\r\n\tDiagnosis and management of complications while on ECMO therapy and weaning to recovery or advanced therapies will be also discussed.
\r\n\r\n\tChapters focusing on specific patient populations, such as cardiogenic shock, thoracic organ transplantation, trauma, and neonates, Covid-19 syndrome, will provide insight into the particular challenges in dealing with the unusual problems of these very diverse groups.
\r\n\r\n\tThe goal of this book is to provide, thanks to the thorough contributions by known experts in the field, a framework for successful program development. Hopefully, this text will also inspire others to further advance this delicate field.
",isbn:"978-1-80356-549-1",printIsbn:"978-1-80356-548-4",pdfIsbn:"978-1-80356-550-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"254c18981115aeda50bdf71829902141",bookSignature:"Dr. Antonio Loforte",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11718.jpg",keywords:"Heart Failure, Cardiogenic Shock, Respiratory Failure, Circulatory Failure, End-Organ Dysfunction, VA-ECMO, VV ECMO, Central ECMO, ECMO Running, Weaning off ECMO, Adverse Events While on ECMO, Survival on ECMO",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 10th 2022",dateEndSecondStepPublish:"April 7th 2022",dateEndThirdStepPublish:"June 6th 2022",dateEndFourthStepPublish:"August 25th 2022",dateEndFifthStepPublish:"October 24th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Loforte is a dedicated and pioneering researcher in the surgical treatment of advanced heart failure in terms of LVAD, BVAD, ECLS, and TAH adoption in different clinical scenarios. 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These include indoor air quality (IAQ), lighting, thermal comfort, acoustics, drinking water, ergonomics, electromagnetic radiation, and many related factors [1], as depicted in Figure 1. Enhanced environmental quality can improve the quality of life of the occupants, increase the resale value of the building, and minimize the penalties on building owners.
\nIEQ components.
IEQ in offices and other workplaces has a crucial role on the return on investment of businesses. A workplace with high IEQ obviously improves the workers’ health and mood, thereby increasing their productivity. Therefore, the additional cost of maintaining high IEQ levels in workplaces will be paid back in a reasonable period and generates additional monetary returns thereafter. It should be noted that buildings being rated as “sustainable and green” do not truly guaranty their compliance with the desired IEQ level [2, 3, 4, 5]. Therefore, IEQ should be given specific focus while designing new buildings as well as in building retrofit plans.
\nIndoor air quality (IAQ), which depends on airborne contaminants inside a building (or in a broader sense, any other enclosure such as a vehicle or an animal house), is one of the crucial factors that determine the quality of the indoor environment. Providing adequate air quality for the occupants is one of the most important functionalities of a building. Lung cancer (due to radon), Legionnaires’ disease, carbon monoxide poisoning, allergy, and asthma are among the serious health implications of poor IAQ [6]. The “sick building syndrome” resulting from inadequate levels of IAQ significantly affects the health and productivity of office employees [7]. Though tremendous efforts are in progress to realize energy-efficient, green, and sustainable buildings, maintaining a safe level of IAQ in these buildings is an ongoing challenge. This is due to the fact that many energy-efficient measures in a building (such as reduced outdoor air ventilation rate, increased thermal insulation, and efficient cooling equipment) can have a detrimental impact on IAQ. Thus, alongside energy efficiency and sustainability, there has been a growing concern over air pollution inside buildings. Therefore, attempts to ensure energy efficiency and sustainability in buildings should simultaneously ensure enhanced health, comfort, and productivity of the occupants [6].
\nThere are two major approaches to tackle IAQ issues in buildings: one is to increase the ventilation rate of outdoor air into the building, and the other is to minimize or control the sources of air pollution within and outside the building. Having said that, the first strategy would work only when the outdoor air is clean enough to improve IAQ [7]. The various sources that affect IAQ are, but not limited to, volatile organic compounds, biological pollutants, oxides of carbon and nitrogen, particulate matter, tobacco smoke, radon, mold, formaldehyde, pesticides, and combustion products. Heseltine and Rosen [8] outlined health issues associated with building moisture and biological agents, and the most important health problems identified are respiratory symptoms, allergies, asthma, and perturbation of the immunological system. A recent review [9] has revealed that carpets play a crucial role in IAQ, as they act as a sink for indoor air pollutants such as particles, allergens, and other biological pollutants.
