from which we derive several relaxed models, including one based on the Benders decomposition technique. Finally, we simulate our models over a set of city-wide IoT hotspots with various traffic percentile thresholds, and evaluate our results.

The emergence of quantum computing poses a formidable security challenge to network protocols traditionally safeguarded by classical cryptographic algorithms.This paper provides an exhaustive analysis of vulnerabilities introduced by quantum computing in a diverse array of widely utilized security protocols across the layers of the TCP/IP model, including TLS, IPsec, SSH, PGP, and more.Our investigation focuses on precisely identifying vulnerabilities susceptible to exploitation by quantum adversaries at various migration stages for each protocol while also assessing the associated risks and consequences for secure communication.We delve deep into the impact of quantum computing on each protocol, emphasizing potential threats posed by quantum attacks and scrutinizing the effectiveness of post-quantum cryptographic solutions.Through carefully evaluating vulnerabilities and risks that network protocols face in the post-quantum era, this study provides invaluable insights to guide the development of appropriate countermeasures.Our findings contribute to a broader comprehension of quantum computing's influence on network security and offer practical guidance for protocol designers, implementers, and policymakers in addressing the challenges stemming from the advancement of quantum computing.This comprehensive study is a crucial step towards fortifying the security of networked environments in the quantum age.

Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.

Blockchain is a promising and revolutionary technology that has the potential to impact almost all industry segments. However, scalability is emerging as one the key challenging issues to its wide adoption. This talk will overview existing solutions to Blockchain scalability, which can be classified into two categories: First layer and second layer solutions. First layer solutions propose modifications to the Blockchain (e.g., Blockchain structure and consensus protocol) while second layer solutions propose mechanisms that are implemented outside of the Blockchain (e.g., state channels and rollups). It will also cover the advantages and tradeoffs of the different scaling solutions.

Internet of Things (IoT) applications depend on reliable external storage and processing such as cloud data centers. In response to high latency from cloud, fog computing has been introduced as a network of microdata centers closer to IoT devices that provides a geo-distributed low-latency response. Current contributions regarding design and dimensioning of fog infrastructures are developed to service a static set of IoT traffic and a reliable fog network. However, these designs are not fault tolerant. This article explores the implementation of reliable and fault-tolerant fog infrastructures via dynamically available fog nodes—standby nodes that activate when a nearby fog node fails. We formulate the design and dimensioning of dynamically available nodes as a set partitioning problem, which is solved via a mixed-integer linear program (MILP). This MILP formulation proves to be intractable; we therefore introduce a column generation approach to increase scalability with little loss to optimal design and dimensioning cost. Compared to other benchmark heuristic methods, our column generation approach yields reduced cost, with proportional solution time.

IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning. Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services. Software-defined networking (SDN) and information-centric networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning. ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive. It provides a novel solution for information propagation and delivery for large-scale IoT services. On the other hand, SDN allocates overall network management responsibilities to a central controller, where network elements act merely as traffic forwarding components. An SDN-enabled network supports ICN without deploying ICN-capable hardware. Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services. This article provides a comprehensive survey on software-defined information-centric Internet of Things (SDIC-IoT) for IoT service provisioning. We present critical enabling technologies of SDIC-IoT, discuss its architecture, and describe its benefits for IoT service provisioning. We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services. We define different taxonomies of SDIC-IoT literature based on various performance parameters. Furthermore, we extensively discuss different use cases, synergies, and advances to realize the SDIC-IoT concept. Finally, we present current challenges and future research directions of IoT service provisioning using SDIC-IoT.

Mobile crowdsensing (MCS) is a promising paradigm of large-scale sensing. A group of mobile users is recruited with their smart devices to accomplish various sensing tasks in specific areas. The mobility and intelligence of mobile users enable MCS to achieve a sufficient coverage ratio of sensing tasks or areas. Currently, MCS is generally proposed and implemented in a centralized way under a platform’s control. However, this centralized structure is vulnerable to a single point of failure. The platform’s failure leads to a shutdown of the entire system. In addition, there is a trust issue between the platform and mobile users because of computational transparency and financial security. It is possible that the platform manipulates the working process of MCS to obtain an improper gain. To overcome these problems, we propose a decentralized MCS framework, named ChainSensing, by leveraging blockchain. In ChainSensing, mobile users interact with blockchain via smart contracts to complete their operations, e.g., publishing sensing tasks and submitting collected data. Since there are computationally intensive problems in ChainSensing, e.g., path planning, path selection, and reward determination, it is significantly expensive to solve such problems in blockchain. Therefore, we propose to leverage smart devices and computing oracles to solve these problems. Specifically, we propose a heuristic algorithm to solve the path planning problem in smart devices of mobile users; we employ computing oracles to solve the path selection and reward determination problems. Finally, we conduct numerical simulations based on Ethereum to evaluate the performance of ChainSensing.

Recently, software defined networks (SDN) has emerged as novel technology that leverages network programmability to facilitate network management. SDN provides a global view of the network, through a logically centralized component, called SDN controller, to strengthen network security. SDN separates the control plane from the data plane, which allows for a more control over the network and brings new capabilities to cope with the new emerging security threats (i.e., zero-day attacks). Existing attack detection schemes are facing obstacles due to high false positive rates, low detection performances, and high computational costs. To address these issues, we propose a multi-module Machine Learning (ML) framework that combines unsupervised ML techniques with a scalable feature collection and selection scheme to effectively/timely detect network security threats in the context of SDN. In particular, our proposed framework consists of: (1) a data flow collection module (DFC) to gather the features of network data in a scalable and efficient way using sFlow protocol; (2) an Information gain Feature Selection (IGF) module to select the most informative/relevant features to reduce training and testing time complexity; and (3) a novel unsupervised ML module that uses a novel outlier detection scheme, called Isolation Forest (ML-IF), to effectively/timely detect network security threats in SDN. The experimental results using the well-known public network security dataset UNSW-NB15, show that our proposed framework outperforms state-of-the-art contributions in terms of accuracy and detection rate while significantly reducing computational complexity; making it a promising framework to mitigate the new emerging network security threats in SDN.

The emergence of Internet of Energy (IoE) paves the way for sustainable and green energy environments that reduce energy costs and integrate Renewable Energy Sources (RESs) as new sources of energy. Electric vehicles (EVs) are one of the main actors of IoE future. The emergence of EVs promises to reduce the environmental crisis (e.g., carbon emissions); however, their charging process will consume massive amounts of electricity and may affect the reliability of the Smart Grid (SG). Recently, vehicle-to-vehicle (V2V) electricity trading approach has gained momentum as a novel strategy that reduces the peak power consumption in SG. In this context, EVs compete to provide electricity with lower prices, while maintaining the V2V electricity trading system secure. However, they lack flexibility, transparency, and authenticity. More importantly, they are based on centralized models (i.e., EV aggregators) which introduce single-point-of-failure and may cause the collapse of the system. In this paper, we propose a fully decentralized blockchain-based system that allows for an automated, fair, and trustworthy V2V electricity trading system; it uses Ethereum’s smart contracts to realize the V2V electricity trading system in a fully distributed, transparent, secure, tamper-proof and trustworthy manner. The proposed system is implemented, tested, and deployed on the Ethereum official test network Ropsten. The experiment results show that the proposed solution achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to new decentralized V2V electricity trading systems in SG.

Mobile crowdsensing (MCS) is a method of data collection by recruiting mobile devices to accomplish various sensing tasks. The mobility and intelligence of mobile devices enable an efficient solution to large-scale sensing, e.g., smart city. Unmanned aerial vehicles (UAVs), as mobile devices, can be used in MCS to perform many sensing tasks (e.g., monitoring). In addition, UAVs provide new business opportunities (e.g., package delivery) with its rapid increasing number. We aim to leverage the package delivery activities of UAVs to solve the task allocation problem of MCS. In the package delivery activities, UAVs must deliver the assigned packages to their destinations. During the package delivery, UAVs travel around to perform sensing tasks with time windows. In this case, the task allocation problem of MCS is considered as a trajectory design problem of UAVs. To plan the trajectories of UAVs, we propose a deep reinforcement learning approach, specifically, double deep Q-network with prioritized experience replay (DDQN-PER). Finally, the results of our numerical simulations show that our proposed solution outperforms two baseline solutions in terms of profit and number of completed tasks.

A steady increase in Internet-of-Things (IoT) applications needing large-scale computation and long-term storage has lead to an overreliance on cloud computing. The resulting network congestion in the cloud, coupled with the distance of cloud data centers from IoT, contributes to unreliable end-to-end response delay. Fog computing has been introduced as an alternative to cloud, providing low-latency service by bringing processing and storage resources to the network edge. In this survey, we sequentially present the phases required in the implementation and realization of practical fog computing systems: 1) design and dimensioning of a fog infrastructure; 2) fog resource provisioning for IoT application use and IoT resource allocation to fog; 3) installation of fog frameworks for fog resource management; and 4) evaluation of fog infrastructure through simulation and emulation. Our focus is on determining the implementation aspects required to build a practical large-scale fog computing infrastructure to support the general IoT landscape.

Nowadays, blockchain technology is seen as one of the main technological innovations to emerge since the advent of the internet. Many applications can benefit from blockchain to protect their exchanges. Nonetheless, applications with more restricted interests cannot use public blockchains. Permissioned blockchains promise to combine effectiveness of blockchains with stricter permissions to join blockchain's network. In permissioned blockchain, the number of participating entities is limited compared to public blockchain. However, by targeting the peers of the blockchain, the attackers can easily take control of consensus process and halt the blockchain operations. In this paper, we propose BrainChain, a scalable and efficient scheme to protect permissioned blockchain nodes from the largest ever Distributed Denial of Service (DDoS) attack (i.e., Domain Name System (DNS) amplification attack) in the context of software defined networks (SDN). BrainChain consists of 4 schemes: (1) Flow statistics collection scheme (FS) to gather the features of flows in an efficient way using sFlow; (2) Entropy based scheme (ES) to measure disorder of network features; (3) Bayes Network based Filtering scheme (BF) to classify, based on entropy values, illegitimate DNS requests; and (4) DNS Mitigation (DM) scheme to mitigate in an effective way the illegitimate flows (i.e., illegitimate DNS requests). Experimental results show that BrainChain can quickly and effectively detect and mitigate the attacks (i.e., DNS amplification attacks) with a high accuracy and a small false positive rate making it a promising scheme to protect blockchain applications from DNS Amplification attacks.

Smart grids (SGs) and advanced metering infrastructures (AMIs) are considered as the new evolution of classical electrical grids. The recent emergence of smart meters is paving the way for the proliferation of smart grids, where billions of smart meters are interconnected to provide novel pervasive services (e.g., real time pricing application and real time energy consumption), and automate diagnostic and daily energy metering (i.e., gas, electric) tasks (e.g., billing, monitoring, planning and predicting of energy usage). The recent explosion in the number of insecure smart meters is changing the view towards SG from enabler of smart homes into a powerful amplifying tool that creates new vectors for cyberattacks (i.e., smart-homes Distributed Denial-of-Service (DDoS) attacks) at large scale. This motivated us to design a new flexible, secure, efficient and trustworthy access control scheme based on blockchain and smart contract. Although access control exists in AMI, it is based on a centralized model (i.e., router/gateway, firewall) which introduces a bottleneck (i.e., single point of failure) and causes the collapse of the system. In this paper, we propose a new decentralized-based access control architecture for SG based on blockchain; it uses smart contracts (i.e., Ethereum's smart contracts) in order to manage permissions in a fully distributed and trustworthy manner. The architecture is implemented, tested and deployed on the Ethereum official test network Ropsten [1]. The results confirm that the proposed blockchain based access control scheme achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to mitigate DDoS attacks in SGs.

The fog-computing paradigm has appeared as a geo distributed response to a growing focus on latency-sensitive Internet-of-Things (IoT) applications and the long delay that may be provided by cloud data centers. Although many researchers have investigated how IoT can interact with a fog, very few have tackled the question of how to construct a fog infrastructure for the expected IoT traffic. This article addresses the design and dimensioning of a fog infrastructure via a mixed-integer linear program (MILP) to construct a physical fog network design by mapping IoT virtual networks to dimensioned fog nodes. Due to the exponential nature of this MILP formulation, we also propose a column generation model with near-optimal results at a significantly reduced design and dimensioning cost. The numerical results show the viability of the column generation method in its proximity to the optimal solution and in its reasonable solution time.

