The integration of Internet of Things (IoT) and Federated Learning (FL) marks a significant step towards pervasiveness, supported by distributed computational resources and enhanced by 5G advancements. However, the efficient and sustainable operation of IoT-enabled FL systems faces critical challenges, particularly in selecting devices that balance energy consumption and system performance. To address this, we propose the Battery-Aware Dynamic and Automated Device Selection (DADS) framework, a novel solution specifically designed for IoT-enabled FL environments. DADS introduces an innovative adaptive mechanism that dynamically adjusts optimization parameters, such as inertia weights and crossover/mutation rates, ensuring a seamless balance between exploration and exploitation. Unlike conventional optimization approaches, DADS employs uniquely developed Adaptive Particle Swarm Optimization (APSO) and Adaptive Genetic Algorithm (AGA) within a cohesive framework. This design enables DADS to respond to dynamic IoT network conditions, such as fluctuating battery levels and device capabilities, ensuring energy-efficient device selection while preserving robust performance. Through extensive performance analysis, DADS demonstrates its novelty by achieving a 20% reduction in energy consumption and a 30% improvement in FL training time compared to state-of-the-art methods. Moreover, DADS significantly enhances battery lifespan, reducing degradation by more than 50%, and optimizes communication efficiency, extending the operational sustainability of IoT devices. These results position DADS as a groundbreaking framework, setting a new benchmark for energy-aware and sustainable IoT-enabled FL systems.

This study addresses the growing need for noninvasive, secure, and efficient biometric identification methods in Internet of Things (IoT) applications, where traditional biometric systems often face challenges due to privacy concerns, environmental constraints, and practical limitations. To tackle these issues, we introduce a novel biometric identification system that leverages custom-built multiplexed Fiber Bragg Grating (FBG) accelerometers and bispectral feature extraction. By applying bispectral analysis to the acquired gait signals, we extract robust and discriminative features for individual identification. Unsupervised clustering algorithms, namely K-means and DBSCAN, were employed to categorize individuals based on these features, successfully identifying ten distinct clusters corresponding to ten participants and demonstrating the system's effectiveness. The K-means model achieved a Davies-Bouldin Index of 0.79 and a Silhouette Score of 0.45, while DBSCAN yielded a Davies-Bouldin Index of 0.87 and a Silhouette Score of 0.36. Given the proprietary nature of the data and the custom-built FBG accelerometers used in this study, direct comparisons to state-of-the-art methods are not available. However, these results underscore the potential of our unique approach and technology to advance biometric identification, providing a promising non-invasive, scalable, and discreet solution with broad applicability in IoT environments.

The transition toward Healthcare Industry 5.0 requires personalized, intelligent, and privacy-preserving solutions. Additionally, the proliferation of home monitoring in the current Healthcare 4.0 framework has been embracing the challenge for invasiveness/privacy issues brought by home monitoring sensors and devices, potentially forsaking the conceived human-centric attributes of Smart Healthcare Industry 5.0. At the intersection of Healthcare Industry 5.0 and secured home monitoring, we propose a multi-service non-invasive solution for distributed home monitoring. A new infrastructure based on Fiber Bragg Grating (FBG) accelerometers is designed, realizing non-invasive/high-privacy distributed home monitoring by solely monitoring floor vibrations. To achieve home monitoring based on the low-dimensional data collected by FBG accelerometers, a multi-class hierarchical support vector machinebased algorithm (H-SVM) is proposed, which achieves simultaneous multi-service classification with FBG noise/interference mitigation. Moreover, an enhanced time difference of arrival (TDoA)-based algorithm (E-TDoA) is proposed for real-time localization of users, with the enhanced accuracy specifically based on FBG-collected data. Experimental results show an overall accuracy of 93.62% for multi-service classification, with a low processing time of 4.1 ms, meanwhile comprising a localization accuracy of 0.715 m (sufficient for home monitoring multi-service delivery).

In the advancing realm of e-health applications powered by 5G networks, the paramount goal of extending the sustainability of e-health services hinges on efficient energy management of Internet of Things (IoT) devices. This paper presents a cutting-edge Deep Reinforcement Learning (DRL)based approach for the dynamic optimization of IoT device selection and power allocation in Federated Learning (FL) tasks, with an emphasis on battery energy conservation to foster AIenabled e-health applications. Adhering to the strict demands of e-health applications in 5G networks, such as ultra-low latency and high data throughput, while prioritizing energy efficiency, this approach aims to prolong IoT device lifespans without sacrificing FL accuracy. By intelligently navigating network dynamics and device constraints through DRL, this framework optimizes energy usage, enhancing the sustainability and reliability of ehealth services within the 5G ecosystem. The comparative analysis reveals that the proposed approach excels in optimizing energy consumption, download and upload throughput, and training efficiency compared to conventional algorithms, establishing a new benchmark for future optimizations in FL applications across IoT devices. Compared to representative algorithms, our approach reduces energy consumption by >80% and training time by >34%, optimizing performance in FL-enabled e-Health applications.

Understanding human movement patterns and evaluating a range of medical disorders depend heavily on the analysis of gait. In this study, we propose a new method for gait analysis, based on multiplexed fiber Bragg grating (FBG) accelerometers. Our work expands the capabilities of FBG-based accelerometers by extracting gait features through the analysis of output signals. In contrast to traditional wearable sensors, our solution offers scalability and discreet monitoring while integrating smoothly into the current infrastructure. Step duration, cadence, peak acceleration, and gait symmetry are among the critical gait metrics that we calculate using MATLAB-based methods to preprocess the accelerometer data. Experiments show that our method is a good fit for precisely capturing gait dynamics. The study revealed significant differences among individuals (p<0.05) in gait parameters based on height and age groups, indicating variations in step time, and normalized cadence. Our findings have important ramifications for biometric identification, rehabilitation, and healthcare applications.

Under the fact that carbon footprint is broadly overlooked in information and communication technologies, multi-domain network orchestration, with even higher carbon footprint for inter-domain management than single-domain scenarios, is urged to be transited to be carbon-aware, towards Green 6G. To begin with, stakeholders are required to reach consensus on metrics for carbon measurement. However, there are limited efforts that discuss the impact of metrics, lack of which directly causes biased and chaotic network orchestration. This paper fills this research gap to provide a comprehensive analytics of metrics and their impacts towards carbon-aware multi-domain network orchestration. To do so, a generic architecture is developed correspondingly, based on which commonly used energy/carbon-aware metrics are summarized and analyzed. Representative scenarios are designed for simulation analysis to comprehensively reflect multi-domain network orchestration. Results reveal that improper metric selection could lead to up to 99.92% biased decision of network orchestration, regarding carbon footprint. In conclusion, this paper provides analytical guidance to the future carbon-aware multi-domain network orchestration, with the impact of metrics.