\nThe term “thermal comfort” refers to a condition that is governed by many environmental and human factors; in other words, physiological, physical, and sociopsychological factors. The environmental factors include air temperature, air velocity, humidity, radiant temperature, and relative humidity, while the major human factors are clothing and metabolic heat. The various other factors include physical health, mental condition, availability of food and drink, and acclimatization. This condition is mostly subjective, which cannot be directly quantified. It has been established that the thermal comfort level is acceptable if at least 80% of the occupants feel comfortable with it. Djongyang et al. [10] and Taleghani et al. [11] provided detailed insights into the thermal comfort in buildings.
\nVisible light falls in a narrow range in the electromagnetic spectrum, between ultraviolet and infrared wavelength ranges. Light has both particle and wave properties; when treated as a wave, light has a frequency that depends on the color of the struck surface. For instance, white surface reflects back most of the incident light, while a black surface absorbs most of it. The main aspects of lighting comfort are light level (intensity or brightness), contrast, and glare. The light intensity requirement depends on the type of activity in the building; for instance, operating rooms need a brighter level than living rooms. The term “contrast” refers to the ease of understanding or legibility; higher contrast gives higher clarity (e.g., black text on white paper provides the highest contrast). Glare is always undesirable as it causes a high level of discomfort in viewing the objects and affects the retina.
\nThe visual comfort level is evaluated by means of some established glare metrics or indices; for example, glare probability (DGP) and daylight glare index (DGI) are used for assessing discomfort due to daylighting, while unified glare index (UGI), visual comfort probability (VCP), and CIE glare index (CGI) are employed for measuring the discomfort level of artificial lighting [12, 13, 14, 15]. Several other indices are also available, as summarized by Carlucci et al. [12]. Galatioto and Beccali [16] reviewed the various aspects and concerns associated with the assessment of indoor daylighting.
\nBuilding acoustics deals with controlling the quality of sound inside a building. It has two parts, namely, room acoustics and building acoustics, which deal with the sound propagation within a room and between rooms (through walls, doors, and floors), respectively. While the room acoustics focuses mainly on the sound quality (e.g., easy communication and high level of intelligibility in office spaces), the building acoustics is concerned with the “unsolicited” sound (e.g., the noise in a room should not be a nuisance to other rooms). The acoustic comfort in a building has a crucial impact on the health, well-being, communication, and productivity of the occupants. The acoustic comfort can be affected by factors such as the geometry and volume of a space, generation of sound within or outside the space, airborne noise transmission, impact noise, and the acoustic characteristics (absorption, transmission, and reflection of sound) of the interior surfaces. The measuring unit of sound intensity is decibels (dB), and of sound pitch is hertz (Hz). The comfortable range of sound for humans is typically 20–20,000 Hz.
\nThe common parameters used for evaluating the acoustic performance of a building are reverberation time (RT), sound pressure level (SPL), early decay time (EDT), clarity (C50 for speech and C80 for music), sound definition or speech intelligibility (D or D50), and speech transmission index (STI). RT is defined as the time for the sound level to decay by 60 dB after a sound source has been switched off. EDT is similar to RT, but it is the initial rate of sound decay in a room, measured as the slope of a line 0–10 dB decay below the maximum sound level. D50 is defined as the ratio of the early received sound energy (0–50 ms after direct sound arrival) to the total received energy. Clarity is defined as the ratio of the energy in the early sound (received in the first 80 ms) to that in the reverberant sound. STI is a measure of speech transmission quality, which indicates the degree to which a transmission channel degrades speech intelligibility. STI ranges from 0 to 1; a speech transferred through a channel with STI of 1 is perfectly intelligible, but the intelligibility reduces as the STI approaches zero. International standards and guidelines (e.g., ISO 18233) are available for the measurement of these characteristics.
\nExtensive researches are in progress, on the acoustic comfort in buildings. In recent works, Tong et al. [17] studied the acoustical performance of classrooms and laboratories in a public school exposed to traffic environment, while Jeong et al. [18] focused on the acoustic design and evaluation of a concert hall. Tan et al. [19] introduced application of building information modeling to improve indoor acoustic performance. Few other studies include those reported by Lam et al. [20], Imran et al. [21], and Renterghem [22].
