Freezing of Gait (FoG) is a prevalent motor dysfunction experienced by patients with Parkinson’s disease, and while extensive research has focused on detecting FoG episodes in both clinical and home environments over the past decade, predictive approaches that enable preemptive prompting are still scarce. Most existing studies rely on fully supervised learning conditions, which pose significant challenges. This study introduces a semi-supervised learning framework utilizing a prototype network designed to leverage a FoG prediction model based on impaired gait patterns indicative of pre-FoG, simultaneously harnessing information from unlabeled sensor data for real-time FoG prediction. To validate our framework, we establish pre-FoG state labeling across four datasets: DAPHNet, CPGDD, PDTURN, and BXHC. Our developed real-time detection model demonstrates strong performance, achieving up to \(96\%\) sensitivity, \(99\%\) specificity, and \(91\%\) average accuracy on the cross-validation dataset. Furthermore, we compare the accuracy between semi-supervised and fully supervised modes, revealing that the semi-supervised approach yields improvements in average accuracy ranging from 0.01 to 0.02 across three datasets. Thus, our proposed method represents an effective strategy for the accurate prediction of FoG in real-time algorithmic settings.

The deployment of reliable maritime communication systems integrated with terrestrial networks is challenging due to the large maritime regions, the difficulty of deploying conventional base stations at sea, and the heterogeneity of proprietary equipment. In this paper, we propose an AI-based network slicing framework for Open Radio Access Network (O-RAN) integrated aerial-terrestrial maritime networks that incorporates non-tethered and tethered unmanned aerial vehicles (UAVs) and marine buoys. The network provides ubiquitous connectivity while addressing diverse maritime user requirements. Specifically, we leverage network slicing to accommodate the needs of two distinct slices: the maritime infotainment slice, which demands high data rates, and the maritime emergency communication slice, which requires high-reliability and low-latency. Moreover, we adopt virtualization principles to enable flexible deployment approaches for virtualized network functions (VNFs), that can be dynamically scaled and/or migrated across virtualized network nodes. Then, we design a network slicing framework based on a Deep Reinforcement Learning (DRL) that takes into account the characteristics of the maritime environment, and present two algorithms using Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). Our findings highlight the importance of the integration of aerial and terrestrial networks with network slicing to enhance the energy efficiency of maritime communications while meeting diverse Quality of Service (QoS) requirements.

Data Center-enabled High Altitude Platforms (DCHAPs) show great potential in reducing the energy consumption of terrestrial data centers by leveraging natural cooling and solar power harvesting. This paper advances the field by analyzing multi-platform architectures with multiple terrestrial data centers (TDCs) and HAPs performing heterogeneous task offloading. We formulate this complex problem mathematically and develop a feasibility-preserving transformation approach to ensure optimization stability. To solve this challenging problem, we propose a novel heuristic sequential algorithm specifically designed for multiplatform DC-HAP systems, while also tailoring two established optimization methods—sequential quadratic programming (SQP) and differential evolution (DE)—to address our unique problem constraints. Comprehensive simulations demonstrate that robust results are guaranteed with our proposed heuristic algorithm, while the SQP algorithm with the transformed problem balances performance and runtime better than the DE approach. The increasing number of HAPs leads to capability degradation for the SQP and DE algorithms, while our proposed heuristic algorithm maintains powerful performance with the highest efficiency for complex system architectures.

Sunlight is commonly used for indoor natural illumination in modern buildings, but it also holds potential as an information carrier in indoor optical wireless communication (OWC) systems. This paper introduces a multi-user, sunlightbased modulation downlink multiple-input single-output (MISO) system employing a recently developed dual-cell liquid crystal shutter (DLS) as the modulator, allowing building facades to simultaneously provide communication and illumination based on user needs. We first outline the system framework, detailing the transmitter and receiver structures. To assess channel performance, we derive an end-to-end transmission model by examining the physical mechanisms of the DLS. A preprocessing linearization method is applied to mitigate nonlinear behavior, enabling us to derive a theoretical lower bound for the system's channel capacity, facilitated by time division multiplexing (TDM). Additionally, we establish an illumination model that accounts for the contributions of active and inactive DLS cells. An optimization problem is formulated to maximize total channel capacity across multiple users, subject to minimum illumination requirements and boundary constraints. Simulation results show that the proposed system achieves high communication throughput by dynamically adjusting the ratio of active to inactive DLS cells. This study establishes a basis for high-speed indoor OWC systems powered by natural sunlight.

Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven HO optimization framework based on high-dimensional Bayesian optimization (HD-BO) and enhanced with transfer learning to reduce training time and improve generalization across different user speeds. Evaluations on a real-world deployment show that HD-BO outperforms 3GPP set-1 and set-5 benchmarks, while transfer learning enables rapid adaptation without loss in performance. This highlights the potential of data-driven, site-specific mobility management in large-scale networks.

Sunlight offers not only illumination but also an eco-friendly solution for indoor optical wireless communication (OWC). This article presents a comprehensive investigation of sunlight modulation using the recently introduced dual-cell liquid crystal shutter (DLS), examining both communication and illumination aspects. We propose a system featuring a glass window equipped with a large array of DLS, including configurable active and non-active communication cells. Our study begins by formulating channel models for DLS configurations in downlink communication based on experimental measurements and thorough analysis. We then derive the illumination expression resulting from the generalized contribution of active and non-active communication cells. Building on these models, we analyze the channel capacity bounds of the proposed communication system and formulate an optimization problem to maximize channel capacity while meeting the minimum indoor illumination constraint. Simulation results demonstrate the superior channel capacity of the DLS-based system compared to conventional light-emitting diode (LED) communication systems. Furthermore, by dynamically adjusting the number of active DLS cells in a multiple DLS system, we achieve high communication throughput for receivers at various locations. Our findings reveal that the discrepancy between our theoretical predictions and empirical findings underscores the potential for elevating the data rate from tens of kbps to tens of Mbps.

Accurate measurement of oxygen uptake ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\) ) dynamics and maximal oxygen consumption ( \(\dot{\mathrm{V}}\mathrm{O}_{2}\max\) ), a vital marker of cardiorespiratory fitness and exercise capacity, requires specialized exercise physiology laboratories with costly equipment. This study develops a Temporal Fusion Network (TFN) approach utilizing easily accessible physiological parameters (heart rate, heart rate reserve, tidal volume, and breathing frequency), which can be measured with wearable sensors, anthropometric variables (age, gender, height, and weight), as well as health status to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics during cardiopulmonary exercise testing (CPET). These input physiological parameters were derived from 140 laboratory CPET of a diverse cohort of adults (90 males, 50 females; 77 healthy, 63 smokers; average age: 26.6 years), to analyze \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics. The TFN model demonstrated high predictive accuracy to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) dynamics, with a Root Mean Square Error (RMSE) of 0.03 L/min and an R-squared ( R 2 ) value of 0.92, indicating robust performance across varied population groups. This TFN model paves the way for practical and cost-effective approach to estimate \(\dot{\mathrm{V}}\mathrm{O}_{2}\) during exercise, with potential integration with consumer health devices to expand accessibility and, enhance its utility for clinical and fitness applications.

Freezing of Gait (FoG) represents a critical and debilitating symptom of Parkinson's disease, posing significant challenges in patient mobility and safety. Numerous research efforts have focused on predicting the onset of FoG using wearable sensors and computational aids. However, the unpredictability and brief nature of FoG episodes complicate the ability to predict their onset in real-time, rendering timely detection a complex goal. In our study, we employed an Autoencoder-Wavenet network architecture designed to effectively utilize data from triaxial accelerometers and tri-axial gyroscopes positioned at the ankle. This approach allowed for a more nuanced analysis of movement patterns associated with FoG. Our findings indicated that this model successfully achieved a high level of accuracy in detecting FoG, with a specificity of 0.81, and sensitivity of 0.87. Furthermore, the model demonstrated the capability to provide real-time warnings of FoG onset, achieving a specificity of 0.68 and a sensitivity of 0.86. And an accuracy of 0.67 within a 1-second timeframe on the test set. Consequently, these results underscore the potential of our model in contributing to the real-time detection and management of FoG in Parkinson's disease patients.

Integrating environmental and health information in the authentication and authorization processes is challenging in the Internet of things (IoT) systems using only wireless radio communications. Recent research plans for IoT future networks shed light on the need for developing new authentication techniques suitable for health care and environmental use cases. To this end, we propose a novel molecular keys (MKs)-based authentication system that uses molecules as data carriers while considering the receiver's types, concentration, and arrival time. To facilitate our proposal, we built a MK generator and detector. The system achieved a decoding accuracy of 86% for sending bit sequences within a distance of 1m. The proposed method can also accommodate dynamic authentication and authorization changes across time and places, emerging marketing applications, and evolving human activities.