Home monitoring has been perceived as a promising service by the framework of Healthcare 4.0, providing remote digitalized network to users in a distributed manner. Small cell (SC), as an emerging technology in 5G, has been commonly acknowledged to support home monitoring, due to its nature of distribution and scalability. Although widely adopted, research efforts are still required towards user-centric on-demand SC deployment for home monitoring. In this paper, on-demand SC deployment is formulated as a multi-criteria optimization problem of task-aware latency and power consumption minimization, meanwhile ensuring network-level Quality of Experience (QoE), simultaneously. The problem is solved by the proposed on-demand SC deployment algorithm, based on the emerging Technique for Order of Preference by Similarity to Ideal Solution with Attribute-based Niche count (TOPANSIS). Simulation results show that the proposed algorithm outperforms the previous work with enhanced latency and power consumption, meanwhile ensuring QoE towards user-centric home monitoring.

This paper presents an analysis of revenue increment strategies for Electric Vehicle Charging Stations (EVCS) within a microgrid context. The proposed methodology takes into account the utilization of Photovoltaic (PV) power and Grid Electricity Price (GEP) in the EVCS revenue increment process. To this end, optimization algorithm is used to provide a systematic and mathematical approach for addressing the revenue increment strategies for EVCS. The paper shows the formulation of the objective functions and constraints in a precise and structured manner, enabling the exploration of various scenarios and providing valuable insights into the revenue-maximizing and State of Charge (SOC) management capabilities of the proposed strategies. A multi-objective function is introduced, including PV power generation, energy price, and additionally, a second objective function is formulated to ensure Electric Vehicles (EVs) reach their desired SOC at departure time. An evaluation of our proposed optimization is conducted, comparing how model formulation impacts the revenue and SOC achievement. Simulation results demonstrate the accuracy and verification of the proposed strategies.

Edge computing brings added benefits for different elements in the overall system (e.g., users, operators and service providers). However, currently there are no proper interfaces and mechanisms to instantiate third-party services within the operators' infrastructure (e.g., as a MEC application), thus hindering edge computing to reach its full potential. To fill this gap, this paper presents architectural enhancements, interfaces and mechanisms to enable dynamic and efficient third-party service deployment within the operators' domain. A simulation-based analysis is presented to showcase the relevance of the proposed solution. Results highlighted the benefits of optimal migration of third-party services into a distributed setting, compared to the unveiled drawbacks of a centralized approach. In addition, the key components of the solution are implemented and experimentally validated through a proof-of-concept prototype showcasing the performance impact of the proposed approach as well as the suitability of its implementation.

Considering that fall accidents are one of the leading causes of non-natural death of elders, it is crucial to design and to implement home’ fall detection systems. Current home monitoring systems are targeting this challenge, pursuing non-invasive, low latency, and simplified fall detection algorithms. Therefore, in this paper, edge-enabled non-wearable and non-invasive fall detection system is proposed. Concretely, outperforming the conventional invasive/privacy-sensitive fall detection technologies, the proposed system comprises four photonic-based accelerometers solely relying on the fiber Bragg grating (FBG) technology, which monitor the vibrations induced by the body impact in the platform by the Bragg wavelength shifts. A newly-developed support vector machine-based multi-class fall detection algorithm is proposed, based on the data collected by the accelerometers. Moreover, feasibility analysis of the proposed fall detection algorithm also reveals the possibility of fall prediction, given the slipping as the pre-falling phenomenon. Experimental results showcase that the proposed fall detection algorithm achieves overall accuracy up to 96.5%, with average processing time achieved as 21.3 ms, indicating the sufficiency to provide high quality of experience (QoE) fall detection services. Besides, fall prediction based on the pre-falling case study of slipping is discussed, revealing that fall can be predicted~197.5 ms beforehand, which is sufficient for further fall prevention (e.g., airbag).

This paper gives a generic architecture and system modeling for full-decentralized on-demand edge computing-based peer offloading. Compared with the conventional centralized peer offloading strategies, the proposed full-decentralized peer offloading, based on federated learning, physically decentralizes peer offloading algorithm into edge computing, fully eliminating the rely on centralized servers (e.g., cloud). Meanwhile, compared with the other previous decentralized offloading schemes (blockchain-based, game theory-based, etc.), edge computing servers in this paper does not require global information to be shared, when they reach consensus of optimal peer offloading. In particular, the adjacent edge computing servers only share property-sensitive data (for the service providers of the edge computing servers) among each other, relying on which the whole edge computing network can reach global optimal peer offloading. In this paper, we consider energy efficiency as a use case to analyze the feasibility and efficiency of the proposed full-decentralized peer offloading architecture.

Small cells have been proven to effectively serve the densification of users, which supports the framework of 5G. Therefore, small cells have drawn great research interest, targeting systemic optimization for their on-demand deployment to handle heterogeneous networks. On top of the previous research efforts, in this paper, we discuss the resilience of the on-demand small cell deployment to server ultra-dense heterogeneous networks, specifically focusing on powering strategies of small cells. Concretely, we formulate the optimization problem, jointly considering the achievable data rate, energy efficiency, and interference mitigation. Based on the formulation, we further propose the on-demand small cell powering strategy, which adopts the unsupervised learningbased algorithm, i.e., k-means clustering, as the backbone. Besides, the proposed powering strategy is further developed to enhance the resilience performance of the system, with the consideration of the situation of malfunctioned small cells. The simulation results show that the proposed system achieves 280 Mbps data rate and 7.58 MB/J, even under the scenarios with malfunctioned small cells, showcasing high resilience. Besides, such a Figure outperforms the selected two benchmarks, by {18.60%, 12.28% and {7.88% (resilience-related scenarios), 28.79%, respectively.