\nErgonomics deals with the design of objects, systems, and environment, in a manner that ensures human comfort. In fact, ergonomics encompasses all components of IEQ, simply because the prime objective of IEQ is human health and comfort. It covers diverse disciplines such as anatomy, physiology, psychology, and design. An indoor ergonomist should be specialized in the interrelationship between the human mind and body and the various aspects of a building such as architecture, interior design, building services, structure, materials, and microclimate. In general, environmental ergonomics deals with the interaction between people and their physical environment with particular importance on thermal comfort, lighting, noise, and vibration. Similar to ergonomics in a residential environment, ergonomics in offices and workplace is also a scientific discipline and a topic of research. Edmonds [23] defines the following factors that affect the workplace ergonomics: tasks, tools, equipment, area and space, environment, and organizational pattern. The Southeast Asian Network of Ergonomics Societies (SEANES) has introduced ergonomic checkpoints for indoor and outdoor workplaces for the purpose of motivating workers to recognize hazards in the work environment and adopt precautionary measures accordingly [24]. Similarly, Ushada et al. [25] developed environmental ergonomic control system for small and medium sized, by using worker workload and workstation temperature difference.
\nElectromagnetic field is created by moving electric charges, microwaves, radio waves, electrical currents, and transformers. The low-frequency electromagnetic radiation prevailing mostly in indoors (due to electrical appliances, computers, wireless devices, etc.) can have detrimental effect on human health, and there are international regulations to deal with this problem (e.g., International Radiation Protection Association (IRPA)) [26]. Most of the regulations agree that exposure to electromagnetic field beyond the safe range of 0–300 Hz is harmful for the human body [27]. The possibility of health hazards such as acute lymphoblastic leukemia in children due to electromagnetic field exposure was well established decades ago [28] and continues to be a significant topic of research [26, 29, 30, 31].
\nAdequate, safe, and accessible supply of drinking water is vital for the sustenance of human life especially in indoor environments where access to natural sources of water such as wells, ponds, rivers, and lakes is limited. Drinking water quality has a direct impact on human health. Infants, young children, weak and elderly people, and those who live in unhygienic environment are largely prone to waterborne deceases [32]. There is no universally applicable legislative framework for the implementation of standards to maintain drinking water quality. An approach that works in one country or region may not be suitable for other countries. Therefore, each country should develop its own legislation according to its requirements and capacity for implementation. However, while developing standards, the most common aspects that need to be taken into account are microbial safety, chemical safety, radiological safety, disinfection, and acceptability [32].
\nA huge number of literatures are available on the research on various aspects of IEQ , and a comprehensive review of these literatures is beyond the scope of this chapter. Many researchers have compiled them in their review articles [7, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]. However, a brief overview of the exemplary researches is presented here. Most of the researches were on post-occupancy evaluation (POE) on IEQ of different types of common buildings (e.g., healthcare, office, educational, residential, etc.), through field measurements and user satisfaction surveys, while many other researchers were interested on POE of sustainable and green buildings. In these researches, the findings are usually compared with the prevailing local or global (as applicable) standards, and recommendations are made to address the issues identified.
\nReynolds et al. [43] measured the physical, mechanical, and environmental factors affecting IEQ of office buildings in the United States (US). The measurements included endotoxin, total bioaerosols, and psychosocial parameters. Addressing the impact of IEQ on the occupant’s productivity in offices, Kang et al. [44] investigated open-plan research offices in 19 Chinese universities by conducting survey on 231 subjects. The study identified five factors that significantly affected the office productivity, which are layout, air quality, thermal comfort, lighting, and acoustic comfort, where the acoustic comfort had the maximum impact. In a similar study [45], experiments were performed on the effect of indoor temperature on the IEQ user perception and productivity in office buildings, by choosing 9 females and 12 males. The parameters measured were air temperature, globe temperature, relative humidity, carbon dioxide (CO2) concentration, and lighting and noise comforts. The indoor air temperature was varied by keeping the other IEQ parameters fixed. It was shown that the thermal environment had a significant impact on the thermal comfort and other IEQ factors. Kim et al. [46] focused on the impact of IEQ and work stress on the physiological responses of office workers and concluded that the most noticeable result of the experiment in this study is that a high CO2 concentration and work stress could detrimentally influence the physiological and physiological responses, leading to abnormal variations in blood pressure. Similar studies on the effect of IEQ on office workers’ performance are those reported by Haapakangas et al. [47], Suk [14], Zuo and Malone Beach [48], Ali et al. [49], Huang et al. [50], Frontczak et al. [51], Wong et al. [52], and Kosonen and Tan [53, 54].
