Power Line Communication (PLC) systems are facing increasing security threats as adversaries leverage low-cost Software-Defined Radios (SDRs) to launch physical-layer attacks, e.g., jamming and Radio Frequency Fingerprinting (RFF), for communication disruption and unauthorized device tracking, respectively. This paper investigates the dual role of Radio Frequency (RF) wireless jamming for PLC environments, through two distinct scenarios: (i) friendly RF jamming for privacy preservation of (cabled) PLC devices against unauthorized RFF, and (ii) adversarial RF jamming to degrade the performance of legitimate RFF-based authentication systems. We conducted various systematic experiments using nine USRP X310 SDRs connected to actual PLC couplers exchanging signals modulated according to the Binary-Phase Shift Keying modulation scheme to analyze the behavior of RFF in PLC scenarios under different RF jamming levels. Our results demonstrate, for the first time, that strategic RF jamming effectively obscures device fingerprints in cabled PLC communications while maintaining communication quality, with bit error rates remaining acceptable across most configurations. We also demonstrate that device identification accuracy degrades significantly as the jamming intensity increases. Our findings establish fundamental trade-offs between privacy protection and authentication reliability, providing insights for the design of robust PLC systems.

Deep Learning-based RF fingerprinting approaches struggle to perform well in cross-domain scenarios, particularly during hardware warm-up. This often-overlooked vulnerability has been jeopardizing their reliability and their adoption in practical settings. To address this critical gap, in this work, we first dive deep into the anatomy of RF fingerprints, revealing insights into the temporal fingerprinting variations during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we then address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our evaluation showcases HEEDFULs efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals–far surpassing traditional models. Furthermore, cross-day and crossprotocol assessments confirm HEEDFUL’s superiority, achieving and maintaining high accuracy during both the stable and initial warm-up phases when tested on WiFi signals. Additionally, we release WiFi type B and N RF fingerprint datasets that, for the first time, incorporate both the time-domain representation and real hardware impairments of the frames. This underscores the importance of leveraging hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more robust RF fingerprinting solutions.

Unmanned aerial vehicles (UAVs) are proven useful for enabling wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices in post-disaster scenarios where conventional communication infrastructure is compromised. As 6G networks emerge, offering ultra-reliable low-latency communication and enhanced energy efficiency, UAVs are poised to play a critical role in extending 6G features to challenging environments. The key challenges in this context include limited UAV flight duration, energy constraints, limited resources, and the reliability of data collection, all of which impact the effectiveness of UAV operations. Motivated by the need for efficient resource allocation and reliable data collection, we propose a solution using UAV swarms combined with reconfigurable intelligent surfaces (RIS) to optimize energy harvesting for IoT devices and enhance communication quality. We formulate the problem of resource optimization, UAVs-RIS trajectory planning, and RIS configuration as a mixed integer nonlinear programming optimization problem and solve it in a dynamic condition by transforming it into a Markov decision process and utilizing a deep reinforcement learning (DRL) approach based on proximal policy optimization (PPO) algorithm to solve it. Simulation results demonstrate that our framework outperforms traditional approaches, including the Actor-Critic (AC) algorithm and a greedy solution, achieving superior performance in energy harvesting efficiency, data collection, and communication reliability.

Deploying transformer models in Personalized Federated Learning (PFL) over wireless networks is challenging due to their large size, which leads to high communication overhead, increased latency, and excessive energy consumption. Traditional pruning and sparsification methods, designed mainly for conventional deep learning architectures, are ineffective for transformers and can cause divergence or degrade performance—especially when applied to self-attention layers or through direct federated averaging. To address these challenges, we propose a novel dual approach called PFL-TPS (PFL with Transformer Pruning and Sparsification). Our approach efficiently reduces communication and computation costs while maintaining model performance, making it suitable for resource-constrained wireless networks. Specifically, we apply adaptive pruning with trainable thresholds to the transformer's Feed-Forward Layers (FFLs), and only these trainable thresholds are shared with the server, resulting in minimal uploaded data. For the Self-Attention Layers (SALs), instead of transmitting bandwidth-intensive model parameters, we employ a server-side hypernetwork that generates personalized parameters based on device-specific embedding vectors sent by the devices, significantly reducing communication overhead and maintaining personalization. Extensive experiments show that PFL-TPS reduces energy consumption by up to 50%, decreases training time by 60.44%, and improves model accuracy by 49.87% compared to baselines in wireless networks.

Machine learning (ML)-based network anomaly detection methods are proven to provide automated network protection from traffic misbehavior and authorized system access through data monitoring and analysis. However, conventional centralized methods present risks for data privacy and breaches. By facilitating distributed model training over a number of network nodes, Federated Learning (FL) emerges as a key enabler for effective anomaly detection yet while preserving the privacy of the data. This paper studies FL-based detection approaches under two different Deep Learning models, CNN and MLP. We use XGBoost for feature selection and the two UNSWNB15 and CICDDoS2019 datasets for assessing the effectiveness of each model through the evaluation of standard performance metric criteria, namely the recall, precision, accuracy, and F1score metrics. Our experimental findings indicate that integrating XGBoost-based feature selection with the CNN model yields superior performance on the UNSW-NB15 dataset, whereas the MLP model benefits more from the same integration when applied to the CICDDoS2019 dataset.

Rising phishing attacks pose serious cybersecurity threats due to their use of fraudulent links to collect confidential user information. In this paper, we evaluate the performance of various Machine Learning (ML) models, including Decision Trees, Random Forest, and Extreme Gradient Boosting, to address this growing threat. Additionally, we assess the effectiveness of different feature selection techniques, such as Analysis of Variance, Correlation-based Selection, Mutual Information, and Recursive Feature Elimination with Cross-Validation. Our findings demonstrate that combining Extreme Gradient Boosting with Recursive Feature Elimination and Cross-Validation outperforms previous methods. The proposed solution achieved an accuracy of 97.33%, a recall of 97.1656%, an F1 score of 97.3%, and a precision of 97.42%, highlighting its potential for effectively identifying phishing attacks

Recently, unmanned aerial vehicles (UAVs) have demonstrated exemplary performance in various scenarios, such as search and rescue, smart city services, and disaster response applications. UAVs can facilitate wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices. However, employing UAVs for such applications poses several challenges, including limited flight duration, constrained energy resources, and the age of information of the data collected. To address these challenges, we employ a UAV swarm to maximize energy harvesting (EH) and data rates for IoT devices by optimizing UAV paths and integrating reconfigurable intelligent surfaces (RIS) technology. We tackle critical constraints, including UAV energy consumption, flight duration, and data collection deadlines, by formulating an optimization problem to find optimal UAV paths and RIS phase shifts. Given the complexity of the problem, its combinatorial nature, and the challenges of obtaining an optimal solution through conventional optimization methods, we decompose the problem into two sub-problems, employing deep reinforcement learning (DRL) to optimize EH and particle swarm optimization (PSO) to optimize RIS phase shifts. Our extensive simulations show that the proposed solution outperforms competitive algorithms, including Brute-Force-PSO, AC-PSO, and PPO-PSO algorithms, providing a robust solution for modern IoT applications.

In recent years, there has been a rising trend towards emerging applications (e.g., brain-computer interaction and haptics-based autonomous cars) with diverse requirements. To effectively enable these applications via autonomous operation and intelligent analytics, one can use a metaverseFor more details on how a metaverse can enable emerging applications and architecture, please refer to khan2024ametaverse. In a metaverse, we have two spaces: (a) a meta space based on a virtual model that performs analysis and resource management and (b) a physical space comprised of real world entities. A metaverse effectively enables emerging applications by performing three main tasks: (a) distributed learning of metaverse models; (b) instantly serving the end-users; and (c) sensing of the physical environment and sharing it with the meta space for synchronized operation. To perform these tasks, efficient wireless resource management is needed. Therefore, a novel resource scheduling framework for the wireless metaverse to enable various applications is proposed. Our aim is to minimize the cost of learning and sensing in metaverse. Subsequently, we formulate a problem. Meanwhile, the reliability as well as latency constraints of the service-requesting devices/users will be fulfilled. We assign multiple resource blocks to learning and sensing devices/units, whereas we use a concept of puncturing for service-requesting devices/users upon arrival. We use a scheme that is based on block successive upper-bound minimization and convex optimization for solving our formulated problem. At the end, we use empirical cumulative distribution function vs. cost and cost vs. metaverse entities for numerical evaluations.

Barrett modular reduction and multiplication are essential primitives for efficient modular computation in cryptographic schemes, including post-quantum standards such as machine learning (ML) key encapsulation mechanism (KEM) and ML-digital signature algorithm (DSA). To protect against faults that compromise correctness and security, we introduce the first efficient fault-detection mechanisms tailored to these operations. For modular reduction, we leverage word-based representations with compact word-sum checks that exploit algebraic input-output relations to ensure computational integrity. For modular multiplication, we adopt a tunable hybrid strategy: early stages apply word-sum checks, while later stages use partial recomputation with encoded inputs, providing robust protection against injected faults. Formal analysis, fault-injection simulations, and hardware/software implementations show that our methods detect a wide range of faults with minimal performance and area overhead. Evaluation results demonstrate overheads of 3.43% and 7.15% for 512-bit and 1024-bit inputs in modular reduction, and 26.47% and 27.22% for 2048-bit inputs in modular multiplication in the number of clock cycles in software. Moreover, in hardware, we observed reasonable overheads: less than 27.5% in area and 2.1% in delay for modular reduction, and less than 23.5% in area and 16.2% in delay for modular multiplication. These results confirm the practicality of our methods for secure yet efficient integration.

After the acceptance of [1], an error was introduced, which we aim to resolve here. The abbreviation ML stands for module lattice-based, not “machine learning.” The first sentence of the first paragraph is corrected from the version that was published in Early Access. It should have read, “Barrett modular reduction and multiplication are essential primitives for efficient modular computation in cryptographic schemes, including postquantum standards such as module lattice-based (ML) key encapsulation mechanism (KEM) and ML-digital signature algorithm (DSA).” In the Introduction, the same correction has been made for the abbreviation ML.

The reciprocity nature of a wireless channel has been leveraged to generate and establish secret keys between two devices communicating over the channel. This is typically done by having the two devices exchange probing signals to measure their channel state information (CSI), which each then uses to derive secret keys. While extensive research on secret key generation primarily focuses on theoretical and simulation analyses, the practical implementation and adoption of key generation encounter obstacles arising from hardware constraints and varying channel conditions. To address this research gap, we experimentally investigate the channel measurements by collecting CSI data using resource-constrained devices. Our experiments reveal a significant degradation in the correlation between the collected CSI from two low-cost devices limiting the key generation performance. Through our experimental investigations, we first demonstrate that wavelet coherence provides insights on the reciprocity of the channel measurements on both time and frequency. We then propose a new wavelet coherence-based CSI data reconstruction technique that uses wavelet coherence and time-lagged cross-correlation to reconstruct CSI data that is consistent between the two participating devices, resulting in significant improvement in the channel measurements. Additionally, we propose a secret-key generation scheme that exploits the proposed wavelet coherence-based CSI data reconstruction, yielding a significant reduction in the bit error rates and an increase in the key generation rates.

The reciprocity of channel state information (CSI) observed by two devices communicating wirelessly has been leveraged to develop security solutions for resource-limited IoT devices. Despite considerable research in this area, much of the attention has been on theoretical and simulated analyses. However, the practical implementation of these security solutions faces significant challenges, primarily due to limited hardware capabilities and varying channel conditions. To bridge this research gap, we revisit the assumption of channel reciprocity from an experimental perspective. Our experimental investigations uncover a notable decline in channel reciprocity for low-cost devices due to the variability in channel conditions and the asynchronous nature of CSI measurements. Through our experiments, we highlight key practical factors contributing to the diminished channel reciprocity and demonstrate that Pearson’s correlation and time-lagged cross-correlation can effectively measure and assess the channel reciprocity property between two communicating devices. Building upon the experiments’ findings, we then introduce a technique that exploits the reciprocity of collected CSI data, as well as its temporal variations and time shifts to enhance device authentication resiliency through an effective detection of replay attacks.

Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8% to 27.8%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.

Software-Defined Networking (SDN) provides the flexibility to dynamically manage network paths, facilitating efficient traffic flow and mutipath routing, thereby improving network resiliency to congestion. This study investigates three distinct SDN-enabled routing strategies: shortest path routing, Equal-Cost Multi-Path (ECMP) routing, and bandwidth-based multipath routing. The comparative result analysis across these three routing strategies revealed that bandwidth-based routing consistently outperforms Shortest Path and ECMP routing across key performance metrics. The study found that Bandwidth-based routing maintains lower delay and jitter, indicating its superior ability to manage network traffic efficiently even as data flow increases. Furthermore, it demonstrates a more modest increase in packet loss, underscoring its effective congestion management. These findings suggest that Bandwidth-based routing provides a more reliable and efficient network performance, particularly in high-traffic conditions, making it a preferable solution for SDN implementations seeking to optimize data flow and network stability.