Small cells have been widely adopted in the fifth generation of wireless communication technology, known as 5G, to boast higher data rates and an ability to support an increased number of connected devices. Studying of the on-demand deployment of small cells provides referrable guidance to the future cell-free network towards 6G, when dealing with ultra-dense heterogeneous networking environment. This paper gives a comprehensive analytical study of on-demand small cell deployment, regarding quality of service-aware load balancing. Concretely, we develop a generic architecture of on-demand small cell deployment, within the coverage area of overloaded macro-cells. Under the architecture, we systemically study the load balancing and quality of service performance, which not only reflects the significance of the proposed architecture, but also provides reference to the future ultra-dense cell-free network deployment.

In order to fit the requirements of future enterprise management networks with multiple service providers, conventional mobile edge computing enabled small cells (MEC-SCs) peer-offloading requires research efforts towards fully-decentralized computation-efficient global-optimal quality of service (QoS) aware load balancing, while ensuring service providers’ privacy protection. In this article, we propose a new fully-decentralized on-demand MEC-SC peer-offloading NETwork (named DEEP-NET), targeting QoS-aware load balancing with enhanced latency and service providers’ privacy protection. Newly developed federated gradient descent based algorithm is fully decentralized to MEC-SCs, which only requires local data and privacy-free inter-MEC-SC data sharing to achieve global optimal QoS-/latency-aware fairness. Result analysis for convergence of the proposed DEEP-NET provides guidance to the future topology optimization of fully-decentralized on-demand MEC-SC deployment. Besides, DEEP-NET outperforms the benchmarks with dynamic user demand to achieve optimal load balancing, with enhanced QoS, latency, and service providers’ privacy.

Network automation has originated in the early 21st century by the International Business Machines Corporation (IBM), which was initialized conceptually, including automated configuration, optimization, healing, and protection of network deployment. In the framework of 5G and upcoming 6G, softwarization and virtualization, as well as the conceived pervasive artificial intelligence (AI), have been activating and further proliferating network automation, supporting ubiquitous applications with diverse network demands, which have recently attracted plenty of research efforts.

Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.

Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies for IIoT in 5G based Industry 4.0 and summary of challenges for next-generation network automation regarding the stricter network requirements of 6G based Industry 5.0. Therefore, in this article, we conduct a comprehensive overview of the state-of-the-art network automation technologies, standardizations, and corresponding impact on IIoT of Industry 4.0. We also forecast the next-generation network automation development toward 6G based Industry 5.0. This article provides blueprint of the next-generation network automation, meanwhile conducting comprehensive overview of the SoA network automation technologies in Industry 4.0, which gains high referable value for the researchers in the relative domain.

In this paper, we modeled the emerging edge-computing-enabled live service migration as a multi-criteria problem optimization, tackling migration costs and benefits, as well as discussion of service providers' data privacy, simultaneously. Based on the optimization formulation, we conducted a small-scale analytical feasibility test, considering widely-utilized multi-criteria decision making algorithms, based on which we proposed a new TOPSIS based service migration algorithm. The algorithm was evaluated using simulations, whose results show that the proposed algorithm is sufficient to support live service migration for heterogeneous edge computing, while outperforming benchmarks in with respect to reducing migration costs and increasing achieved benefits, by 34.52% and 60.21%, respectively.

The detection of protein aggregates and its concentration, plays a relevant role in several fronts, namely at the detection of neurodegenerative diseases such as Alzheimer's, and the assessment of cellular stress linked to cell death. Evaluation of protein aggregation is an essential step of quality control in all stages of biopharmaceutical synthesis and transport, in order to guarantee the activity and desired immunogenicity of their products. The detection of protein aggregates often requires the use of expensive and complex techniques. Here we propose a fast and easy implementation solution, based on an optical fibre Fabry-Perot Interferometer (FPI) end tip resonator. The solution proposed revealed to be effective on the detection of protein aggregates, providing similar results as the ones obtained using a commercial spectrometer for fluorescence detection.

Small cell (SC) resource allocation for the next-generation cellular networks embraces ultra-low latency, energy efficiency, and reliable challenges. Conventional optimization algorithms may not be capable of supporting the abovementioned scenarios, with aggregated and centralized traffic burden causing excessive latency, especially for the conceived large-scale eHealth networks in Healthcare 4.0. In this paper, we propose a new Decentralized Integer-based Non-Dominated Sorting Genetic Algorithm (DI-NSGA), on top of the authors’ previous work. Integer-based resource allocation process are formulated, and decentralized to mobile edge computing embedded SCs for releasing centralized traffic burden. Overall latency and achieved data rate are considered as the optimization objectives. Simulation analysis shows that the proposed DI-NSGA achieves low computation cost while maintaining high optimality by searching for the Pareto Front, compared with the selected benchmarks.

Abstract In this article, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover, the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this article, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an advanced encryption standard (AES)‐256 encryption technique to encrypt data. Moreover, an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.

Peer-offloading of ultra-large-scale mobile edge computing enabled small cell (ULS-MEC-SC) will be highly demanded with optimal quality of service (QoS), and cost efficiency. Modeling and analysis of inter-MEC-SC topology has been foreseen to gain dominant influence to ULS-MEC-SC peer-offloading. Although complex network shows its strength revealing topological information, efforts are still lacked for topological analysis of ULS-MEC-SC. Therefore, in this paper, for the first time, we model and analyze inter-MEC-SC topology for ULS-MEC-SC peer-offloading, by considering complex network. We also propose systemic translation between graph theory based indicators and network-layer performance, for correlating graph theory based topological analysis with service-centric performance in ULS-MEC-SC peer-offloading. Comprehensive scenarios of ULS-MEC-SC peer-offloading are designed, comparing and analyzing performance of complex network modeling, which is referable of revealing unique strengths of typical complex networks (random graph, small world, and scale free network), regarding operation cost, latency, and convergence speed. Therefore, this paper paves the way to complex network based topological modeling, determination, and analysis of future ULS-MEC-SC peer-offloading.

In this paper, we present an optical fiber based architecture for non-invasive home monitoring of elder citizens. The approach is based on a network of optical fiber sensors distributed along the space/room to be monitored. The sensing mechanism is based on optical fiber Bragg grating (FBG) sensors, produced by the phase mask method and integrated within an accelerometer structure. This type of sensing solution has high sensitivity, allied with an extra resilience. Here we present the proposed architecture, the evaluation of different parameters that influence the accelerometer feedback, and the theoretical approach for indoor localization using this type of sensing mechanism. One advantage of the proposed solution is that it does not depend on wearables, which are considered burden for elders.