\nAlmeida and De Freitas [55] performed onsite measurements of temperature, relative humidity, CO2 concentration, and ventilation rates in the classrooms of nine retrofitted and non-retrofitted school buildings in Portugal. The measurements were done during winter, mid-season, and summer conditions. In their observations, the non-retrofitted schools lack in the desired IEQ level, while retrofitted buildings did not have mechanical ventilation systems. Shan et al. [56] investigated the influence of indoor thermal condition and IAQ on students’ health and performance through life cycle costing (LCC) approach, by considering two university classrooms. In the proposed LCC approach, metrics were defined for students’ health (or well-being) and performance, which were subsequently translated into monetary values to quantify the impact of IEQ. The indicators considered for health and performance were sick leave and students’ grade achievement, respectively. The findings of this study indicated the significance of incorporating students’ health and performance into the design and operation of educational buildings. Few other researches focusing on educational buildings are those of Kim et al. [57], Vilčeková et al. [58], Jamaludin et al. [59], De Giuli et al. [60], and Nasir et al. [61].
\nLai et al. [62] developed an IEQ assessment model for residential buildings in Hong Kong. The empirical model developed by using the data collected from 125 occupants from 32 residential buildings was useful to assess the acceptance level in terms of operative temperature, CO2 concentration, and acoustic and lighting comforts. The study revealed that both thermal and acoustic comforts were the decisive contributors, while IAQ was the least. Huang et al. [63] studied the effect of IEQ of long-term care (LTC) facilities on the occupants’ behavior, through survey. Garcia et al. [64] performed retrospective descriptive secondary analyses on the data collected (air exchange rates, temperature, and humidity) from indoor, outdoor, and personal air in residential buildings. Addressing the IEQ of healthcare buildings, Andrade et al. [65] performed user perception survey on hospital buildings in Portugal, considering physical and social aspects. De Giuli et al. [66] conducted survey and field measurements of three medical wards in a general hospital in Italy.
\nAs already mentioned, the IEQ level of sustainable and green buildings has been a concern of many researchers. Choi [67] proposed an explanatory model to understand the relationships among the occupants’ perceptions on the IEQ level, overall facility, productivity, and sustainability ethic, in sustainable buildings. Hwang and Kim [68] performed post-occupancy evaluation (POE) of open offices in a Korean building that was certified as “1st Grade Building” Green. The studied parameters were indoor temperature, relative humidity, vertical temperature distribution, air velocity, predicted mean vote (PMV), radiant temperature, outdoor temperature, and humidity. Measurements were also done on the major indoor air contaminants, illuminance, and SPL. An online survey was also conducted among the occupants to know their perception on the IEQ level. The performance of this building was found to be satisfactory in terms of PMV and lighting, while it was weak for IAQ and acoustic comfort. Ravindu et al. [69] explored the IEQ level of a LEED-certified factory building in Sri Lanka, through questionnaire survey. They found that the building was performing low with regard to thermal comfort, ventilation, and ability to control indoor the environment. Altomonte et al. [3] studied the occupant satisfaction on IEQ in LEED- and BREEAM-certified office buildings and highlighted the importance of incorporating IEQ in the criteria for sustainable and green building certifications.
\nIndoor environmental quality is a very important scientific domain that deals with various aspects that govern the health, comfort, and productivity of the occupants and determine the value of a building. However, even though there is increasing awareness on the demand for sustainable, green, and high-performance buildings, ensuring the desired level of IEQ is often not given the deserving care. Consequently, most of the sustainable and green buildings lack in complying with the IEQ requirements. The building owners should rewrite their mindset to take into account the enormous potential for monetary returns and health benefits through improving the IEQ of the building. The following good practices are generally recommended to ensure a comfortable level of IEQ:
Follow scientific practices of design, construction, renovation, operation, and maintenance, in compliance with the international standards.
Adopt “source control” by minimizing the causes that lead to poor IEQ.
Enhance the esthetics and indoor environment by proper integration of natural and man-made facilities.
Minimize the dependence of artificial lighting and electrical equipment such as air conditioner, elevator, and fans, with a view to improve human health and minimize energy consumption.
Ensure thermal comfort through proper design of the interior and microclimate.
Facilitate proper ventilation and maintain acceptable air quality, by following standard guidelines.