Mobile crowdsensing (MCS) is a new and promising paradigm of data collection due to the growing number of mobile smart devices. It can be utilized in applications of large-scale sensing by employing a group of mobile users with their smart devices. Since a large number of mobile users are recruited, the allocation of sensing tasks to mobile users has a critical influence on the performance of MCS applications. To efficiently assign sensing tasks to mobile users, we propose a novel MCS framework named DeepSensing. This framework consists of six executive phases, i.e., registration of sensing tasks, the announcement of reward rule, collection of users' information, task allocation, execution of sensing activities, and distribution of data and rewards. Here, the phase of task allocation is a key component, which directly determines the performance of DeepSensing, e.g., the platform's profit. DeepSensing aims to maximize the platform's profit by taking into account the various constraints of sensing tasks and mobile users. Therefore, we propose a deep reinforcement learning (DRL) method to optimally assign sensing tasks to mobile users. Specifically, we employ a double deep Q-network with prioritized experience replay (DDQN-PER) to address the task allocation problem, which is also formulated as a path planning problem with time windows. To evaluate our proposed DDQN-PER solution, three baseline solutions are provided, i.e., the ant colony system (ACS), E-greedy, and random solutions. Finally, the results of numerical simulations show that our proposed DDQN-PER solution outperforms the baseline solutions in terms of the platform's profit and it plans better organized traveling paths for mobile users.

Vehicular safety applications based on DSRC/802.11p have strict reliability requirement (greater than 0.99). However, it is difficult to achieve high reliability in wireless medium as the transmission is vulnerable to various wave propagation issues. To the best of our knowledge, none of the existing emergency message dissemination schemes in the literature, achieves a predefined reliability in lossy channel. In this paper, we propose a novel scheme, called reliable emergency message dissemination scheme (REMD), which achieves a predefined reliability for message dissemination while satisfying delay requirements, for various channel conditions. We aim to guarantee very high reliability (e.g., 99%) in each hop, with low control overhead while keeping low end-to-end latency for time-critical applications. We employ zero-correlated unipolar orthogonal codes to combat hidden terminal problem. We exploit periodic beacons, to accurately estimate reception quality of 802.11p wireless link in each cell; then, we use this information to determine the optimal number of broadcast repetitions in each hop. In addition, to ensure reliability in multi-hop, we utilize cooperative communication. The simulation results show that REMD outperforms the existing well-known schemes in the literature. Furthermore, REMD satisfies latency requirements for time-critical vehicular applications and has less network overhead than the existing schemes.

As one of the most devastating types of Distributed Denial of Service (DDoS) attacks, Domain Name System (DNS) amplification attack represents a big threat and one of the main Internet security problems to nowadays networks. Many protocols that form the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. DNS, one of the most critical elements of the Internet, is among these protocols. It is vulnerable to DDoS attacks mainly because all exchanges in this protocol use User Datagram Protocol (UDP). These attacks are difficult to defeat because attackers spoof the IP address of the victim and flood him with valid DNS responses coming from legitimate DNS servers. In this paper, we propose an efficient and scalable solution, called WisdomSDN, to effectively mitigate DNS amplification attack in the context of software defined networks (SDN). WisdomSDN covers both detection and mitigation of illegitimate DNS requests and responses. WisdomSDN consists of: (1) a novel proactive and stateful scheme (PAS) to perform one-to-one mapping between DNS requests and DNS responses; it operates proactively by sending only legitimate responses, excluding amplified illegitimate DNS responses; (2) a machine learning DDoS detection module to detect, in real-time, illegitimate DNS requests. This module consists of (a) Flow statistics collection scheme (FSC) to gather the features of flows in an efficient and scalable way using sFlow protocol; (b) Entropy calculation scheme (ECS) to measure randomness of network traffic; and (c) Bayes Network based Filtering scheme (BNF) to classify, based on entropy values, illegitimate DNS requests; and (3) DNS Mitigation scheme (DM) to effectively mitigate illegitimate DNS requests. The experimental results show that, compared to state-of-art, WisdomSDN can effectively detect/mitigate DNS amplification attack quickly with high detection rate, less false positive rate, and low overhead making it a promising solution to mitigate DNS amplification attack in a SDN environment.

The recent proliferation of Internet of Things (IoT) is paving the way for the emergence of smart cities, where billions of IoT devices are interconnected to provide novel pervasive services and automate our daily lives tasks (e.g., smart healthcare, smart home). However, as the number of insecure IoT devices continues to grow at a rapid rate, the impact of Distributed Denial-of-Service (DDoS) attacks is growing rapidly. With the advent of IoT botnets such as Mirai, the view towards IoT has changed from enabler of smart cities into a powerful amplifying tool for cyberattacks. This motivates the development of new techniques to provide flexibility and efficiency of decision making on the attack collaboration in a software defined networks (SDN) context. The new emerging technologies, such as SDN and blockchain, introduce new opportunities for low-cost, efficient and flexible DDoS attacks collaboration for the IoT based environment. In this paper, we propose Co-IoT, a blockchain-based framework for collaborative DDoS mitigation; it uses the concept of smart contracts (i.e., Ethereum's smart contracts) to facilitate the collaboration among SDN-based domains and transfer attacks information in a decentralized manner. The implementation of Co-IoT is deployed on Ethereum official test network Ropsten [1]. The experimental results confirm that Co-IoT achieves flexibility, efficiency, security and cost effectiveness making it a promising approach to mitigate large scale DDoS attacks.

Mobile edge computing is considered as a promising solution to augment computational capabilities of mobile devices. Mobile users can offload computation intensive tasks to edge clouds collocated with base stations. When mobile users want to offload mobile tasks through base stations, software defined network can provide a centralized control on distributed base stations and mobile devices. In this paper, we investigate the computation offloading problems in software defined network, wherein mobile users compete for limited bandwidth resources of base stations and computation resources in edge clouds. As mobile users are self-interested in making offloading decisions, we formulate the computation offloading problem as a population game in order to analyze the aggregate offloading decisions. We analyze the aggregate offloading decisions of mobile users through evolutionary game dynamic and show that the game always admits a Nash equilibrium. Numerical results demonstrate the effectiveness of the proposed mechanism.

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

In channel-hopping (CH)-based distributed cognitive radio networks (CRNs), the time duration that secondary users (SUs) spend for establishing communication links is called access delay. To evaluate access delay, we propose an access delay model by jointly considering imperfect spectrum sensing and multi-channel multi-SU transmission, from the cross-layer perspective. The model considers two typical scenarios. The first scenario assumes that the SUs do not use contention scheme (CS) which indicates that the time slot is relatively shorter to just allow a transmission. The second scenario assumes that the SUs employ CS [i.e., modified Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) in this paper], which indicates that the time slot is long enough to regulate multiple transmissions. We then propose a bio-inspired algorithm for the first scenario and a self-adaptive step-length algorithm for the second scenario to search for the optimal values of spectrum sensing parameters. The theoretical analysis and simulation results validate the proposed access delay model and show that the proposed algorithms can reduce the most redundant computation. They also show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, we conduct a cost-benefit analysis to evaluate the performance of the two scenarios.

Nowadays, blockchain is seen as one of the main technological innovations. Many applications can rely on the blockchain to secure their exchanges. However, applications with private interest cannot rely on public blockchains. First, in a public blockchain, anyone can read the whole data of the blockchain. Second, anyone can participate to the "consensus process"; the process for determining the validity of each transaction. Consortium and fully private blockchains aim to combine forcefulness of blockchains with controlled consensus process and stricter permissions for deploying a node and joining the blockchain network. In both consortium and fully private blockchains, the number of peers on the blockchain network is very small in comparison with public blockchain. Nonetheless, by targeting the nodes of blockchains, an attacker can easily manage the whole blockchain and takes control of the consensus process to validate his illegitimate transactions. In this paper, to defend blockchain nodes from DNS amplification attacks, we propose a scalable and proactive solution in the context of software defined networks (SDN), named ChainSecure. ChainSecure consists of 3 schemes: (1) StateMap, a novel stateful mapping scheme (SMS) to perform a mapping one-to-one between DNS request and response; (2) Entropy calculation scheme (ECS) to measure the disorder / randomness of data using sFlow in order to detect illegitimate flows; (3) DNS DDoS Mitigation (DDM) module to effectively mitigate illegitimate DNS requests. The experimental results show that ChainSecure protects blockchain nodes and can detect/mitigate the attack quickly to achieve high accuracy in detecting illegitimate DNS traffic making it a promising solution to protect blockchain nodes from DNS amplification attacks.

In this paper, we propose an optimization approach on access delay by jointly modeling imperfect spectrum sensing and multi-channel multi-secondary user (SU) access contention, from a cross-layer perspective, for channel-hopping (CH) based cognitive radio networks (CRNs). Specifically, we first employ an absorbing Markov chain to model the multi-SU access contention process by combining the impacts of spectrum sensing and rendezvous; furthermore, we propose a methodology to derive the enclosed expression of rendezvous probability by analyzing the regularity and the periodicity of a CH rendezvous algorithm. We further analyze the access delay model and compute the optimal parameter combination (e.g., sensing duration and access contention duration) to build a communication link within minimum time duration Theoretical analysis and simulation results validate the proposed optimization approach and show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, the impact of the numbers of SUs and channels is also analyzed to study the relationship between CRN size and SUs' minimum access delay.

Computation offloading is envisioned as a promising solution to resource scarcity problem on mobile devices. Mobile users can offload computation intensive tasks to remote cloud with stronger capabilities. In order to execute tasks in cloud, mobile users have to upload computational data through cellular networks. When large amounts of mobile users in the same cell attempt to offload mobile tasks through the base station, the communication latencies for data transmissions may be high due to limited bandwidth resources. However, since the task completion times are constrained by hard deadlines, this restricts the feasible set of computational tasks that can be uploaded. In this paper, we propose a population game based approach to achieve efficient computation offloading for large amounts of competing mobile users, where each user is aimed to minimize his energy consumption. This game is subject to the task execution deadlines, user specific data rates, and the competition over the shared communication channel. We analyze the evolutionary dynamic of the game and show that the game always admits a Nash equilibrium. We then design a computation offloading mechanism that can achieve a Nash equilibrium of the game. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.

The wireless access for vehicular environment (WAVE) mandates that data packets of non-safety applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels; yet, owing to vehicles' high mobility, there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to mitigate this problem. Nevertheless, they are either inefficient or cost-ineffective. In this paper, we propose a novel approach called altruistic service channel selection (ASSCH) that compels vehicles to cooperate in order to select the least congested service channels for vehicle-to-vehicle (V2V) non-safety applications. ASSCH has three phases: 1) identifying the channel's current state (i.e., free or occupied); 2) predicting channels that are likely to be free in the near future; and 3) selecting the least used channel among them. We then propose a stochastic analytical model for the throughput of V2V non-safety applications considering various factors, including the busy channel at zero, discarded by all existing IEEE 802.11p EDCA models. Simulation results demonstrate that ASSCH outperforms existing allocation-based schemes as it incurs low capture delay, low ratio of overlapping WBSS, and high throughput. Simulation results also show that our analytical model closely matches the throughput of EDCA access categories.

Cellular network is facing a severe traffic overload problem caused by the phenomenal growth of mobile data. Offloading part of the mobile data traffic from the cellular network to alternative networks is a promising solution. In this paper, we study the mobile data offloading problem under the architecture of mobile cloud computing, where mobile data can be delivered by WiFi network and device-to-device communication. In order to minimize the overall cost for the data delivery task, it is crucial to reduce cellular network usage while satisfying delay requirements. In our proposed model, we formulate the data offloading task as a finite horizon Markov decision process. We first propose a hybrid offloading algorithm for mobile data with different delay requirements. Moreover, we establish sufficient conditions for the existence of threshold policy. Then, we propose a monotone offloading algorithm based on threshold policy in order to reduce the computational complexity. The simulation results show that the proposed offloading approach can achieve minimal communication cost compared with the other three offloading schemes.

Having seamless Internet connection is highly desirable for wireless sensor network (WSN). IPv6 offers the most promising solution to deal with the large quantity of nodes or "things". This demonstration presents a versatile research and development (R&D) or prototyping platform, on which we implement generic hardware and software framework for sensing/monitoring as well as remote control applications.