In this paper, we present an optical fiber based approach to accurately evaluate the respiratory capacity of individuals through spirometry. The proposed system converts the air flow rate into strain variations in an optical fiber, which will modulate the spectral response of a Fabry-Perot interferometer (FPI) micro-cavity, created in the fiber core. Simulation studies were performed to verify the proposed theoretical approach. Additionally, preliminary results concerning the study of the system's reliability are also presented, which confirm the suitability of using FPI sensors for the evaluation of spirometry parameters. The simulation and implementation results showcase the accuracy of the measurements performed by the proposed solution, confirming its appropriateness to be used as a smart low-cost eHealth spirometer. This is the first time such FPI approach is used with this purpose.

5G wireless networks have been designed to provide high reliability, ultra-low latency, and support of massive amount of connected devices, in the scope of the Internet of Things (IoT). Advances in electronics and networking are enabling the wide adoption of multiple types of verticals, under the umbrella of IoT. One area of IoT, which is gaining lots of attention, is e-Health. Within e-Health, users’ monitoring in general, and monitoring of vital signs, such as heartbeat rate, are very useful in saving many lives; however, they require ultra-low latency. Cloud-based networking and computing have been proposed to achieve the required low latency. Furthermore, fog computing was proposed to further decrease the achieved latency for critical tasks and services; however, this adds more complexity to the control of the network, in addition to the task scheduling among both the cloud and fog layer. In this paper, we tackle this problem by proposing a task classification and scheduling scheme in a fog-cloud networking environment, by considering comprehensively modeled characteristics of tasks, user profile, environmental exposure and networking features, targeting the improvement of overall latency for higher priority critical tasks. Moreover, we perform an analytical study on the execution comparison between cloud and fog computing services, which paves the way to further develop an orchestrator for task scheduling, among the multi-layer fog-cloud based e-Health systems. Simulation results show that the proposed task scheduling scheme outperforms the benchmark, by guaranteeing ultra-low latency for critical tasks (high-priority), while ensuring sufficient latency performance for latency-tolerant tasks.

Traffic proliferation induced by mobile video streaming has been evoking tremendous challenges to resource management in 5G Internet-of-Things (IoT) networks. Although small cell based heterogeneous network is capable of traffic offloading, it still requires a systemic deployment with optimal allocation and resource management to deal with high data rates and network densification of mobile video streaming users. In this paper, we propose a two-tier hierarchical small cell based heterogeneous network infrastructure to tackle the aforementioned challenges. In particular, we develop an offline tier for the long-term small cell allocation, formulated with energy efficiency and predicted quality of experience (QoE) maximization. We also propose an online tier, characterizing the real-time on-demand network-slicing based small cell deployment, to optimize the dynamic QoE of users. Simulation results show that the proposed scheme achieves optimal energy efficiency and real-time improved QoE performance simultaneously, for different scale of heterogeneous IoT networks with dynamic mobile video streaming requirements by users, and clear advantages in highly dense networks with high percentage of video streaming services in IoT HetNets.

With the popularity of mobile edge computing (MEC), video streaming's peer-offloading strategy significantly affects the Quality of Experience (QoE) performance of video streaming for mobile users (MUs). Improving the service quality regarding the MUs' demand has become a vital challenge, reflected by QoE. This paper proposes a QoE-aware MEC-based peer-offloading method for HAS-based video streaming, called QOMECS. The proposed method considers dynamic MUs' demands and corresponding QoE requirements. We categorize the QoE KPIs into perceptual and systemic sectors. We formulate the corresponding transmission, computation, and offloading for MEC-based HAS into a QoE maximization problem. We propose a reverse-fuzzied particle swarm optimization (R-FPSO), to solve the highly nonlinear and perceptual-oriented optimization formulation. Unlike conventional fuzzy logic, R-FPSO reverses the fuzzification process by fuzzifying PSO's output (i.e. translated QoE KPIs into satisfactory levels) and further updates the particle values and velocities in the PSO process. Simulation results show that the proposed QOMECS dramatically improves the edge computing efficiency, with optimized QoE performance.

This paper proposes a device-to-device communication based small cell (SC) deployment scheme in Smart Grid, named as JODI-D2D, to achieve high QoS, high reliability, and high energy efficiency. SC deployment is formulated as a joint optimization of data rate maximization and interference minimization, serving the distributed smart grid user equipment (SGUE), while reducing the computation latency. The SC powering strategy is determined with the k-means clustering, which considers the features of SGUEs, endowed by both power and communication layers. Simulation results show that the proposed JODI-D2D achieves high QoS with improved data rate performance, sufficient computation latency and maximized energy efficiency for SG applications.

Breast cancer (BC) treatments are often aggressive for the patients, causing severe impairments to their physical condition. Physical supervised exercise has proven to be beneficial for the patients' physical and emotional health recovery. In this work, we present an eHealth solution based on optical fiber sensors (OFSs), to monitor the physical activity of BC patients, during their exercises. Such solution will allow the patients to exercise from the comfort of their home, with the continuous supervision of the physiotherapist, and with a possibility to connect remotely with other patients. The use case, presented in this work, is focused on the design of optical fiber based eHealth enablers to monitor the exercises prescribed in the recovery of the handgrip force, often affected by the treatments that BC patients are subjected to.

This paper presents an improved algorithm for small cell (SCs) deployment in the heterogeneous network (HetNet) future generations of mobile networks. The sequential and fixed number of the SCs deployment is formulated as a joint optimization of load balancing and interference (JOLBI) minimization over the number and locations of the distributed user equipment (UE) forming a hotspot (HS). Then, the SCs are adaptively powered ON or OFF according to outcomes of the proposed clustering algorithms applied to the UEs' distribution. The conceived integrated solution of the JOBLI and clustering algorithm does not only satisfy the coverage constraint and maximize the minimum user throughput of the HetNet, but also enables an energy effective SCs deployment. The simulation results show that this JOLBI and clustering algorithm enables a power saving of the whole network by 35% when HS of UEs are considered in the network simulations.