Adopt proper design and maintenance of HVAC system, and proper design and construction of the envelope, to prevent mold, fungi, airborne bacteria, and radon.
Minimize the spread of pathogens by minimizing exposure to washrooms and by proper maintenance procedures.
Avoid using products and materials, which contain harmful ingredients (such as formaldehyde) and produce harmful emissions.
Ensure noise comfort and privacy, by suitably adopting the materials for walls, floors, and ceiling, and other standard means for acoustic comfort.
Avoid unpleasant odors through selective use of products, regular and safe waste disposal, careful selection of cleaning products, isolation of contaminants, prohibition of smoking, and related measures.
Establish a comfortable and healthy indoor lighting, through optimum integration of artificial and natural lightings, and use of energy-efficient, user-friendly, and eco-friendly artificial lighting.
Maintain availability and accessibility of safe and clean drinking water in compliance with the water quality standards.
Restrict and be aware of exposure to electromagnetic field and radiation, in the indoor environment.
Ensure indoor ergonomic quality by providing ergonomic furniture and other facilities.
Regularly conduct occupant surveys and post-occupancy evaluations.
In the last decade, industrial manufacturing such as healthcare, smart grids based on Cyber-Physical Internet of Thing Systems (CPIoTS) has been widespread [1]. In this context, IIoT network, which is characterized by the unified network physical layer, the QoS constraints, the autonomous connection requirements, is considered one of the key issues. The rapid increase in data amounts with diverse QoS requirements [2] brings several challenges in order to meet the complex requirements, as well as resource and QoS requirements with high data rate and low latency. In fact, the advanced 5G technology has a significant potential to provide IIoT QoS satisfactory [3]. With their architectural approaches which are founded on a unified physical layer, addressing the diverging performance requirements in terms scalability and availability still a hot challenges topic. Today’s drastic digital transformations empowered by emerging technology like Edge Computing, Software Defined Networking (SDN), Network Function Virtualization (NFV), and LoRaWAN can bring smart services for network candidates [4]. Network slicing (NS) is the key solution that provides smart service’s connectivity with diverse QoS requirements. Using deep RL at each LoRa agent in the environment. Each agent considered to be a Deep Q-learning (DQL) brain interacts with the environment to find the best action on their parameters that brings the best reward. In addition, it introduces the FL approach to provide better RL based action on each agent, to maximize QoS, and hence throughput revenue. However, NS provides the network availability as a service following the slice instances exploiting NFV and SDN [5]. In this context, a Mini Batch GD and GMM framework is proposed in [7] to provides radio resources for the virtual slice member. In addition, a LoRa network slicing technique based on Maximum Likelihood Estimation proposed, in [6], to allocate network resources in inter and intra mode. Meanwhile, recently supervised learning approach-based resource allocation is also proposed to manage network resources, but due to the training data unavailability or the high computational training process, are not appropriate for large-scale network and cannot satisfy dynamic slices requirements.
RL technique can improve efficient resource management by interacting with the environment, in which Q-learning is the widely used. The RL agent learns the association between taken action and the received feedback in terms of reward. It follows a policy, which is updated according to the maximized revenue via several action series. Therefore, high-quality policies building in a centralized network architecture faces a major challenge, especially when the space of state features is restricted. To deal with these issues, Federated Learning (FL) has been suggested as a decentralized tool for machine learning, which is designed to be a global learning system. In this context, the aim of this work is to propose a deep federated reinforcement learning (DFRL), to equip the slice member with the required channel resources, by tuning LoRa TP and SF parameters [8].
The leftover of this chapter is organized in six sections. Section 2 presents the related work of this chapter. In Section 3, we give a brief overview on the Industrial IoT, the federated learning, and the network slicing. We highlight, in Section 5, the proposed slicing architecture and the system model. After that, in Section 6, the relation between wireless sensor network (WSN) and the IIoT is well highlighted. Next, the proposed slicing resource reservation-based DFRL framework is presented in Section 6. Section 7 evaluates the simulation results. Finally, Section 8 concludes the chapter.