In this paper, we study mobile data offloading problem under the architecture of mobile cloud computing (MCC), where mobile data can be delivered by cellular, WiFi and Device-to-Device (D2D) communication networks. In order to minimize the overall cost for data delivery task, it is crucial to reduce cellular network usage while satisfying delay requirements. In the proposed model, a portion of the cellular data traffic is offloaded through WiFi and D2D networks. We formulate the data offloading problem as a finite horizon Markov Decision Process (FHMDP). We solve the problem using hybrid offloading algorithm for delay sensitive and delay tolerant applications. The simulation results show that the proposed offloading scheme can achieve minimal total cost compared with other three offloading schemes.

The Wireless Access for Vehicular Environment (WAVE) specifies that data packets of infotainment applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels. Nevertheless, owing to high mobility in vehicular ad hoc networks (VANET), there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to avoid such a scenario. Yet, they are either inefficient or cost-ineffective. In this paper, we propose ASSCH, an altruistic mechanism that impels vehicles to collaborate in order to select the least congested (i.e., used) service channel. ASSCH also includes a mechanism for WBSS termination, which is not specified in the WAVE standard. To the best of our knowledge, none of the existing works have proposed something similar. Simulation results demonstrate that our scheme handles the overlapping problem better and incurs high channel efficiency.

This paper proposes two Markovian models to analyze the performance of IEEE 802.11p EDCA mechanism for vehicle-to-vehicle (V2V) infotainment applications. The first model describes the backoff procedure and is used to compute the transmission probability of each access category (AC) while the second illustrates the contention phase after a busy channel and is used to derive the probability of collision. Both models consider backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the case where the channel is sensed busy once the counter backoff reaches 0, which is not considered in almost all of existing works in the literature. Using both models, we derive an accurate formula for the normalized throughput for each AC. Simulation results are provided to demonstrate the accuracy of our analytical model.

Recently, Jung et al. [1] proposed a data access privilege scheme and claimed that their scheme addresses data and identity privacy as well as multi-authority, and provides data access privilege for attribute-based encryption. In this paper, we show that this scheme, and also its former and latest versions (i.e. [2] and [3] respectively) suffer from a number of weaknesses in terms of finegrained access control, users and authorities collusion attack, user authorization, and user anonymity protection. We then propose our new scheme that overcomes these shortcomings. We also prove the security of our scheme against user collusion attacks, authority collusion attacks and chosen plaintext attacks. Lastly, we show that the efficiency of our scheme is comparable with existing related schemes.

The tradeoff between decreasing interference to primary user (PU) and increasing secondary user's (SU's) throughput is of great importance for cooperative sensing in cognitive radio networks. Non‐ideal spectrum sensing in PHY and multiple SUs' access contention in MAC jointly impact SUs' transmission and the tradeoff. In this study, the authors investigate the joint impact from a cross‐layer perspective. First, they quantify the reliability of cooperative sensing and compute SUs' transmission probability under the conditions of non‐ideal sensing and access contention. Closed‐form expressions of the interference probability to PU and SUs' throughput are derived. Specially, two widely‐used contention‐based MAC protocols, i.e. slotted Aloha and distributed coordination function, are studied. Then, they formulate the sensing‐throughput tradeoff problem by using interference probability to PU, rather than the detection probability, as the constraint. Finally, a 2‐dimension search algorithm is proposed to obtain the optimal solution, including the optimal fusion rule, sensing duration and detection threshold. Simulation results validate the outperformance of the cross‐layer scheme. They also demonstrate how the optimal solution varies with some key parameters, i.e. PU's signal‐to‐noise ratio and the number of contending SUs.

Vehicular sensing has gained prominence in recent years with its use in entities, including traffic management centers, forensic authorities, and air pollution control units. It also provides end users with real-time street images, parking summaries, and road congestion status. To reduce bandwidth usage and improve the content value, the sensed data must be aggregated. Data aggregation is said to be efficient when the destination (i.e., a node that serves as a data collection point in the network) is capable of receiving sensed data from a significant proportion of vehicles. However, when a large number of vehicles attempt to send sensed data, the network becomes congested eventually causing packet losses and collisions. Thus, if aggregation is performed without considering key factors, such as number of vehicles and network dynamics, it is difficult to ensure the efficient collection of sensed data at the destination. In this paper, we propose a dynamic hierarchical aggregation scheme in which sensed data is aggregated using a hierarchy. Moreover, the hierarchy is dynamically updated based on theoretically estimated delivery efficiency. In particular, we perform partition and merge operations within the hierarchy to achieve an improved value of delivery efficiency. The simulation results show that the proposed scheme ensures efficient data collection even with stringent delay requirements and achieves scalability with respect to a number of vehicles in the network.

Mobile cloud computing is a revolutionary computing paradigm for mobile application which enables storage and computation migration from mobile users to resources rich and powerful cloud servers, but emerges various privacy concerns. Attribute based encryption is a public key encryption that ensures the security of stored data in the cloud and provides fine grained access control using defined policies and constraints. Location of a device is one of the contextual policies which is used to improve data security, authenticate users and provide access to services and useful information for mobile users. However, unlike other policies and attributes used in attribute based encryption, location of mobile users are dynamic. In this paper, we investigate providing Location Based Services (LBS) for attribute based access control in mobile cloud. More specifically, we propose a multi-authority attribute based access control scheme and protect users privacy against malicious authorities. The proposed scheme uses dynamic location of a mobile user as contextual information about that user, employs coarse location as an attribute in attribute based encryption to achieve K-anonymity, and filters the returned results for more accuracy. The attribute based encryption is integrated with proxy re-encryption to outsource the computation to a cloud server with “unlimited” computational power. The proposed scheme achieves efficiency by reducing computational cost on resource-constrained mobile users.

With the advancement of technology in smartphones and their popularity, users are expecting higher performance and functionality from mobile devices just as powerful non-mobile devices, especially in the context of multimedia applications. However, despite significant advances in handheld devices, there still exists a gap between device capabilities and the requirements of applications. Mobile Cloud Computing emergence has overcome the barriers of the mobile device restrictions. To improve the performance of media services processed in clouds, an efficient resource allocation mechanism is needed. In this paper, we propose a resource allocation scheme in Mobile Cloud Computing based on Learning Automata technique, taking into account three fundamental concerns: total response time of various types of mobile media services, if they are to be executed on cloud; level of uncertainty (i.e. prone to failure measurement); and computational capacity requirement of services. Simulation results demonstrate that the proposed resource allocation scheme can optimally allocate cloud resources to each media service while ensuring minimal QoS requirements of applications.

In wireless networks, spectral efficiency is a vital issue in the wake of ever increasing bandwidth usage. To improve throughput, in wired and wireless networks, network coding is one of the promising solutions which has been used for a quite some time. On getting coding opportunity in a hop, coding gain is around 100% if physical layer networking, unlike linear network coding which offers maximum coding gain up-to 33%. However, PNC has symbol-level synchronization, carrier-frequency synchronization, and carrier-phase synchronization. The throughput elevate with number of nodes involved in physical layer network coding. In the literature, there is no proposal which increases the involvement of number nodes to elevate overall throughput of the network. In this paper, we are forcing multiple opposite direction routes to share common road segments without compromising delay and reliability requirements. We designed a new particle swarm optimization (PSO) mechanism which offers maximum throughput gain for the entire network. Through network coding such voluntary congestion is solved. Thus, higher packet delivery ratio can be achieved while keeping lower end to end delay which is verified by graphs.

This paper presents an analytical comparison of active and passive receiver models in diffusive molecular communication. In the active model, molecules are absorbed when they collide with the receiver surface. In the passive model, the receiver is a virtual boundary that does not affect molecule behavior. Two approaches are presented to derive transforms between the receiver signals. As an example, two models for an unbounded diffusion-only molecular communication system with a spherical receiver are unified. As time increases in the three-dimensional system, the transform functions have constant scaling factors, such that the receiver models are effectively equivalent. Methods are presented to enable the transformation of stochastic simulations, which are used to verify the transforms and demonstrate that transforming the simulation of a passive receiver can be more efficient and more accurate than the direct simulation of an absorbing receiver.

In this paper, we propose methods to enable efficient data delivery in vehicular ad-hoc networks (VANETs). We target scenarios where data are collected by vehicles and need to be transmitted. Dynamic clusters are formed while vehicles move in order to make data transmissions more robust and scalable. We use mathematical optimization solutions to optimize transmission scheduling so as to maximize throughput and minimize delay in delivering data. This paper defines an optimization model which addresses the max-min flow allocation problem by decomposing it into a master problem and a subproblem. Our proposed scheme implements a contention free based medium access control where physical conditions of channel have been fully analyzed. Extensive simulations were performed for different scenarios to show the performance of the proposed Distributed Cluster Based (DCB) transmission scheduling scheme.

We propose a multi-hop reliable broadcasting (M-HRB) scheme suitable for a wide range of vehicular ad-hoc network (VANET) applications in urban setting. Multi-hop reliability is performed using local state information. Basically, a street is divided into multiple cells to form grid-like zones. We apply a proactive local state processing scheme exploiting features of periodic beacons to estimate wireless link quality of the neighbours. With availability of local state information, adequate numbers of forwarders are selected to achieve desired reliability in each hop for a multi-hop broadcast. Furthermore, M-HRB saves bandwidth consumption by enabling cooperation among forwarders. Simulation results show the superior performance of M-HRB in city environment in terms of reliability and bandwidth consumption. M-HRB also satisfies transmission latency requirements for time-sensitive vehicular applications.

Follow me cloud (FMC) is one of the solutions to the limitations of the service quality and type of functionality due to the inherent constraints of mobility management. In this paper, we address the tradeoff between the overhead and Quality of Experience (QoE) by proposing a mobility-based services migration prediction (MSMP). The aim of MSMP is to plan, for a given service, the sequence of data to transfer from distinct micro data centers (MDC) to the user according to his mobility pattern and the estimated load of data centers. MSMP consists of (1) a data transfer throughput estimation scheme, called DTT, that aims to estimate, in advance, the throughput that the user could receive; (2) MDC service area handoff time estimation scheme, called AHT, that aims to estimate the time windows when the user will perform MDC service area handoffs; and (3) service migration management scheme, called SMM, that aims to select optimal MDCs in terms of offered throughput and to split user requested service into several portions to be processed by these MDCs. We evaluate MSMP and compare it against the most recent related approaches; the simulation results show that MSMP outperforms existing approaches in terms of data latency.

This paper uses two Markov chains to model IEEE 802.11p EDCA throughput over service channels. A 2-D Markov chain is constructed first to describe the backoff procedure of each access category and to infer its transmission probability. Then, a 1-D discrete Markov chain is used to describe the contention phase of an access category and to derive its collision probability. Both models consider the backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the transmission opportunity (TXOP) parameter, unexploited by IEEE 802.11p, to enhance the performance of infotainment applications. Using both models, we derive an accurate model of the normalized throughput for each access category. Simulation results show that our model generates higher throughput for access categories with high priority compared to IEEE 802.11p standard.

This paper presents a distributed clustering algorithm, called DCEV, which constructs multi-hop clusters. DCEV places vehicles into non-overlapping clusters which have adaptive size based on their relative mobility. The cluster formation is based on a D-hop clustering scheme where each node selects its cluster head in at most D-hop distance. To create clusters, DCEV uses a new metric to let vehicles choose the most stable route to their desired cluster head within their D-hop neighbourhood. For this purpose, each node calculates the mean relative mobility value of each discovered route (end-to-end relative mobility). DCEV considers the route which has the least end-to-end relative mobility as the most stable route. Extensive simulations were conducted for different scenarios to validate the performance of DCEV clustering algorithm. Results show that DCEV efficiently manages to build stable clusters.

Internet of Vehicles requires reliable Inter-Vehicular communications. Such a requirement is challenging since the wireless communication channel is very erroneous and lossy in city environments. A lot of solutions for connecting vehicles to the internet have been proposed. However, existing multi-hop gateway discovery solutions do not consider, a key issue, the unreliability of broadcast in city environments. In this paper, our objective is to find out the minimum communication hops, with very high reliability (e.g., 97%), to gateways. To accomplish this, we model the gateway placement problem (called GP) as a k-center optimization problem. We solve it in O(n^2*log(n)) time using a (2-1) dimension reduction technique. We make use of M-HRB to discover reliable multi-hop paths to gateways. Simulation results demonstrate that applying M-HRB with GP provides high packet reception rate and generates smaller end-to-end delay compared to existing solutions. Furthermore, our proposal makes efficient use of wireless channel bandwidth.