C-RAN presents an advanced mobile networking architecture that promises to tackle various challenging aspects of 5G such as increasing energy efficiency and providing high capacity. Indeed, C-RAN paves the way toward better energy efficiency by centralizing the baseband processing at cloud computing based servers (BBU pool). In this paper, we propose an RRH group based mapping (RGBM) that aims to minimize the power consumption at the BBU pool, while considering users' QoS and BBU capacity constraints. To achieve this, the proposed scheme uses two key steps: i) the formation of RRH groups aimed at improving the QoS of weak users, ii) the formation of RRH cluster to be mapped for minimal number of BBUs requirement. The proposed scheme uses an efficient greedy heuristic to solve the optimization problem. The performance of the proposed approach was evaluated using simulations, which indicate a significant gain in terms of BBU minimization, power reduction and energy efficiency, while preserving QoS constraints, against well studied legacy solutions.

With the latest advances in electronics and networking, eHealth has become a reality and is enhancing the life quality of people, by enabling them to live healthier and a more fulfilling life. On the other hand, the continuous population’s aging is pushing the limits on our health system, increasing the need for eHealth solutions, especially those enabling continuous monitoring of senior citizens and patients. Towards such goal, this paper presents an innovative eHealth system for ankle angular movement monitoring for physical rehabilitation. The proposed solution has a non- invasive design, due to its small size and ease of use; hence, it provides users with full autonomy to live a fully active life. The system is composed of optical fiber Bragg grating (FBG) sensors, used for the angular movement monitoring. The proposed system was tested in a healthy volunteer, and the obtained results show the accuracy of the proposed system, by displaying the expected behavior of normal healthy ankles. The system can be used in continuous ankle monitoring during routine daily activity in rehabilitating patients or elder citizens’ scenarios, or can even be integrated into an exoskeleton, along with other sensors, for complete monitoring of all movements of the subject.

This paper investigates two-coil inductive channel for an efficient Wireless Power Transfer (WPT), while intelligently enabling high-data rate communication. The analysis is based on the power efficiency and channel capacity performance, which are determined based on the multiport scattering (S)-parameters numerical simulation results, using a lumped equivalent circuit model. Different access points and configuration scenarios, along with the decoupling filters design consideration of power and data links, were compared in order to cognitively share the two-coil inductive channel, for handling both high-power signal and low-power modulated high- frequency data signal. The performance of the transmission and isolation between the power and data link is evaluated. The theoretical channel capacity of the proposed two-coil circuit is calculated, as a function of the coupling coefficient k12, as well as in the presence of an additive white Gaussian noise and signal to noise ratio. This paper adds to the intelligence and cognition of the radio, to efficiently use the limited available spectrum for broadband communications, in addition to power transmission, simultaneously.

The advances and fast spread of mobile devices and technologies, we witness today, have extended its advantages over medical and health practice supported by mobile devices, giving rise to the growing research of Internet of Things (IoT), especially the e-Health field. The features provided by mobile technologies revealed to be of major importance when we consider the continuous aging of population and the consequent increase of its debilities. In addition to the increase of lifetime span of population, also the increase of health risks and their locomotive impairments increases, requiring a close monitoring and continuous evaluation. Such monitoring should be as noninvasive as possible, in order not to compromise the mobility and the day-to-day activities of citizens. Therefore, we present the development of a noninvasive optical fiber sensor (OFS) architecture adaptable to a shoe sole for plantar pressure remote monitoring, which is suitable to be integrated in an IoT e-Health solution to monitor the well-being of individuals. This paper explores the production of the OFS multiplexed network (using fiber Bragg gratings) to monitor the foot plantar pressure distribution during gait (walking movement). From the acquired gait data, it is possible to infer health conditions of the patient's foot and spine posture. To guarantee the patients mobility, the proposed system consists of an OFS network integrated with a wireless transceiver to enable efficient ubiquitous monitoring of patients. This paper shows the calibration and measurement results, which reflect the accuracy of the proposed system, under normal walking in controlled area.

Network coding (NC)-enabled mobile small cells are observed as a promising technology for fifth-generation (5G) networks that can cover the urban landscape by being set up on-demand at any place and at any time on any device. Nevertheless, despite the significant benefits that this technology brings to the 5G of mobile networks, major security issues arise due to the fact that NC-enabled mobile small cells are susceptible to pollution attacks; a severe security threat exploiting the inherent vulnerabilities of NC. Therefore, intrusion detection and prevention mechanisms to detect and mitigate pollution attacks are of utmost importance so that NC-enabled mobile small cells can reach their full potential. Thus, in this article, we propose for the first time, to the best of our knowledge, a novel intrusion detection and prevention scheme (IDPS) for NC-enabled mobile small cells. The proposed scheme is based on a null space-based homomorphic message authentication code (MAC) scheme that allows detection of pollution attacks and takes proper risk mitigation actions when an intrusive incident is detected. The proposed scheme has been implemented in Kodo and its performance has been evaluated in terms of computational overhead.

The continuous aging of the population has increased health risks, mostly translated in mobility impairments. New therapeutic options have come to light in the past years, with the rise of e-health architectures. The use of optical fiber based sensors has been considered as a possible e-health enabling technology, due to their advantages compared to electronic based sensors. In this paper, we present an e-health optical fiber based solution to monitor the knee angular flexion/extension during gait. The presented design is an accurate non-invasive solution, using kinesio tape and optical fiber Bragg grating sensors, in order not to limit the movement of the individuals. The paper shows the accuracy of the acquired measurements during gait using the proposed designed sensor.

Internet of Things (IoT) and cloud computing are steadily growing in support of recent and projected revolutionary Internet applications. Cloud computing has the ability to meet the performance expectations of different applications. In this paper, we present the implementation of applications based on cooperative resource and energy-constrained objects with optimized performances. To dynamically incorporate objects into IoT applications' execution, task scheduling should be implemented for resource allocation in an optimized manner. We propose a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully-informed particle swarm (FIPS) algorithms in order to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT cloud computing. Our objective is to satisfy the Quality of Service (QoS) in terms of throughput and delay, by performing optimal task scheduling taking into consideration different classes of data traffic. Performance evaluation of experiments show that throughput and delay can be significantly improved by dynamic dedicated servers (DDSS) and heterogeneous DDSS (h-DDSS) using FIPS optimization algorithm compared to CPSO optimization algorithm.

The emerging vehicular ad-hoc networks (VANETs) have paved the way to Cooperative Adaptive Cruise Control (CACC) applications. With such application, cars can travel in platoons with very small headways and thus achieve considerable capacity and fuel consumption gains. In order to ensure safety while exploiting such gains, intra/inter-platoon communications have to rely on a fast and reliable information exchange. Thus, it is important to define relaying schemes which can ensure that reliable platoon applications can be run over the unreliable wireless channel. In this paper, we propose a new dissemination algorithm for the Decentralized Environmental Notification Message (DENMs) over platoons. Our dissemination algorithm is responsible for electing the vehicles among platoon members for relaying the DENMs. The reliability is met by a proper selection of the best relay based on bidirectional link quality and distance criteria. Unlike existing method for the estimation of the bidirectional link quality (BDSC), which is more suitable for unicast context, we propose an algorithm that accounts for the dissemination context in platoons. A performance comparison with four state-of-the-art relaying approaches, based on simulations, shows the high performance of our approach in terms of end to end delay, reachability, and average number of hops.

This paper investigates the optimal number of antennas at a base station, in contrast to what has been accepted in the past: that increasing the number of antennas at base station always enhances performance. In this study, we show that increasing the number of antennas does not always improve the desired performance. Additionally, such increase in antennas consumes more power in transmission and adds to the computation complexity, which in turn needs more time and is more difficult to implement. The optimum number of antennas has been evaluated using simulations. The simulation results show that the optimal ratio equals to 1.2 times the number of active users in each time frame.

Coordinated vehicles travelling in platoons can improve road capacity, reduce fuel consumption and offer a more comfortable travelling experience for the driver. However, for platooning to achieve such gains, headway distances between travelling cars have to be kept small even at high speeds. The feasibility of such platooning approach, hence relies entirely on the availability of fast and reliable information exchange between vehicles, which occurs over an unreliable wireless communication channel. In this paper, we analyse the dissemination of safety related messages in platoon-based VANETs. We propose an enhancement of the reliability of the dissemination algorithm by modifying the relay election procedure to include the bidirectional link quality in addition to distance criteria. A performance comparison is presented using simulations, which shows that our proposed enhancement provides comparable end-to-end delay with higher reliability for DENMs dissemination over platoons.

The continuous aging of the population has increased health risks, leading to the need for close monitoring of elder citizens and patients. Fortunately, the field of e-Health has been experiencing great progress, although it still faces challenges, since it needs to be applied without compromising the mobility or lifestyle of monitored patients; i.e. non-invasive. Addressing such challenges, we present a non-invasive energy- efficient insole optical fiber sensor architecture for plantar pressure monitoring. The paper shows the design and implementation of a non-invasive "in-sole" optical fiber sensor network (using Fiber Bragg Gratings), which is able to monitor health conditions by observing plantar pressure distribution in the foot. The system integrates the optical fiber sensor network with an energy efficient wireless transceiver, in order to allow continuous monitoring of monitored patients, during their uninterrupted daily routine. The paper presents the calibration and laboratory measurements of the sensing network. The recorded measurements show the suitability and high accuracy of the proposed system in monitoring the plantar pressure distribution during walking movement (i.e. gait).

Mobile computing proved to be essential in today's cyber communications. However, entities in mobile computing are known of having limited energy, physical, and logical resources. This imposes various challenges that greatly affect communication quality and performance of those mobile entities, especially when applying computationally-intensive security measures that are essential for protecting the communication sessions. Therefore, it becomes vital to seek suitable security techniques that balance between the communication performance and the resource context of those mobile entities. This paper investigates some possible options toward implementing an adaptive security measures that work with various mobile and next generation Internet entities. The paper basically studies the communication performance of mobile entities when security functions are running on them with and without operating adaptations. While the focus in this paper is about the Message Authentication Code group of security functions, the work can be generalized to include any resource-intensive security measures including both other cryptographic (such as encryption) and non-cryptographic measures (such as challenges).

In the context of the Smart City concept, mobile e-Health applications can play a pivotal role towards the improvement of citizens' quality of life, since they can enable citizens to access personalized e-Health services, without limitations on time and location. However, accessing personalized e-Health services through citizens' mobile e-Health applications, running on their mobile devices, raises many privacy issues in terms of citizens' identity and location. These privacy issues should be addressed so that citizens, concerned about privacy leakage, will embrace Smart City mobile e-Health applications and reap their benefits. Hence, in this paper we propose an OAuth2-based protocol with strong user privacy preservation that addresses these privacy issues. Our proposed protocol follows the OAuth2 protocol flow and integrates a pseudonym-based signature scheme and a delegation signature scheme into the user authentication phase of the OAuth2 protocol. The proposed protocol enables citizens authentication towards the servers providing personalized e-Health services, while preserving their privacy from malicious mobile applications and/or eavesdroppers. Moreover, the proposed protocol does not require to store sensitive information in the citizens' mobile devices.

Security is of paramount importance for vehicular communications to verify regular updates from authorized vehicles while detecting misleading reports from malicious or malfunctioning on board units (OBUs). This should however be provided without violating the privacy of the users. Using frequently changing pseudonyms for the vehicles is an effective solution to preserve their privacy. However, in current systems the vehicles need to be always connected to the Certification Authority (CA) to receive the pseudonyms. This might be unfeasible due to the sparse infrastructure deployment, especially in the early stage of the technology, that can lead to availability and scalability problems. In this paper, we solve this issue by proposing efficient protocols for authentication, pseudonym generation, message verification and anonymity revocation that do not require permanent contact with the CA. Even in case of network availability, this feature also reduces the communication overhead for the pseudonym generation and revocation list distribution.

Abstract Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

Interference management is an important subject in Device to Device (D2D) communication when underlying a LTE-A cellular band. By default, in LTE-A there is negligible intra-cell interference due to the orthogonality of the subcarriers but this orthogonality will be lost when D2D communication takes place under cellular users (CU). Therefore, one of the key aspects of D2D communication is the set of spectrum bands in which D2D communication takes place. Hence, in this paper we will propose two novel resource allocation (RA) schemes: the first RA scheme (cell level) mitigates the interference between D2D and CU and the second RA scheme (user level or scheduling) schedules the resources in an energy efficient way between D2D and CU. These RA schemes increase the throughput and reduce the overall energy cost per bit of the system. Afterwards, these schemes are compared with the conventional methods and the simulation results show that the proposed schemes obtain higher throughput and save significant amount of energy per bit.

Multi-standard mobile terminals (MTs) allow mobile users to experience ubiquitous connectivity and better quality of service. However, these advances come at a price of higher power consumption for MTs due to holding multiple active interfaces. In this paper, we apply multihop relaying through pervasive short range interfaces (e.g., Bluetooth, WiMedia. etc.) in the presence of an infrastructure network (e.g., LTE, WiFi, etc.) to extend the battery lifetime of multi-standard MTs. To this end, MTs exchange context information (conveying channel state information, battery level, etc.) and, whenever beneficial, form a coalition, pool their resources, and perform their tasks cooperatively to enhance their efficiency. We introduce a novel utility function to assess the profitability of cooperation. To incentivize MTs to cooperate, the achieved common utility of the coalition can be distributed among the cooperative MTs by means of energy credits using a solution concept from coalitional game theory. The simulation results validate the effectiveness of the proposed approach to extend the battery lifetime of MTs to more than double.

Hierarchical ad hoc networks have gained popularity due to the huge number of mobile devices, as well as the social content distribution which is frequently location based. One major issue of hierarchical networks due to mobility is the overlapping of clusters. The issue is often resolved by merging of overlapping clusters. Some efforts propose immediate merging of clusters, while others suggest contention intervals before merging without detailed analysis of the length of such intervals. The literature lacks a thorough study of the efficiency of merging procedures. This paper represents a comprehensive study of the merging procedures. The paper derives an analytical model to compare the merging procedure with and without applying contention intervals. The effect of varying the length of contention intervals on the cluster stability and energy efficiency is also studied. The results show that adopting contention intervals is not recommended. Instead, to achieve the best energy efficiency when two clusters overlap, the clusters should either merge immediately or avoid merging, depending on the characteristics of the scenario. The analytical results are supported with simulations.

Cooperative communications have been gaining huge attention lately, due to the increase in the number of mobile devices and the advancement in their capabilities. In this framework, a commonly suggested approach to benefit from cooperation is the formation of virtual groups of mobile terminals. Mobility-aware algorithms are commonly proposed, where nodes form cooperative clusters according their basic mobile characteristics. However, more detailed mobility pattern information is not used to improve the performance of these systems. The reason is the complexity of current mobility pattern recognition algorithms that require previous set up and advanced knowledge on the scenario. This paper presents a novel cognitive algorithm for the identification of mobility patterns that can be used for mobile group formation and that requires no configuration. The proposed algorithm, based on data mining techniques and cooperative optimization, can evaluate the number of mobility patterns in the scenario and classify nodes according these patterns in a distributive fashion. The algorithm is mathematically presented and tested with extended simulations in both Matlab and ns-2.

Packet forwarding is an essential service in a multihop cellular network (MCN), which relies on the cooperation of all participating mobile terminals (MTs). Although this service can effectively improve the energy efficiency of MTs by dividing a long radio link to several shorter hops, cooperation of MTs cannot be taken for granted as they are normally controlled by rational players who seek to maximize their own benefit by taking advantage of the service while minimizing their contribution. To ensure proper operation of the network, there should be a mechanism to encourage cooperative nodes while punishing free riders. To this end, we propose a credit scheme based on coalitional game model. We provide credit to cooperative nodes proportional to the core solution of the game, which distributes the common utility among the players in a way that everyone is satisfied. Simulation results validate that the proposed technique can successfully detect and isolate selfish nodes.

In an era where spectral resources are at a premium, the underutilization of LTEA spectrum on the Uplink (UL) band due to the asymmetric nature of internet traffic is a major concern. On the other hand, Ad-hoc mode, Device to Device (D2D) or Direct-LTEA communication underlying a cellular infrastructure has recently started gaining attention as a new technology that supports machine-to-machine scenarios that can be network unassisted providing a tentative solution towards filling in the spectral gaps. Although D2D communication can share the same licensed frequency band with cellular users, it requires stringent interference management to avoid diminishing the communication quality with primary users. In this paper, we consider secondary D2D users that operate over LTEA UL bands. D2D users sense the pathloss between their location and LTEA eNB. Based on this sensing information, a novel resource allocation scheme is applied to mitigate the interference with the LTEA system. Simulation results show that there is about a 50

Energy is becoming a vital resource in wireless networks and with the introduction of high data rate wireless technologies the future Wireless Personal Area Networks (WPANs) will be able to support demanding applications e.g. interactive games, video sharing etc. and as a result the energy saving mechanisms will become more demanding and challenging. The mobile devices with hybrid character, containing ad hoc and infrastructure access, will need mechanisms that keep them connected in an energy efficient way. For the node to be connected to either a cluster or a solitary node knowledge of the neighbor devices are required to choose a suitable node for cooperative communication; this discovery of information is under the scope of this paper. We present a context based beaconing mechanism both for node discovery and cooperative cluster formation. We have exploited the available beacon Information Elements (IEs) of UWB to embed our own information elements of interest e.g. Energy level, status of node, willingness, etc. to facilitate node discovery and cooperation. A Context Extraction Function (CxEF) has been created to insert and extract information in the beacon. We have also presented a detailed representation of bit mapping for energy and cooperation in beacons. The simulation results show that our mechanism saves a considerable amount of energy compared to other techniques which perform blind node discovery without using context information.

Recent years have witnessed an explosive growth of wireless technologies and applications. The adoption of a wide range of multimedia applications on wireless devices has been made possible through the advances in wireless technologies. Only one aspect is not catching up, which is re-chargeable battery for mobile devices. Without disruptive new approaches for energy saving, mobile users will relentlessly be searching for power outlets rather than network access, and become once again bound to a single location. In this paper, we address the cooperation among wireless nodes in a cluster for the main goal of saving the battery power of mobile terminals. This paper studies the possibility of decreasing energy consumption through relaying data over short range technologies through cluster head. Relaying nodes sacrifice resources for the sake of other devices; hence a trading model based on Cournot model is proposed to offer incentives for relays. Simulation results demonstrate the gains in energy saving. The proposed algorithm also guarantees that the required QoS is always maintained.

Multi-standard mobile devices are allowing users to experience higher data rates and ubiquitous connectivity. These advances are achieved on the expense of higher energy consumption due to the multiple active wireless interfaces. In this paper, we use one advantage of the multiple interfaces, namely short range communications. Mobile terminals (MTs) form short range cooperative network to take advantage of the good channel quality of short range links to save energy of MTs. In this cooperative network, the energy of all MTs is treated as a pool of resources, which is available for all MTs in the network. Toward this end, we propose a combinatorial optimization game to identify the problem. We derive the core solution for the relay selection game in cooperative networks. A mathematical framework to compute the achieved energy saving is derived. Simulation results using the solution of the game show that energy saving can be achieved using short range cooperation in wireless networks. The results also show the effectiveness of the proposed node selection game.

Multihop communication has been proposed in cellular networks to overcome some inherent limitations. Congestion relief is amongst the promised gains. In this paper, the concept of inter-cell relay, which uses multihop communication to divert calls from heavy loaded cells to less loaded adjacent cells, is introduced. We show that using inter-cell relay, the number of supported calls inside a congested cell can be significantly increased. We devise two approaches for congestion relief based on the conditions of the network, to maximize the number of supported calls inside a congested cell. The distribution-based approach determines the number of extra hops for inter-cell relay based on call distribution. On the other hand, the delay sensitive approach assumes that the number of extra hops for inter-cell relay is limited by calls quality of service requirements. By imposing a limit on the number of extra hops, the approach decides the number of inter-cell relayed calls and the number of calls connected to the congested BS. Our results illustrate the benefits gained from inter-cell relay in congestion relief. We demonstrate that inter-cell relay can decrease congestion of a cell by fully utilizing the available resources in surrounding cells.

Multihop communication has been proposed in cellular networks to overcome some inherent limitations. Congestion relief and load balancing are amongst the promised gains. In this paper, we investigate inter-cell relay - which means diverting calls from one congested (heavy loaded) cell to an adjacent less loaded cell for congestion relief (load balancing) — in multihop CDMA cellular networks. The application of inter-cell relay changes the resulting interference inside the heavy loaded cell as well as at the supporting BSs. Toward analyzing the gains from inter-cell relay, we derive formulas for interference experienced at the congested and supporting BSs. Upper bound on number of calls supported inside the area of the congested cell is derived based on interference formulas. We show that inter-cell relay can increase the number of accepted calls inside a congested cell.

In this paper, we utilize rate adaptation for Quality of Service (QoS) provisioning in multi-hop CDMA networks where multiple classes are considered. We quantify the interference resulting from each class. Based on the requirements and the resulting interference from ongoing calls of each class, achievable data rate and bit error rate (BER) are determined. We quantify the level of degradation required of a given class to achieve a desirable QoS of the other class. Maintaining a certain QoS also requires power adjustment. To this effect, we propose a generic rate adaptation and power adjustment (RAPA) scheme. RAPA adapts the quality of classes within pre-determined thresholds. The operation of RAPA is illustrated through examples.

With the advent of voice over internet protocol (VoIP) service, assessing its voice quality is an area of intense research interest. Due to time-consuming and expensive natures of widely accepted subjective mean opinion score (MOS) test, objective methods are often used as an alternative. This paper presents applicability and accuracy analysis of several leading objective methods in quality testing. Particularly, the paper focuses on packet loss, which is one of major impairments in VoIP. A speech database covering typical packet loss conditions is designed. The subjective MOS test is conducted and the results are evaluated with objective MOS. The database is also used to verify a non-intrusive VoIP speech quality assessor the authors developed (El-Hennawey et al., 2006). The results show that perceptual evaluation of speech quality (PESQ) based algorithms are generally acceptable for quantifying the effects of packet loss. In addition, we also find that their performance is limited for codec G.711 without packet loss concealment (PLC).

The capacity of CDMA cellular networks is interference limited. Multi-hop communication promises to reduce interference, hence increasing capacity. However, such capacity gains depend on the actual interference. The location of a call determines its interference effect on the network. In this paper, we study the effect of call distribution on the capacity of multi-hop CDMA cellular networks. The capacity of multi-hop case is compared to that of single hop case. The effect of non-uniform call distribution is studied. It is shown that in the multi-hop case call distribution in a cell affects the capacity of this cell but hardly the capacity of its neighboring cells. The case is reversed in the single-hop case. Call distribution in a cell has no effect on the capacity of this cell, but it can have a significant effect on the capacity of surrounding cells. It is shown that if calls tend to originate near the border of one cell, this can seriously degrade the capacity of the whole network. This scenario is alleviated in the multi-hop case due to the shorter distances signals have to travel, resulting in lower interference. This paper also highlights scenarios where multi-hop communication is deeply needed.

Multi-hop cellular communication has been proposed mainly to overcome some of the short-comings of cellular networks. Increasing network capacity and reducing energy consumption are amongst the promised advantages of this concept. In this paper, energy consumption in multi-hop CDMA cellular networks is investigated with the objective of quantifying the resulting effect. Reducing energy consumption in CDMA cellular networks, using multi-hop communication, is shown to be possible. Furthermore, comparing multi-hop cellular networks with and without power control, it is shown that power control further reduces energy consumption - especially with small number of hops. The effect of varying the hardware specifications is also studied. It is shown that multi-hopping is more effective in environments with high path loss.

The Multi-hop CDMA cellular concept has been proposed to overcome cellular drawbacks, like congestion and load imbalance. Although it is a widely accepted that multi-hopping increases cellular capacity, it has never been quantified. In this paper, the capacity increase in multi-hop CDMA cellular networks is quantified. To this end, and since CDMA networks are interference limited, we derive equations for interference in multi-hop cellular networks at base stations (BSs) and relaying mobile terminals (MTs) in the uplink. The interference formulas are used to verify that capacity can be increased, by increasing either the number of simultaneous calls or data rate. A 10% increase in the number of simultaneous calls is shown to be possible even under worst-case scenarios. This increase is achievable while keeping interference at relaying MTs below acceptable thresholds. The novelty of this paper is that it quantifies, and for the first time, interference levels at MTs and BSs as well as potential capacity enhancements.

The concept of multi-hop CDMA cellular networks has been around for sometime now. It is a widely accepted assumption that using multi-hopping in cellular networks will increase the cellular capacity. This capacity increase has yet to be quantified. In this paper, this quantification is done, for the first time, for multi-hop CDMA cellular networks with power control. CDMA networks are interference limited. For this reason, interference is calculated at BSs and relaying MTs during an uplink slot, assuming power control is in use in all hops. The results for interference calculations show the possible increase in capacity, by increasing either the number of simultaneous calls or data rates. An increase of 23% in the number of supported simultaneous calls is shown to be possible even with relaying MTs being different than active MTs sending their own data. This paper derives formulas to calculate interference at BSs and MTs using power control in all hops. It also quantifies the potential capacity increase using multi-hopping with power control.