Deep Learning-based RF fingerprinting has emerged as a game-changer for offering robust network device authentication and identification solutions. However, it struggles in cross-time scenarios, particularly during hardware warm-up phases. This often-overlooked vulnerability jeopardizes the reliability of these solutions. In response to this critical gap, we dive deep into the anatomy of RF fingerprints, revealing insights into temporal variations in DL-based RF fingerprinting during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our extensive evaluation showcases HEEDFUL's efficacy, achieving remarkable classification accuracies of up to 96% during the initial intervals of device operation-far surpassing traditional models. Cross-domain assessments confirm HEEDFUL's superiority, achieving a steady 87% classification accuracy across warm-up intervals on the Day 2 dataset. Additionally, we release a WiFi RF fingerprinting dataset that, for the first time, incorporates both the time-domain representation and real hardware impairments of the frames. This inclusion underscores the importance of leveraging actual hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more resilient solutions.

As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.

Blockchain emerges as a potential solution for enabling distributed management and accountability of wireless Internet of Thing (IoT) networks. In addition, blockchain offers increased IoT security, as it involves every single node around the network to verify and approve new transactions. On the other hand, IEEE 802.11ax, a recently concluded WiFi standard aimed at achieving high network efficiency, plays a key role for solving spectrum sharing and cross-technology coexistence among WiFi and 6G cellular users, one of the key challenges faced by next-generation wireless systems. In this paper, we leverage the power of blockchain and 802.11ax technologies to propose a medium access control (MAC) protocol design for future IoT wireless local area networks (WLANs), also referred to as wireless IoT-Blockchain networks setup. Our simulation results show that the proposed protocol design enhances transmission latency and achievable network throughput.

Recently, Explicit Congestion Notification (ECN) has been leveraged by most Datacenter Network (DCN) protocols for congestion control to achieve high throughput and low latency. However, the majority of these approaches assume that each switch port has one queue while current industry trends towards having multiple queues per each switch port. To this end, we propose ML-ECN, a Multi-Level probabilistic ECN marking scheme for DCNs enabled with multiple-service, multiple-queue switch ports. The main design of ML-ECN relies on the separation between small, medium, and large flows by dedicating multiple queues for each flow class to ensure fairness enqueueing. ML-ECN employs a single threshold for each queue in small service-queue class and multiple thresholds with a probabilistic marking for each queue in medium and large service-queue classes to achieve low latency for mice (small) and high throughput for elephant (large) flows. In addition, ML-ECN performs fairness-aware ECN marking that ensures small flows never get marked at early queue build up. Large-scale ns-2 simulations show that ML-ECN outperforms existing approaches for different performance metrics.

The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT networks. RF fingerprinting has emerged as a potential solution for device identification by leveraging the transmitter unique manufacturing impairments of the RF components. Although deep learning is proven efficient in classifying devices based on the hardware impairments, trained models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification. For the proposed technique we show that, for Rayleigh channels, blind partial channel estimation enabled by MIMO increases the testing accuracy by up to 40% when the models are trained and tested over the same channel, and by up to 60% when the models are tested on a channel that is different from that used for training.

The Internet of Things (IoT) is starting to integrate deeply into our daily lives thanks to the different services it provides. This technology has already made us more closely linked to the external environment through ubiquitous communication devices. However, even though this proximity has numerous benefits, it also has a significant security impact, where the cyber-attack surface has grown dramatically. In this regard, we present, in this paper, our results toward the development of a decision tree-based machine learning model for intrusion detection in Massive-IoT networks. The principal objective of this work is to provide a highly accurate detection model, while preserving resource consumption by developing a real prototype of the intrusion detection system. To this end, we first propose and apply our pre-processing methodology on the well-known Avast IoT-23 dataset, allowing us to reach a high detection rate with 99.99% of accuracy and just 1804KB of the model’s size. Then, we propose a new machine learning model based on the decision tree classifier and deploy it in a real environment with malicious attack traffic. Obtained results show that our proposed model allows 88% of real-traffic-based precision rate and up to 90% of specificity.

Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural networks (RVNNs). Although this improvement is (intuitively) attributed to the complex nature of the input RF data (i.e., IQ symbols), no prior work has taken a closer look into analyzing such a trend in the context of wireless device identification. Our study provides a deeper understanding of this trend using real LoRa and WiFi RF datasets. We perform a deep dive into understanding the impact of (i) the input representation/type and (ii) the architectural layer of the neural network. For the input representation, we considered the IQ as well as the polar coordinates both partially and fully. For the architectural layer, we considered a series of ablation experiments that eliminate parts of the CVNN components. Our results show that CVNNs consistently outperform RVNNs counterpart in the various scenarios mentioned above, indicating that CVNNs are able to make better use of the joint information provided via the in-phase (I) and quadrature (Q) components of the signal.

Multicast offers a prime means to support group communication in data center (DC) networks, as it saves network traffic and improves application throughput. Unfortunately, traditional IP multicast routing does not scale well with the number of supported multicast groups when used for cloud DC networks. Prior attempts are shown to scale multicast by taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets. However, when considering large multicast group sizes, these approaches incur high network and/or CPU overheads, and they struggle to balance between network overhead (i.e., switch memory and packet size overheads) and end-host CPU overhead. For instance, in these approaches, keeping network overhead minimal leads to inflating end-host CPU load and vice versa. This paper proposes Ernie, a source-routed multicast routing approach that addresses the multicast scalability limitations while keeping both the network and end-host overheads at low levels. Ernie further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic.

Blockchain technology has attracted the attention of many researchers in several different application fields. The use of blockchain in Internet of Things (IoT) and Wireless Sensors Networks (WSN) has been analyzed and considered as a promising solution to many of the IoT limitations. Communication is a very basic essence of the blockchain network and must be carefully planned while integrating with IoT, where an extremely large number of devices are interconnected and could have a serious impact on security and performance of the network. In this work, blockchain nodes are assumed to use wireless channels to communicate among themselves and other elements of the IoT setup. These communications can be in unicast and broadcast manner, where transmission latency and throughput are significant metrics that might jeopardize the overall system. This paper will propose a Medium Access Control (MAC) mechanism for wireless IoT-Blockchain system, while addressing these performance metrics. The proposed MAC protocol is based on the widely used IEEE 802.11ax MAC protocol, Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) basic access mechanism. The model is then simulated within NS-3 to observe the delay and throughput over several different setups.

Wireless device classification techniques play a vital role in supporting spectrum awareness applications, such as spectrum access policy enforcement and unauthorized network access monitoring. Recent works proposed to exploit distortions in the transmitted signals caused by hardware impairments of the devices to provide device identification and classification using deep learning. As technology advances, the manufacturing impairment variations among devices become extremely insignificant, and hence the need for more sophisticated device classification techniques becomes inescapable. This paper proposes a scalable, RF data-driven deep learning-based device classification technique that efficiently classifies transmitting radios from a large pool of bit-similar, high-end, high-performance devices with same hardware, protocol, and/or software configurations. Unlike existing techniques, the novelty of the proposed approach lies in exploiting both the in-band and out-of-band distortion information, caused by inherent hardware impairments, to enable scalable and accurate device classification. Using convolutional neural network (CNN) model for classification, our results show that the proposed technique substantially outperforms conventional approaches in terms of both classification accuracy and learning times. In our experiments, the testing accuracy obtained under the proposed technique is about 96% whereas that obtained under the conventional approach is only about 50% when the devices exhibit very similar hardware impairments. The proposed technique can be implemented with minimum receiver design tuning, as radio technologies, such as cognitive radios, can easily allow for both in-band and out-of band sampling.

The ever-increasing growth of urban populations coupled with recent mobile data usage trends has led to an unprecedented increase in wireless devices, services and applications, with varying quality of service needs in terms of latency, data rate, and connectivity. To cope with these rising demands and challenges, next-generation wireless networks have resorted to cloud radio access network (Cloud-RAN) technology as a way of reducing latency and network traffic. A concrete example of this is New York City's LinkNYC network infrastructure, which replaces the city's payphones with kiosk-like structures, called Links, to provide fast and free public Wi-Fi access to city users. When enabled with data storage capability, these Links can, for example, play the role of edge cloud devices to allow in-network content caching so that access latency and network traffic are reduced. In this paper, we propose HybridCache, a hybrid proactive and reactive in-network caching scheme that reduces content access latency and network traffic congestion substantially. It does so by first grouping edge cloud devices in clusters to minimize intra-cluster content access latency and then enabling cooperative-proactively and reactively-caching using LSTM-based prediction to minimize in-network content redundancy. Using the LinkNYC network as the backbone infrastructure for evaluation, we show that HybridCache reduces the number of hops that content needs to traverse and increases cache hit rates, thereby reducing both network traffic and content access latency.

Production data centers operate under various workload sizes ranging from latency-sensitive mice flows to long-lived elephant flows. However, the predominant load balancing scheme in data center networks, equal-cost multi-path (ECMP), is agnostic to path conditions and performs poorly in asymmetric topologies, resulting in low throughput and high latencies. In this paper, we propose CAFT, a distributed congestion-aware fault-tolerant load balancing protocol for 3-tier data center networks. It first collects, in real time, the complete congestion information of two subsets from the set of all possible paths between any two hosts. Then, the best path congestion information from each subset is carried across the switches, during the Transport Control Protocol (TCP) connection process, to make path selection decision. Having two candidate paths improve the robustness of CAFT to asymmetries caused by link failures. Large-scale ns-3 simulations show that CAFT outperforms Expeditus in mean flow completion time (FCT) and network throughput for both symmetric and asymmetric scenarios.

Data centers (DCs) nowadays house tens of thousands of servers and switches, interconnected by high-speed communication links. With the rapid growth of cloud DCs, in both size and number, tremendous efforts have been undertaken to efficiently design the network and manage the traffic within these DCs. However, little effort has been made toward measuring, understanding and chattelizing how the network-level traffic of these DCs behave. In this paper, we aim to present a systematic taxonomy and survey of these DC studies. Specifically, our survey first decomposes DC network traffic behavior into two main stages, namely (1) data collection methodologies and (2) research findings, and then classifies and discusses the recent research studies in each stage. Finally, the survey highlights few research challenges related to DC network traffic that require further research investigation.

Traditional IP multicast routing is not suitable for cloud data center (DC) networks due to the need for supporting large numbers of groups with large group sizes. State-of-the-art DC multicast routing approaches aim to overcome the scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. However, although these scale well with the number of multicast groups, they do not do so with group sizes, and as a result, they yield substantial traffic control overhead and network congestion. In this paper, we present Bert, a scalable, source-initiated DC multicast routing approach that scales well with both the number and the size of multicast groups, and does so through clustering, by dividing the members of the multicast group into a set of clusters with each cluster employing its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much lesser traffic control overhead by significantly reducing the packet header sizes and the number of extra packet transmissions, resulting from the need for compacting forwarding rules across the switches.

IoT networks can be viewed as collections of Internet-enabled physical devices and objects, embedded with sensor, actuator, computation, storage and communication components, that are capable of connecting and exchanging data to one another. In recent years, organizations have allowed more and more IoT devices to be connected to their networks, thereby increasing their risks of and exposure to security vulnerabilities and threats. Therefore, it is important for such organizations to be able to identify which devices are connected to their network and which ones are legitimate and pose no risk. Leveraging network traffic to identify devices through supervised learning has recently been gaining popularity, where feature information is first extracted by intercepting device traffic and then exploited to provide device classification. The main limitation of prior works is that they can only identify previously seen types of devices, and any newly added device types are treated as abnormal types. In the real world, hundreds of millions of new IoT devices are produced each year, and the lack of a large amount of training data makes a system based solely on supervised learning unrealistic. In this paper, we propose a hybrid supervised and unsupervised learning method that enables secondary classification of unseen device types. Our technique combines deep neural networks with clustering to enable both seen and unseen device classification, and employs autoencoder technique to reduce dimensionality of datasets, thereby providing a good balance between classification accuracy and overhead.

We propose a blockchain-based, distributed protocol for enabling the deployment of Internet-of-Things (IoT) networks on-demand on top of IoT devices. Specifically, the proposed protocol leverages blockchain technology to: 1) enable distributed and secure authentication, registration, and management of participatory IoT devices; 2) provide fast discovery of IoT resources and scalable and secure instantiation of IoT networks-on-demand; and 3) manage payment operations and ensure reliable fund transfers among the network entities. The proposed protocol relies on a peer-to-peer network communication infrastructure to allow communications among the IoT devices in a distributed manner and uses a self-recovery/self-healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor registered devices to keep track of their service delivery quality so that their service delivery reputations could be leveraged for future device selection and mapping. We implemented and evaluated the proposed protocol intensively using simulations and showed that it scales well with network parameters, is resilient to faulty devices, and is robust to 51% attack.

This paper proposes a distributed resource sharing protocol for enabling dynamic deployment of IoT networks on-demand in smart cities. The proposed protocol leverages Blockchain technology to: (i) enable distributed and secure management of IoT devices; (ii) provide fast discovery of IoT resources and scalable on-demand networks; and (iii) ensure reliable fund transfers for service payment among the network entities. The protocol relies on a peer-to-peer network infrastructure to allow communication among the IoT devices in a distributed manner, and uses a self-recovery/self- healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor and keep track of services delivered by registered devices for quality of service delivery assurance. We implemented and evaluated the proposed protocol intensively using simulations to assess its effectiveness in terms of scalability to network sizes and robustness to device failures.

As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless spectrum sharing between secondary users (SUs) and incumbent users. Despite its benefits, SAS's design requirements, as set by FCC, present privacy risks to SUs, merely because SUs are required to share sensitive operational information (e.g., location, identity, spectrum usage) with SAS to be able to learn about spectrum availability in their vicinity. In this paper, we propose TrustSAS, a trustworthy framework for SAS that synergizes state-of-the-art cryptographic techniques with blockchain technology in an innovative way to address these privacy issues while complying with FCC's regulatory design requirements. We analyze the security of our framework and evaluate its performance through analysis, simulation and experimentation. We show that TrustSAS can offer high security guarantees with reasonable overhead, making it an ideal solution for addressing SUs' privacy issues in an operational SAS environment.

This paper proposes efficient strategies that shave Data Centers (DCs)' monthly peak power demand with the aim of reducing the DCs' monthly expenses. Specifically, the proposed strategies allow to decide: i) when and how much of the DC's workload should be delayed given that the workload is made up of multiple classes where each class has a certain delay tolerance and delay cost, and ii) when and how much energy should be charged/discharged into DCs' batteries. We first consider the case where the DC's power demands throughout the whole billing cycle are known and present an optimal peak shaving control strategy for it. We then relax this assumption and propose an efficient control strategy for the case when (accurate/noisy) predictions of the DC's power demands are only known for short durations in the future. Several comparative studies based on real traces from a Google DC are conducted in order to validate the proposed techniques.

Resource overbooking-based approaches have been adopted as one of the key technique for reducing power consumption of data centers by minimizing the number of physical machines (PMs) that need to be kept active. They do so by assigning virtual machine (VM) requests to a PM in excess of the PM's supported capacity, with the anticipation that these assigned VMs will not fully utilize their requested resources and hence the aggregate amount of needed resources will not surpass the PM's capacity. However, although resource overbooking improves PM utilization, it may lead to PM overloads where the actual VMs' demanded amount of resources exceeds the PM's capacity, thus resulting in violating service- level agreements (SLAs) of some of the hosted VMs. In this paper, we propose an integrated resource allocation framework for data centers that minimizes the number of active PMs through dynamic VM placement while ensuring that SLAs of admitted VMs are not violated by reducing the number of PM overload occurrences through VM resource prediction, resource scaling, and VM migration. Experimental evaluations based on real Google traces show that our proposed framework outperforms existing techniques by incurring lesser overload events with no to very few SLA violations of admitted VMs while yielding energy consumptions similar to those achieved under existing approaches.

Data center network (DCN) topologies have recently been the focus of many researchers due to their vital role in achieving high DCN performances in terms of scalability, power consumption, throughput, and traffic load balancing. This paper presents a comprehensive comparison between two most commonly used DCN topologies, Fat-Tree and BCube, with a focus on structure, addressing and routing, and proposes a new DCN topology that is better suited for nowadays data center networks. We show that our proposed topology, termed Circulant Fat-Tree, alleviates traffic congestion at the core switches, improves network latency, and increases robustness against switch and server failures when compared to traditional Fat-Tree DCN topologies.

We propose a distributed compressive sampling technique for cooperative wideband spectrum sensing that requires lesser numbers of measurements while overcoming time-variability of spectrum occupancy and the hidden terminal problem. First, we prove that the wideband spectrum occupancy information can almost surely be recovered with a reduced number of spectrum measurements. Second, we propose nonuniform sensing matrix design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. Using simulations, we confirm our theoretic results and show that cooperation leads to high detection probability, even with each secondary user taking only a small number of measurements. We also show that it is sufficient to consider a subset of close-by secondary users to obtain comparable performances.

We propose AirMAP, a framework for enabling scalable database-driven dynamic spectrum access and sharing. We bring together the merits of compressive sensing and collaborative filtering to provide accurate radio occupancy map while reducing the network overhead cost and overcome the scalability issue with conventional approaches. We start from an observation that close-by users have a highly correlated spectrum observation and we propose to recover the spectrum occupancy matrix in the borough of each sensing node by minimizing the rank of local sub-matrices. Then, we combine the recovered matrix entries using a similarity criterion to get the global spectrum occupancy map. Through simulations, we show that the proposed framework minimizes the error while reducing the network overhead. We also show that the proposed framework is scalable when considering high frequencies.

Mobile video streaming has become an essential application in mobile wireless networks, making up most of the mobile data of today's Internet traffic. Studies have shown that mobile video data is projected to make up about 78 percent of the global mobile data traffic, and that global mobile data traffic is expected to increase sevenfold by 2021. Massive small cell base station (SBS) deployments have emerged as a potential solution promising to fulfill these unprecedented mobile data demands, by offering great coverage enhancements and maintaining high quality of video streaming. However, due to relatively small cell sizes and high user mobility, mobile video streaming in dense SBS networks faces fundamental challenges such as intermittent connectivity and frequent handoffs, causing degradation in video streaming quality. In this paper, we tackle this issue by introducing a hybrid proactive in-network caching framework that stores some popular videos at the edge of the network, namely at the SBSs, while also pre-caching video contents in advance to better service mobile users. The proposed framework essentially reduces the need for bringing every requested video from the core (original) network, which results in alleviating network congestion by reducing back-haul traffic and in improving mobile video streaming experience by avoiding service discontinuity during handoffs. We develop a simulation framework using MATLAB to study the performance of the proposed caching technique and show that the proposed technique can effectively improve video quality of experience and reduce back-haul traffic.

FogMQ is a message brokering and device cloning service in fog and edge computing. Excessive tail end-to-end latency occurs with conventional message brokers when a massive number of geographically distributed devices communicate through a message broker. Latency of broker-less messaging is highly dependent on computational resources of devices. Deviceto-device messaging does not necessarily ensure low messaging latency and cannot scale well for a large number of resourcelimited and geographically distributed devices. For each device, FogMQ provides a high capacity device cloning service that subscribes to device messages. The clones facilitate near-theedge data analytics in resourceful cloud compute nodes. Clones in FogMQ apply Flock; an algorithm mimicking flocking-like behavior and allows the clones to autonomously migrate between heterogeneous cloud platforms. Flock controls and minimizes the weighted tail end-to-end latency. We have implemented FogMQ and evaluated it in a geographically distributed testbed. In our functional evaluation, we show that FogMQ is stable and achieves a bounded tail end-to-end latency that is up to 34% less than existing brokering methods.

We propose an integrated, energy-efficient, resource allocation framework for overcommitted clouds. The framework makes great energy savings by 1) minimizing Physical Machine (PM) overload occurrences via VM resource usage monitoring and prediction, and 2) reducing the number of active PMs via efficient VM migration and placement. Using real Google data consisting of a 29-day traces collected from a cluster containing more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by reducing the number of unpredicted overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.

In this paper, we propose a dual-hop radiofrequency (RF)/free-space optical system with multiple relays employing the decode-and-forward and amplify-and-forward with a fixed gain relaying scheme. The RF channels are subject to a Rayleigh distribution while the optical links experience a unified fading model emcopassing the atmospheric turbulence that follows the Málaga distribution (or also called the M-distribution), the atmospheric path loss, and the pointing error. Partial relay selection with outdated channel state information is proposed to select the candidate relay to forward the signal to the destination. At the reception, the detection of the signal can be achieved following either heterodyne or intensity modulation and direct detection. Many previous attempts neglected the impact of the hardware impairments and assumed ideal hardware. This assumption makes sense for low data rate systems but it would no longer be valid for high data rate systems. In this paper, we propose a general model of hardware impairment to get insight into quantifying its effects on the system performance. We will demonstrate that the hardware impairments have small impact on the system performance for low signal-to-noise ratio (SNR), but it can be destructive at high SNR values. Furthermore, analytical expressions and upper bounds are derived for the outage probability and ergodic capacity while the symbol error probability is obtained through the numerical integration method. Capitalizing on these metrics, we also derive the high SNR asymptotes to get valuable insight into the system gains, such as the diversity and the coding gains. Finally, analytical and numerical results are presented and validated by the Monte Carlo simulation.

With the emergence of smart grids, adopting dynamic energy pricing models has become both possible and desirable. With such a pricing dynamicity, great savings in energy costs can be achieved in telecommunication systems when energy is procured efficiently through carefully designed real-time resource schedulers. Broadly speaking, existing scheduling algorithms can be categorized into two classes: online and off-line. Off-line algorithms are not practical merely because of their need for prior knowledge of future system information. In this paper, we propose an efficient online power procurement and allocation scheduler that maximizes a long-term system utility function without the need for prior knowledge of future system information, where the system utility function is expressed in such a way that the gain coming from serving the users and the cost of the procured energy are traded off for one another. We propose an approach that allows us to derive closed-form instantaneous energy procurement and resource allocations that are functions only of the actual instantaneous system parameters. Our approach computes the optimal power procurement and users' allocation per time slot in an online fashion with very low computational complexity. Using simulations, we study the efficiency of the proposed approach under various parameters and quantify the energy costs that our approach can potentially save.

There have recently been research efforts that leverage compressive sampling to enable wideband spectrum sensing recovery at sub-Nyquist rates. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar primary user traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different bands could present different occupancy patterns. In fact, applications of similar types are often assigned spectrum bands within the same block, dictating that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum and exploit its inherent block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ℓ1-minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches, while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a smaller number of sensing measurements when compared to the state-of-the-art approaches.

Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly benefit end users, cities, and the environment. LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City (NYC) with kiosk-like structures providing free public Wi-Fi. We consolidate these networks with standalone edge cloud devices known as cloudlets and introduce geographically distributed content delivery cloudlets (CDCs) to store popular Internet content closer to end users; essential in environments with diverse and dynamic content interests. A content-centric and delivery framework is proposed leveraging NYC's population densities and CDCs for interest-based in-network caching. Analysis shows that although the adoption of multiple CDCs dramatically improves overall network performance, advanced caching policies are needed when considering increased content heterogeneity. Thus, we propose popularity-driven and cooperation-based caching policies at individual CDCs to account for user and content dynamics over time. The amalgamation of urban population densities, multiple CDC placements and smarter caching techniques helps exploit the ultimate benefits of a content-centric urban communications network and dramatically improves overall network performance and responsiveness. Our proposed solutions are validated using LinkNYC as a use-case.

In this work, we provide a detailed analysis of a dual-hop fixed gain (FG) amplify-and-forward relaying system, consisting of a hybrid radio frequency (RF) and free-space optical (FSO) channels. We introduce an impairment model which is the soft envelope limiter (SEL). Additionally, we propose the partial relay selection (PRS) protocol with outdated channel state information (CSI) based on the knowledge of the RF channels in order to select one relay for the communication. Moreover, the RF channels of the first hop experience Rayleigh fading while we propose a unified fading model for the FSO channels, called the unified Gamma Gamma (GG), taking into account the atmospheric turbulence, the path loss and the misalignment between the transmitter and the receiver aperture also called the pointing error. Novel closed-forms of the outage probability (OP), the bit error probability (BEP) and the average ergodic capacity (EC) are derived in terms of Meijer-G and Fox-H functions. Capitalizing on these metrics, we also derive the asymptotical high signal-to-noise ratio (SNR) in order to get engineering insights into the impacts of the hardware impairments and the system parameters as well. Finally, using Monte Carlo simulations, we validate numerically the derived mathematical formulations.

We propose Flock; a simple and scalable protocol that enables live migration of Virtual Machines (VMs) across heterogeneous edge and conventional cloud platforms to improve the responsiveness of cloud services. Flock is designed with properties that are suitable for the use cases of the Internet of Things (IoT). We describe the properties of regularized latency measurements that Flock can use for asynchronous and autonomous migration decisions. Such decisions allow communicating VMs to follow a flocking-like behavior that consists of three simple rules: separation, alignment, and cohesion. Using game theory, we derive analytical bounds on Flock's Price of Anarchy (PoA), and prove that flocking VMs converge to a Nash Equilibrium while settling in the best possible cloud platforms. We verify the effectiveness of Flock through simulations and discuss how its generic objective can simply be tweaked to achieve other objectives, such as cloud load balancing and energy consumption minimization.

Cognitive communication networks, or simply cognitive networks (CNs), are one of the enabling technologies for next generation networks. Through dynamic spectrum access, CNs can make the best out of the underutilized radio spectrum resource. However, security threats can severely affect spectrum utilization in those networks. For instance, jamming can disrupt CN communications if not addressed properly. Addressing jamming while maintaining a desired quality of service (QoS) is not an easy task. In this paper, we propose a time-based countermeasure technique which, unlike its existing frequency-based counterparts, does not assume accessibility to multiple channels. Furthermore, while existing mechanisms assume static users, our proposed approach accounts for mobility of cognitive users (CUs). Our proposed anti-jamming solution exploits and controls the mobility of CUs to improve QoS. Our findings show that our technique is as jamming-resilient as other existing cryptographic techniques, it however achieves better QoS levels.

In this work, we present a global framework of a dual-hop RF/FSO system with multiple relays operating at the mode of amplify-and-forward (AF) with fixed gain. Partial relay selection (PRS) protocol with outdated channel state information (CSI) is assumed since the channels of the first hop are time-varying. The optical irradiance of the second hop are subject to the Double-Weibull model while the RF channels of the first hop experience the Rayleigh fading. The signal reception is achieved either by heterodyne or intensity modulation and direct detection (IM/DD). In addition, we introduce an aggregate model of hardware impairments to the source (S) and the relays since they are not perfect nodes. In order to quantify the impairment impact on the system, we derive closed-form, approximate, upper bound and high signal-to-noise ratio (SNR) asymptotic of the outage probability (OP) and the ergodic capacity (EC). Finally, analytical and numerical results are in agreement using Monte Carlo simulation.

In this paper, we consider the use of CUDA-based Graphics Processing Units (GPUs) as high performance parallel computing for the purpose of accelerating the application task scheduling in Vehicular Cloud Computing (VCC) systems. We leverage the Single Instruction Multiple Data (SIMD) mode in General-Purpose Graphic Processing Units (GPGPUs) to solve the value iteration algorithm of the defined Markov Decision Process (MDP) of task scheduling on real VCC. We consider opportunistically available Vehicle to Infrastructure (V2I) communication in Dedicated Short Range Communication (DSRC) used in Vehicular ad hoc networks (VANETs) for the vehicular clouds.

This paper proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative wideband spectrum sensing. Our technique requires lesser number of sensing measurements while still recovering spectrum occupancy information accurately. It does so by leveraging compressive sampling theory to exploit the block-like occupancy structure of wideband spectrum access. Our technique is also adaptive in that it accounts for the variability of spectrum occupancy over time. It exploits supervised learning to provide and use accurate realtime estimates of the spectrum occupancy. Using simulations, we show that our proposed technique outperforms existing approaches by making accurate spectrum occupancy decisions with lesser sensing communication and energy overheads.

Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make spectrum availability decisions are highly correlated to the users' locations. It is therefore important that cooperative spectrum sensing schemes be empowered with privacy preserving capabilities so as to provide SUs with incentives for participating in the sensing task. In this paper, we propose privacy preserving protocols that make use of various cryptographic mechanisms to preserve the location privacy of SUs while performing reliable and efficient spectrum sensing. We also present cost-performance tradeoffs. The first consists on using an additional architectural entity at the benefit of incurring lower computation overhead by relying only on symmetric cryptography. The second consists on using an additional secure comparison protocol at the benefit of incurring lesser architectural cost by not requiring extra entities. Our schemes can also adapt to the case of a malicious fusion center as we discuss in this paper. We also show that not only are our proposed schemes secure and more efficient than existing alternatives, but also achieve fault tolerance and are robust against sporadic network topological changes.

In this paper, we investigate the performance analysis of dual hop relaying system consisting of asymmetric Radio Frequency (RF)/Free Optical Space (FSO) links. The RF channels follow a Rayleigh distribution and the optical links are subject to Gamma-Gamma fading. We also introduce impairments to our model and we suggest Partial Relay Selection (PRS) protocol with Amplify-and-Forward (AF) fixed gain relaying. The benefits of employing optical communication with RF, is to increase the system transfer rate and thus improving the system bandwidth. Many previous research attempts assuming ideal hardware (source, relays, etc.) without impairments. In fact, this assumption is still valid for low-rate systems. However, these hardware impairments can no longer be neglected for high-rate systems in order to get consistent results. Novel analytical expressions of outage probability and ergodic capacity of our model are derived taking into account ideal and non- ideal hardware cases. Furthermore, we study the dependence of the outage probability and the system capacity considering, the effect of the correlation between the outdated CSI (Channel State Information) and the current source-relay link, the number of relays, the rank of the selected relay and the average optical Signal to Noise Ratio (SNR) over weak and strong atmospheric turbulence. We also demonstrate that for a non- ideal case, the end-to-end Signal to Noise plus Distortion Ratio (SNDR) has a certain ceiling for high SNR range. However, the SNDR grows infinitely for the ideal case and the ceiling caused by impairments no longer exists. Finally, numerical and simulation results are presented.

The spread of the wireless communication devices resulted in a remarkable growth of power consumption for telecommunication systems to satisfy the users' demands. Thus, new revolutionary solutions are needed to enhance their energy efficiency. In this paper, we propose an enhanced energy efficiency scheme for wireless systems based on sharing the energy between different cells to optimize the overall cost. The objective is to encourage cooperation between cells/micro-grids to exchange additional/needed power function of their respective throughput demand and energy availability. We propose a pricing scheme for the energy exchange and a power procurement and resource allocation strategy per operator to maximize the revenue of each operator. We derive analytic expressions for power procurement considering uniform power allocation over users. Then, we consider adaptive power allocation per user depending on channels and Quality of Service (QoS) requirements and propose a scheme to derive the global power procurement solution. Our simulation results show the feasibility of the proposed scheme allowing to enhance the energy efficiency of the hybrid powered wireless system while optimizing the revenue of the different involved operators.

The Dedicated Short Range Communication (DSRC) technology has been used in Vehicular communication to enable short-lived safety and non-safety applications based on Vehicle to Vehicle (V2V) communications over the Control Channel (CCH) and Vehicle to Infrastructure (V2I) communications over the Service Channels (SCHs) in Vehicular Ad hoc Networks (VANETs). With the under-utilized advanced computation, communication and storage resources in On-Board Units (OBUs) of modern vehicles, Vehicular Clouds (VCs) are used to manage coalitions of affordable resources in Vehicles in order to host infotainment applications used by other vehicles on the move. In this paper, we introduce an Advanced Activity-Aware (AAA) scheme for Multi-Channel Operations based on 1609.4 in MAC Protocol in Wireless Access in Vehicular Environments (WAVE). The AAA aims at dynamically achieving an optimal setup of Control Channel Interval (CCHI) and Service Channel Interval (SCHI) by reducing the inactivity interval while maintaining a default Synchronization Interval (SI) between all vehicles. We evaluate the performance of our proposed scheme through real-time simulation of vehicular cloud load and VANET communications using NS3. The simulation results indicate that our proposed scheme increases significantly the throughput and reduces the average delay of uploaded packets of non-safety applications from the VC to the Road Side Unit (RSU) while maintaining a V2V communication for safety similar to that of the 1609.4 standard.

LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City with kiosk-like structures providing free super fast gigabit Wi-Fi to everyone. This work proposes and investigates the applicability of shifting LinkNYC from a traditional IP network to a content-centric network by upgrading all or a subset of their kiosks with standalone cloudlets, which cache content as it disseminates throughout the network, and content delivery cloudlets which are geographically distributed throughout the boroughs and store popular internet content. With this shift content is brought much closer to the end user than traditional methods which is essential in highly mobile environments. Analysis shows that adopting multiple content delivery cloudlets dramatically improves overall network performance and stability. Finally, given a cloudlet-aware path a mobile content delivery scheme is designed to offset service continuity issues that are amplified when a mobile user encounters multiple cloudlets with intermittent connectivity. Results show an overall improvement in a mobile user's throughput, response time and cache hit percentage.

We propose efficient control strategies for deciding the amount of energy that a battery needs to charge/discharge over time with the objective of minimizing the Peak Charge and the Energy Charge components of the Data Center (DC) electricity bill. We consider first the case where the DC's power demands throughout the whole billing cycle are known and we present an optimal peak shaving control strategy for a battery that has certain leakage and conversion losses. We then relax this assumption and propose an efficient battery control strategy when we only know predictions of the DC's power demands in a short duration in the future. Several comparative studies are conducted based on real traces from a Google DC in order to validate the proposed techniques.

Adjacent channel interference (ACI) is often not considered when spectrum sharing schemes are designed for cognitive radio networks (CRNs). In practice, it is necessary to avoid interference by deploying guard bands between two distinct receptions. However, using guard bands typically reduces spectrum efficiency. In this work, we study the impact of guard bands on spectrum efficiency under different spectrum sharing schemes. Specifically, we model cognitive radio network as a continuous-time Markov process. We derive blocking probability, forced termination probability, spectrum efficiency and average number of guard bands using our Markov model. Using these metrics, we then study and analyze the impact of using guard bands on the performance of cognitive radio networks. We show that taking guard bands into consideration is critical as disregarding this realistic issue results in inaccurate conclusions and outcomes.

Augmenting the long-term evolution (LTE)-evolved NodeB (eNB) with cloud resources offers a low-latency, resilient, and LTE-aware environment for offloading the Internet of Things (IoT) services and applications. By means of devices memory replication, the IoT applications deployed at an LTE-integrated edge cloud can scale its computing and storage requirements to support different resource-intensive service offerings. Despite this potential, the massive number of IoT devices limits the LTE edge cloud responsiveness as the LTE radio interface becomes the major bottleneck given the unscalability of its uplink access and data transfer procedures to support a large number of devices that simultaneously replicate their memory objects with the LTE edge cloud. We propose Replisom; an LTE-aware edge cloud architecture and an LTE-optimized memory replication protocol which relaxes the LTE bottlenecks by a delay and radio resource-efficient memory replication protocol based on the device-to-device communication technology and the sparse recovery in the theory of compressed sampling. Replisom effectively schedules the memory replication occasions to resolve contentions for the radio resources as a large number of devices simultaneously transmit their memory replicas. Our analysis and numerical evaluation suggest that this system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.

We propose Cloud of Things for sensing-as-a-service: a global architecture that scales up cloud computing by exploiting the global sensing resources of the Internet of Things (IoT) to enable remote sensing. Cloud of Things enables in-network distributed processing of sensors data offered by the globally available IoT devices and provides a global platform for meaningful and responsive data analysis and decision making. We propose a distributed sensing resource discovery and virtualization algorithms that efficiently deploy virtual sensor networks on top of a subset of the selected IoT devices. We show, through analysis and simulations, the potential of the proposed solutions to realize virtual sensor networks with minimal physical resources, reduced communication overhead, and low complexity. We also design an uncoordinated, distributed algorithm that relies on the selected sensors to estimate a set of parameters without requiring synchronization among the sensors. Our simulations show that the proposed estimation algorithm, when compared to conventional alternating direction method of multipliers (ADMMs), reduces communication overhead significantly without compromising the estimation error. In addition, the convergence time, though increases slightly, is still linear as in the case of conventional ADMM.

We develop an efficient virtual network embedding (VNE) algorithm, termed Bird-VNE, for mobile wireless networks. Bird-VNE is an approximation algorithm that ensures a close to optimal virtual embedding profit and acceptance rate while minimizing the number of virtual network migrations resulting from the mobility of wireless nodes. Bird-VNE employs a constraint satisfaction framework by which we analyze the constraint propagation properties of the VNE problem and design constraint processing algorithms that efficiently narrow the solution space and avoid backtracking as much as possible without compromising the solution quality. Our evaluation results show that the likelihood that Bird-VNE results in backtracking is small, thus demonstrating its effectiveness in reducing the search space. We analytically and empirically verify that Bird-VNE outperforms existing VNE algorithms with respect to computational efficiency, closeness to optimality, and its ability to avoid potential migrations in mobile wireless networks.

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access (DSA) users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for DSA users with multiple channel access and adaptive power allocation capabilities. We also propose simple two-phase heuristics for allocating spectrum and power resources among users. The proposed heuristics split the spectrum and power allocation problem into two subproblems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase using learning. Two procedures to learn the channel selection are proposed and compared in terms of optimality, scalability, and robustness. The power allocation, on the other hand, is formulated as a real optimization problem and solved, during the second phase, by traditional optimization solvers. Simulation results show that energy and cross-layer awareness and multiple channel access capability improve the performance of the system in terms of the per-user average rewards received from accessing the dynamic spectrum access system. In addition, the two proposed methods for channel selection via learning represent a tradeoff between optimality, scalability, and robustness.

Multipath TCP (MPTCP) is a new evolution of TCP that enables a single MPTCP connection to use multiple TCP subflows transparently to applications. Each subflow runs independently allowing the connection to be maintained if endpoints change; essential in a dynamic network. Differentiating between congestion delay and delay due to handoffs is an important distinction overlooked by transport layer protocols. Protocol modifications are needed to alleviate handoff induced issues in a growing mobile culture. In this paper, findings are presented on transport layer handoff issues in currently deployed networks. MPTCP as a potential solution to addressing handoff- and mobility-related service continuity issues is discussed. Finally, a handoff-aware cross-layer-assisted MPTCP (CLA-MPTCP) congestion control algorithm is designed and evaluated.

In this paper, we introduce a new green resource allocation problem using hybrid powering of communication systems from renewable and nonrenewable sources. The objective is to efficiently allocate the power delivered from the different micro-grids to satisfy the network requirements. Minimizing a defined power cost function instead of the net power consumption aims to encourage the use of the available renewable power through collaboration between the base stations within and outside the different micro-grids. The different degrees of freedom in the system, ranging from assignment of users to base stations, possibility of switching the unnecessary base stations to the sleep mode, dynamic power allocation, and dynamic allocation of the available bandwidth, allow us to achieve important power cost savings. Since the formulated optimization problem is a mixed integer-real problem with a nonlinear objective function, we propose to solve the problem using the branch and bound (B&B) approach, which allows to obtain the optimal or a suboptimal solution with a known distance to the optimal. The relaxed problem is shown to be a convex optimization which allows to obtain the lower bound. For practical applications with large number of users, we propose a heuristic solution based on decomposing the problem into two subproblems. The users-to-base stations assignment is solved using an algorithm inspired from the bin-packing approach while the bandwidth allocation is performed through the bulb-search approach. Simulation results confirm the important savings in the nonrenewable power consumption when using the proposed approach and the efficiency of the proposed disjointed algorithms.

Abstract Cognitive radio networks emerge as a promising solution for overcoming shortage and inefficient use of bandwidth resources by allowing secondary users (SUs) to access the primary users' (PUs) channel so long as they do not interfere with them. The dynamical spectrum availability makes SU's packet average delay one of the most important performance measures of a cognitive network. It is important to understand the nature of delay, as well as its dependence on PU behaviors. In this paper, we analytically model and analyze the dynamics of the spectrum availability and their impact on the SU's packet delay. The cognitive network is modeled as a discrete‐time queueing system. PU channel occupancy is modeled as a two‐state Markov chain. Our contribution in this paper is defining and characterizing the properties of the random process that describes the availability of the opportunistic resources. In addition, we apply the mean residual service time concept to achieve an analytical solution for the queueing delay. Moreover, inspired by the slotted Aloha system, we model the packet service mechanism and determine the manner in which it depends on the resource availability. The delay becomes unbounded if the spectrum availability dynamics are not carefully considered in network design. Copyright © 2015 John Wiley & Sons, Ltd.

5G wireless networks will support massive connectivity mainly due to device-to-device communications. An enabling technology for device-to-device links is the dynamical spectrum access. The devices, which are equipped with cognitive radios, are to be allowed to reuse spectrum occupied by cellular links. The dynamical spectrum availability makes cognitive users switch between channels. Switching leads to energy consumption, latency, and communication overhead in general. The performance degrades even more when the network is under jamming attack. This type of attack is one of the most detrimental attacks. Addressing jamming while maintaining a desired quality of service is a challenge. While existing anti-jamming mechanisms assume stationary users, in this paper, we propose and evaluate countermeasures for mobile cognitive users. We propose two time-based techniques, which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on switching to countermeasure jamming. We achieve analytical solutions of jamming, switching, and error probabilities. Based on our findings, the proposed techniques out perform other existing frequency-based techniques. Copyright © 2016 John Wiley & Sons, Ltd.

We propose Cloud of Things for Sensing as a Service: a global architecture that scales up cloud computing by exploiting the global sensing resources of the highly dynamic and growing Internet of Things (IoT) to enable remote sensing. The proposed architecture scales out by augmenting the role of edge computing platforms as cloud agents that discover and virtualize sensing resources of IoT devices. Cloud of Things enables performing in-network distributed processing of sensing data offered by the globally available IoT devices and provides a global platform for meaningful and responsive sensing data analysis and decision making. We design cloud agents algorithmic solutions bearing in mind the onerous to track dynamics of the IoT devices by centralized solutions. First, we propose a distributed sensing resource discovery algorithm based on a gossip policy that selects IoT devices with predefined sensing capabilities as fast as possible. We also propose RADV: a distributed virtualization algorithm that efficiently deploys virtual sensor networks on top of a subset of the selected IoT devices. We show, through analysis and simulations, the potential of the proposed algorithmic solutions to realize virtual sensor networks with minimal physical resources, reduced communication overhead, and low complexity.

Managing cloud resources in a way that reduces the consumed energy while also meeting clients demands is a challenging task. In this paper, we propose an energy-aware resource allocation framework that: i) places the submitted tasks (elastic/inelastic) in an energy-efficient way, ii) decides initially how much resources should be assigned to the elastic tasks, and iii) tunes periodically the allocated resources for the currently hosted elastic tasks. This is all done with the aim of reducing the number of ON servers and the time for which servers need to be kept ON allowing them to be turned to sleep early to save energy while meeting all clients demands. Comparative studies conducted on Google traces show the effectiveness of our framework in terms of energy savings and utilization gains.

In this paper, we introduce a new green resource allocation problem using hybrid powering of the communication system from renewable and non-renewable sources. The objective is to efficiently allocate the power delivered from different micro-grids to satisfy the users' requirements. Minimizing a defined power cost function instead of the net power consumption aims to encourage the use of the available renewable power through collaboration between base stations within and outside the different micro-grids. The different degrees of freedom in the system, ranging from assignment of users to base stations, possibility of switching the unnecessary base stations to the sleep mode, and dynamic allocation of the available bandwidth, allow us to achieve important power cost savings. Although the formulated optimization problem is a mixed integer-real problem with a non-linear objective function, we propose an efficient two-step algorithm to jointly assign users to base stations and the shared bandwidth among users. The users-to-base stations assignment is inspired from the bin-packing approach while the bandwidth allocation is performed through the bulb-search approach. Simulation results confirm the important savings in non-renewable power consumption when using the proposed approach.

Cognitive Radio Networks (CRNs) enable opportunistic access to the licensed channel resources by allowing unlicensed users to exploit vacant channel opportunities. One effective technique through which unlicensed users, often referred to as Secondary Users (SUs), acquire whether a channel is vacant is cooperative spectrum sensing. Despite its effectiveness in enabling CRN access, cooperative sensing suffers from location privacy threats, merely because the sensing reports that need to be exchanged among the SUs to perform the sensing task are highly correlated to the SUs' locations. In this paper, we develop a new Location Privacy for Optimal Sensing (LPOS) scheme that preserves the location privacy of SUs while achieving optimal sensing performance through voting-based sensing. In addition, LPOS is the only alternative among existing CRN location privacy preserving schemes (to the best of our knowledge) that ensures high privacy, achieves fault tolerance, and is robust against the highly dynamic and wireless nature of CRNs.

Multi-Path TCP (MPTCP) is a new evolution of TCP that enables a single MPTCP connection to use multiple TCP subflows transparently to applications. Each subflow runs independently allowing the connection to be maintained if endpoints change; essential in a dynamic network. Differentiating between congestion delay and delay due to handovers is an important distinction overlooked by transport layer protocols. Protocol modifications are needed to alleviate handoff induced issues in a growing mobile culture. In this article, findings are presented on transport layer handoff issues in currently deployed networks. MPTCP as a potential solution to addressing handoff- and mobility-related service continuity issues is discussed. Finally, a handoff-aware cross-layer assisted MPTCP (CLA-MPTCP) congestion control algorithm is designed and evaluated.

The focus of this paper is to investigate the fundamental limits of power allocation when taking into account a dynamic power pricing scheme. This paper proposes an optimal power allocation analysis for wireless systems when real time power pricing is available. We propose to minimize the total power consumption cost while ensuring minimum individual and total throughput limits. We consider different models for the power pricing function. Analytic solutions for the power allocation are derived for each model. The derived solutions are shown to be modified versions of the water-filling solution. Low-complexity algorithms are proposed for the resource allocation with each pricing model. Performance comparison and pricing effect are shown through simulations.

In this paper, we propose to minimize the total energy consumption cost of a simultaneous data transmission and power delivery from different sources. The receiver is designed to simultaneously process information and harvest energy from the received signal through a power splitter. We derive an optimal power allocation and splitting ratios for each source node that minimizes the total power cost while ensuring the required data rates for each link. The solution profits from the variability between the channel gains and data requirements. Numerical simulations allow to analyze the performance of the proposed solution.

In this paper, we propose dynamic power pricing for distributed resource allocation in large-scale Dynamic Spectrum Access (DSA) systems. The dynamic power pricing is considered to influence the users' spectrum assignment and power allocation in two resource allocation problems. In the first scenario, the objective is to maximize the reward of the obtained throughput over the time window while not exceeding a fixed budget for the power cost. The second problem consists of minimizing the total power cost while guaranteeing a minimum achieved throughput. Since the optimal solutions are of high computational complexity, we propose a distributed two-step algorithm to solve the optimization problems. In the first step, we rely on "learning" to determine the best channel selection for each user. In the second step, we optimize the allocated power to be used for the selected channels. Using simulations, we show that dynamic power pricing models allow achieving better DSA throughput when compared to the case of a static pricing for the same budget. Likewise, it results on power consumption cost's saving when trying to achieve a target throughput.

Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data centers. The proposed framework: i) predicts the number of virtual machine (VM) requests, to be arriving at cloud data centers in the near future, along with the amount of CPU and memory resources associated with each of these requests, ii) provides accurate estimations of the number of physical machines (PMs) that cloud data centers need in order to serve their clients, and iii) reduces energy consumption of cloud data centers by putting to sleep unneeded PMs. Our framework is evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. These evaluations show that our proposed energy-aware resource provisioning framework makes substantial energy savings.

We propose a credit-based resource allocation technique for dynamic spectrum access that is robust against malicious and selfish behaviors and ensures good overall system fairness performance while also allowing spectrum users to achieve high amounts of service. We also propose a new objective function that, when combined with the proposed credit-based technique, leads to further improvements of the system fairness performance. Our proposed techniques overcome user misbehavior by masking the impact of the users' pursued private objectives on the overall system performance. They also improve fairness among users by allocating service to users adaptively by accounting for how much service each user has received in the past. Our simulation results show that our proposed techniques maintain high system performance by allowing users to achieve high amounts of service and by ensuring fair allocation of spectrum resources among users even in the presence of misbehaved users. Using simulations, we also show that these high performances are also achievable under various different network scenarios.

In this paper, we implement and analyze a resource allocation protocol for distributed dynamic spectrum allocation (DSA) systems. The DSA protocol is a learning-based protocol that allows secondary users (SU) to exploit the spectrum bands efficiently in a distributed manner without the need of information exchange. The implementation and test of the proposed protocol is done using ns3 assuming that the SUs selecting the same band share it in accordance with a carrier sense multiple access (CSMA) scheme. The evaluation of the proposed protocol is done under various traffic models. We show the importance of the objective function's choice; used as a utility to be maximized in the learning. We also show the impact of various practical aspects taken into consideration while implementing the protocol on the system's achieved performance.

The virtual network embedding (VNE) problem is known to be NP-hard, and as a result, several heuristic approaches have been proposed to solve it. These heuristics find sub-optimal solutions in polynomial time, but have practical limitations, low acceptance rates, and high embedding costs. In this paper, we first propose two heuristics that exploit the constraint propagation properties of the VNE problem to ensure both topological and capacity disjoint consistencies, thereby avoiding backtracking while increasing acceptance rates. Then, combining these two heuristics, we design a polynomial-time VNE algorithm (we term it BIRD-VNE) that, in addition to avoiding backtracking and increasing acceptance rates, incurs a low embedding cost when compared to existing approaches.

Cognitive radio networks (CRNs) emerge as a promising solution for overcoming the shortage and inefficient use of bandwidth resources by allowing secondary users (SUs) to access the primary users' (PUs) channels so long as they do not interfere with them. The random availability of the PU channels makes the delay analysis of the SU, which accesses the channels opportunistically, plays a crucial role as a quality of service measure. In this paper, we model and characterize the total average delay the SUs experience in a CRN. The cognitive radio system is modeled as a discrete-time queueing system. The availability of the N independent and identical PU channels is modeled as a two states Markov chain. Our contributions in this paper is that we provide a solid performance evaluation that gives a closed-formula for the two delay components experienced by the SUs, namely the waiting delay and the service delay. We derive the waiting delay using the residual time concept. We characterize the service time distribution by considering the buffered-slotted-ALOHA systems. We also provide numerical results to show the effects of the analysis on the CRN design.

We propose a credit-based resource allocation technique for wireless systems with dynamic spectrum access (DSA) capability, such as multichannel wireless sensor networks. The proposed technique is robust against selfish and malicious behaviors by achieving high performance independently of what users choose to pursue as their objectives. It also improves fairness by ensuring that different users are allocated equal amounts of spectrum service. Using simulations, we show that the proposed credit-based technique allows users to achieve high rewards/service even in the presence of misbehaved users that choose to pursue their greedy and selfish goals. We also show that it reduces the standard deviation of users' amounts of received service drastically, and hence, improves fairness among users substantially.

Linear wireless sensor networks (LWSN) are special class of wireless sensor networks where sensor nodes are deployed in a straight line. Monitoring industrial pipelines, railroads, tunnels, power lines, and borders are applications of LWSNs. Wireless sensors are tiny devices with limited energy resources; therefore, efficient energy routing in LWSNs is critical. In this paper, we propose energy-efficient routing schemes for time-sensitive data traffic in linear wireless sensor networks. We further simulate LWSNs and analyze the behavior of lifetime against varying network parameters.

We propose a cross-layer data-aggregation approach that maximizes the lifetime of wireless sensor networks with multiple sinks while ensuring the feasibility of the routing solution. The proposed approach accounts for the coupling effects between MAC- and network-layers to ensure the physical feasibility of the obtained solutions, and maximizes the network lifetime by avoiding nodes with critical energy resources. Using simulations, we demonstrate the impact of not including medium access contention constraints in the routing formulation on the physical feasibility of the routing solution. We also study the impact of several network parameters on the network lifetime.

The recent emergence and success of cloud-based services has empowered remote sensing and made it very possible. Cloud-assisted remote sensing (CARS) enables distributed sensory data collection, global resource and data sharing, remote and real-time data access, elastic resource provisioning and scaling, and pay-as-you-go pricing models. CARS has great potentials for enabling the so-called Internet of Everything (IoE), thereby promoting smart cloud services. In this paper, we survey CARS. First, we describe its benefits and capabilities through real-world applications. Second, we present a multilayer architecture of CARS by describing each layer's functionalities and responsibilities, as well as its interactions and interfaces with its upper and lower layers. Third, we discuss the sensing services models offered by CARS. Fourth, we discuss some popular commercial cloud platforms that have already been developed and deployed in recent years. Finally, we present and discuss major design requirements and challenges of CARS.

This paper provides a measurement-based performance evaluation of the Optimized Link State Routing (OLSR) protocol.Two versions of OLSR, OLSR-ETX and OLSR-ETT, are implemented and evaluated on a mesh network that we built from off-the-shelf commercial components and deployed within our department building.OLSR-ETX uses the Expected Transmission Count (ETX) metric, whereas OLSR-ETT uses the Expected Transmission Time (ETT) metric as a means of assessing link quality.The paper describes our implementation process of the ETT metric using the plug-in feature of OLSRd, and our calculation method of link bandwidth using the packet-pair technique.A series of measurements are conducted in our testbed to analyze and compare the performance of ETX and ETT metrics deemed useful for quality of service.Our measurements show that OLSR-ETT outperforms OLSR-ETX significantly in terms of packet loss, end-toend delay, jitter, route changes, bandwidth, and overall stability, yielding much more robust, reliable, and efficient routing.

A micro-grid is a small scale power supply network that is designed to provide electricity to a small community with its own renewable energy sources. Due to distributed generation variability, security and load sharing issues, an efficient communication infrastructure is necessary between its agents (load, generation and storage units). Numerous research efforts are being developed to come up with such communication techniques that can overcome the barriers to implement the concept of micro-grids. This paper covers the features, characteristics and challenges of micro-grids and their associated communication techniques.

In this paper, we derive and evaluate private objective functions for large-scale, distributed opportunistic spectrum access (OSA) systems. By means of any learning algorithms, these derived objective functions enable OSA users to assess, locate, and exploit unused spectrum opportunities effectively by maximizing the users' average received rewards. We consider the elastic traffic model, suitable for elastic applications such as file transfer and web browsing, and in which an SU's received reward increases proportionally to the amount of received service when the amount is higher than a certain threshold. But when this amount is below the threshold, the reward decreases exponentially with the amount of received service. In this model, SUs are assumed to be treated fairly in that the SUs using the same band will roughly receive an equal share of the total amount of service offered by the band. We show that the proposed objective functions are: near-optimal, as they achieve high performances in terms of average received rewards; highly scalable, as they perform well for small- as well as large-scale systems; highly learnable, as they reach up near-optimal values very quickly; and distributive, as they require information sharing only among OSA users belonging to the same band.

We derive theoretical performance limits of densely covered delay-tolerant networks (DTNs). In the DTN model we study, a number of fixed (data collector) nodes are deployed in the DTN region where mobile (data generator) nodes move freely in the region according to Brownian motion. As it moves, each mobile is assumed to continuously generate and buffer data. When a mobile comes within the communication coverage range of a data collector node, the mobile immediately and completely uploads its buffered data to the data collector node, and then resumes generating and buffering its data. In this paper, we first derive analytic bounds on the amount of time a mobile spends without communication coverage. Then, using these derived bounds, we derive sufficient conditions on node density that statistically guarantee that the expected amount of time spent in the uncovered region remains below a given threshold. Additionally, we derive sufficient conditions on node density to keep the probability of buffer overflow below a given tolerance.

This paper studies cooperative techniques that rely on femtocell user diversity to improve the downlink communication quality of macrocell users. We analytically derive and evaluate the achievable performance of these techniques in the downlink of Rayleigh fading channels. We provide an approximation of both the bit-error rate (BER) and the data throughput that macrocell users receive with femtocell user cooperation. Using simulations, we show that under reasonable SNR values, cooperative schemes enhance the performances of macrocells by improving the BER, outage probability, and data throughput of macrocell users significantly when compared with the traditional, non-cooperative schemes.

ABSTRACT Trends in wireless networks are increasingly pointing towards a future with multi‐hop networks deployed in multi‐channel environments. In this paper, we present the design for iMAC—a protocol targeted at Medium Access Control in such environments. iMAC uses control packets on a common control channel to facilitate a three‐way handshake between the sender and the receiver for every packet transmission. This handshake enables the sender and the receiver to come to a consensus on a channel to use for data transmission and also signals to neighboring nodes about the contention on that channel. iMAC then uses a mechanism similar to 802.11 for data communication. Our evaluation of iMAC shows that it provides significant gains in throughput in comparison with uninformed channel selection, especially when contention for channel bandwidth is neither too low nor too high; intelligent selection of channels by iMAC is necessary to harness available bandwidth resources in the presence of medium levels of contention. Copyright © 2011 John Wiley & Sons, Ltd.

Femtocells (FCs) are small-area cellular networks deployed over a macrocell (MC) network. Typically, FCs are independent of each other and of the underlying MC, thereby necessitating distributed non-cooperative resource allocation schemes. This paper develops a new distributed autonomous uplink (UL) power control (PC) scheme for femto users (FUs). Our work aims at maintaining the minimum required signal to interference plus noise ratio (SINR) for a maximum number of FUs via distributed QoS-aware stochastic power allocation to FUs. We evaluate the performance of our proposed solution and compare it with existing power allocation techniques. Results show that our scheme yields a significant performance improvement in terms of percentage of satisfied users when compared with these existing solutions.

We derive theoretical bounds on expected hitting times in densely covered delay-tolerant networks (DTNs). We consider a number of fixed (data collector) nodes deployed in the DTN region, and a number of mobile (data generator) nodes that move freely in the region according to Brownian motion. As it moves, each mobile node is assumed to continuously generate and buffer data. When a mobile node comes within the communication coverage range of a data collector node, it downloads its buffered data to it. Otherwise, it keeps generating and buffering its data. In this paper, we derive analytic bounds on the amount of time a mobile node spends without communication coverage. Then, using these derived bounds, we derive sufficient conditions on node density that statistically guarantee that the expected hitting times remain below a given threshold.

Femtocell (FC) is a new networking paradigm that differs from the traditional, macrocell (MC) network in many ways: size/coverage, random deployment, autonomous operation, signal propagation environment, etc. Inter-cell interference is a major issue in FCs operating over the same channel as the underlying MC. In this paper, we present an analytic study of the uplink (UL) physical interference and other related metrics, namely the signal to interference ratio (SIR) and the outage probability in FCs. We consider a stochastic model in which the spatial distribution of the femto users (FUs) and the macro users (MUs) is described by two independent, homogeneous Poisson Point Processes (PPPs). We first characterize the UL interference at the FC by deriving its first and second order statistics, its probability density function (PDF) and its tail distribution. Second, we derive the PDF of the per-FU SIR, its temporal autocorrelation and the outage probability. Finally, we validate the derived outage probability via Monte-Carlo simulations and show how this probability constrains the system capacity in terms of number (or density) of FUs that could be accepted in the MC.

We propose an energy-aware MAC protocol, referred to as EM-MAC, for multi-hop wireless networks with multi-channel access capabilities. EM-MAC relies on iMAC's efficient channel selection mechanism to resolve the medium contention on the common control channel, enabling wireless devices to select the best available data channel for data communication. Our protocol saves energy by allowing devices that have not gained access to the medium to switch to doze mode until the channel becomes idle again. Simulations results show that EM-MAC reduces energy consumption when compared with iMAC.

We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access (DSA) networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or “rewards” to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby “hopefully” maximizing their long-term cumulative received reward. However, when DSA users' objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this paper, we derive efficient objective functions that DSA users can aim to maximize, and that by doing so, users' collective behavior also leads to good overall system performance, thus maximizing each user's long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner.

This paper is concerned with the design and analysis of delay-tolerant networks (DTNs) deployed for free-roaming animal monitoring, wherein information is either transmitted or carried to static access-points by the animals whose movement is assumed to be random. Specifically, in such mobility-aided applications where routing is performed in a store-carry-and-drop manner, limited buffer capacity of a carrier node plays a critical role, and data loss due to buffer overflow heavily depends on access-point density. Driven by this fact, our focus in this paper is on providing sufficient conditions on access-point density that limit the likelihood of buffer overflow. We first derive sufficient access-point density conditions that ensure that the data loss rates are statistically guaranteed to be below a given threshold. Then, we evaluate and validate the derived theoretical results through comparison with both synthetic and real-world data.

In this paper, we develop cross-layer techniques suitable for wireless sensor networks (WSNs) that are capable of multichannel access. More specifically, we propose energy and cross-layer aware routing schemes for multichannel access WSNs that account for radio, MAC contention, and network constraints. By doing so, we guarantee to meet data rate requirements of end-to-end flows while maximizing the network lifetime. When MAC contention constraints associated with the shared wireless medium are not included in routing formulations, routing solutions may not be feasible, in that the shared medium may not be able to support the required data rates of these flows. In this paper, we first derive three sets of sufficient conditions that ensure feasibility of data rates in multichannel access WSNs. Then, utilizing these sets, we devise three different MAC-aware routing optimization schemes, each aiming to maximize the network lifetime. Finally, we perform extensive simulation studies to evaluate and compare the performance of the proposed routing approaches under various network conditions.

We develop resource and service management techniques to support secondary users (SUs) with QoS requirements in large-scale distributed dynamic spectrum access (DSA) systems. The proposed techniques empower SUs' to seek and exploit spectrum opportunities dynamically and effectively, thereby maximizing the SUs' long-term received service satisfaction levels. Our techniques are efficient in terms of optimality, scalability, distributivity, and fairness. First, they enable SUs to achieve high service satisfaction levels by quickly locating and accessing available spectrum opportunities. Second, they are scalable by performing well in systems with small as well as large numbers of SUs. Third, they can be implemented in a decentralized manner by relying on local information only. Finally, they ensure fairness among SUs by allowing them to receive equal amounts of service.

Abstract Wireless sensor networks (WSNs) typically consist of a large number of battery‐constrained sensors often deployed in harsh environments with little to no human control, thereby necessitating scalable and energy‐efficient techniques. This paper proposes a scalable and energy‐efficient routing scheme, called WCDS‐DCR, suitable for these WSNs. WCDS‐DCR is a fully distributed, data‐centric, routing technique that makes use of an underlying clustering structure induced by the construction of WCDS (Weakly Connected Dominating Set) to prolong network lifetime. It aims at extending network lifetime through the use of data aggregation (based on the elimination of redundant data packets) by some particular nodes. It also utilizes both the energy availability information and the distances (in number of hops) from sensors to the sink in order to make hop‐by‐hop, energy‐aware, routing decisions. Simulation results show that our solution is scalable, and outperforms existing schemes in terms of network lifetime. Copyright © 2010 John Wiley & Sons, Ltd.

Femtocells (FCs) are low power, small-area cellular networks typically designed for use in a home or small business. The design of FC networks is challenging due to their independence of each other and of the underlying macrocell (MC) network thereby, necessitating non-cooperative resource allocation schemes. This paper develops a new distributed non-cooperative uplink (UL) power allocation scheme for FC users. Our scheme aims at fairly maximizing the throughput of femto users (FUs) based on periodic interference estimation performed by the femto access points (FAPs). We compare our scheme to the optimal centralized one. Simulation results show that our scheme presents good performances in terms of throughput and fairness.

This paper investigates the performance limits of delay tolerant networks (DTNs) with intermittently connected nodes deployed for wildlife monitoring, wherein information is either transmitted or carried to static accesspoints by free-ranging animals whose movement is assumed to be random. Specifically, in such mobility-aided applications where routing is performed in a store-carry-and-drop manner, limited buffer capacity of a carrier node plays a critical role, and data loss due to buffer overflow heavily depends on access-point density. Driven by this fact, our focus in this paper is on providing sufficient conditions on accesspoint density that limit the likelihood of buffer overflow. Specifically, we first derive and prove sufficient access-point density conditions that ensure that the data loss rates are statistically guaranteed to be below a given threshold. We consider studying both the square and hexagonal accesspoint deployment structures. Then, we validate the derived theoretical results for each of the two studied structures through simulations.

This paper develops cross-layer techniques for routing rate-constrained traffic in wireless sensor networks (WSNs) with multichannel access capability. We first derive and prove sufficient conditions that ensure feasibility of data rates in multichannel access WSNs. Then, we use these conditions to devise routing approaches that maximize the network lifetime while ensuring that the obtained routing solutions satisfy the required data rates. Finally, we evaluate and compare the performances of the proposed routing approaches under various network parameters.

We characterize some fundamental performance limits of partially covered, intermittently connected, delay-tolerant networks (DTNs) that are comprised of a hybrid mix of mobile and static nodes (i.e., access points). Specifically, we derive theoretic bounds on the expected hitting time between two consecutive visits of a mobile node to access points for both the square and hexagonal access point deployment structures. For each of these two models, we use the Poisson Clumping technique to derive theoretic bounds on the data loss rate, under the assumption that a mobile node has a finite buffer that overflows after a certain amount of time. Based of these obtained results, we provide asymptotic analysis of the expected hitting time in these partially covered DTNs. We test the applicability of the Poisson Clumping technique, and our hitting time results, with simulations.

We develop objective functions for large-scale distributed dynamic spectrum access (DSA) networks that, by means of any learning algorithm, enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). We show that the proposed functions are: (i) optimal by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributed by being implementable in a decentralized manner.

We develop resource and service management techniques to support spectrum users (SUs) with quality of service requirements in large-scale distributed dynamic spectrum access (DSA) systems. The proposed techniques empower SUs to seek and exploit spectrum opportunities dynamically and effectively, thereby maximizing the long-term service satisfaction levels that SUs receive from accessing and using the DSA system. Our techniques are efficient in terms of optimality, scalability, distributivity, and fairness. First, they enable SUs to achieve high service satisfaction levels by quickly locating and accessing available spectrum opportunities. Second, they are scalable by performing well in systems with small as well as large numbers of SUs. Third, they can be implemented in a decentralized manner by relying on local information only. Finally, they ensure fairness among SUs by allowing them to receive equal amounts of service.

In this paper, we present and evaluate learning schemes that allow multiple secondary users to locate and use spectrum opportunities effectively, thus improving efficiency of dynamic spectrum access (DSA) systems. Using simulations, we show that the proposed schemes achieve good performances in terms of throughput and fairness, and does so by interacting with and learning from the environment only, without requiring prediction models of the environment's dynamics and behaviors.

ABSTRACT The expected shortage in spectrum supply is well understood to be primarily due to the inefficient, static nature of current spectrum allocation policies. In order to address this problem, Federal Communications Commission promotes the so called opportunistic spectrum access (OSA) to be applied on cognitive radio networks (CRNs). In short, the idea behind OSA is allowing unlicensed users to use unused licensed spectra as long as they do not cause interference to licensed users. In this paper, we present and evaluate learning schemes that allow unlicensed users to locate and use spectrum opportunities effectively, thus improving efficiency of CRNs. We separately consider two models: single and multiple unlicensed user(s). For the latter model, we present two schemes: noncooperative and cooperative Q‐learning. All proposed schemes do not require prior knowledge or prediction models of the environment's dynamics and behaviors, yet can still achieve high performance by learning from interaction with the environment. Using simulations, we show that the proposed schemes achieve good performances in terms of throughput and fairness. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes medium contention aware routing schemes for rate-constrained traffic in wireless sensor networks (WSNs) that maximize network lifetime. Three sufficient conditions, referred to as rate-based, degree-based, and mixed constraints, are incorporated into the routing formulations to guarantee medium access feasibility of the routing solutions. Simulations show that the mixed condition based approach always achieves better network lifetimes than the other two approaches for larger networks, but at the cost of larger execution times. Our results show that the rate-based condition approach is the best choice in terms of balancing between solution quality and complexity.

This paper proposes an auction mechanism for dynamic spectrum allocation that balances between two conflicting objectives: revenue and efficiency. The spectrum resources are allocated by FCC (i.e., the auctioneer) with the objective of maximizing revenue while also increasing efficiency or social welfare. Unfortunately, revenue and efficiency are often not aligned with one another. Truthful mechanisms, such as Vickrey-Clarke-Groves (VCG), are shown to be efficient in terms of social welfare, but poor in terms of revenue generation. On the other hand, mechanisms, such as uniform-price, can generate higher revenues than VCG, but less efficient. We show that the incorporation of adequate reservation price to the uniform-price auction mechanism gives enough incentives to spectrum buyers to be truthful, thus providing a good balance between efficiency and revenue. We investigate the impact of reservation price not only on the revenue and efficiency, but also on the spectrum utilization. Simulation results show that the proposed auction mechanism maximizes revenue while also increasing efficiency and spectrum utilization.

The expected shortage in spectrum supply is well understood to be primarily due to the inefficient, static nature of current spectrum allocation policies. In order to address this problem, FCC promotes the so-called Opportunistic Spectrum Access (OSA). In short, the idea behind OSA is to allow unlicensed users to use unused licensed spectra so long as they do not cause interference to licensed users. In this paper, we propose Q-OSA, a learning scheme that enables effective OSA, thus improving spectrum efficiency. Q-OSA does not require prior knowledge of the environment's dynamics, yet can still achieve high performance by learning from interaction with the environment.

We propose a joint optimization of Network Coding and MIMO techniques to improve the downlink channel throughput of a wireless base station. Specifically, we consider a MIMO base station with multiple transmit antennas that serves multiple users simultaneously by generating multiple signal beams with well-defined beamforming weight vectors where each beam intends for a particular user. Given a large number of users and a small number of transmit antennas, a base station must decide, at any transmission opportunity, which group of users it should transmit packets to, in order to maximize the overall throughput. To that end, we propose a method for grouping users that takes advantages of NC technique and the orthogonality of user channels to improve the overall throughput on both unicast and broadcast transmissions. Our simulation results indicate that the proposed method can efficiently increase the throughput over existing techniques, especially in highly lossy environments.

This paper provides a measurement-based performance evaluation of the Optimized Link State Routing (OLSR) protocol. Two versions of OLSR, OLSR-ETX and OLSR-ETT, are implemented and evaluated on a mesh network that we built from off-the-shelf commercial components. OLSR-ETX uses the Expected Transmission Count (ETX) metric whereas, OLSR-ETT uses the Expected Transmission Time (ETT) metric as a means of assessing link quality. The paper describes our implementation process of the ETT metric using the plug-in feature of OLSRd, and our calculation method of link bandwidth using the packet-pair technique. A series of measurements are conducted in our testbed to analyze and compare the performance of ETX and ETT metrics. Our measurements show that OLSR-ETT outperforms OLSR-ETX significantly in terms of packet loss, end-to-end delay, and stability, yielding a much more robust, reliable, and efficient routing.

We have recently witnessed a rapidly increasing demand for, and hence, a shortage of, wireless network bandwidth due to rapidly growing wireless services and applications. It is, therefore, important to develop an efficient way of utilizing this limited bandwidth resource. Fortunately, recent technological advances have enabled software-defined radios (SDRs) to switch from one frequency band to another at minimum cost, thereby making dynamic multiband access and sharing possible. On the other hand, recent advances in signal processing combined with those in antenna technology provide multiple-input multiple-output (MIMO) capabilities, thereby creating opportunities for enhancing the throughput of wireless networks. Both SDRs and MIMO together enable next-generation wireless networks, such as mesh networks, to support dynamic and adaptive bandwidth sharing along time, frequency, and space. In this paper, we develop a new framework that 1) identifies the limits and potential of SDRs and MIMO in terms of achievable network throughput and 2) provides guidelines for designers to determine the optimal parameters of wireless mesh networks equipped with multiband and multiantenna capabilities.

By Lingyang Song, Yan Zhang, Nirwan Ansari, Jianwei Huang and Bechir Hamdaoui, Guest Editors Welcome to this special issue of Wiley Journal of Wireless Communications and Mobile Computing (WCMC). The title of this special issue literally adopts the theme of the 2010 International Wireless Communications and Mobile Computing Conference (IWCMC 2010), as it attempts to represent the ‘best’ of IWCMC 2010 by soliciting representative quality research works presented at the conference for inclusion in this issue via a rigorous selection and review process. This special issue covers a quite broad range of topics of wireless networks, wireless communications, and mobile computing, from the physical layer through application and system design. The goal of this special issue is to create a great opportunity for high impact research from both the mobile communications industry and academia to present and discuss new trends, developments, emerging technologies, and new industrial standards. To guarantee the quality, in this special issue, we have selectively collected 11 expanded papers from the proceedings of IWCMC 2010, and clustered them in three groups: three papers dealing with physical layer aspects, six papers investigating MAC and network layer issues, and two papers focusing on applications as well as prototypes. Detailed overview of the selected works is given below. The first group includes three papers, which provide physical layer results for wireless communications and mobile computing. The first paper, by Takeda et al., studies joint transmit and iterative receive frequency-domain equalization for DS-CDMA. In the proposed scheme, simple one-tap frequency-domain equalization at the transmitter and iterative one-tap FDE at the receiver are jointly performed using the common knowledge of channel state information, and at the same time taking channel estimation constraints into account. The paper by Fan et al. investigates the relay position selection problem for the diamond network over Nakagami-m fading channels. This paper discusses the impact on the performance of diamond network caused by the relays' position for a general Nakagami-m fading channel, which extends the previous work for a special Rayleigh fading case, and gives clear restrictions of their positions based on the requirement of the throughput improvement and network stability. The third paper, by Stuber et al., studies outage probability for cooperative diversity with selective combining in cellular networks. The analysis mainly focuses on outage probability for amplify-and-forward and decode-and-forward cooperative diversity systems with selective combining, for the case of a log-normal Nakagami faded desired signal and log-normal Rayleigh faded co-channel interferers. The second group of papers mainly investigates MAC and network layer issues. The first paper, by Wong et al., deals with switching cost minimization in the IEEE 802.16e mobile WiMAX sleep mode operation in order to improve the battery lifetime of the mobile station. The paper proposes a novel approach to resolve this issue by making a heuristic decision during the listening interval to minimize the switching frequency for better energy efficiency. The second paper by Lin et al. studies Multicast Broadcast Service (MBS) zone configuration for wireless multicast and broadcast service. Two schemes, the overlapping scheme and the enhanced overlapping scheme, are provided for more flexible MBS zone configuration to achieve better performance for MBS in terms of QoS and radio resource utilization. The third paper, by Kumar et al., investigates the issue of trust advisory and its establishment in mobile networks, with application to ad hoc networks, including DTNs. The authors utilized encounters in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. The fourth paper by Znati et al. proposes robust multicast routing algorithms for mobile wireless networks by considering more practical challenges, e.g., the mobility of nodes, the tenuous status of communication links, limited resources, and indefinite knowledge of the network topology. This paper addresses these difficulties by providing a framework and architecture with proactive and reactive components to support multicasting to guarantee reliability and efficiency of end-to-end packet delivery. The fifth paper, by Pu et al., redefines the fairness concept regarding the application utility for time-constraint flows and then presents novel utility-based fair bandwidth sharing approaches in vehicular networks. Accordingly, two practical bandwidth-sharing schemes are provided for transferring data by fast-moving wireless nodes such as vehicles in order to guarantee QoS. The sixth paper, by Ali et al., provides a MAC protocol for cognitive wireless sensor body area networks to increases the critical traffic throughput. The proposed cognitive radio based MAC protocol prioritizes the critical packets access to the transmission medium by transmitting them with higher power while transmitting lower priority packets using lower transmission power. The third group consists of two papers focusing on applications and prototypes. The first paper by Fantacci et al. introduces a novel communication infrastructure for emergency management to interconnect several heterogeneous systems and provide multimedia access to groups of people involved in emergency operations as foreseen by the In.Sy.Eme. (Integrated System for Emergency) project. The main scope of the In.Sy.Eme system is to facilitate functional integration of new technologies with actual or off-the-shelf technologies to provide fast responses to any emergency situations and efficient use of all available resources. The second paper, by Manfrin et al., demonstrates the CalRAdio-Based advanced Spectrum Scanner, an open platform developed to monitor the ISM 2.4-2.499 GHz band, and reveals opportunities for a better utilization of the available spectrum resources. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it capitalizes on the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. In conclusion, this issue of WCMC offers a state-of-the-art view of recent advances in wireless network, wireless communications, and mobile computing. It also offers both academic and industry appeal—the former as a basis toward future research directions and the latter toward viable commercial applications. In the long term, innovative wireless communications and mobile computing techniques will be characterized by their criticalness in consumer, business, and government applications to enhance the development of the whole world in realizing a better future. Finally, we would like to thank all the authors who have submitted their papers for consideration for publishing their work in this issue. We would like to extend our gratitude to the anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also would like to thank the devoted staff of Wiley for their high level of professionalism, and particularly express our gratitude to the Editor-in-Chief of WCMC, Professor Mohsen Guizani, for his advice, patience, and encouragement from the beginning until the final stage. We hope you will enjoy reading the great selection of papers in this issue.

The enormous success of wireless technology has recently led to an explosive demand for, and hence a shortage of, bandwidth resources. This expected shortage problem is reported to be primarily due to the inefficient, static nature of current spectrum allotment methods. As an initial step towards solving this shortage problem, FCC1opens up for the so-called opportunistic spectrum access (OSA), which allows unlicensed users to exploit unused licensed spectrum, but in a manner that limits interference to licensed users. Fortunately, technological advances enabled cognitive radios, which are viewed as intelligent communication systems that can learn from their surrounding environment by themselves, and adapt their internal operating parameters in real-time also by themselves to improve spectrum efficiency. Cognitive radios have recently been recognized as the key enabling technology for realizing OSA. In this work, we propose a machine learning-based scheme that will exploit the cognitive radios' capabilities to enable effective OSA, thus improving the efficiency of spectrum utilization. Our proposed learning technique does not require prior knowledge of the environment's characteristics and dynamics, yet can still achieve high performances by learning from interaction with the environment.

The next generation of radio wireless networks is expected to support spectrum decision operations that will allow a cognitive radio terminal to share the frequency bandwidth with primary users without impairing their QoS requirements. This can indeed enhance the utilization of the limited spectrum resources, and help meet the growing bandwidth demand. In this paper, we propose a new, cross-layer algorithm that allows a cognitive terminal to share and access to either HSDPA or WiMAX licensed spectrum while giving priority to the primary users. This algorithm not only considers the interference caused by the cognitive terminal, but also enhances the achievable throughput of this cognitive terminal. Simulation results show the effectiveness of our spectrum access scheme. Under our proposed scheme, the achievable data rate of the cognitive radio terminal is increased by 30% when compared with that obtained under common access schemes.

Multiantenna or MIMO systems offer great potential for increasing the throughput of multihop wireless networks via spatial reuse and/or spatial multiplexing. This paper characterizes and analyzes the maximum achievable throughput in multihop, MIMO-equipped, wireless networks under three MIMO protocols, spatial reuse only (SRP), spatial multiplexing only (SMP), and spatial reuse and multiplexing (SRMP), each of which enhances the throughput, but via a different way of exploiting MIMO's capabilities. We show via extensive simulation that as the number of antennas increases, the maximum achievable throughput first rises and then flattens out asymptotically under SRP, while it increases "almost" linearly under SMP or SRMP. We also evaluate the effects of several network parameters on this achievable throughput, and show how throughput behaves under these effects.

Studies showed that the static nature of the traditional spectrum allocation methods, currently being used to share the radio spectrum, resulted in a plenty of unused spectrum opportunities that wireless devices can still potentially exploit. Fortunately, recent technological advances enabled software-defined radios (SDRs) that can switch from one spectrum band (SB) to another at minimum cost, thereby promoting dynamic and adaptive spectrum access and sharing. In this paper, we derive and study an adaptive spectrum assessment approach that allows devices to decide how to seek spectrum opportunities effectively. In the event when a decision is made in favor of discovering new opportunities, the proposed approach allows devices to determine the optimal number of SBs to be explored so that the device benefits from such an opportunistic spectrum access. This approach is optimal in that it strikes a balance between two conflicting needs: keeping spectrum assessment overhead low while increasing the likelihood of discovering spectrum opportunities. We study the effect of several network parameters, such as the primary traffic load, the secondary traffic load, and the collaboration level of the sensing method, on the optimal number of SBs that devices need to explore.

This paper proposes an adaptive approach that allows wireless devices with multi-band capabilities to determine the optimal number of spectrum bands (SBs) to be explored so that its net benefit from accessing the spectrum is maximized. This approach is optimal in that it strikes a balance between the two conflicting objectives of (1) keeping spectrum sensing overhead low and (2) increasing the chances of finding spectrum opportunities. We also study the effect of several network parameters, such as the primary and secondary traffic loads, on the optimality of the proposed approach.

Recent technological advances have enabled SDRs to switch from one frequency to another at low cost, thus making dynamic multi-band access possible. On the other hand, recent advances in signal processing combined with those in antenna technology provide MIMO capabilities, thereby creating opportunities for enhancing the throughput of wireless networks. Both SDRs and MIMO together enable next-generation wireless networks, such as wireless mesh networks, to support dynamic and adaptive bandwidth sharing along time, frequency, and space. In this paper, we develop a new framework that identifies the limits and potentials of SDRs and MIMO. We characterize and analyze the maximum throughput that wireless networks can achieve when they are SDR-capable and MIMO-equipped.

Wireless networks and devices have been rapidly gaining popularity over their wired counterparts. This popularity, in turn, has been generating an explosive and ever-increasing demand for, and hence creating a shortage of, the radio spectrum. Existing studies indicate that this foreseen spectrum shortage is not so much due to the scarcity of the radio spectrum, but due to the inefficiency of current spectrum access methods, thus leaving spectrum opportunities along both the time and the frequency dimensions that wireless devices can exploit. Fortunately, recent technological advances have made it possible to build software-defined radios (SDRs) which, unlike traditional radios, can switch from one frequency band to another at little or no cost. We propose a MAC protocol, called Opportunistic Spectrum MAC (OS-MAC), for wireless networks equipped with cognitive radios like SDRs. OS-MAC (1) adaptively and dynamically seeks and exploits opportunities in both licensed and unlicensed spectra and along both the time and the frequency dimensions; (2) accesses and shares spectrum among different unlicensed and licensed users; and (3) coordinates with other unlicensed users for better spectrum utilization. Using extensive simulation, OS-MAC is shown to be far more effective than current access protocols from both the network's and the user's perspectives. By comparing its performance with an Ideal-MAC protocol, OS-MAC is also shown to not only outperform current access protocols, but also achieve performance very close to that obtainable under the Ideal-MAC protocol.

Use of multiple antennas or MIMO has great potential for enhancing the throughput of multi-hop wireless networks via spatial reuse and/or spatial division multiplexing. In this paper, we characterize and analyze the maximum achievable throughput in multi-hop wireless MIMO networks under three MIMO protocols, spatial reuse only (SRP), spatial multiplexing only (SMP), and spatial reuse & multiplexing (SRMP), each of which enhances throughput via a different way of exploiting the MIMO's potential. We show via extensive simulation that as the number of antennas increases, the maximum achievable throughput first rises and then flattens out asymptotically under SRP, while it increases almost linearly under SMP or SRMP. We evaluate the effects of several network parameters on this achievable throughput. We also demonstrate how these results can be used by designers to determine the optimal parameters of multi-hop wireless MIMO networks.

Recent advances in antenna technology made it possible to build wireless devices with more than one antenna at affordable costs. Because multiple antennas offer wireless networks a potential capacity increase, they are expected to be a key part of next-generation wireless networks to support the rapidly emerging multimedia applications characterized by their high and diverse QoS requirements. This paper developed methods that exploit the benefits of multiple antennas to enable multi-hop wireless networks with flow-level QoS capabilities. The authors first propose a cross-layer table-driven statistical approach that allows each node to determine the amount of spatial reuse and/or multiplexing, offered by the multiple antennas that are available to it. The authors then use the developed statistical approach to derive sufficient conditions under which flow rates are guaranteed to be feasible. The derived conditions are multi-layer aware in the sense that they account for cross-layer effects between the PHY and the MAC layers to support QoS at higher layers. The authors evaluate and compare the derived sufficient conditions via extensive simulations. The authors show that the conditions result in high flow acceptance rates when used in multi-hop wireless networking problems such as QoS routing and multicommodity flow problems. The authors also demonstrate the importance and the effect of considering cross-layer couplings into the development of flow acceptance methods.

Unlike single omnidirectional antennas, multiple antennas offer wireless ad-hoc networks potential increases in their achievable throughput and capacity. Due to recent advances in antenna technology, it is now affordable to build wireless devices with more than one antenna. As a result, multiple antennas are expected to be an essential part of next-generation wireless networks to support the rapidly emerging multimedia applications characterized by their high and diverse QoS needs. This paper develops an admission control framework that exploits the benefits of multiple antennas to better support applications with QoS requirements in wireless ad-hoc networks. The developed theory provides wireless ad-hoc networks with flow-level admission control capabilities while accounting for cross-layer effects between the PHY and the MAC layers. Based on the developed theory, we propose a mechanism that multiple antenna equipped nodes can use to control flows' admissibility into the network. Through simulation studies, we show that the proposed mechanism results in high flow acceptance rates and high network throughput utilization.

Multimedia applications over IEEE 802.11 wireless LANs (WLANs) such as wVoIP have recently attracted the focus of many researchers. Unlike best-effort applications, multimedia applications are delay- and/or bandwidth-sensitive. In order for these applications to achieve an acceptable QoS, the network must provide them with certain QoS guarantees. This paper proposes an admission control heuristic for applications with delay requirements in IEEE 802.11e EDCA WLANs. To develop the proposed heuristic, we first derive an analytical approximation of delays experienced by packets when delivered via IEEE 802.11e EDCA WLANs. We validate the heuristic through simulations of voice traffic

Multi-hop hotspot networks consist typically of one or few wireless access nodes (ANs) and many self-organizing, self-coordinating, and battery-powered portable nodes (PNs). Nodes maintain cooperative connectivity among each other without any need for a wired infrastructure. On the one hand, because PNs are power-limited, efficient use of their available energy resources is crucial to their lifetimes. On the other hand, because higher rates of elastic flows provide higher QoS, it is desirable to maximize the throughput. Unfortunately, increasing lifetime and maximizing throughput are two conflicting objectives which cannot he optimized simultaneously. In this paper, we propose an extension of our earlier-presented scheme (B. Hamdaoui et al., IEEE Press Monograph on Sensor Network Op., 2004) to support elastic traffic routing in multi-hop hotspot networks. The proposed routing scheme strikes a balance between the need to keep the nodes operational with sufficient energy resources and the desire to allocate higher throughput to elastic flows. The proposed scheme also deals systematically with both objectives of maximizing the network lifetime and minimizing the total consumed energy.

Handoff is the process during which a mobile node (MN) needs to change its connectivity point to the wireless internetwork from one access node (AN) to another during an ongoing communication. If MNs are allowed to have two or more simultaneous connections to the internetwork through different ANs, then the handoff is said to be soft; otherwise, it is said to be hard. Traditionally, during forward-link soft handoff, multiple identical copies of each packet are simultaneously transmitted to the MN through the associated ANs. At the MN's physical-layer, the received signals are combined on a bit-by-bit basis resulting in improving the bit-error rate. However, this approach requires tight synchronization of the ANs involved in the soft handoff. In addition, as shown in the literature, the capacity often decreases due to the increase of the number of channels used by MNs during soft handoff. In this paper, we propose, analyze, simulate, and implement a soft handoff scheme called soft handoff over IP (SHIP) for forward-link that 1) overcomes the need for synchronization and 2) increases the capacity of the network. Through both analytic and simulation studies, we show that SHIP achieves significant performance improvements. We derive analytic expressions of the power-capacity relationship for two-dimensional (2-D) and one-dimensional (1-D) cell models. By comparing our scheme with the hard handoff, we empirically show that the capacity increases by about 30% and 20%, respectively, for the 2-D and 1-D cell models. Further, the simulation results show that SHIP saves up to 30% of the total power consumed by the ANs.

Wireless nodes within the same vicinity contend for accessing the shared medium. The contention constraints on sharing the medium depend on the medium access control (MAC) protocol. For example, in IEEE 802.11 MAC protocol-based networks, if node i is in communication with node j, then all nodes within the same transmission range of i or j cannot communicate. On the other hand, if nodes within each other's transmission range can use different frequencies (e.g., FDMA) or different codes (e.g., CDMA), then neighbor nodes can communicate simultaneously. Furthermore, if nodes are equipped with two radios (e.g., WINS sensor networks), then nodes not only can communicate concurrently but also can receive while they are transmitting. In this paper, we prove a sufficient condition under which a flow rate vector is feasible given the MAC protocol. We also prove that the sufficient condition is necessary for some MAC protocols such as those used by WINS sensor and Bluetooth [Marsan, M., 2002] networks. We give illustrative and real examples for which these conditions apply.