In this paper, we propose parallel interference cancellation (PIC) for link activation in VANETs. Link activation (LA) stands for activating a set of communication links which can transmit simultaneously without transmission collisions. We consider multi-hop VANET scenarios where vehicles are clustered using d-hop clustering algorithms such as proposed in [1]. We model the interference cancellation as a mixed integer programming (MIP) optimization problem where wireless link conditions are analyzed. The proposed parallel interference cancellation method can be used for scheduling of transmissions and resource sharing inside the constructed clusters. Simulations were performed for different scenarios to show the performance of the improved LA.

Vehicular Ad hoc Networks (VANETs) are one of the building blocks for future Intelligent Transportation Systems (ITS). They will support applications for safety and entertainments on the streets. VANETs also provide an excellent potential for on-demand services. Such services in VANETs require real-time request-reply routing between vehicle client and service provider, and also tracking of client location. The vehicle requesting for a service acts as a client. The service provider communicates with clients through fixed base stations. Due to frequent link break- ups in VANET communications, maintaining a one-to- one communication path between service provider and client is an open issue. We refer to such one- to-one communication as the unicast service. In this paper, we propose a novel architecture to solve the unicast service provisioning and vehicle tracking in an integrated manner. Simulation results show that our proposed model reduces network overhead dramatically, and provides an on- demand unicast service with acceptable delivery delay and delivery ratio.

For energy harvesting cognitive radio (EHCR), secondary user's (SU's) energy harvesting duration and spectrum sensing parameters jointly impact SU's capacity but have not been studied yet. In this paper, we investigate the joint impact. First, we obtain the closed-form expression of SU's capacity considering both limited energy supply and imperfect spectrum sensing. Then, we maximize SU's capacity while satisfying detection probability constraint and guaranteeing that the harvested energy is sufficient to transmit a certain amount of data payload. Simulation results prove that the optimized energy harvesting duration and spectrum sensing parameters dramatically improve SU's capacity.

A major challenge in vehicular ad-hoc networks (VANETs) is the ability to account for resource sharing and location management so that multicasting/routing functions and bandwidth reservations can be organized efficiently. By creating clusters of vehicles we are able to control resource sharing and management functions in VANETs that are highly dynamic. In this paper, a D-hop clustering algorithm, called DHCV is presented which organizes vehicles into non overlapping clusters which have adaptive sizes according to their respective mobility. The D-hop clustering algorithm creates clusters in such a way that each vehicle is at most D hops away from a cluster head. To construct multi-hop clusters, each vehicle chooses its cluster head based on relative mobility calculations within its D-hop neighbours. The algorithm can run at regular intervals or whenever the network formation changes. One of the features of this algorithm is tendency to re-elect the surviving cluster heads whenever the network structure changes. Extensive simulation results have been done under different scenarios to show the performance of our clustering algorithm.

VANETs) that provides a vehicle with data about 1-hop neighboring vehicles. Data provided by this service can be used to support safety applications, such as the efficient selection of forwarders for safety messages and dissemination of early warnings to drivers about potential dangers of the road. This paper discusses the limitations of the beaconing service in providing vehicles with safety-related information. It also proposes a mechanism to let each vehicle have additional information about its surroundings in order to get an extended perception of its environment. This can help in considerably reducing accidents on the roads. Through simulations, we show that the additional overhead caused by the exchange of additional data can be kept low enough to prevent significant impact on overall network performance.

The tradeoff between increasing secondary users’ (SUs’) throughput and decreasing interferences to primary user (PU) is an important problem in cognitive radio networks. Joint design of sensing duration and frame duration has a crucial impact on both these two conflicting attributes but has not been studied yet. In this study, using a cross‐layer approach, the authors investigate joint design of sensing duration and frame duration for the tradeoff. Specially, they consider that PU's traffic randomly changes within a secondary frame and multiple SUs contend to use the licensed channel based on widely used large/small‐scale‐backoff‐based MAC protocols. By modelling more realistic PU's traffic, imperfect spectrum sensing in PHY and multiple SUs’ access contention in MAC, the authors reformulate the sensing‐throughput tradeoff problem to maximise SUs’ throughput while restricting interference probability to PU under a tolerable level. Moreover, the optimal solution is analysed and a bi‐dimensional search algorithm is presented. Simulation results show that the authors’ proposal achieves better throughput performance than conventional approaches. They also show how the optimal solution varies with received PU's signal‐to‐noise ratio and PU's traffic distribution.

This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks (VANETs). Most existing schemes aim at reducing E2ED via greedy-based techniques (i.e., shortest path, connectivity, or number of hops), which make them prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that computes E2ED for the entire routing path before sending data messages. To do so, SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected information of delay and connectivity. Routes with the lowest aggregated weights are selected to forward data packets. Simulation results show that SCRP outperforms some of the well-known protocols in literature.

The tradeoff between the interference to primary users' (PUs) and secondary users' (SUs) throughput is of great importance in cognitive radio networks. Both imperfect spectrum sensing and multi-channel access contention impact the tradeoff. In this paper, jointly considering imperfect spectrum sensing and multi-channel access contention from cross-layer perspective, we obtain the expressions of interference probability to PUs' and SUs' throughput for both slotted Aloha and distributed coordination function-based Medium Access Control (MAC) protocols. Compared against related contributions, which use detection probability as constraint, we formulate the sensing-throughput tradeoff problem by taking the interference probability as the optimization constraint. We further propose to use the access strategies set and exhaustive search on sensing duration to jointly optimize spectrum sensing parameters and access parameters with the objective of a completely cross-layer design. Numerical results show that the proposed cross-layer method can improve SUs' throughput performance significantly by relaxing the requirement of sensing reliability. Moreover, SUs' throughput performance when the realistic multi-channel scenario is taken into account is worse than the predicted performance in related contributions assuming single-channel scenario. In addition, the optimal solution varies with the number of divided sub-channels and frame duration, and thus needs to be carefully designed.

Thanks to their flexibility and their simplicity of installation, Wireless Mesh Networks (WMNs) allow a low cost deployment of network infrastructure. They can be used to extend wired networks coverage allowing connectivity anytime and anywhere. However, WMNs may suffer from drastic performance degradation (e.g., increased packet loss ratio and delay) because of interferences and congestion. Generally, the network may be unexpectedly congested at one or more gateways (GWs) since their number is limited and most traffic is oriented to/from Internet and passes through them. In this paper, we propose a combination between a selective gateway scheme and an adaptive routing scheme in WMNs, called SGRL (Selective Gateway and Reinforcement Learning-based routing). SGRL (1) considers a probabilistic gateway selection strategy to avoid route flapping which improves network stability and traffic fairness between gateways and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio and load at the gateways. Simulation results show that SGRL can significantly improve the overall network performance compared to interference and channel switching (MIC), Reinforcement Learning-based Distributed Routing (RLBDR), Expected Transmission count (ETX), load at gateways as routing metrics.

This paper deals with channel assignment for cloud network users based on the information from application layer. We assume a multiple user network where the services requested by the users are categorized into real-time (RT) and delay- tolerant (DT) services. We assign each wireless channel to a user who needs RT service. Inspired by the cognitive networks, we aim to assign the same channel to a second user who needs DT service, while minimizing the interference. Our objective is to simultaneously perform these assignments such that the SNR value of the worst user is maximized. This assignment significantly outperforms the separate assignment of the channels to RT users and then, to DT users. The resulting assignment problem turns out to be NP- hard. We propose a very simple, yet effective algorithm to solve this problem. In the second part of the paper, we will prove that the results of our suggested solution stay in a specific neighborhood of the optimal answer. In order to prove this fact, we statistically analyze the optimal solution, where we derive a general framework to express the statistical behavior of the optimal solution. Then we prove that both optimal solution and our solution achieve the same physical channel diversity.

In recent years, many routing protocols have been proposed to enable infotainment applications for urban VANET. These schemes deploy greedy-based techniques that rely on various routing metrics (e.g., driving distance, road segment connectivity, or number of hops) to meet the requirements of the aforementioned applications, i.e., low end-to-end delay and high throughput. Yet, most of these protocols are exposed to the local maximum problem, inciting them to employ the carry-and-forward mechanism. This leads to high end-to-end delay and significant packet losses. To address this issue, we propose a Stable and Reliable CDS-based Routing Protocol (SRCP) that selects paths with high connectivity and low delivery delay. To accomplish this, SRCP builds backbones over road segments and estimates the delivery delay for transmitting data packets over them. It, then, assigns weights to the road segments and selects the routing path with the lowest total weight to forward data packets. Extensive simulations demonstrate that SRCP generates less end-to-end delay and provides better packet delivery ratio compared to existing schemes.

From cross-layer perspective, the impact of imperfect spectrum sensing and access contention on the cooperative sensing in cognitive radio networks is investigated in the context of the tradeoff between interferences to PUs and aggregated secondary throughput. Jointly considering imperfect spectrum sensing and access contention, we reformulate the sensing-throughput tradeoff problem via taking the interference probability, rather than the detection probability, as the optimization constraint, and further obtain the optimal combination of fusion rule, sensing duration and detection threshold to maximize the secondary throughput under the interference probability constraint. Numerical results show that the proposed cross-layer method can improve the secondary throughput performance significantly, especially in the case of low signal-to-noise ratio.

International audience

Vehicular sensing has gained significant attention in recent years, thanks to its enormous benefits to many entities including traffic management centers, forensic authorities and air pollution control units. To reduce redundancy and improve the content, the collected data must be aggregated. Delay-sensitive sensing applications require the aggregated data be collected with a certain delay. In this paper, we propose a hierarchical aggregation scheme for image sensing in vehicular networks. The hierarchy is dynamically updated based on observed network conditions. In particular, partition and merge operations are performed on the hierarchy to satisfy the delay requirement. The simulation results show that the proposed scheme outperforms existing scheme in terms of efficient data collection and redundancy elimination.

In wireless networks, spectral efficiency is an important issue due to limited available bandwidth. Traditionally, when a receiver collects simultaneous transmissions, packets collide and the much meaningful information cannot be retrieved. Thus, through suitable scheduling, simultaneous transmissions to the receiver are avoided. However, in physical layer network coding, simultaneous transmissions are allowed and the combined signal is equivalent of XOR operation on air. A path from a source to a destination in unicast routing in VANET is called a flow; multiple flows may have common road segments. There will be heavy contention in such shared road segments resulting in lower throughput, lower packet delivery ratio and higher end to end delay. In this paper, we propose a multihop physical layer network coding structure to be used by multiple flows of unicast routing paths. We analyze throughput, end-to-end delay and coding gain with respect to number of routing flows and number of forwarders involved in network coding.

In multi-channel cognitive radio networks, it is important to investigate the sensing-throughput tradeoff from cross-layer perspective. Jointly considering imperfect spectrum sensing in physical layer and multi-channel access contention in MAC layer, we reformulate this tradeoff via taking the interference probability, rather than the detection probability, as the optimization constraint. And we further obtain the optimal sensing duration and detection threshold depending on the number of available channels to maximize the secondary throughput under the interference probability constraint. Numerical results show that the proposed cross-layer method can improve the secondary throughput performance significantly.

Vehicle density information is crucial for efficient functioning of many vehicular applications, including emergency notification, driver assistance, and infotainment applications. This information is used for evacuation planning in accident scenarios, finding alternate routes in the case of road congestion, and providing stable routing paths for uninterrupted internet connections. In a city scenario, it is a tedious task to continuously collect and share large volumes of data containing density information. In this paper, we propose a mechanism to create a density map for city environments. A hierarchy (i.e., tree) is established, where a node represents a road segment and the density is used to determine the height of the node; the root node represents the road segment having highest density. The purpose of this tree is to collect and aggregate density information starting from the leaves until the root node is reached. Then, the aggregated density information (i.e., density map) is forwarded down the hierarchy. For efficient aggregation of density information, we adopt an effective curve-fitting method where data are represented in an equation. Simulation results show that the proposed mechanism allows highly accurate computing of density map while generating low network overhead.

An urban scenario is the center stage for vehicles to roam around the concrete jungle. Any sorts of wireless communication would be affected by hidden terminal problems, fading and interferences. Unintended nodes are unnecessarily bothered by such huge volume of microwave communications. The most common forms of communication are beaconing messages, which let the vehicles know about its neighboring vehicles and possibly choose an appropriate forwarder for safety and non-safety messages. However, such influx of broadcast messages may lead to beacon overhead and congestion resulting in low message reception as well as excessive delay. Interferences due to inter-street beacon messages may affect emergency messages, channel arbitration messages and other control messages which share a common channel as specified by DRSC/WAVE. This paper proposes a scheme to cancel interferences due to inter-street beacon communications by adaptive transmission control, while maintaining application layer transmission range, through multi-hop beacon forwarding and network coding. The simulations show that our scheme has higher packet delivery ratio and higher successful channel utilization compared to CSMA/CA protocols.

Urban Sensing has become an increasingly popular service in vehicular networks. It allows consumers to access a repository of data collected by sensors embedded in vehicles. Aggregation is a viable approach to convert the sensed data into a usable form. An efficient way to perform aggregation is to divide the network into a number of geographical regions, called aggregation regions. In this paper, we propose a novel mechanism to construct aggregation regions based on the location of RSUs (road-side units). Besides, we propose an optimization strategy to determine the optimal location of aggregation that minimizes the delay of vehicular communications.

Beacon messages are periodic 1-hop broadcast messages which are sent by each vehicle to let neighboring vehicles/infrastructures be aware of its position, speed, and change of direction. Beacon information plays a crucial role for many applications including active safety applications through which vehicles can predict the position of neighboring vehicles and be able to take instant decisions to avoid any emergency situation. However, such influx of broadcast messages may lead to beacon overhead and congestion. These result in low message reception as well as excessive delay. Beacon overhead and congestion may affect emergency messages, channel arbitration messages and other control messages which share a common channel as specified by DRSC/WAVE. This paper proposes a mechanism for controlling beacon overhead by adopting packet level network coding. The simulations prove that our scheme has higher packet delivery ratio and higher successful channel utilization compared to CSMA/CA protocol.

Smart cities leverage technology to analyze data to make decisions, anticipate problems and coordinate resources to operate efficiently. Data produced by sensors embedded in vehicles moving on streets enable sensing applications for smart cities that were infeasible in the past due to high deployment costs. In this paper, we propose a novel framework for collection, aggregation and retrieval of data. The framework considers vehicles and road-side units as the main entities. To collect data, the city road network is divided into a number of sensing regions. We discuss the aggregation operations for each type of event. A retrieval mechanism is also proposed to deliver content in real-time. The simulations results demonstrate that the proposed framework outperforms existing vehicular sensing approaches in terms of delay and accuracy.

For safety applications, fast delivery is the key requirement that broadcasting protocols in VANET must fulfill. To achieve this goal, most of the existing schemes try to select the optimal next forwarder through a distributed contention phase. This latter adds few units of delay at each hop which is substantial in case of multihop transmission. A more pragmatic approach is to build a stable virtual infrastructure, ahead of time, which will be in charge of relaying messages. In this regard, this paper presents the Minimum Stable CDS-based Virtual Backbone, labeled CDS-SVB, which guarantees fast and efficient broadcasting. It selects vehicles with identical velocities to assure stability as well as low dissemination delay, and it deploys acknowledgments to ensure reliability. Simulation results show that CDS-SVB outperforms DBA-MAC in terms of dissemination delay, backbone's lifetime, and packets delivery ratio.

Wireless technologies are becoming an essential part of our daily life. These technologies are expected to provide a wide variety of real-time applications; hence, there is a vital need to provide quality-of-Service (QoS) support. One of the key mechanisms to support QoS is traffic regulation. The basic idea behind traffic regulation is to measure the network state (e.g., load) in order to adapt the rate of carefully selected application flows. In this paper, we propose a novel model, called FuzzyWMN, which can be used to implement traffic adaptation in Wireless Mesh Networks (WMNs).The objective of FuzzyWMN is to compute the rate adaptation to apply to application flows according to the current network state; it relies on two parameters to meet this objective: (1) packet delays between sources and destinations; and (2) buffer occupancy of network nodes. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to realize traffic adaptation in networks characterized by information uncertainty and imprecision due to the dynamic traffic behavior, channel interferences, etc. Extensive simulations show that FuzzyWMN achieves stable end-to-end delay and good throughput under different network conditions.

It has been reported that the IEEE 802.11 MAC protocol and the TCP congestion control are highly problematic in terms of flow starvation in wireless mesh networks (WMNs). However, the economic features of IEEE 802.11 make it the commonly-used MAC protocol in WMNs. Therefore, solving starvation at the transport layer seems to be more appropriate. Indeed, the main starvation cause in TCP is that congestion is managed as a link-based problem. However, since bandwidth is a spatially-shared resource in WMNs, congestion is a neighborhood phenomenon that should be handled using mutual cooperation within a congested neighborhood. Such cooperation considerably consumes the already scarce bandwidth of WMNs causing more congestion. In this paper, we propose a neighborhood-aware and overhead-free congestion control scheme (NICC) that solves the starvation problem without impacting the scarce bandwidth of WMNs. NICC makes use of some underexploited fields in the IEEE 802.11 frame header, without modifying the standard frame size, to provide an overhead-free multi-bit congestion feedback; being overhead-free, this feedback allows performing neighborhood cooperation without generating control overhead. Furthermore, being multi-bit, it yields source nodes a fine-grained indication of the congestion degree, providing accurate rate control. The NICC performance in terms of starvation avoidance and bandwidth efficiency is proven through extensive simulations.

Bandwidth is an extremely valuable and scarce resource in wireless networks. Therefore, efficient bandwidth management is necessary to support service continuity, guarantee acceptable quality of service and ensure steady quality of experience for users of mobile multimedia streaming services. Indeed, the support of uniform streaming rate during the entire course of a streaming service, whereas the user is on the move is a challenging issue. In this paper, we propose a framework, together with schemes, which integrates user mobility prediction models with bandwidth availability prediction models to support the requirements of mobile multimedia services. More specifically, we propose schemes that predict paths to destinations, times when users will enter/exit cells along predicted paths, and available bandwidth in cells along predicted paths. With these predictions, a request for a mobile streaming service is accepted only when there is enough (predicted) available bandwidth, which is along the path to destination, to support the service. Simulation results show that the proposed approach outperforms existing bandwidth management schemes in better supporting mobile multimedia services.

In this paper, we propose an efficient forwarding node selection and density estimation mechanisms for vehicular networks (VANETs). Density estimation is performed without using beacons. Basically, the transmission range is divided into segments. Then, we apply a superposition based analog signal transmission and exploit multi-user capability of OFDM to collect density in each lane and in each segment. With such fine-grained information, a farthest node can be successfully located and selected as the forwarder. The one hop delay is substantially reduced by reducing the contention time and relying mostly on signals instead of control packets to assist the forwarder node selection process. In the simulations, it is observed that the proposed protocol accurately estimates the node density. Also, it outperforms existing multi-hop broadcast protocols in terms of end-to-end delay.

A good understanding of the behaviour of the traffic of a mobile network is essential for an efficient planning and management of the mobile network's scarce bandwidth resources. In this paper, we propose a probabilistic approach, called Handoff Time Estimation MODel (HTEMOD), to estimate the time window when a user will perform handoffs along his/her movement/path to a destination. We derive the probability distribution function of time taken to transit each road segment along the path, using a sample of users that is selected according to navigation zone characteristics, current data on road segments, and current behaviour of users on the road segment. We evaluate our model via simulations, and compare it with the model proposed in [1]. Regardless of the given probability value to obtain a time window, the road segment density and the number of road segments to handoff, HTEMOD provides a better accuracy and good duration of predicted time window when handoff will occur. Whilst the proposed HTEMOD model can be applied to any type of user equipment, its efficiency becomes more appealing in the context of vehicles (i.e., for the support of road to vehicles communications - RVC) or highly mobile nodes travelling in urban areas constrained by predefined roads and whose velocities are also restricted according to speed limits, level of congestion in roads, and traffic control mechanisms (e.g., stop signs and traffic lights).

Thanks to their flexibility and their simple installation, Wireless Mesh Networks (WMNs) allow a low cost deployment of a network infrastructure. They can be used to extend the wired network coverage allowing connectivity anytime and anywhere. Network stability is a key performance metric in supporting real time communication over the network. Because of high bandwidth demand and dynamic traffic variation, several paths in WMNs are expected to be unstable. High levels of network instability can lead to interferences, packet losses and high delays. In this paper, we address the stability problem of WMNs; instability in these networks is caused mainly by link quality fluctuations and frequent route flapping. Indeed, most routing protocols try to optimize a routing metric locally or globally without considering network stability. First, we present the key factors that may cause network instability; then, we propose a new technique, called Local Node Stability-based Routing (LNS), using the entropy function (known as a measure of the uncertainty and the disorder in a system) to define a node stability. Simulation results show that the stability can be improved in WMNs using LNS compared to other routing schemes namely RLBDR, MIC and ETX.

IEEE 802.11p Wireless Access in the Vehicular Environment (WAVE) standard is being developed, in order to support Intelligent Transportation System (ITS) applications including safety applications. This includes data exchange between vehicles (V2V), and between vehicles and infrastructure (V2I). Safety applications, e.g., collision and other safety warnings, rely on broadcast communication. Unfortunately, 802.11p does not allow mechanisms such as sending RTS/CTS and acknowledgements for broadcast communication. Therefore, several collisions can be caused by hidden nodes/vehicles. Moreover, the possibility of using the binary exponential back-off technique to reduce congestion is not supported due to the lack of acknowledgements. In this paper, we propose a new 802.11 based Vehicular Multi-hop Broadcast protocol, called Highway Multihop Broadcast (HMB) that addresses the broadcast storm, hidden node, and reliability problems of multi-hop broadcast in VANET. HMB selects the farthest vehicle, with the least speed deviation with respect to the source, to forward and acknowledge broadcast frames. Simulation results show that HMB has a very high success rate in delivering safety messages, and efficient channel utilization when compared with existing broadcast based protocols.

Along with the recent and ongoing advances in the wireless and mobile access technologies, a wide plethora of mobile multimedia services have emerged. Ensuring an acceptable level of Quality of Service (QoS) is a crucial requirement to allow users enjoy these mobile multimedia services. One means to ensure QoS is to minimize the frequency and magnitude of fluctuations in the mobile multimedia streaming rates during the multimedia service and while users are on the move. For this purpose, there is need for tools to predict a user's long-term movement. In this vein, this paper proposes a Path Prediction Model (PPM) to predict a user's movement path. PPM is based on historical movement trace, current movement data and spatial conceptual maps; it assumes a priori knowledge of the destination. At each road intersection, the probability of selecting the next road segment is evaluated, based on historical data, towards the destination. These probabilities are computed via (a) filtering historical data according to the day of the week (e.g., weekend, holiday) and the time of the day; and (b) applying conditional probability rules taking into account the path used between the origin of movement, current position, and the destination. Simulations are conducted using real-life data to evaluate the performance of the proposed model. Encouraging results are obtained in terms of average prediction accuracy and mitigation of the impact of learning period and the remaining distance to reach the destination on the path prediction performance.

Mobile Wireless Network technology has enabled the development of increasingly diverse applications and devices resulting in an exponential growth in usage and services. One challenge in mobility management is the movement prediction. Prediction of the user's longer-term movement (e.g., 10 min in advance) with reasonable accuracy is very important to a broad range of services. To cope with this challenge, this paper proposes a new method to estimate a user's future destination, called Destination Prediction Model (DPM). This method combines two types of approaches: one based on the use of filtered historical movement pattern and another based on contextual knowledge; both approaches use spatial conceptual maps. The filter is based on the day and the time of the day to increase accuracy. The current movement direction, that takes into account the recent data, is used by the proposed method to reduce historical and contextual knowledge mistakes. Simulations are conducted using real-life data to evaluate the performance of the proposed model. For subjects with low predictability degree, DPM reaches an average prediction accuracy of 79%; it reaches 91% for subjects with high predictability and 86% for other subjects. Simulation results also indicate that DPM significantly reduces the impact of learning period and the remaining distance to reach the destination on prediction performance. In the future, we plan to extend our research work by proposing a full Path Prediction Model (PPM) based on the Destination Prediction Model (DPM).

Voice over IEEE 802.11 becomes much more cost effective when deployed over IEEE 802.11-based Wireless Mesh Network (WMN) due to the license-free backhaul wireless links. However, medium access control (MAC) based on IEEE 802.11 traditional random backoff protocols that are suitable for throughput-sensitive data services cannot satisfy the performance of voice services. Therefore, in this paper we propose an improved MAC based on medium reservations, called EMDA. EMDA improves significantly the optional Mesh Deterministic Access (MDA) MAC, to provide a much higher voice call capacity thanks to voice packet aggregation feasibility and much less MDA signaling overhead. EMDA is based on a per-node reservation of a block of contiguous transmission opportunities that are properly dimensioned. Moreover, we further improve the near-deterministic access of EMDA by using short jamming periods just before these reservations. Extensive simulation results show that our proposed MAC enlarges the voice capacity and uniformly distribute it over the WMN. It also provide a better performance in guaranteeing hard delay constraints, lower jitter and lower packet losses compared to other MAC.

Abstract Mesh‐based multicast routing protocols for mobilead hocnetworks (MANETs) build multiple paths from senders to receivers to deliver packets even in the presence of links breaking. This redundancy results in high reliability/robustness but may significantly increase packet overhead. This paper proposes a mesh‐based multicast protocol, called centered protocol for unified multicasting through announcements (CPUMA), that achieves comparable reliability as existing mesh‐based multicast protocols, however, with significantly much less data overhead. In CPUMA, a distributed core‐selection and maintenance algorithm is used to find the source‐centric center of a shared mesh. We leverage data packets to center the core of each multicast group shared mesh instead of using GPS or any pre‐assignment of cores to groups (the case of existing protocols). The proposed centering scheme allows reducing data packet overhead and creating forwarding paths toward the nearest mesh member instead of the core to reduce latency. We show,viasimulations, that CPUMA outperforms existing multicast protocols in terms of data packet overhead, and latency while maintaining a constant or better packet delivery ratio, at the cost of a small increase in control overhead in a few scenarios. Copyright © 2010 John Wiley & Sons, Ltd.

In mobile multimedia streaming services, important requirements consist of the support of service continuity, the guarantee of acceptable Quality of Service (QoS) and insurance of steady Quality of Experience (QoE). How to get a uniform data exchange rate during the entire (or partial) course of a streaming service while a user is on the move is an important challenge. Generally speaking, the streaming rate of a multimedia service may heavily fluctuate due to the unavailability or deficiency of resources along the movement path of a user. To cope with this challenge, this paper proposes a framework that integrates user mobility prediction models with resource availability prediction models to keep a constant or less fluctuating streaming rate and to ultimately ensure steady QoE. Simulations are conducted to evaluate the performance of the proposed framework in achieving its design objectives and encouraging results are obtained.

In the last few years, extensive research has been performed to extend Internet connectivity to VANETS. Indeed, several routing protocols have been proposed to determine routes between vehicles and gateways (e.g., Road Side Units: RSUs). However, most of these protocols do not use efficiently the bandwidth which is a scarce resource in VANETS. In this paper, we propose a routing protocol to connect vehicles to Internet through mobile gateways with the objective to make efficient use of the network bandwidth. Indeed, the protocol significantly reduces the communication overhead required to establish and maintain the routes relying on the mobility of the gateways. The simulation results show that the proposed protocol outperforms existing protocols in terms of normalized routing load, packet delivery ratio, and end-to-end delay.

This paper addresses the problem of optimal route selection in wireless mesh networks (WMNs) where intra-flow and inter-flow interferences have a negative impact on the network performance. Several routing metrics have been proposed in the literature to find paths that minimize interferences and thus maximizes throughput. However, most of these metrics consider either inter-flow interferences or intra-flow interferences and a few consider both types of interferences. In this paper, we propose an efficient new routing metric, called MBP (Metric Based on Probabilities), that aims to choose routes with high throughput, low intra-flow and inter-flow interferences between a source and a destination. MBP is based on the Minimum Loss (ML) metric and a well known physical interference model. Simulation results show that MBP outperforms some existing metrics, namely hop count, WCETT and iAWARE, in terms of throughput, delay and packet loss.

The emergence of real-time applications and their widespread usage in communication have generated the need to provide quality-of-Service (QoS) support in wireless networks environments. One of the most crucial mechanisms of a model for providing QoS support is the traffic regulation. In the aim of better representing and analyzing the decision making policy of the traffic adaptation process in wireless mesh networks (WMN), we propose a novel model named FuzzyWMN. The proposed model combines the essential notions of both fuzzy logic theory and Petri nets; this enables FuzzyWMN to achieve the traffic adaptation process in the context of dynamic network events characterized by the uncertainty and imprecision information, due to the dynamic traffic behavior, channels interference, etc. The evaluation of FuzzyWMN performances, compared to AIMD-SWAN and IEEE 802.11, was studied under different network and traffic conditions. The promising results obtained from extensive simulations confirm that the traffic adaptation based on the fuzzy design can achieve stable end-to-end delay, and good throughput under different network conditions.

Described in the specifications of WAVE (Wireless Access in Vehicular Environments) standards, broadcast is the main traffic in vehicular networks when all vehicles monitor the control channel. In this paper, we show a simple Markov model to analyze the saturation throughput of control channel broadcast, our analysis reveals that existing IEEE 802.11p parameter settings can result in degraded network performance. In particular, the argument that the contention window size determines the performance of broadcast is concluded. Moreover, we propose a novel scheme which can achieve an optimal throughput by adapting the contention window size to the networks size. Both theoretical analyses and simulation results show the effectiveness of our proposal.

Most existing research has made use of simulation and analytical methods to study Wireless Local Area Networks (WLAN). In this paper, we use FPGA to model and implement the 802.11 Distributed Coordination Function (DCF). Firstly, we define the functional blocks and their working behavior, then, we describe the corresponding implementation onto FPGA devices. The design of all functional blocks strictly follows the specifications of the 802.11 standard. Thus, the proposed FPGA-based DCF module can seamlessly interconnect with existing commercial WLAN chipsets, furthermore all parameters of the 802.11 DCF are wide-open to system designers (i.e., can be easily configured/modified). The proposed implementation provides a normative prototype by which the development of DCF based systems can be tested and evaluated conveniently.

This paper addresses the problem of optimal gateways selection and route selection to Internet in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to Internet. Several schemes have been proposed to route packets in WMNs or to select appropriate gateways to connect clients to Internet. However, most of these schemes consider packet loss, interferences, load at gateways, or ETX (Expected Transmission Count) as routing metrics; only a few schemes consider two some of these metrics at the same time. In this paper, we propose an efficient gateway and path selection scheme, called BP2BG (Best Path to Best Gateway), that takes into account load at gateways, ETX and interferences in order to select best path to best gateway. Simulation results show that BP2BG can significantly improve the overall network performance compared to schemes using either ETX, nearest gateway (i.e., shortest path to gateway), load at gateways or interferences as metrics for path and gateway selection.

This paper addresses the problem of optimal routing in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count, load at routers and interference. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously is needed. In this paper, we propose an efficient new routing scheme, called RLBPR (Reinforcement Learning-based Best Path Routing), that adaptively learns an optimal routing policy, depending on multiple optimization metrics such as loss ratio, interference ratio and load at the gateways. Simulation results show that RLBPR can significantly improve the overall network performance compared to schemes using either Metric of interference and channel switching (MIC), Best Path to Best Gateway (BP2BG), Expected Transmission count (ETX), nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.

Voice over IEEE 802.11 networks is a costeffective solution compared to cellular telephony in small areas and therefore considered among the killer applications of emergent Wireless Mesh Networks (WMNs). However, traditional random medium access control (MAC) protocols used in IEEE 802.11-based WMNs, which are suitable for throughput-sensitive data services, are far from guaranteeing the low delays and the low packet losses required by voice traffic. Therefore, in this paper we propose a new enhanced MAC, called EMDA, combined with a simple scheduling and admission control algorithm, called SAC, that provides a high capacity and quality guarantees for voice over WMNs. EMDA improves significantly the optional Mesh Deterministic Access (MDA) MAC protocol, to provide a much higher voice call capacity thanks to voice packet aggregation and less MDA signaling overhead. EMDA is based on a per-node's radio interface transmission opportunity reservations that are well dimensioned. Moreover, we further improve the deterministic access of EMDA by proposing a jamming scheme that provides more robustness against interfering non-MDA nodes. The proposed SAC algorithm guarantees hard delay constraints and a uniform voice capacity over WMNs regardless of the number of hops to a gateway (i.e., a mesh router connecting WMN to Internet). Simulation results show that our proposed scheme provides a bigger voice capacity that is uniformly distributed over the WMN and a better performance in guaranteeing hard delay constraints and lower packet losses compared to other schemes.

The WAVE (Wireless Access in Vehicular Environments) concept includes seven channels within the DSRC band. One of them, known as the Control Channel (CCH), is the one used to exchange all safety-related messages. Messages sent over the CCH have to be processed with different priorities depending on how critical they are for vehicle safety. However, the MAC protocol currently adopted for WAVE, namely EDCA, stops short of that requirement; it does not establish strict priorities, but only relative advantages for some types of messages over the others. Another problem is that, since messages are broadcasted on the CCH, there are no acknowledgments. This means that it is not possible to know whether a transmission was successful or not, which eliminates the possibility to use the binary exponential backoff technique to reduce congestion. In this paper, we propose a simple but effective solution to both of these problems. We use simulations to analyze the performance of the modified MAC protocol and compare it to that of the original EDCA. The results show that the proposed scheme outperforms EDCA. Our comparison focuses on the reduced probability of collision for high-priority frames (gain) and on the increased delays for lower-priority frames (price to pay).

Optical Burst Switching (OBS) networks are candidate to play an important role in next generation optical networks. Routing and wavelength assignment may have a decisive impact on the performance of these all-optical networks. We propose a novel Integer Linear Programming (ILP) formulation for the routing problem which is, at the contrast of existing models, specific to the nature of OBS networks and is based on the network topology. To resolve efficiently this model, we propose a genetic algorithm. Also, we propose a wavelength assignment heuristic which uses the solution of the routing optimization model. Simulation results show the effectiveness of our model in improving the performance of OBS networks.

In this paper, we address the issue of multicast cooperative communications over WMNs. Specifically, we propose a new framework where mesh router is in charge of the cooperation of mesh clients during a multicast session. Under this framework, we identify the selection of relaying nodes as a critical step to success. To solve above problem, we further develop the concept of in/out-group cooperation as well as the heuristics to find the optimal relay solution. Extensive simulation results show that our approach can considerably mitigate the transmission errors in wireless multicast environment with high efficiency.

In cellular networks it is the mobile node's responsibility to update the network about its location change, especially when this one enters idle mode. We developed a new framework [8] where the idle interface is powered-off to save energy and thus could neither detect its location change nor perform a location update. This framework uses a proxy entity at the network and at the mobile node the active radio interface for proxying the idle interface. In this paper, we present an approach that relies on the proxy entity and the active interface for proxying the location update procedure of the proxied interfaces. More specifically, we propose two algorithms. The first algorithm considers proxying periodic location update of idle interface without considering its mobility if there is any. The second algorithm is based on sending the location to the Information Server which determines the list of paging/tracking areas that serves the current location. We present the design of our architecture for 4G systems, namely WiMAX and LTE. The algorithms are analytically evaluated to evaluate the power savings compared with single-radio power management.

Ad-hoc multi-hop broadcast protocols are usually used in vehicular networks to provide safety services. However, these protocols face several issues, namely broadcast storms, hidden nodes, and message delivery failures, that prevent safety applications from guaranteeing their required high message delivery ratio and low delays. In this paper, we tackle these issues using a novel cluster-based contention-free broadcast protocol. Particularly, we propose an efficient time slot reservation protocol, centralized in stable cluster heads that continuously adapts to vehicles dynamics. Thus, using a centralized protocol, we ensure an efficient utilization of the time slots for the exact number of active vehicles including hidden nodes; our protocol also ensures a bounded delay for safety applications to access communication channel. We reduce the overhead of our reservation protocol using a directed broadcast propagation and a single reservation request for a periodic medium access during a vehicle's cluster session. During the recurrent service interval, a contention-based period follows the efficiently-used contention free period; it is dynamically adjusted to improve throughput-sensitive non-safety applications. Extensive simulation results show that the proposed scheme can significantly improve the periodic safety application performance in terms of safety message delivery ratio and delay.

The rapid growth of routing tables represents a major challenge facing the scalability of BGP and indeed the whole Internet infrastructure. In this paper, we introduce a novel distributed algorithmic scheme for partitioning the BGP routing table on multiple controller cards, where we exploit parallelism to enhance both the lookup speed and the scalability of the RIB (Routing Information Base). The proposed scheme increases the lookup performance by letting unrelated tasks, such as the Best Match Prefix (BMP) lookup and the BGP decision process to be executed in parallel at different controller cards. Simulations show that our proposal outperforms classical central lookup mechanisms with a reasonably acceptable cost, while it increases considerably the space scalability of the BGP routing table.

Channel assignment schemes in Multi-Radio Wireless Mesh Networks (MR-WMNs) usually leave several links sharing the same channel within overlapped transmissions or interference ranges; this is especially true when only one radio is used or when the number of radios is very small compared to the number of orthogonal channels. In this paper, we propose a new multi-objective optimization model for channel assignment (CA) performed during the MR-WMNs planning process. Given the expected traffic demand, the goal is to (1) minimize user handoff overhead; (2) minimize traffic load variances to achieve load balancing; (3) maximize overall throughput; and (4) maximize Jain's fairness index to achieve fairness among mesh clients. We also propose a variable neighborhood search (VNS) meta-heuristic to solve our model. Simulation results show that our proposed approach achieves good performance in terms of delay, loss rate, overall throughput and fairness in the MR-WMNs.

Wireless Mesh Networks (WMN) have attracted increasing attention from the research community as a high-performance and low-cost solution to last-mile broadband Internet access. In the other side, Optical Burst Switching (OBS) is a promising access technology that uses optical fiber with burst switching paradigm. In this paper, we propose a novel Metropolitan Area Network (MAN) architecture called Optical Burst Wireless Mesh Architecture (OBWMA) which integrates WMN at the user access side and OBS at the core of the MAN. OBWMA aims to combine advantages of both WMNs and OBS networks, such as large coverage at low cost and bandwidth availability. We develop an analytical model to compute the end-to-end delay in OBWMA in order to support flow requests with delay constraints. Furthermore, we propose a novel QoS provisioning scheme for the WMN part (WQP), and a novel bandwidth provisioning scheme for the OBS part (WPBP). Simulation results using ns-2 demonstrate the feasibility of OBWMA, the accuracy of our analytical model and the effectiveness of WQP and WPBP to provide QoS provisioning for the converged network.

n order for a mesh-based routing protocol, in a mobile ad hoc network, to perform well it must achieve a high level of robustness without excessive overhead. We present the centered protocol for unified multicasting through announcements (CPUMA) for mobile ad hoc networks. A distributed coreselectio

Optical Burst Switching (OBS) is a promising switching paradigm for the next generation Internet. A bufferless OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologica

Optical Burst Switching (OBS) is a promising switching technology for the next generation all-optical networks. An OBS network without wavelength converters and fiber delay lines can be implemented simply and cost-effectively using the existing technology. However, this kind of networks suffers from a relatively high burst loss probability at the OBS core nodes. To overcome this issue and consolidate OBS networks with QoS provisioning capabilities, we propose an absolute QoS differentiation scheme, called Absolute Fair Quality of service Differentiation (AFQD), which is based on a wavelength partitioning scheme, called Optimization Topology-aware Wavelength Partitioning scheme (OTWP). AFQD is the first absolute QoS provisioning scheme that guarantees loss-free transmission for high priority traffic inside the OBS network. Simulation results show that AFQD not only guarantees loss-free transmission for high priority traffic but also substantially decreases the loss probability of best effort traffic to a remarkable level compared to the existing schemes.

In the design of wireless mesh networks (WMNs), one of the fundamental considerations is the reliability and availability of communication paths between network pairs in the presence of nodes failure. The reliability and deployment cost are important and are largely determined by network topology. Usually, network performance and reliability are considered separately. In this paper, we propose a new algorithm based on ear decomposition for constructing reliable WMN infrastructure that resists the failure of a single mesh node and ensures full coverage to all mesh clients (MCs). Via a case study, we show the tied relationship between network deployment cost, performance and reliability in a simultaneous optimization of cost and load balance over network channels. The optimization model proposed is solved using metaheuristics which provides the network operator with a set of reliable tradeoff solutions.

Applications using grid computing infrastructure usually require resources allocation to satisfy their quality of service (QoS) requirements. Given that the grid infrastructure is a set of computing resources geographically distributed, the support of grid applications requires the allocation of computing resources and bandwidth to enable communication among these resources. The objective is to accommodate as many applications as possible while still satisfying their requirements. Ideally, we would like to accommodate a given Grid application using a set of computing resources (e.g., one server) that are not geographically distributed (e.g., in the same LAN); however, this is not always possible. Indeed, to increase the probability of accommodating grid applications, we may need to use computing resources scattered all over the network; in this case, bandwidth allocation is required to enable communication among these resources. In this paper, we propose an optimization model that enables the "simultaneous" allocation of computing resources and bandwidth for grid application while maximizing the number of grid applications being accommodated. A heuristic is proposed to solve the model with an acceptable response time; simulations show that the proposed approach outperforms existing classical approaches.

Choosing strategic locations to optimally place gateways prior to network deployment in wireless mesh networks (WMNs) can alleviate a number of performance related problems; it can also lead to better handling of network scalability. Existing solutions that address the optimal gateway placement problem differ mainly in terms of the set of constraints that the placed gateways has to satisfy; the resulting placements influence, differently, the network quality of service (QoS). In this paper, we study the WMN topology design and we propose a clustering based gateway placement algorithm (CBGPA) that guarantees end-to-end bounded delay communications with a good handling of network scalability. We show, via a case study, that CBGPA is constraints-independent algorithm that can effectively be coupled with a WMN design model; for that, we propose a multi-objective optimization model to design WMNs topologies from scratch. The two objectives of deployment cost and average congestion of gateways are simultaneously optimized in the model. The optimization model proposed is solved using a nature inspired meta-heuristic algorithm coupled with CBGPA, which provides the network operator with a set of bounded-delay tradeoff solutions. A comparative experimental study, using large size networks (up to 169 nodes) and different key parameter settings is conducted to show the effectiveness of CBGPA and to evaluate the performance of the proposed model.

Wireless mesh networks (WMNs) have attracted increasing attention from the research community as a high-performance and low-cost solution to last-mile broadband Internet access. However, it remains an open challenge to provide mesh clients with efficient handoff among different mesh routers. In this paper, we address channel switching efficiency and load balancing capability during the handoff process. More specifically, (1) we introduce the concept of clique into WMNs to handle channel conflicts among neighboring mesh routers and thus achieve scalability; and (2) we propose a dynamic load balancing strategy for the handoff process, which integrates the mechanisms of mesh router selection and traffic admission control. Simulation results show that our proposed approach achieves significant gain in the handoff delay. Moreover, it provides good performance in terms of load balancing, loss rate and overall throughput.

Recently, wireless mesh networks (WMNs) have drawn significant attention from academia and industry as a fast, easy, and inexpensive solution for broadband wireless access. In WMNs, many important applications, such as mobile TV and video/audio conferencing, require the support of multicast. In this paper, we address the issue of multicast cooperative communications over WMN. Specifically, we identify the selection of relay nodes as a critical step to the success of cooperative multicast. To solve this problem, we develop the concept of in-group/out-group cooperation as well as a distributed relay selection algorithm for the mobile computing environment. Extensive simulation results show that our approach can efficiently mitigate the multicast transmission errors in wireless mesh networks.

A number of key issues arise in the implementation of scalable multicast protocols for wireless mobile ad hoc networks (MANETs), namely energy consumption and data delivery over unstable/mobile nodes. To improve scalability of these protocols, clustering has been proposed. Clustering allows reducing the number of mobile nodes participating in multicast routing algorithms, which in turn significantly reduces the routing-related control overhead. In this paper, we propose a clustering algorithm, called RSIDS (restful stability based insomnious distributed sensors), which considers both stability and residual energy of neighboring nodes when selecting critical nodes (i.e. cluster heads and gateways). RSIDS uses passive clustering (in opposition to active clustering) to form the clustering structure. The critical nodes selection enables the selection of most stable nodes with high residual energy as critical nodes; the goal is to minimize re-clustering (and thus re-branching for multicast protocols) that may generate considerable overhead and packet losses and increase the lifespan of the network. We show, via simulations, that RSIDS outperforms existing clustering schemes, in terms of packet delivery ratio and network lifetime, when used with the MAODV (multicast ad hoc on demand distance vector) routing protocol.

Recently, we proposed a multi-objective approach to optimize the planning of Wireless Mesh Networks (WMNs). Unlike other approaches where the deployment cost is the pivotal concept to optimize under typical network constraints, this approach tends to simultaneously optimize the two objectives of network deployment cost and network throughput. Optimal WMN planning solutions under this approach are more realistic and much preferred by network planners in that they have to be both cost-effective and efficient (the deployment cost is minimized while the throughput is maximized). While the deployment cost objective is straightforward, the throughput objective can be viewed from different perspectives: either minimizing the aggregation of network interferences or maximizing the culmination of the flows over the entire network. Here, we propose a third perspective that maximizes the throughput by balancing the load over the network channels. We perform a thorough comparative experimental study on these three instance models with different key-parameter settings. Preliminary results presented in this paper show that this new proposed model totally supersedes the flow aggregation based model and should be used as a contender to the interference based model.

The new generation of handheld devices is equipped with multi-radio interfaces that enable radio link connections to a variety of wireless networks (i.e. Wi-Fi, WiMAX).This allows for seamless connectivity; however, this also raises a serious issue concerning the short longevity of the handheld usability due to the high power consumption of the wireless interfaces. In this paper, we present a framework that increases considerably the battery longevity of the handheld devices; it enables efficient power management from a global point of view (and not a single radio interface view) of multi-radio devices. The basic idea behind our proposal is to power off the idle interface but at the same time to keep it in virtual idle mode in the network by extending IEEE 802.21 on both sides (the mobile node side and the network side). On the network side, the 802.21 entity acts as a proxy of the powered-off interface to insure the assigned resources for the interface are always maintained during the proxying period. In the context of the proposed framework, we present (details of) the mechanisms to proxy an idle interface (after powering it off) and to wake up a proxied interface respectively. The proposed solution is analytically evaluated to quantify the power savings compared with single-radio power management.

A modern wireless network can be neither successfully deployed nor successfully expanded without proper planning. In this paper we consider the wireless mesh network (WMN) planning problem where not much work has been done. We propose a more realistic multi-objective approach to model this problem where the two conflicting objectives of total deployment cost and network throughput are to be optimized while guaranteeing full coverage to all mesh clients. Previous contributions have mainly formulated and solved this problem by using single-objective integer linear programming formulations and exact methods. The main limitation of these approaches resides in their restriction to small sized instances. We propose a population-based meta-heuristic algorithm to solve the problem. This algorithm produces a set of good planning solutions for real-size networks thus enlarging the decision perspective of a network planner. We also discuss the effect of different parameters on the characteristics of the solutions.

Admission control is a key management function in wireless networks, particularly wireless mesh networks (WMNs), in order to support multimedia applications that require quality of service (QoS) guarantees. Even using state of the art schemes to provide QoS, if the amount of traffic in the network is allowed to increase in an uncontrolled manner, network performance will deteriorate significantly degrading the QoS for all network traffic. With admission control, a new flow is admitted only if the QoS requirements of all flows in the network still can be met after the new flow begins. This paper introduces a distributed admission control scheme, called RCAC (routing on cliques admission control) for WMNs. We propose an analytical model that enables computing the appropriate admission ratio to guarantee that the loss rate in the network does not exceed a target value; the model also allows computing end-to-end delay necessary to process flow requests with delay constraints. RCAC achieves scalability since it partitions the network into cliques; only clique heads are involved in the admission control procedure. Simulations, using ns-2, demonstrate that RCAC accepts new incoming flows only when the network target loss rate and end-to-end delay are satisfied and maintains relatively high resource utilization in a dynamic traffic load environment.

Optical burst switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This issue could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. Deflection routing is one of the contention resolution approaches that have been proposed to tackle this problem. In addition to be cost-effective, it is also efficient in reducing loss probability, especially with low and moderate traffic loads. In this paper, we propose an adaptive reinforcement learning-based deflection routing scheme (RLDRS) which focuses on the route selection issue by choosing the optimal alternative output port in terms of both loss probability and delay when deflection is performed. Moreover, RLDRS limits the number of authorized deflections of each burst in order to reduce the additional traffic caused by deflection routing and to prohibit excessive deflections. Simulation results show that RLDRS reduces effectively loss probability and outperforms shortest path deflection routing (SPDR).

Our goal is to propose a unified/generalized model for the Wireless Mesh Networks (WMNs) design problem taking into account all the parameters that have a significant impact on WMNs. In the WMNs design literature, different studies take into account traffic demand, interference, multi-channel, transmission power, potential locations of wireless routers/gateways and node's characteristics (number of radios/node and number of channels/radio). In this paper, we introduce two new parameters: expansion and reliability. We will focus on refining the WMN design taking into account traffic expansion, geographic expansion and reliability without increasing the investment cost of the design.

A wise network planning becomes the most important phase in determining the network efficiency. In this paper we consider the wireless mesh network (WMN) planning problem where no much work has been done. We propose a new multi-objective optimization model for planning WMNs, where the two conflicting objectives, namely network deployment cost and network channels' interferences, are simultaneously minimized while guaranteeing end- users' full coverage and robust topologies. We also propose a novel performance metric to evaluate the network interference level and a population-based optimization heuristic to solve our model, whereby many WMN planning solutions are provided to the end-planner to choose among. We use realistic-size instances (up to 81) mesh nodes to test our multi-objective optimization model, and discuss the impact of the key parameters on the characteristics of the solutions.

Applications utilizing Grid computing infrastructure usually require resources allocation (e.g., bandwidth and CPU) to satisfy their quality of service (QoS) requirements. Given the dynamic nature of grid computing, QoS support and adaptation must be a high priority to successfully support those applications. In this paper, we present an adaptive resources provisioning scheme that optimizes the resources utilization while satisfying the required QoS. More specifically, it minimizes the request blocking probability and, thus, maximizes the revenues of the infrastructure provider.

The design/planning of WMNs is a key phase before any deployment. Few proposals can be found in the open literature that deals with the design problem; however, they do not take into account all the parameters that have an impact on the outcome of the design and they assume the existence of a physical topology where the location and the characteristics of nodes (e.g., number of channels, number of radios) are fixed.In this paper, we define a generalized model for the WMNs design problem that takes into account all the parameters that have a significant impact on the network (interference, multi-channel, transmission power, etc.), expected traffic, the constraints of the physical environment (potential locations of wireless routers and gateways), etc. To resolve the generalized model, we propose a combination of genetic and tabu search algorithms. The objective is to minimize the cost of the network and its operations while satisfying the requirements.

Design/planning of WMNs is the key phase before any deployment. Few proposals can be found in the open literature that deal with the design problem; moreover, they do not take into account all the parameters that have an impact on the outcome of the design and they assume the existence of a physical topology where the location and the characteristics of nodes (e.g., number of channels, number of radios) are fixed. In this paper, we define a generalized model for the WMNs design problem that takes into account all the parameters that have a significant impact on the network (interference, multi-channel, transmission power, etc.), the requirements of providers (expected amount of traffic/users), the constraints of the physical environment (potential locations of wireless routers, e.g., poles, and gateways, e.g., data centers), etc. The objective is to minimize the cost of the network and its operations while satisfying the requirements. The proposed model is shown to outperform considerably existing solutions.

In this paper, we propose a new explicit multicast protocol for MANET, called EM2NET. The objective of EM2NET is to efficiently support large number of small multicast groups in MANETs. EM2NET strikes a nice balance between the characteristics of explicit multicast protocols (e.g., Xcast and E2M) and these of tree/mesh-based multicast protocols (e.g., ODMRP and MAODV). Indeed, it combines the encoding of destinations addresses in the packet header and the maintenance of multicast state information in carefully selected nodes. Simulations show that EM2NET outperforms (a) explicit multicast protocols and (b) tree/mesh based protocols in the case of small size multicast groups.

Optical burst switching (OBS) is an optical switching paradigm which offers a good tradeoff between the traditional optical circuit switching (OCS) and optical packet switching (OPS) since it has the relatively easy implementation of the first and the efficient bandwidth utilization of the second. Hence, OBS is a promising technology for the next generation optical Internet. A buffer-less OBS network can be implemented using ordinary optical communication equipment without the need for either wavelength converters or optical memories. However, OBS networks suffer from a relatively high blocking probability, a primary metric of interest, because of contention. In this paper we propose a new contention resolution scheme for buffer-less OBS networks using deflection routing and reinforcement learning agents to dynamically assign an appropriate offset time (OT) to each burst in order to reduce losses caused, for example, by insufficient offset time (IOT) in case only deflection is used. Simulation results demonstrate that our approach reduces effectively blocking probability, whereas it maintains a reasonable end-to-end delay for each burst. Hence, it establishes an appropriate tradeoff between loss rate and delay.

Thanks to recent technological progress, autonomous wireless sensor networks have experienced considerable development. Currently, they are used in the areas of health care, environment, military etc. For a number of sensor-based applications, the knowledge of the positions of sensors is required or, at least, preferable. In this paper, we propose a new method to locate a large number of nodes in wireless sensor networks where only a subset of them are landmarks (i.e., know their positions). Our method is AOA-based (angle of arrival) and it is called A2L (angle to landmark). Compared, via simulations, to previous methods such as APS and AHLoS, A2L considerably increases the number of located nodes with accurate precision while using a smaller node degree.

In sensor networks, several applications such as habitat monitoring and moving objects tracking, require the knowledge of nodes positions. Position estimation most often includes errors due to the measurements of distance and incoming angles between neighbors. Erroneous positions are propagated from a node to other nodes exacerbating the degree of errors in the estimation of the positions of these nodes. In this paper, we propose a new localization method, called HA-A2L. Compared, via simulations, to previous methods, such as APS and A2L, HA-A2L considerably increases the number of located nodes with far better accuracy.

This demo will present both real and mocked-up usecases of a software application that enables accurate probabilistic modeling of new IP based services and their impact on network resources. The demonstration should be of general interest to Network Service Providers (NSP), device manufacturers, and application developers. Network planning and service analysis is a very important issue for today’s NSP’s; new communications devices and services emerge quickly (e.g., VoIP, Peer-to-peer filesharing, etc.) and end-users demand network resources that are up to the task. For the analyst, our tool leverages familiar UML style views, used to capture prototypical service “sessions”. It then allows annotation of the service model with a rich grammar, model conversion into semi-Markov form, and algorithm execution for network resource usage estimation. Ultimately, analysts gain insight into the impact of new consumer devices and services on their networks.

The novel resources provisioning scheme consists of scheduling and allocating resources earlier than the time this reservation takes place. The resources are effectively reserved at the agreed upon scheduled time. In this paper we describe a novel scheme for providing resource reservation in advance in time slotted optical network. In this network a flows of time slots are established between source nodes and destinations. The user specifies the number of slots he/she requires, the start time of the reservation, and the duration of the reservation. The proposed scheme provides the user with the number of slots that can be reserved at the start time and other times in the future carefully selected.

Quality of service (QoS) support in Web services plays a great role for the success of this emerging technology. In this paper, we present a QoS broker-based architecture for Web services. The main goal of the architecture is to support the client in selecting Web services based on his/her required QoS. To achieve this goal, we propose a two-phase verification technique that is performed by a third party broker. The first phase consists of syntactic and semantic verification of the service interface description including the QoS parameters description. The second phase consists of applying a measurement technique to compute the QoS metrics stated in the service interface and compares their values with the claimed one. This is used to verify the conformity of a Web service from the QoS point of view (QoS testing). A methodological approach to generate QoS test cases, as input to QoS verification is used. We have implemented a prototype that includes the verification and certification components of the broker. We performed experiments to evaluate the importance of verification and certification features in the selection process using real Web services.

Abstract Some applications utilizing Grid computing infrastructure require the simultaneous allocation of resources, such as compute servers, networks, memory, disk storage and other specialized resources. Collaborative working and visualization is one example of such applications. In this context, quality of service (QoS) is related to Grid services, and not just to the network connecting these services. With the emerging interest in service‐oriented Grids, resources may be advertised and traded as services based on a service level agreement (SLA). Such a SLA must include both general and technical specifications, including pricing policy and properties of the resources required to execute the service, to ensure QoS requirements are satisfied. An approach for QoS adaptation is presented to enable the dynamic adjustment of behavior of an application based on changes in the pre‐defined SLA. The approach is particularly useful if workload or network traffic changes in unpredictable ways during an active session. Copyright © 2004 John Wiley & Sons, Ltd.

Access to multimedia servers is commonly done according to a client/server model where the end user at the client host retrieves multimedia objects from a multimedia server. In a distributed environment, a number of end users may need to access a number of multimedia servers through one or several communication networks. Such a scenario reveals the requirement for a distributed access platform. In addition, the demand for multimedia information is increasing beyond the capabilities of high performance storage devices. Therefore, load distribution and scalability issues must be addressed while designing and implementing the distributed access platform. This paper introduces a scalable access platform (SAP) for managing user access to multimedia-on-demand systems while optimizing resource utilization. The platform is generic and capable of integrating heterogeneous multimedia servers. SAP operation combines static replication and dynamic load distribution policies. It provides run time redirecting of client requests to multimedia servers according to the workload information dynamically collected in the system. To support multimedia-on-demand systems with differing quality-of-service (QoS) requirements, the platform also takes into account, as part of the access process, user QoS requirements and cost constraints. This paper also presents an application of the generic platform implementing a scalable movie-on-demand system, called SMoD. Performance evaluation based on simulation shows that in many cases SMoD can reduce the blocking probability of user requests, and thus can support more users than classical video-on-demand (VoD) systems. It also shows that the load is better distributed across the video servers of the system.

The World-Wide Web has been a remarkably successful system for distributing hypertext documents. The basic model for Web interaction is that a client requests a page of data which can include images and hyperlinks within it. This interaction model is inadequate for real-time multimedia (MM) applications, since the Web and its associated set of protocols, e.g. HTTP, do not support the real-time transfer of the continuous media (Audio/Video). Several solutions have been proposed to support real-time playout of continuous media via the Web, e.g. Netscape. Most of these solutions do not provide means to the user to negotiate the desired presentation quality (in terms of quality of service (QoS) parameters settings); even the proposals that provide QoS negotiation (more generally QoS management) are used in a rather static manner, that is, the video/audio servers are a priori known. The authors propose to integrate in the WWW a dynamic QoS management approach that allows (1) the user to negotiate the desired QoS; and (2) and to select the "best" video/audio server which might support the user requirements. This activity is based on the general structure of multimedia documents and associated QoS parameters, called meta-data, which they developed under an ongoing CITR project. The main objective of the paper is to integrate meta-data associated with MM document in WWW e.g. Netscape; this will allow one to use dynamic QoS management protocols.

Emerging high-speed networks will support a number of real-time services required by distributed multimedia applications, such as video-on-demand. To support these services, appropriate routing procedures should be used; upon receipt of a request to open a new connection with certain quality of service (QoS) requirements, an appropriate routing procedure calculates an "optimal" route (with respect to a certain cost function) which satisfies the connection requirements. Most existing routing procedures for real-time connections determine routes which satisfy only a subset of QoS requirements and optimize in terms of QoS information (e.g. a route with the smallest end-to-end delay). The authors present a routing procedure which finds a route, if it does exist, which satisfies the whole set of QoS requirements; the route could be optimal in terms of QoS information, money to pay for the utilization of network resources, or network reliability; it is up to the network operator to select the optimization criteria to be used by the routing procedure. They also describe how the proposed routing procedure can be used in ATM environment.

Most current approaches in designing and implementing distributed multimedia (MM) presentational applications have concentrated on the performance of the continuous media file servers in terms of seek-time overhead and real-time disk scheduling; particularly, the quality of service (QoS) negotiation mechanisms they provide are used in a rather static manner, i.e. these mechanisms are restricted to the evaluation of the capacity of certain system components. In contrast to those approaches, we propose a general QoS negotiation framework that supports the dynamic choice of a configuration of system components to support the QoS requirements of the user of a specific application: we consider different possible system configurations and select an optimal one to provide the appropriate QoS support. We document the design and implementation of a QoS negotiation procedure for distributed MM presentational applications, such as news-on-demand. The negotiation procedure described is an instantiation of the general framework for QoS negotiation. Our proposal differs in many respect with the negotiation functions provided by existing approaches: (1) the negotiation process uses an optimization approach to find a configuration of system components which supports the user requirements, (2) the negotiation process supports the negotiation of a MM document and not only a single monomedia object, (3) the QoS negotiation takes into account the cost to the user; (4) the negotiation process may be used to support automatic adaptation to react to QoS degradations, without intervention by the user/application.