Recently, several articles have investigated the many challenges typical of the network slicing approach. In particular, in [9] the authors propose an online auction algorithm to realize a resource allocation framework, capable of guaranteeing the diversity of services to users and high levels of social welfare. Differently, the work in [10] deals with a new resource allocation framework to automatically and automatically size the capacity and size of network slices. In this chapter, resource partitioning is done based on both available network bandwidth and LoRa configurations parameters resulting in an optimal trade-off between traffic and network aspects. The authors of [11] focus on the design and implementation of a dynamic slicing sharing system to ensure minimum user throughput requirements. Therefore, it addresses three sub-issues: admission control, resource allocation, and user abandonment issues. The contextualization of the placement of VNFs to the network slicing problem is presented in [12], where, in particular, the topological information of the network is exploited to provide an appropriate deployment of functions, with regard to different service classes. Moreover, the placement problem of VNFs has also been addressed in [13], which considers the function decomposition and the sub-functions sharing, a profitable heuristic algorithm is proposed based on Linear integer programming formulation of the VNF placement problem. Further, in the work [14], a formulation of mixed integer linear program is exploited to process the number identification of VNFs to use, which aims to meet specific service requirements. To solve the placing VNFs problem in a federated cloud, the coalition formation game is proposed in [14]. Alternatively, a Pareto analysis of the VNF placement problem is the subject of [15]. In [16], a network partitioning policy is developed to take into account the social well-being and the supplier profit of the network. Finally, special cases for the VNF placement problem are discussed and analyzed in [17].
The problem of bandwidth slicing in software-defined networks is studied in [18], where price spikes are exploited to indicate the presence of traffic spikes and network congestion. This work provides a time-based price analysis combined with a Stackelberg game, in which the gain of SP Internet is the gain of income. In a different way, the work cited in [19] studies the correlation between the network slices size and the resource pricing strategy. In addition, an algorithm to vary the prices is proposed by the authors in [19], with the aim of maximizing both the customer profit and the SP. Although FL has not been used in the field of network slicing research, FL has recently reached attention and several papers have presented its use, the methods cited in [20, 21] being prime examples of’such a branch of literature. In [20], a new aggregation data scheme for wireless computation is proposed. The strategy exploits the signal overlay property of the wireless channels. Differently, articles [22, 20] focus on maximizing the number of devices involved in the aggregation process, also taking into account the minimization of aggregation error. Thus, it contextualize the FL in an MEC system and apply the distributed gradient descent method to identify the best compromise between local updates and global aggregations, aiming to minimize the loss function, in taking into account certain resource constraints. Likewise, the article in [21] analyzes the MEC environment and presents the application of hybrid filtering on stacked encoders to predict fluctuation in file popularity in the content caching problem. Moreover, the article cited [23] modifies the proposed, federated averaging algorithm with the stochastic gradient descent algorithm, to train the data in a distributed way, thus reducing communication costs. The multitasking learning problem is studied in the work cited in [24], authors proposed a new Mocha contextual optimization approach that used in combination with the FL system. The work cited in [25] analyzed the End-to-end delay in a blockchain framework, in which an FL blockchain structure is developed to perform a distributed consensus strategy. In order to improve the transmission and computational costs in a hybrid IoT-MEC network, authors in [26] proposed to use the FL powered by the multiple deep reinforcement learning agents. In addition, ultra-dense scenarios are also considered in [27], where an approach based on the technique of deep learning of short-term long-term memory is applied to forecast local network traffic in order to avoid congestion.
This chapter aims to address the problem of network slicing using deep federated RL at each LoRa agent in the environment. Each agent considered to be a Deep Q-learning (DQL) brain interacts with the environment to find the best action on their parameters that brings the best reward. In addition, it introduces the FL approach to provide better RL-based action on each agent, to maximize QoS, and hence throughput revenue.
The development and evolution of modern information and communication technologies lead us to the Fourth Industrial Revolution, in which the Industrial Internet of Things (IIoT) is supposed to be one of the key aspects to realize the industry 4.0. With an unprecedented increase in the number of Internet of Things (IoT) devices and emerging applications, a large amount of traffic is created every day. Such an increase represents a heavy load on the Internet network and also requires significant investments for the upgrade of the infrastructure. However, with the development of big data analysis and artificial intelligence (AI) techniques such as deep learning (DL) and machine learning (ML), the data collected can be effectively exploited for many purposes. From a communication point of view, the last few years have seen the emergence of AI applications in various fields. For example, ML is used to study efficient antenna selection in multi-antenna wireless systems [28], DL is used to handle the computational offload problem in IoT systems with edge computing [29], and Deep reinforcement learning (DRL) is used to optimize resource allocation issues at the edge of the network, such as traffic classification, edge caching, network security, and data offload [30]. However, conventional AI models generally require central processing of the data collected from all users on the network, i.e. users have to upload their own data to a central server to train the learning model. However, a key concern with central learning is data privacy, i.e., some users want to keep track of their local data and do not want to transmit their local data to the central server. Training the learning model centrally requires a central cloud with extremely powerful compute and storage capabilities. Meanwhile, recent advancements in computer hardware and the proliferation of smart devices in our daily lives have shown that every IoT device can be equipped with reasonable levels of compute and storage, which is closely comparable to a desktop computer there was. is 10 years old [31]. Therefore, the standard ML model is not easily applicable to large scale IoT networks and cannot exploit the availability of distributed computing. This requires a new learning model that leaves training data distributed across individual IoT devices instead of being centralized.
Motivated by this problem, Google invented the concept of federated learning (FL) for on-device learning and data privacy preservation [32]. Using the FL approach, each IoT device can train its model based on locally collected data. Local data from IoT devices does not need to be sent to the centralized cloud. The centralized cloud only needs to collect the updated local training model from individual users. Due to its characteristics, FL has been adopted in many applications, for example FL for improving Google keyboard suggestions [33], FL for healthcare [34], and FL for smart city detection [35]. To illustrate the concept of FL, an overview of FL in IoT systems is shown in Figure 1. In general, each IoT device has its own set of data and the aggregation server can either be located at the edge of the network or in a virtual cloud in the remote cloud computing system [36]. Each FL model has its own advantages and disadvantages, depending on various factors. For example, FL with the server at the edge of the network is suitable for applications requiring low latency, location awareness and contextual information on the network while cloud-based FL is suitable for applications with IoT devices massive over multiple regions and computing power requirements/storage capacities.
An overview of FL in IoT systems.
Recently, the integration of DL models with IoT and edge devices has become more popular, which provides real-time analytics with limited resources [60]. Thus, Federated DL (FDL) allows Industry 4.0 companies to integrate DL into IoT devices and provides a secure framework using FL, as shown in Figure 2. DL is computationally expensive, which requires resources and an expensive framework. Thus, the decentralization of DL models is a multidimensional problem that requires a framework of new technologies to integrate DL with advanced computing and the IIoT. The main goal of FDL is to provide the IIoT with advanced capabilities using optimized DL that would turn Industry 4.0 factories into smart factories. Some of the parameters required to create the FDL model in IIoT are the FDL model, FDL networking, FDL security, and FDL optimization.
Federated DL in IIoT.
An FDL model can be implemented on both client side and server side. On the client side, private networks are defined, the DL model of which is tuned and optimized from the general model present in the cloud. Optimized and fitted models are then deployed on the client side, where the model is trained with data generated locally from the end device. Finally, the final device contains a highly quantized and compressed FDL model. On the server side, the model in the cloud is continuously updated by differentially integrating the gradients of each private network. Each local DL network in turn is responsible for continuously uploading and uploading the currently updated gradients to the cloud model. Thus, a distributed selective stochastic gradient descent approach is presented in [37] which can be applied in the cloud model to frequently update the local private model. The first decentralized model called”Model chain” uses blockchain technology [38, 39] to allow the preservation of confidentiality in the transfer of data. In addition to this, asynchronous stochastic gradient descent can also be used when a single model can be trained in parallel among all devices, aggregated and processed.
Regarding FDL communication and networking, is that the main benefit of using FDL is to run DL models in IoT devices and involve the model in the decision making process. This type of decentralized DL process improves the robustness, operational efficiency and reliability of IoT devices. FDL provides two types of communication, namely intra-communication channel and inter-communication channel. Train transmits data between all levels of the framework. FDL communicates between the IoT and the cloud tier where the cloud-optimized model is deployed on the end device. However, security and confidentiality must be maintained in the FDL during communication. In inter communication channels, the components of each layer communicate with each other in three different ways, such as cloud, edge, and end device. The main objective of FDL is to minimize intra-communication and to maximize inter-communication, which would greatly reduce the cost of communication. By the way, to maintain privacy and security, FDL builds DL models that do not expose information about the data to the cloud. Security issue on the server side includes sharing of DL models on the cloud that leads to confidentiality and security risk. Security issue on the client side is done by encrypting the data during the training process before sending it to the cloud server. