Under scope of the EXIGENCE SNS JU project, this paper presents the design and tests on two carbon-aware orchestration mechanisms — service throttling and spatial (workload) shifting — built directly into ETSI's Open Source MANO (OSM) platform. They validate it on a real-world edge/cloud video pipeline (Automatic License Plate Recognition for maritime port monitoring), and show up to 47.3% reduction in daily operational carbon emissions without breaking service requirements.

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.

Low Power Wide Area Network (LPWAN) has been one of the most widely applied wireless technologies supporting large-scale IoT applications in Industry 4.0 era. In which, 3 major LPWAN protocols play leading roles in actual deployments: Long Range (LoRa), NarrowBand-IoT (NB-IoT), and Sigfox. Currently, promoting interoperability among different LPWANs is regarded as an evolution for IoT constructions. Hitherto, there lacks of an industrial standard to produce multi-protocol LPWANs in systematical level, subsequently introduces challenges to interoperate LPWANs. To address this challenge, this article proposes an IEEE 2668 based LPWAN infrastructure to improve interoperability regarding the in-compliance issue raised by different LPWAN standards. Thus, an interoperable framework for LPWANs (IF-LPWANs) is presented to embrace and cooperate multiple LPWANs. The framework was tested to provide a 8.3% energy consumption reduction and a 13% PID transmission latency reduction. Such a development is the first of its kind in both industrial and research area.

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.

The edge computing market has been proliferating, which is foreseen to embrace more solution providers joining under the framework of 6G. Many research efforts exist, focusing on edge computing peer offloading among a single provider. However, there are limited discussions on multi-provider scenarios, which raises new challenge associated with providers' synchronization and property protection. Moreover, carbon footprint for multi-provider edge computing peer offloading will be even higher, which is also overlooked. Therefore, this paper provides a generic architecture and system model of full-decentralized on-demand edge computing peer offloading, specifically targeting multi-provider scenarios with providers' protection and carbon reduction. The carbon-aware peer offloading algorithm is formulated as an optimization process with Lagrangian modeling, which is physically embedded in edge computing servers, thus reaching consensus without data aggregation or property-sensitive data sharing. Simulation results reveal that the proposed algorithm achieves reduced carbon footprint, while maintaining a high quality of service.

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.

Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small‐scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL‐MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL‐MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL‐MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.

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.

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.

LoRa wide area network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications, given its long-range and low-power properties. Hitherto, there is no appropriate traffic model for LoRaWAN to estimate the heterogeneous arriving traffic at the network server cluster (NSC). Inefficient computation power planning or even processing failure might be further caused. Radio replication, commonly existed in the arriving traffic at NSC in LoRaWAN, also causes difficulty estimating the makespan (i.e., mean processing time in NSC). To overcome the abovementioned limitations, a heterogeneous radio-replication-aware traffic aggregation model is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Furthermore, a nondominated sorting genetic algorithm based on multiobjective optimization is developed to simultaneously minimize cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with four radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.

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.

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.

The received signal strength (RSS) finger-print-based approaches are widely used for indoor location-based services (LBSs). The emerging long range wide area network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant LBSs attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, in this article we propose the extreme RSS (ERSS) to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the ERSS is further defined to cultivate a highly stable and robust fingerprint database which withstands environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-m localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.

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.

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.

With the rapid exhaustion of fossil energy, electric vehicles (EVs) become one of the key candidates for the next generation of transportation. Increasingly perfect technology developed makes EVs grow significantly. Therefore, Charging Stations (CSs), as accessories and necessities of EVs, should form a network with optimal planning. Inappropriate CS network design could cause series of negative effects to the popularization of EVs, the layout of the city traffic network and the financial cost of CS network construction, etc. Besides, the charging infrastructure planning for cities with large population and high EV density becomes even difficult. In this paper, an Effective Planning of CSs Network (CSN) is proposed. Comprehensive environmental elements (e.g. cities' and CSs' information, EV charging status, etc.) are considered in CSN. The CSN deals with complicated city planning in giant cities (i.e. large population, high EV density, etc.). Hong Kong is selected as the case study because it can be regarded as a typical giant city. Results show that the proposed CSN can ensure the EVs can find a CS before it is out of power. Besides, the CSN saves ~18% financial cost for the charging infrastructure planning in giant cities.

Intelligent sensing and actuation in building applications has been considered by many Heating, ventilation and air conditioning (HVAC) experts. Wireless Sensor Network (WSN) performs as an efficient communication tool for human comfort (e.g. indoor thermal control). In this paper, an analysis of Packet Error Rate (PER) in WSN for HVAC system was implemented. Based on the collected PER information, a WSN network was designed and built for achieving low PER. The network provided guidance for large-scale WSN design in HVAC systems.

A new network applies on high traffic ZigBee based wireless network. The new network incorporates multi-radio multi-channel technology and improves the efficiency of data transmission. The latency performance of the proposed network is analyzed by OPNET.

Healthcare problems is a popular topic nowadays in both the market and the research area. The transmission of the healthcare data is one of the most urgent topics that requires to be solved. Hence, in this paper, a new ZigBee based wireless sensor network is designed under the hospital environment. The network is under beacon mode in the MAC layer to ensure the security issue. The network delay and the battery consumption of the devices are considered as the key objectives that need to be achieved. Therefore, Evidential reasoning method is adopted as the method to give trade off solutions for the network. The simulation shows that the network can perform better on the delay and battery consumption when count of backoff=2 and backoff index=1. Therefore, the multi-criteria making method is very suitable for this model.

Indoor Air quality (IAQ) is an urgent topic that worsens because of several of pollutants in the product indoor. World Health Organization (WHO) has standardized the harmful level indicating the potential health risk caused by hazardous substance. The conventional schemes consider single substance only which have no significant contribution to IAQ practically as IAQ depends on lots of parameters. As a result, a new comprehensive control scheme which deal with the IAQ and indoor environmental controlling, is designed.

The application of localization in healthcare system is a crucial topic which helps to locate the position of patent or the elderly in case urgency happens. From this aspect, a wireless technology is adopted to provide an efficient localization monitoring system for patients or the elderly in indoor area. The location of patients can be obtained through the developed algorithm. Fuzzy C-Means clustering (FCM) is one of the applicable techniques to locate the position of patients. However, low accuracy of FCM is the main problem. For this reason, the revised FCM localization algorithm, Calibrated Fuzzy C-Means Clustering Algorithm (C-FCM) is proposed in this investigation based on received signal strength (RSS) in wearable device. The proposed algorithm is evaluated through experiment and it has a percentage improvement of 14% compared with FCM.

World Health Organization informed that traffic accidents potentially become the 5thleading cause of death if there is no effective way to restrict drunk driving. It is reported that 51 million people are injured or dead because of the traffic accidents every year. These traffic accidents lead to the expenditures of $500 billion dollars. Among these traffic accidents, drunk driving is one of the leading cause that drunk drivers can be found in 40 % of total traffic accidents. To protect the public from drunk driving, drunk driving detection (DDD) is considered as one of the effective ways. Among various types of DDD, electrocardiogram-based (ECG-based) detection can provide real-time monitor and response. In this paper, ECG-based drunk driving detection scheme was proposed. Among various types of DDD, the proposed work is able to provide early detection and fully automated detection with satisfied accuracy.

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ijand the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ijand Kconv,ijby more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

To meet the requirements on data collection from wireless sensor network for healthcare application especially in hospital, the performance of wireless communication is an important issue. In particular, simultaneous transmissions from numerous sensors will cause serious collision which leads to transmission packets loss and delay. In this paper, a management scheme, multi-criteria decision making method using TOPSIS, for IEEE 802.15.4 is proposed for wireless sensor network. The performance of the network is determined by beacon order, superframe order, contention window, number of backoffs and backoff exponent. By analyzing slotted CSMA-CA mechanism, which is in the beacon-enabled mode, through OPNET, the proposed scheme can estimate the best combination of the parameters. The results show that the proposed scheme achieves the best combination of low end-to-end delay, high throughput and high successful probability.

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

This paper proposes a novel method for detecting the Parkinson's diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinson's diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinson's diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinson's diseases, our proposed method allows to use a flexible number of features for detecting the Parkinson's diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here in this paper, we proposed a framework of employing IoT technique to construct a free time navigation system. The system aims at providing a real-time quantification of traffic conditions and suggests optimal route based on the information retrieved. The system can be basically separated into two major components: (i) the traffic condition estimation module and the (ii) real-time routing algorithm. In the first component, traffic conditions of roads will be estimated based the information collected from sensors installed on vehicles. Based on these location and speed information, the traffic condition can be quantified using a weighted kernel density estimation (WKDE) function. This function is a function of time and provides a real time insight of the overall traffic condition. By combining this information and the topological structure of the road network, a more accurate time consumption on each road can be estimated and hence enable a better routing.

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.

Log-distance path loss model have been using as a simple positioning method because of its simplicity which is mainly depends on Received Signal Strength (RSS) along with path loss exponent and random Gaussian noise variable with zero-mean. This model can be extensively used in urban and remote area because it is related to energy representation. On the other hand, fingerprint positioning is also an alternative solution in positioning due to it reliable performance. It is noticed that antenna gain and background thermal noise should be included into the model such that the accuracy could be improved. In this investigation, a sub-urban route was selected as testing area and carried the Particle Swarm Optimization (PSO) for an optimal coordinate. Experimental results show that the new scheme was implemented successfully in RSS positioning resulting in about averaged 100 meters and 84 meters in daytime and evening time respectively in same experiment scene. This new scheme provides a more reliable way in calculating a sensors position.

A multi-interface ZigBee building area network (MIZBAN) for a high-traffic advanced metering infrastructure (AMI) for high-rise buildings was developed. This supports meter management functions such as Demand Response for smart grid applications. To cater for the high-traffic communication in these building area networks (BANs), a multi-interface management framework was defined and designed to coordinate the operation between multiple interfaces based on a newly defined tree-based mesh (T-Mesh) ZigBee topology, which supports both mesh and tree routing in a single network. To evaluate MIZBAN, an experiment was set up in a five-floor building. Based on the measured data, simulations were performed to extend the analysis to a 23-floor building. These revealed that MIZBAN yields an improvement in application-layer latency of the backbone and the floor network by 75% and 67%, respectively. This paper provides the design engineer with seven recommendations for a generic MIZBAN design, which will fulfill the requirement for demand response by the U.S. government, i.e. a latency of less than 0.25 s.

Dual Radio Streaming ZigBee Homecare Gateway (DRS-ZHG) was devised and implemented to support remote medical services. The novelty of DRS-ZHG is increases the transmission data rate of ZigBee. Consequently, the DRS-ZHG design furnishes low latency and highly accurate telehealth service at home. More important, Zero packet loss has been recorded during the functional testing of DRS-ZHG,