Information technology organizations and companies are seeking greener alternatives to traditional terrestrial data centers to mitigate global warming and reduce carbon emissions. Currently, terrestrial data centers consume a significant amount of energy, estimated at about 1.5% of worldwide electricity use. Furthermore, the increasing demand for data-intensive applications is expected to raise energy consumption, making it crucial to consider sustainable computing paradigms. In this study, we propose a data center-enabled High Altitude Platform (HAP) system, where a flying data center supports the operation of terrestrial data centers. We conduct a detailed analytical study to assess the energy benefits and communication requirements of this approach. Our findings demonstrate that a data center-enabled HAP is more energy-efficient than a traditional terrestrial data center, owing to the naturally low temperature in the stratosphere and the ability to harvest solar energy. Adopting a data center-HAP can save up to 14% of energy requirements while overcoming the offloading outage problem and the associated delay resulting from server distribution. Our study highlights the potential of a data center-enabled HAP system as a sustainable computing solution to meet the growing energy demands and reduce carbon footprint.

Hyperloop or evacuated-tube transportation is a groundbreaking technology that can reach aircraft-like speeds. Its uncommon configuration of a steel-made tube isolates the moving pod from the outside wireless world. In this work, we propose an inner tube network architecture that can provide the moving pod with a seamless and reliable connection. The proposed network consists of successive access points (APs) and intelligent reflecting surfaces (IRS) strategically positioned along the tube and connected to a control station (CS) through wired links to improve the wireless cell coverage. The subsequent entities of the proposed design are intelligently placed along the movement path, steering the transmitted beam towards the receiver, while a soft handover is achieved between consecutive cells. First, we optimize each IRS's positioning and phase shifts to maximize cell coverage thanks to the IRS scanning abilities while keeping a minimum quality of service. Afterward, we exploit the centralized operation at the CS and design a soft handover scheme for the inner-tube wireless network. The numerical results show that the proposed approach provides good cell coverage and spectral efficiency with different IRS scanning ranges.

Industry and academics have shown a strong interest in satellite-terrestrial networks combined with new technologies such as free-space optics (FSO). We propose a downlink satellite-terrestrial network solution combining FSO with site diversity and multi-hop hybrid radio-frequency (RF)/FSO links. Although the proposed network architecture is a cost-effective solution with wide coverage and immunity against different weather conditions, the multi-hop links can degrade its delay performance. Hence, we analyze the network dropping probability and latency to address the hop-count problem for various applications. We validated the derived network performance by Monte-Carlo simulation and showed that this setting could support ultra low latency applications such as medical applications with latency between 3–10 milliseconds, audio/video 2–50 milliseconds, and augmented reality 7–20 milliseconds. Also, the proposed network guarantees 98 % data delivery with 20 nodes.

The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at different platforms (even in different languages) may share similar topics. Second, the user preference over these topics is transferable across different platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a many-shot source domain to a few-shot target domain. To bridge two domains that are even in different languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the baselines. The source code can be found at https://github.com/taichengguo/Few-shot-NewsRec.

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

Terahertz (THz) band spans the frequencies lying between 0.1 and 10 THz and represents the gap between millimeter waves and Infrared bands. THz will play an influential role in mitigating the spectrum resources shortage to meet the exponential growth of services and wireless devices. Standardization activities are being carried out to regulate the THz band's exploitation under the IEEE 802.15 standardization project. Despite the generous bandwidth, THz communications suffer from high pathloss and attenuation due to Molecular Absorption. As such, THz systems need to use extra power, suitable antennas, and enhanced signal processing and communication techniques to compensate for different signal attenuation sources. In this paper, we propose a new operation mode for the IEEE standard 802.15 to save the power transmission requirement while achieving the required data rate. Hence, less battery or antenna size is needed to support the same communication link quality. To this end, we optimize the power, modulation scheme, and channel allocation to minimize the total transmitted power keeping a minimum quality-of-service. Moreover, the design captures the effect of humidity on the system performance.

Pretraining large models on generous multi-modal corpora has accelerated the development of visual-linguistic (VL) representation and achieved great success on various vision-and-language downstream tasks. Learning these models is usually executed by predicting the randomly masked words of captions or patches in images. Such approaches, nevertheless, seldom explore the supervision of causalities behind the caption descriptions or the procedure of generating events beyond still images. In this work, we endow the pretrained models with high-level cognition by delving into dynamic contexts to model the visual and linguistic causalities uniformly. Specifically, we format thedynamic contextsof an image as the sentences describing the eventsbefore,on, andafterimage. Unlike traditional caption-wise similarity, we propose a novel dynamic contexts-based similarity (DCS) metric, in which the correlation of potential causes and effects besides immediate visual content are considered to measure the relevance among images. DCS can be further simplified by parameterizing event continuity to relax the requirements on dense contextual event annotations. A new pre-task is designed to minimize the feature distances of dynamically contextual relevant images and incorporate the event causality and commonsense knowledge into the VL representation learning. Models based on our dynamic contexts significantly outperform typical VL models on multiple cross-modal downstream tasks, including the conventional visual commonsense reasoning (VCR), visual question answering (VQA), zero-shot image-text retrieval, and extended image / event ordering tasks.

Internet of Underwater Things (IoUT) is a technological revolution that could mark a new era for scientific, industrial, and military underwater applications. To mitigate the hostile underwater channel characteristics, this article considers a multimodal underwater network that hybridizes acoustic and optical wireless communications to achieve an ubiquitous control and high-speed low-latency networking performance, respectively. Since underwater optical wireless communications (UOWCs) suffer from limited range, it requires effective multihop routing solutions. In this regard, we propose a sector-based opportunistic routing (SectOR) protocol. Unlike the traditional unicast routing (TUR) techniques, which send packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in TUR. Contingent upon the performance characterization of a single-hop link, we obtain a variety of local and global metrics to evaluate the fitness of a candidate set (CS) and develop candidate prioritization techniques for various OR metrics. Since rate$\leftrightarrow $error and range$\leftrightarrow $beamwidth tradeoffs yield different CS diversities, we develop a candidate filtering and searching algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that the SectOR protocol can perform even better than optimal unicast routing protocols in well-connected underwater networks.

This paper studies the temporal characteristics of the intelligent reflecting surface (IRS)-based visible light communication (VLC) channel using radiometric concepts. Throughout this study, we account for the delays experienced by the transmitted power along the continuum of paths originating at the source, passing through the IRS, reaching the detector. Then, we derive the impulse response of multi-element phase-tunable metasurface and orientation-tunable mirror array-based reflector setups for a general setting of source, reflector, and detector dimensions and relative positions. In addition, we derive simpler expressions for the two special cases, namely, the point source and the large-source small-reflector. Moreover, we present the exact expression for the delay spread and derive lower, upper bounds and asymptotic expressions when the number of reflecting elements increases for both reflector types. Finally, we study the impact of several system parameters on the temporal characterization of the two IRS-based VLC systems.

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results.

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.

Reconfigurable intelligent surfaces (RISs) appear as one of the most promising paradigms for future wireless communications, because of their high adjustability for diverse communication demands and the additional information-carrying capability by reflecting patterns. This paper investigates the capacity of RIS-assisted multiple-input multiple-output (MIMO) symbiotic communications utilizing multiple reflecting patterns, where each reflecting pattern is non-uniformly activated to carry additional information. To enhance transmission performance, the reflecting patterns, reflecting activation probability, and the transmit covariance matrix are jointly designed. Since the exact expression of the system capacity is intractable, the lower and upper bounds on the capacity are derived and used for optimization in this paper. Based on the lower bound on the capacity, a gradient ascent algorithm is developed to find the optimal reflecting patterns, reflecting activation probability, and the transmit covariance matrix. By taking advantage of the concise-form upper bound on the capacity, closed-form solutions of the reflecting activation probability and transmit covariance matrix can be derived after optimizing the reflecting patterns. The superiority of the proposed design is investigated and verified by computer simulations. Some selected numerical results demonstrate that the proposed design can achieve a higher capacity than the benchmark adopting only one reflecting pattern.

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named Dual Attention-Based Federated Learning (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra-and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10% and 30%, respectively.

This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum rate of the users. The agents' utility in the potential game is their marginal contribution to the global welfare or their so-called wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3-D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks.

The controversy and argument on the usefulness of the physical layer (PHY) academic research for wireless communications are long-standing since the cellular communication paradigm gets to its maturity. In particular, researchers suspect that the performance improvement in cellular communications is primarily attributable to the increases in telecommunication infrastructure and radio spectrum instead of the PHY academic research, whereas concrete evidence is lacking. To respond to this controversy from an objective perspective, we employ econometric approaches to quantify the contributions of the PHY academic research and other performance determinants. Through empirical analysis and the quantitative evidence obtained, albeit preliminary, we shed light on the following issues: 1) what determines the cross-national differences in cellular network performance; 2) to what extent the PHY academic research and other factors affect cellular network performance; 3) what suggestions we can obtain from the data analysis for the stakeholders of the PHY research.

In this paper, we investigate the soft-reconfiguration of optical wireless data centers (WDCs). In the considered physical topology, edge top-of-rack (ToR) switches in the leaf layer are inter-connected with core switches in the spine layer via wavelength division multiplexing (WDM) based free-space optical (FSO) links. We propose an agile load balancing (LB) solution, namely SoftFG, to cope with the dynamically changing link load variations and the low-utilization time intervals within the wireless data centers (DCs). SoftFG executes flow grooming (FG) and soft reconfigurations on the virtual topology depending upon the fine-grain network statistics. Unlike the long-term LBs, SoftFG offloads large flows of congested paths onto underutilized links without making any hardware reconfiguration on path capacity and routes. Flows can be offloaded to other wavelengths within the same FSO link (i.e., intra-link), to other FSO links (i.e., inter-link), or within/across topologies (i.e., intra/inter topology). To do so, SoftFG ensures clear visibility on network paths, early congestion detection, and fast-accurate reaction to reroute offloaded flows onto underutilized wavelengths or links. Therefore, SoftFG is designed as a kernel module installed on the virtual switches/hypervisor. The module collects flow statistics based on a source-destination collaborative scheme and records them in flow and path information tables. SoftFG accordingly makes quick decisions on offloading and reroutes flows with high accuracy. Emulation results show that SoftFG delivers about 12 and 17 faster flow completion time (FCT) than LetFlow and CONGA LBs, respectively.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

LightFDG is an integrated approach to flow detection (FD) and flow grooming (FG) in optical wireless data center networks (DCNs), which is interconnected via wavelength division multiplexing (WDM) based free-space optical (FSO) links. Since forwarding bandwidth-hungry elephant flows (EFs) and delay-sensitive mice flows (MFs) on the same path can cause severe performance degradation, the LightFDG optically grooms flows of each class into rack-to-rack (R2R) flows. Then, R2R-MF and R2R-EF flows are separately forwarded over lightpaths of separate MF and EF virtual topologies, respectively. Lightpaths are provisioned by jointly determining the capacity and route based on flows' arrival rate, size, and completion time request. To prevent EFs from congesting the MF lightpaths, high speed and accurate flow-detection mechanisms are also necessary for classifying EFs as soon as possible. Therefore, a fast-lightweight-and-accurate flow detection framework is developed by leveraging the transmission control protocol (TCP) behaviors. The proposed FD scheme has the flexibility of being implemented as in-network or centralized to classify flows of modifiable and unmodifiable hosts, respectively. Since the centralized scheme incurs considerable overhead, the processing and communication overhead is also mitigated by proposed techniques. Numerical results show that LightFDG outperforms traditional load balancers by about 3× for EFs and 10× for MFs. Along with the developed overhead mitigation methods, the centralized scheme is shown to provide up to 62× lower overhead with 100% accuracy and with about 224× higher detection speeds than the existing centralized solutions.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems in a multi-cell indoor scenario. We investigate the energy harvesting and data rate performance of multiple users while meeting the lighting constraints. To this end, we develop optimization frameworks that tune the parameters of transmitters or receivers to improve the SLIPT system performance. Firstly, we model the relationship between tunable lens-based optical receivers concentrator gain and their fields of view. Next, we develop algorithms to maximize the spectral efficiency (SE) considering per-user minimum harvested energy requirements and lightning constraints by controlling the average light emitting diode (LED) excitation current or tuning the optical receivers' fields of view. Then, we study the joint system performance limits of SE and total harvested energy. Towards this aim, we formulate multi-objective optimization problems and propose algorithms to find the best SE - energy harvesting tradeoff for both designing strategies. Finally, we present some extensive simulations to explore the benefits of the proposed algorithms comparing with basic benchmarks. Besides, we monitor the effect of changing several system parameters on the two objectives and the behavior of the inherent trade-off between them under both transmitter and receiver sides designing strategies.

On the contrary of low speed and high delay acoustic systems, underwater optical wireless communication (UOWC) can deliver a high speed and low latency service at the expense of short communication ranges. Therefore, multihop communication is of utmost importance to extend the range, improve degree of connectivity, and overall performance of underwater optical wireless networks (UOWNs). In this regard, this paper investigates relaying and routing techniques and provides their end-to-end (E2E) performance analysis under the location uncertainty. To achieve robust and reliable links, we first consider adaptive beamwidths and derive the divergence angles under the absence and presence of a pointing-acquisitioning-and-tracking (PAT) mechanism. Thereafter, important E2E performance metrics (e.g., data rate, bit error rate, transmission power, amplifier gain, etc.) are obtained for two potential relaying techniques; decode & forward (DF) and optical amplify & forward (AF). We develop centralized routing schemes for both relaying techniques to optimize E2E rate, bit error rate, and power consumption. Alternatively, a distributed routing protocol, namely Light Path Routing (LiPaR), is proposed by leveraging the range-beamwidth tradeoff of UOWCs. LiPaR is especially shown to be favorable when there is no PAT mechanism and available network information. In order to show the benefits of multihop communications, extensive simulations are conducted to compare different routing and relaying schemes under different network parameters and underwater environments.

In this paper, we propose an enhanced Huffman coded orthogonal frequency-division multiplexing with index modulation (EHC-OFDM-IM) scheme. The proposed scheme is capable of utilizing all legitimate subcarrier activation patterns (SAPs) and adapting the bijective mapping relation between SAPs and leaves on a given Huffman tree according to channel state information (CSI). As a result, a dynamic codebook update mechanism is obtained, which can provide more reliable transmissions. We take the average block error rate (BLER) as the performance evaluation metric and approximate it in closed form when the transmit power allocated to each subcarrier is independent of channel states. Also, we propose two CSI-based power allocation schemes with different requirements for computational complexity to further improve the error performance. Subsequently, we carry out numerical simulations to corroborate the error performance analysis and the proposed dynamic power allocation schemes. By studying the numerical results, we find that the depth of the Huffman tree has a significant impact on the error performance when the SAP-to-leaf mapping relation is optimized based on CSI. Meanwhile, through numerical results, we also discuss the trade-off between error performance and data transmission rate and investigate the impacts of imperfect CSI on the error performance of EHC-OFDM-IM.

By incorporating the merits of satellite, aerial, and terrestrial communications, the space-air-ground integrated network (SAGIN) emerges in recent years as a promising solution to support seamless, high-rate, and reliable transmission with an extremely larger coverage than a classic terrestrial network. In essence, SAGIN is a cooperative relay network, in which high-altitude platforms (HAPs) and terrestrial base stations (BSs) serve as intermediates relaying signals between end device and satellite. In this article, we thereby view the SAGIN from the perspective of cooperative communications and introduce relay networking technologies to model and construct the framework of SAGIN. Meanwhile, we take the realistic propagation environment, HAP mobility and mathematical tractability into account and reconstruct the cooperative channel models for SAGIN, including the space-air, space-ground and air-ground links. Based on the constructed framework of SAGIN, we analyze the outage performance and approximate the outage probability as well as asymptotic outage probability in closed form. Numerical results generated by computer simulations verify our analysis and provide insight into the applicability of SAGIN. Although the relaying scenarios considered in this work are simplistic, the good tractability and expandability of the constructed framework provide a solid foundation for further research of advanced systems with complex configurations.

Internet of Multimedia Things (IoMT) applications such as real-time multimedia based security and monitoring in smart homes, hospitals, cities, and smart transportation management systems are of the most difficult systems to deploy. These services are highly time sensitive and require Quality of service (QoS) guarantees. QoS requirements are key factors that lead to variations of multimedia traffic quality and the Quality of Experience (QoE) for the end users. IoMT devices transmit measurements to a predefined IoMT application server subject to maximum QoS constraint. The delay and dropping are essential constraints as delayed packets are considered useless for the IoMT applications. Our objective is to obtain an approximate expression of the blocking probability due to either buffer overflow or violating certain end-to-end threshold. For this purpose, we employ M/G/1 framework for our network. We validate the proposed analytical model and demonstrate the blocking probability and end-to-end delay. We anticipate that our results are critical for optimizing IoMT network design and deployment.

The considerable growth in demands for wireless services have led to spectrum scarcity challenge. Cognitive radio came into practice to deal with the scarcity problem by granting cognitive users access to the licensed spectrum. However, this solution requires efficient power allocation strategies to guarantee QoS for cognitive system, reduce power consumption, and protect primary users from the cognitive users' interference impact. In this paper, we investigate the energy efficient power allocation problem for cognitive radio networks in underlay mode. We propose a novel approximated online Q-learning scheme for power allocation in which cognitive users learn with conjecture feature to select the most appropriate power level. The power allocation problem is formulated as an optimization problem with the goal to maximize energy efficiency under QoS and interference constraints. The scheme is evaluated using software defined radio testbed and simulations. The evaluation results demonstrate the scheme capability to guarantee SINR for both primary and cognitive systems and mitigate interference with minimum power consumption in comparison with other schemes.

Underwater optical wireless communications (UOWC) is an emerging technology to provide underwater applications with high speed and low latency connections. However, it suffers from limited range and requires effective multi-hop routing solutions for the proper operation of underwater optical wireless networks (UOWNs). In this regard, this paper proposes a distributed Sector-based Opportunistic Routing (SectOR) protocol. Unlike the traditional routing techniques which unicast packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR is especially suitable for UOWNs as the link connectivity can be disrupted easily due to the underwater channel impairments (e.g., pointing errors, misalignment, turbulence, etc.) and sea creatures passing through the transceivers' line-of-sight. In such cases, OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in conventional unicast routing. Contingent upon the performance characterization of a single-hop link, we obtain distance progress (DP) and expected (DP) metrics to evaluate the fitness of a candidate set (CS) and prioritize the members of a CS. Since rate↔error and range↔beamwidth tradeoffs yield different candidate set diversities, we develop a candidate selection and prioritization (CSPA) algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that SectOR protocol can perform even better than an optimal unicast routing protocol in well-connected UOWNs.

In this paper, we study the joint 3D placement of unmanned aerial vehicles (UAVs) and users association under bandwidth limitation and quality of service constraints. In order to allow to UAVs to dynamically change their 3D locations in a distributed fashion while maximizing the network's sum-rate, we break the underlying optimization into 3 subproblems where we separately solve the 2D UAVs positioning, the altitude optimization, and the UAVs-users association. First, given fixed 3D positions of UAVs, we propose a fully distributed matching based association that alleviates the bottlenecks of the bandwidth and guarantees the required quality of service. Next, to address the 2D positions of UAVs, we adopt a modified version of K-means algorithm, with a distributed implementation, where UAVs dynamically change their 2D positions in order to reach the barycenter of the served users cluster. In order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited-interferences, the UAVs altitudes game is a non-cooperative potential game where the players (UAVs) can maximize the limited-interference sum-rate by only optimizing a local utility function. Our simulation results show that, using the proposed approach, the network's sum rate of the studied scenario is improved by 200% as compared with the trivial case where the classical version of K-means is adopted and users are assigned, at each iteration, to the closest UAV.

Providing security and privacy for the Internet of Things (IoT) applications while ensuring a minimum level of performance requirements is an open research challenge. Recently, blockchain offers a promising solution to overcome the current peer-to-peer networks limitations. In the context of IoT, Byzantine fault tolerance (BFT)-based consensus protocols are used due to the energy efficiency advantage over other consensus protocols. The consensus process in BFT is done by electing a group of authenticated nodes. The elected nodes will be responsible for ensuring the data blocks' integrity through defining a total order on the blocks and preventing the concurrently appended blocks from containing conflicting data. However, the blockchain consensus layer contributes the most performance overhead. Therefore, a performance study needs to be conducted especially for the IoT applications that are subject to maximum delay constraints. In this paper, we obtain a mathematical expression to calculate the end-to-end delay with different network configurations, i.e., number of network hops and replica machines. We validate the proposed analytical model with simulation. Our results show that the unique characteristics of IoT traffic have an undeniable impact on the end-to-end delay requirement.

A novel modulation scheme termed orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM) has been proposed and regarded as one of the promising candidate modulation schemes for next generation networks. Although OFDM-SNM is capable of having a higher spectral efficiency (SE) than OFDM with index modulation (OFDM-IM) and plain OFDM under certain conditions, its reliability is relatively inferior to these existing schemes, because the number of active subcarriers varies. In this regard, we propose an enhanced OFDM-SNM scheme in this paper, which utilizes the flexibility of placing subcarriers to harvest a coding gain in the high signal-to-noise ratio (SNR) region. In particular, we stipulate a methodology that optimizes the subcarrier activation pattern (SAP) by subcarrier assignment using instantaneous channel state information (CSI) and therefore the subcarriers with higher channel power gains will be granted the priority to be activated, given the number of subcarriers is fixed. We also analyze the proposed enhanced OFDM-SNM system in terms of outage and error performance. The average outage probability and block error rate (BLER) are derived and approximated in closed-form expressions, which are further verified by numerical results generated by Monte Carlo simulations. The high-reliability nature of the enhanced OFDM-SNM makes it a promising candidate for implementing in the Internet of Things (IoT) with stationary machine-type devices (MTDs), which are subject to slow fading and supported by proper power supply.

The continuous improvement in optical energy harvesting devices motivates the development of visible light communication systems that utilize such available free energy. In this paper, an outdoor visible light communications (VLC) system is considered where a VLC base station sends data to multiple users that are capable of harvesting optical energy. The proposed VLC system serves multiple users using time division multiple access (TDMA) with unequal time and power allocation, which are allocated to achieve the system communications and illumination objectives. In an outdoor setup, the system lighting objective is to maximize the average illumination flux, while the communication design objective is to maximize the spectral efficiency (SE). A multiobjective optimization problem is formulated to obtain the Pareto front of the SE-illumination region. To this end, the marginal optimization problems are solved first using low complexity algorithms. Then, based on the proposed algorithms, a Karush-Kuhn-Tucker-based algorithm is developed to obtain an inner bound of the Pareto front for the SE-illumination tradeoff. The inner bound for the Pareto-front is shown to be close to the optimal Pareto-frontier via several simulation scenarios for different system parameters.

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing capable free-space optic channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution, which grooms mice flows (MFs), mission-critical flows (CFs), and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high-priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming, the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.

In this paper, we analyze the performance degradation of a multi-hop decode-and-forward full-duplex relaying system caused by the residual self-interference (RSI) and hardware distortions (HWD) imposed by the FDR operation and imperfect hardware, respectively. In addition, we study the benefits of employing improper Gaussian signaling (IGS) in the MH-FDR system. Different from the traditional symmetric signaling scheme, i.e., proper Gaussian signaling (PGS), IGS has non-zero pseudo-variance that can limit the impact of RSI and HWD in the MH-FDR system. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate of the MH system as the minimum rate supported by all participating links. Then, we optimize the pseudo-variance of all participating transmitters, including source and relays to compensate the interference impact and improve the end-to-end achievable rate. We propose two network optimization schemes based on the system characteristics, i.e., joint optimization framework and distributed optimization scenario. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using IGS in single-user detection systems that suffer from both RSI and HWD can effectively mitigate the degradation in the achievable rate performance.

Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications usage on the Internet (i.e., online gaming, VoIP, and safety-critical applications), latency becomes a major issue for the development of wireless technology since it has an enormous impact on user experience. In fact, in a phenomenon known as bufferbloat, large static buffers inside the network devices results in increasing the time that packets spend in the queues and, thus, causing larger delays. Concerns have arisen about designing efficient queue management schemes to mitigate the effects of over-buffering in wireless devices. In this paper, we advocate the exploitation of machine learning techniques for dynamic buffer sizing. We propose LearnQueue, a novel reinforcement learning design that can effectively control the latency in wireless networks. LearnQueue adapts quickly and intelligently to changes in the wireless environment using a sophisticated reward structure. The latency control is performed dynamically by tuning the buffer size. Adopting a trial-and-error approach, the proposed scheme penalizes the actions resulting in longer delays or hurting the throughput. In addition, the scheme parameters are designed for an optimized operation depending on different applications requirements. Using the latest generation of WARP hardware, we investigated LearnQueue performance in various network scenarios. The testbed results prove that LearnQueue can grantee low latency while preserving throughput under various congestion situations. We also discuss the feasibility and possible limitations of large-scale deployment of the proposed scheme in wireless devices.

Today's modern communication technologies such as cloud radio access and software defined networks are key candidate technologies for enabling 5G networks as they incorporate intelligence for data-driven networks. Traditional content caching in the last mile access point has shown a reduction in the core network traffic. However, the radio access network still does not fully leverage such solution. Transmitting duplicate copies of contents to mobile users consumes valuable radio spectrum resources and unnecessary base station energy. To overcome these challenges, we propose huMan mObility-based cOntent Distribution (MOOD) system. MOOD exploits urban scale users' mobility to allocate radio resources spatially and temporally for content delivery. Our approach uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. Furthermore, a human activity model is presented and statistically analyzed for simulating people daily routines. The proposed approach is evaluated via simulations and compared with a generic broadcast strategy in an actual existing deployment of base stations as well as a smaller cells environment, which is a trending deployment strategy in future 5G networks. MOOD achieves 15.2 and 25.4 percent of performance improvement in the actual and small-cell deployment, respectively.

Visible light communications (VLCs), in general, and resource allocation for VLC networks in particular, have gained lots of attention recently. In this paper, we consider the resource allocation problem of a VLC downlink transmission system employing dynamic time division multiple access, where time and power variables are tuned to maximize the downlink spectral efficiency (SE). As for the operational conditions, we impose constraints on the average optical intensity, the energy budget, and the quality-of-service. To solve this non-convex problem, we transform the objective function into a difference of concave functions by solving a second-order differential inequality. Then, we propose a low-complexity algorithm to solve the resource allocation problem. Finally, we show by simulations the SE performance gains achieved by optimizing time and power allocation over the initial total power minimization solution for the considered system.

In this paper, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network's sum-rate. Our approach is referred to as 'Learn-As-You-Fly' (LAYF) algorithm. LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching-based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted. Finally, in order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited interference, the UAVs altitudes game is a potential game where UAVs can maximize the limited interference sum-rate by only optimizing a local utility function. Our simulation results show that the network's sum-rate is improved as compared to both a centralized suboptimal solution and a distributed approach that is based on closest UAVs association.

Cognitive radio attracts researchers' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system parameters' adaptation using optimization and machine learning techniques. However, these techniques have strengths and weaknesses depending on the experienced network scenario that make one more appropriate than others. In this paper, we propose a novel design for the cognitive system called supervised cognitive system (SCS), which aims to perform radio parameters adaptation with the most appropriate CE learning technique for the encountered network scenario. To realize SCS, it is required to evaluate the performance of different CEs in different network scenarios and according to certain performance objectives. In addition, the ability to select the most appropriate CE learning technique for adaptation in the current network scenario is also a priority in our design. Therefore, SCS investigates the relationship between learning and performance improvement and it employs online learning to classify scenarios and select the most appropriate CE learning technique. The testbed implementation and evaluation results in terms of goodput, packet error rate, and spectral efficiency show that the proposed SCS achieves more than 50% in performance gain compared to the best standalone CE.

In this paper, we study the performance of simultaneous lightwave information and power transfer (SLIPT) systems of multi-cell indoor scenario. We aim to investigate the energy harvesting and data rate performance of multiple users while meeting the lightning constraints. To this end, we develop optimization frameworks and tune the light emitting diodes average currents to improve the performance of the SLIPT system. Firstly, we propose an algorithm to maximize the spectral efficiency (SE) subject to lighting and minimum harvested energy per user requirements. The proposed algorithm can be implemented in a distributed fashion with a reduced computational burden at each node. Then, we consider the energy harvesting maximization problem to investigate the maximum possible energy gain and its corresponding SE performance. Finally, we present some extensive simulations to explore the benefit of the optimization frameworks with respect to standard equal allocation setting. In addition, we monitor the effect of changing several system parameters on the two objectives and highlight the underlying trade-off between them.

Wireless data centers (DCs) are enablers of reconfigurable data center network (DCN) topologies by augmenting the cabling complexity and inflexibility of traditional wired DCs. In this paper, we propose an optical traffic grooming (TG) for mice flows (MFs) and elephant flows (EFs) in a wireless DCN which is interconnected with free-space optical (FSO) links operating on wavelength division multiplexing (WDM). Since handling the bandwidth-hungry EFs along with delay-sensitive MFs over the same network resources have undesirable consequences, proposed TG policy treat MFs and EFs separately. MFs/EFs destined to the same rack are groomed into larger rack-to-rack MF/EF flows over dedicated lightpaths whose routes and capacities are jointly determined taking the load balancing into account. Performance evaluations of proposed TG policy show a significant throughput improvement thanks to bandwidth efficient utilization of the wireless links. Therefore, proposed TG requires expeditious flow detection mechanisms which can immediately classify EFs with very high accuracy. Since these demands cannot be met by existing sampling and port-mirroring based solutions, we propose a lightweight and fast in-network flow detection (LightFD) mechanism. LightFD is designed as a module on the Virtual-Switch/Hypervisor, which detects EFs based on acknowledgment sequence number of flow packets. Emulation results show that LightFD can provide up to 110 times faster detection speeds than sampling-based methods with %100 detection accuracy. We also demonstrate that the EF detection speed has a considerable impact on achievable EF throughput.

Internet of Things (IoT) can be defined as the interconnection of any device to the Internet that collects and exchanges information. With the rapidly growing heterogenetic IoT applications and its associated devices, a massive amount of data is being transmitted in the network. Often, a large spike in network traffic to a particular destination causes a widespread disruption of Internet services for end users, which can cause online businesses billions of dollars in losses. In this paper, we analyze an intelligent edge that can identify volumetric traffic and address them in real-time using an instantaneous detection method for IoT applications. This approach can easily detect a large surge and a potential variation in traffic patterns for an IoT application, which can contribute to safer and more efficient operation of the overall system. As per our results, we gave a closer insight on the advantage of having an intelligent edge to serve as a detection mechanism.

In this work, we propose FLight, a fast, lightweight and adaptive mechanism for detecting elephant-flows while improving the detection accuracy and speed. FLight leverages the TCP communication behavior for its detection algorithm, it demonstrates a 100% elephant-flow detection accuracy, and is 242× faster than other centralized solutions.

Software Defined Networking (SDN) provides network engineers with a high-level of abstraction to manage network traffic and control the associated network resources. Unfortunately, the data-plane devices communicate with the controller for every new flow, which adds an extra overhead and causes excessive delays. Such communication relies on the probability called matching probability. In this work, we propose a mathematical model for the SDN flow-setup process with the consideration of all factors that contribute into the matching probability, such as proactive/reactive flow setup modes. Finally, we attempt at deriving the overall system capacity and blocking probability.

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach for design and provisioning of optical wireless DCNs. While switches are modeled as hybrid opto-electronic cross-connects, links are modeled as wavelength division multiplexing (WDM) capable free-space optic (FSO) channels. Using the standard TG terminology, we formulate the optimal mixed integer linear problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast sub-optimal solution where mice flows (MFs) are groomed and forwarded on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server (S2S) express lightpaths whose routes and capacity are dynamically determined based on wavelength and capacity availability. Emulation results show that proposed models and algorithms provide a significant throughput improvement upon traditional DCNs for both MFs and EFs.

5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users' priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users' QoS.

Network efficiency and proper utilization of its resources are essential requirements to operate wireless networks in an optimal fashion. Cognitive radio aims to fulfill these requirements by exploiting artificial intelligence techniques to create an entity called cognitive engine. Cognitive engine exploits awareness about the surrounding radio environment to optimize the use of radio resources and adapt relevant transmission parameters. In this paper, we propose a hybrid cognitive engine that employs Case Based Reasoning (CBR) and Decision Trees (DTs) to perform radio adaptation in multi-carriers wireless networks. The engine complexity is reduced by employing DTs to improve the indexing methodology used in CBR cases retrieval. The performance of our hybrid engine is validated using software defined radios implementation and simulation in multi-carrier environment. The system throughput, signal to noise and interference ratio, and packet error rate are obtained and compared with other schemes in different scenarios.

The inevitable deployment of 5G and the Internet of Things (IoT) sheds the light on the importance of the energy efficiency (EE) performance of Device-to- Device (DD) communication systems. In this work, we address a potential IoT application, where different prioritized DD system, i.e., Low-Priority (LP) and High-Priority (HP) systems, co-exist and share the spectrum. We maximize the EE of each system by proposing two schemes. The first scheme optimizes the individual transmission power and the spatial density of each system. The second scheme optimizes the transmission power ratio of both systems and the spatial density of each one. We also construct and analytically solve a multi- objective optimization problem that combines and jointly maximizes both HP and LP EE performance. Unique structures of the addressed problems are verified. Via numerical results we show that the system which dominates the overall EE (combined EEs of both HP and LP) is the system corresponding to the lowest power for low/high power ratio (between HP and LP systems). However, if the power ratio is close to one, the dominating EE corresponds to the system with higher weight.

The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.

Heterogeneous cloud radio access networks (H-CRAN) is a new trend of SC that aims to leverage the heterogeneous and cloud radio access networks advantages. Low power remote radio heads (RRHs) are exploited to provide high data rates for users with high quality of service requirements (QoS), while high power macro base stations (BSs) are deployed for coverage maintenance and low QoS users support. However, the inter-tier interference between the macro BS and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRAN. Therefore, we propose a centralized resource allocation scheme using online learning, which guarantees interference mitigation and maximizes energy efficiency while maintaining QoS requirements for all users. To foster the performance of such scheme with a model-free learning, we consider users' priority in resource blocks (RBs) allocation and compact state representation based learning methodology to enhance the learning process. Simulation results confirm that the proposed resource allocation solution can mitigate interference, increase energy and spectral efficiencies significantly, and maintain users' QoS requirements.

Today's mobile service providers aim at ensuring end-to-end performance guarantees. Hence, ensuring an efficient content delivery to end users is highly required. Currently, transmitting popular contents in modern mobile networks rely on unicast transmission. This result into a huge underutilization of the wireless bandwidth. The urban scale mobility of users is beneficial for mobile networks to allocate radio resources spatially and temporally for broadcasting contents. In this paper, we conduct a comprehensive analysis on a human activity/mobility model and the content broadcasting system in 5G mobile networks. The objective of this work is to describe how human daily activities could improve the content broadcasting efficiency. We achieve the objective by analyzing the transition probabilities of a user traveling over several places according to the change of states of daily human activities. Using a real-life simulation, we demonstrate the relationship between the human mobility and the optimization objective of the content broadcasting system.

In this paper, we propose a cross-layer design, which optimizes the energy efficiency of a potential future 5G spectrum-sharing environment, in two sharing scenarios. In the first scenario, underlying sharing is considered. We propose and minimize a modified energy per good bit (MEPG) metric, with respect to the spectrum sharing user's transmission power and media access frame length. The cellular users, legacy users, are protected by an outage probability constraint. To optimize the non-convex targeted problem, we utilize the generalized convexity theory and verify the problem's strictly pseudoconvex structure. We also derive analytical expressions of the optimal resources. In the second scenario, we minimize a generalized MEPG function while considering a probabilistic activity of cellular users and its impact on the MEPG performance of the spectrum sharing users. Finally, we derive the associated optimal resource allocation of this problem. Selected numerical results show the improvement of the proposed system compared with other systems.

The demand on mobile Internet related services has increased the need for higher bandwidth in cellular networks. The 5G technology is envisioned as a solution to satisfy this demand as it provides high data rates and scalable bandwidth. The multi-tier heterogeneous structure of 5G with dense base station deployment, relays, and device-to-device (D2D) communications intends to serve users with different QoS requirements. However, the multi-tier structure causes severe interference among the multi-tier users which further complicates the resource allocation problem. In this paper, we propose a cooperative scheme to tackle the interference problem, including both cross-tier interference that affects macro users from other tiers and co-tier interference, which is among users belong to the same tier. The scheme employs an online learning algorithm for efficient spectrum allocation with power and modulation adaptation capability. Our evaluation results show that our online scheme outperforms others and achieves significant improvements in throughput, spectral efficiency, fairness, and outage ratio. © 2016 IEEE.

Recent SDN researches suffer from over-accumulation of unhealthy flow-load. Instead, we leverage the SDN controller network view to encode the end-to-end path information into the packet address. Our solution EncPath significantly reduces the flow-table size and the number of control messages. Consequently, the power consumption of network switches is in orders of magnitude less than other evaluated solutions. It also provides flow management flexibility and scalability. We compare EncPath with single and multipath routing solutions and single path solution. Also, we operated them in proactive and reactive modes. We find that EncPath flow entries in core switches in a multihomed fat-tree with 144 hosts is approximately 1000 times smaller than Equal-Cost MultiPath (ECMP) and random routing. Additionally, the number of control messages to setup the network is reduced by a factor of 200×. This, consequently, affords data-plane and control-plane devices space to process other tasks.

We propose a cross layer design that optimizes the energy efficiency of spectrum sharing systems. The energy per good bit (EPG) is considered as an energy efficiency metric. We optimize the secondary user's transmission power and media access frame length to minimize the EPG metric. We protect the primary user transmission via an outage probability constraint. The non-convex targeted problem is optimized by utilizing the generalized convexity theory and verifying the strictly pseudo-convex structure of the problem. Analytical results of the optimal power and frame length are derived. We also used these results in proposing an algorithm, which guarantees the existence of a global optimal solution. Selected numerical results show the improvement of the proposed system compared to other systems.

In this paper, we consider a cognitive radio system in which a block-fading channel is assumed. Each transmission frame consists of M blocks and each block undergoes a different channel gain. Instantaneous channel state information about the interference links remains unknown to the primary and secondary users. We minimize the secondary user's targeted outage probability over the block-fading channels. To protect the primary user, a statistical constraint on its targeted outage probability is enforced. The secondary user's targeted outage region and the corresponding optimal power are derived. We also propose two sub-optimal power strategies and derive compact expressions for the corresponding outage probabilities. These probabilities are shown to be asymptotic lower and upper bounds on the outage probability. Utilizing these bounds, we derive the exact diversity order of the secondary user outage probability. Selected numerical results are presented to characterize the system's behavior.

The flexibility of radio access network facilitated by 5G networks opens the gateway of deploying dynamic strategies for broadcasting TV channels in an efficient way. Currently, spectrum efficiency and bandwidth efficiency are the two common metrics measuring the efficiency of a system. These metrics assess the net bitrate for a unit of spectrum bandwidth. However, there is a lack of measurement, quantifying the effectiveness of a broadcasting strategy from the user perspective. In this paper, we introduce a novel measurement approach, called broadcast efficiency which considers the mobile user as a main reference. Broadcast efficiency is calculated as the number of served audiences per unit of radio resource. From numerical analysis, we show that broadcasting unpopular TV channels dramatically decreases the broadcast efficiency. This finding is evaluated by employing multiple distributions on the size of audience among TV channels. Furthermore, by conducting a real-life simulation, we discover that a high broadcast efficiency may result in a low percentage of served audiences if the audiences of TV channels are quite evenly distributed.

Frame aggregation is a major enhancement in the IEEE 802.11 family to boost the network performance. The increasing awareness about energy efficiency motivates the re-think of frame aggregation design. In this paper, we propose a novel Green Frame Aggregation (GFA) scheduling scheme that optimizes the aggregate size based on channel quality in order to minimize the consumed energy. GFA selects an optimal sub-frame size that satisfies the loss constraint for real-time applications as well as the energy budget of the ideal channel. This scheme is implemented and evaluated using a testbed deployment. The experimental analysis shows that GFA outperforms the conventional frame aggregation methodology in terms of energy efficiency by about 6× in the presence of severe interference conditions. Moreover, GFA outperforms the static frame sizing method in terms of network goodput while maintaining the same end-to-end latency.

This work proposes a novel blind void (idle periods) filling in Long-Reach Ethernet Passive Optical Networks (LR-EPONs) namely Size Controlled Batch Void Filling (SCBVF). We emphasize on reducing grant delays and hence reducing the average packet delay. SCBVF delay reduction is achieved by early flushing data during the idle time periods (voids) between allocated grants. The proposed approach can be integrated with almost all of the previously reported dynamic bandwidth allocation schemes. SCBVF is less sensitive to differential distance between ONUs and can work well in case of small differential distances compared to previously reported void filling schemes. We support our work by extensive simulation study considering bursty traffic with long range dependency. Numerical results show a delay reduction up to 35% compared to non-void filling scheme outperforming its main competitors that can achieve up to 7% delay reduction.

Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. However, the optimal block size varies based on the wireless channel conditions. Further, blocks within a single frame may have different optimal sizes based on variations in interference patterns. This paper proposes a hybrid interference-resilient frame fragmentation (Hi-Frag) link-layer scheme for wireless sensor networks. It effectively addresses the challenges associated with dynamic partitioning of blocks while accounting for the observed error patterns. Hi-Frag is the first work to introduce an adaptive frame fragmentation scheme with hybrid block sizing, implemented and evaluated on a real WSN testbed. Hi-Frag shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5× improvement in throughput when the channel condition is noisy, while reducing network delays by up to 14% of the observed delay. On average, Hi-Frag shows 35% gain in throughput compared to static fragmentation approaches across all channel conditions used in our experiments. Also, Hi-Frag lowers the energy consumed per useful bit by 66% on average compared to conventional protocols, which increases the energy efficiency.

In this paper, an energy efficient cognitive radio system is proposed. The proposed design optimizes the secondary user transmission power and the sensing duration combined with soft-sensing information to minimize the energy per goodbit. Due to the non-convex nature of the problem we prove its pseudo-convexity to guarantee the optimal solution. Furthermore, a quantization scheme, that discretize the soft-sensing information, is proposed and analyzed to reduce the overload of the continuously adapted power. Numerical results show that the energy per goodbit performance of the proposed system outperforms the benchmark systems. The impact of the quantization levels and other system parameters is evaluated in the numerical results.

Modern smartphones are limited by their short battery life. The advancement of the graphical performance is considered as one of the main reasons behind the massive battery drainage in smartphones. In this paper we present a novel implementation of the GPUFreq Scaling Governors, a Dynamic Voltage and Frequency Scaling (DVFS) model implemented in the Android Linux kernel for dynamically scaling smartphone Graphical Processing Units (GPUs). The GPUFreq governors offer users multiple variations and alternatives in controlling the power consumption and performance of their GPUs. We implemented and evaluated our model on a smartphone GPU and measured the energy performance using an external power monitor. The results show that the energy consumption of smartphone GPUs can be significantly reduced with a minor effect on the GPU performance.

Wireless full-duplex radios can simultaneously transmit and receive using the same frequency. In theory, this can double the throughput. In fact, there is only little work addressing aspects other than throughput gains in full-duplex systems. Over-buffering in today's networks or the so-called “bufferbloat” phenomenon creates excessive end-to-end delays resulting in network performance degradation. Our analysis shows that full-duplex systems may suffer from high latency caused by bloated buffers. In this paper, we address the problem of buffer management in full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. We compare the performance of WQM in full-duplex environment to Drop Tail mechanism over various scenarios. Our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases.

In this paper, two resource allocation schemes for energy efficient cognitive radio systems are proposed. Our design considers resource allocation approaches that adopt spectrum sharing combined with soft-sensing information, adaptive sensing thresholds, and adaptive power to achieve an energy efficient system. An energy per good-bit metric is considered as an energy efficient objective function. A multi-carrier system, such as, orthogonal frequency division multiplexing, is considered in the framework. The proposed resource allocation schemes, using different approaches, are designated as sub-optimal and optimal. The sub-optimal approach is attained by optimizing over a channel inversion power policy. The optimal approach utilizes the calculus of variation theory to optimize a problem of instantaneous objective function subject to average and instantaneous constraints with respect to functional optimization variables. In addition to the analytical results, selected numerical results are provided to quantify the impact of soft-sensing information and the optimal adaptive sensing threshold on the system performance.

Radio resource management becomes an important aspect of the current wireless networks because of spectrum scarcity and applications heterogeneity. Cognitive radio is a potential candidate for resource management because of its capability to satisfy the growing wireless demand and improve network efficiency. Decision-making is the main function of the radio resources management process as it determines the radio parameters that control the use of these resources. In this paper, we propose an adaptive decision-making scheme (ADMS) for radio resources management of different types of network applications including: power consuming, emergency, multimedia, and spectrum sharing. ADMS exploits genetic algorithm (GA) as an optimization tool for decision-making. It consists of the several objective functions for the decision-making process such as minimizing power consumption, packet error rate (PER), delay, and interference. On the other hand, maximizing throughput and spectral efficiency. Simulation results and test bed evaluation demonstrate ADMS functionality and efficiency.

Leak and backflow detection are essential aspects of Water Distribution System (WDS) monitoring. Most existing solutions for leak/backflow detection in WDSs focus on the placement of expensive static sensors located strategically. In contrast to these, we propose a solution whereby mobile sensors (i.e., their movement aided only by the inherent water flow in the system) detect leaks/backflow. Information about the leaks/backflow is collected from the sensors either by physically capturing them, or through wireless communication. Specifically, we propose models to maximize leak/backflow detection given a cost constraint (a limit on the number of sensors). Through extensive simulations, we demonstrate the superior performance of our proposed solution when compared with the state of the art solutions (e.g., algorithms/protocols and analysis).

Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all ТСРЯР stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of ТСРЯР layers as whole not only the physical layer.

In this paper we consider a cognitive radio multi-input multi-output environment in which we adapt our beamformer to maximize both energy efficiency and signal to interference plus noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with the sensing information to achieve an optimal energy efficient system. The proposed schemes maximize the energy efficiency and SINR metrics subject to cognitive radio and quality of service constraints. Since the optimization of energy efficiency problem is not a convex problem, we transform it into a standard semi-definite programming (SDP) form to guarantee a global optimal solution. Analytical solution is provided for one scheme, while the other scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

In this paper we propose an energy efficient cognitive radio system. Our design considers an underlaying resource allocation combined with soft sensing information to achieve a sub-optimum energy efficient system. The sub-optimality is achieved by optimizing over a channel inversion power policy instead of considering a water-filling power policy. We consider an Energy per Goodbit (EPG) metric to express the energy efficient objective function of the system and as an evaluation metric to our system performance. Since our optimization problem is not a known convex problem, we prove its convexity to guarantee its feasibility. We evaluate the proposed scheme comparing to a benchmark system through both analytical and numerical results.

In this paper, a delay-sensitive multi-hop wireless sensor network is considered, employing an M/G/1 with vacations framework. Sensors transmit measurements to a predefined data sink subject to maximum end-to-end delay constraint. In order to prolong the battery lifetime, a sleeping scheme is adopted throughout the network nodes. The objective of our proposed framework is to present an expression for maximum hop-count as well as an approximate expression of the probability of blocking at the sink node upon violating certain end-to-end delay threshold. Using numerical simulations, we validate the proposed analytical model and demonstrate that the blocking probability of the system for various vacation time distributions matches the simulation results.

In this paper, we analyze the impact of large, persistently-full buffers (`bufferbloat') on various network dynamics in IEEE 802.11n wireless networks. Bufferbloat has mostly been studied in the context of wired networks. We study the impact of bufferbloat on a variety of wireless network topologies, including wireless LAN (WLAN) and multi-hop wireless networks. Our results show that a single FTP transfer between two Linux wireless hosts can saturate the buffers in the network stack, leading to RTT delays exceeding 4.5 s in multi-hop configurations. We show that well-designed Aggregate MAC Protocol Data Unit (A-MPDU) MAC-layer frame aggregation can reduce RTT delays while simultaneously increasing network throughput. However, additional measures may still be required to meet the constraints of real-time flows (such as VoIP). Our experiments show that large buffers can deteriorate the fairness in rate allocation in parking lot based multi-hop networks.

Next-generation passive optical network (PON) has been considered in the past few years as a cost-effective broadband access technology. With the ever-increasing power saving concern, energy efficiency has been an important issue in its operations. In this paper, we propose a novel sleep-time sizing and scheduling framework for the implementation of green bandwidth allocation (GBA) in TDMA-PONs. The proposed framework leverages the batch-mode transmission feature of GBA to minimize the overhead due to frequent ONU on-off transitions. The optimal sleeping time sequence of each ONU is determined in every cycle without violating the maximum delay requirement. With multiple ONUs possibly accessing the shared media simultaneously, a collision may occur. To address this problem, we propose a new sleep-time sizing mechanism, namely Sort-And-Shift (SAS), in which the ONUs are sorted according to their expected transmission start times, and their sleep times are shifted to resolve any possible collision while ensuring maximum energy saving. Results show the effectiveness of the proposed framework and highlight the merits of our solutions .

In this paper, we consider a cognitive radio multi-input-multi-output environment, in which we adapt our beamformer to maximize both energy efficiency (EE) and signal-to-interference-plus-noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with sensing information to achieve optimal energy-efficient systems. The proposed schemes maximize EE and SINR metrics subject to cognitive radio and quality-of-service constraints. The analysis of the proposed schemes is classified into two categories based on knowledge of the secondary-transmitter-to-primary-receiver channel. Since the optimizations of EE and SINR problems are not convex problems, we transform them into a standard semidefinite programming (SDP) form to guarantee that the optimal solutions are global. An analytical solution is provided for one scheme, while the second scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.

In this paper we consider a wireless multimedia system by mapping scalable video coded (SVC) bit stream upon superposition coded (SPC) signals, referred to as (SVC-SPC) architecture. Empirical experiments using a software-defined radio(SDR) emulator are conducted to gain a better understanding of its efficiency, specifically, the impact of the received signal due to different power allocation ratios. Our experimental results show that to maintain high video quality, the power allocated to the base layer should be approximately four times higher than the power allocated to the enhancement layer.

With the increasing adoption of wireless communication technologies, there is a need to improve management of existing radio resources. Cognitive radio is a promising technology to improve the utilization of wireless spectrum. Its operating principle is based on building an integrated hardware and software architecture that configures the radio to meet application requirements within the constraints of spectrum policy regulations. However, such an architecture must be able to cope with radio environment heterogeneity. In this paper, we propose a cognitive resource management architecture, called CogWnet, that allocates channels, re-configures radio transmission parameters to meet QoS requirements, ensures reliability, and mitigates interference. The architecture consists of three main layers: Communication Layer, which includes generic interfaces to facilitate the communication between the cognitive architecture and TCP/IP stack layers; Decision-Making Layer, which classifies the stack layers input parameters and runs decision-making optimization algorithms to output optimal transmission parameters; and Policy Layer to enforce policy regulations on the selected part of the spectrum. The efficiency of CogWnet is demonstrated through a testbed implementation and evaluation.

The explosive growth in mobile Internet and related services has increased the need for more bandwidth in cellular networks. The Long-Term Evolution (LTE) technology is an attractive solution for operators and subscribers to meet such need since it provides high data rates and scalable bandwidth. Radio Resource Management (RRM) is essential for LTE to provide better communication quality and meet the application QoS requirements. Cognitive resource management is a promising solution for LTE RRM as it improves network efficiency by exploiting radio environment information, intelligent optimization algorithms to configure transmission parameters, and mitigate interference. In this paper, we propose a cognitive resource management scheme to adapt LTE network parameters to the environment conditions. The scheme optimizes resource blocks assignment, modulation selection and bandwidth selection to maximize throughput and minimize interference. The scheme uses constrained optimization for throughput maximization and interference control. It is also enhanced by learning mechanism to reduce the optimization complexity and improve the decision-making quality. Our evaluation results show that our scheme achieved significant improvements in throughput and LTE system capacity. Results also show the improvement in the user satisfaction over other techniques in LTE RRM.

Frame fragmentation techniques aim to achieve higher throughput by reducing retransmissions. Using experiments on a WSN testbed, we show that frame fragmentation also helps to reduce energy consumption. In this paper we propose Green-Frag, a new energy-efficient protocol based on efficient frame fragmentation technique. Green-Frag allows sensor nodes to transmit data with optimal transmit power and frame structure based on environmental conditions. Green-Frag takes into consideration the channel conditions, interference patterns and level, as well as the distance between sender and receiver. The paper discusses various design and implementation considerations for Green-Frag. Using experimental evaluation on a sensor mote testbed, we show that Green-Frag achieves the least energy consumption by choosing the best transmit power according to the channel conditions.

Multicopy routing strategies have been considered the most applicable approaches to achieve message delivery in Delay Tolerant Networks (DTNs). Epidemic routing and two-hop forwarding routing are two well-reported approaches for delay tolerant networks routing which allow multiple message replicas to be launched in order to increase message delivery ratio and/or reduce message delivery delay. This advantage, nonetheless, is at the expense of additional buffer space and bandwidth overhead. Thus, to achieve efficient utilization of network resources, it is important to come up with an effective message scheduling strategy to determine which messages should be forwarded and which should be dropped in case of buffer is full. This paper investigates a new message scheduling framework for epidemic and two-hop forwarding routing in DTNs, such that the forwarding/dropping decision can be made at a node during each contact for either optimal message delivery ratio or message delivery delay. Extensive simulation results show that the proposed message scheduling framework can achieve better performance than its counterparts.

This paper introduces a novel multicopy routing protocol, called Self-Adaptive Utility-based Routing Protocol (SAURP), for Delay Tolerant Networks (DTNs) that are possibly composed of a vast number of devices in miniature such as smart phones of heterogeneous capacities in terms of energy resources and buffer spaces. SAURP is characterized by the ability of identifying potential opportunities for forwarding messages to their destinations via a novel utility function-based mechanism, in which a suite of environment parameters, such as wireless channel condition, nodal buffer occupancy, and encounter statistics, are jointly considered. Thus, SAURP can reroute messages around nodes experiencing high-buffer occupancy, wireless interference, and/or congestion, while taking a considerably small number of transmissions. The developed utility function in SAURP is proved to be able to achieve optimal performance, which is further analyzed via a stochastic modeling approach. Extensive simulations are conducted to verify the developed analytical model and compare the proposed SAURP with a number of recently reported encounter-based routing approaches in terms of delivery ratio, delivery delay, and the number of transmissions required for each message delivery. The simulation results show that SAURP outperforms all the counterpart multicopy encounter-based routing protocols considered in the study.

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

We propose TMAC, a timestamp-ordered MAC protocol for Wireless Mesh Networks (WMNs). TMAC extends CSMA/CA by scheduling data packets based on their age. Prior to transmitting a data packet, a transmitter broadcasts a request control message appended with a timestamp to a selected list of neighbors. It can proceed with the transmission only if it receives a sufficient number of grant control messages from these neighbors. A grant message indicates that the associated data packet has the lowest timestamp of all the packets pending transmission at the local transmit queue. We demonstrate that a loose ordering of timestamps among neighboring nodes is sufficient for enforcing local fairness, subsequently leading to flow rate fairness in a multi-hop WMN. We show that TMAC can be implemented using the control frames in IEEE 802.11 stack, and thus can be easily integrated in existing 802.11-based WMNs. Our simulation results show that TMAC achieves excellent resource allocation fairness while maintaining over 90% of maximum link capacity in parking lot and large grid topologies.

We analyze the problem of buffer sizing for TCP flows in 802.11-based Wireless Mesh Networks. Our objective is to maintain high network utilization while providing low queueing delays. The problem is complicated by the time-varying capacity of the wireless channel as well as the random access mechanism of 802.11 MAC protocol. While arbitrarily large buffers can maintain high network utilization, this results in large queueing delays. Such delays may affect TCP stability characteristics, and also increase queueing delays for other flows (including real-time flows) sharing the buffer. In this paper we propose sizing link buffers collectively for a set of nodes within mutual interference range called the 'collision domain'. We aim to provide a buffer just large enough to saturate the available capacity of the bottleneck collision domain that limits the carrying capacity of the network. This neighborhood buffer is distributed over multiple nodes that constitute the network bottleneck; a transmission by any of these nodes fully utilizes the available spectral resource for the duration of the transmission. We show that sizing routing buffers collectively for this bottleneck allows us to have small buffers (as low as 2 - 3 packets) at individual nodes without any significant loss in network utilization. We propose heuristics to determine these buffer sizes in WMNs. Our results show that we can reduce the end-to-end delays by 6× to 10× at the cost of losing roughly 5% of the network capacity achievable with large buffers.

We present the Whirlpool Routing Protocol (WARP), which efficiently routes data to a node moving within a static mesh. The key insight in WARP's design is that data traffic can use an existing routing gradient to efficiently probe the topology, repair the routing gradient, and communicate these repairs to nearby nodes.

This paper introduces a novel routing protocol in Delay Tolerant Networks (DTNs), aiming to solve the online distributed routing problem. By manipulating a collaborative reinforcement learning technique, a group of nodes can cooperate with each other and make a forwarding decision for the stored messages based on a cost function at each contact with another node. The proposed protocol is characterized by not only considering the contact time statistics under a novel contact model, but also looks into the feedback on user behavior and network conditions, such as congestion and buffer occupancy sampled during each previous contact with any other node. Therefore, the proposed protocol can achieve high efficiency via an adaptive and intelligent routing mechanism according to network conditions. Extensive simulation is conducted to verify the proposed protocol, where a comparison is made with a number of existing encounter-based routing protocols in term of the number of transmissions of each message, message delivery delay, and delivery ratio. The results of the simulation demonstrate the effectiveness of the proposed technique.

This paper introduces a novel congestion control scheme for TCP over OBS networks, called Statistical Additive Increase Multiplicative Decrease (SAIMD), which aims to improve the throughput performance for high-bandwidth TCP flows in OBS networks. We show through analytic model and extensive simulations that the proposed scheme can effectively solve the false congestion detection problem and significantly outperform the conventional TCP counterparts without losing fairness.

Transport control protocol (TCP) has served as a reliable, self-regulated, and congestion tolerant transport protocol for many Internet applications. Relatively, limited knowledge has been gained in terms of the impacts encountered in TCP when optical burst switching (OBS) is adopted in the network backbone. A novel scheme, called TCP with Explicit Notification Generalized Additive Increase Multiplicative Decrease (TCP-ENG), is introduced, which is considered as the first study that integrates the explicit notification platform with the GAIMD approach. The proposed scheme aims to solve the false congestion detection problem in the IP over OBS networks. An analytical model is developed for the proposed scheme and is verified through extensive simulation.

It has been shown that burst retranmsission scheme and burst deflection scheme can effectively reduce the burst loss probability, thereby improving the performance of loss-based TCP implementations. However, both schemes introduce additional delays for bursts that are retransmitted or deflected. The additional delays could result in delay-based TCP falsely detecting network congestion, which may negatively impact the performance of delay-based TCP implementations, such as TCP Vegas. In this paper we investigate the delay-based TCP Vegas behavior over OBS networks. Furthermore, we analyze the throughput of TCP Vegas over a barebone OBS network and an OBS network with burst retransmission.

Additive Increase Multiplicative Decrease (AIMD) window adjustment mechanism has been embedded in TCP in order to regulate the transmission rate in modern communication networks. In recent years, the AIMD (1,0.5) traffic regulation mechanism along with possibly additional enhancements, such as false timeout detection and explicit notification, has been considered in the carriers with Optical Burst Switching (OBS) as the underlying transmission technology. This paper introduces a novel rate control mechanism based on Generalized AIMD (α,β), called Burst AIMD (BAIMD), for tuning the rate control parameters (α, β) at each sender. BAIMD is designed to improve throughput while maintaining friendliness with co-existing AIMD (1,0.5) flows, and is characterized in the following two folds: (1) no burst window is required in the TCP sender's level; (2) no explicit notifications are required. The above characteristics make the proposed scheme distinguished from all the past reported counterparts by minimizing the signalling efforts and control complexity. The simulation result shows that BAIMD can solidly outperform the past reported AIMD-based (1,0.5) rate control schemes under a wide range of traffic loads. We also suggest that BAIMD rate control mechanism may serve as a better choice than AIMD (1,0.5) in the bufferless OBS networks due to its dynamic and flexible (α,β) parameter pair.

Due to the bufferless nature of optical burst switched network, contentions occur even at low traffic loads, leading to burst losses. Contention resolution schemes, such as burst retransmission and deflection, can reduce burst losses, especially at low traffic loads. However, both schemes result in additional packet delay for the packets in bursts that are retransmitted or deflected. The additional packet delay affects the performance of delay-based TCP implementations that rely on packet delay to estimate available bandwidth in networks and to detect network congestion state. In this paper, we discuss the issues of TCP Vegas over OBS networks and propose a threshold-based TCP Vegas version that is suitable for the characteristics of OBS networks. The threshold-based TCP Vegas are able to distinguish whether the increases in packet delay are due to network congestion, or due to burst contentions at low traffic loads. Our simulation results show that the threshold-based TCP Vegas has higher throughput for a TCP connection compared to TCP Vegas and the loss-based TCP implementations, such as TCP Sack.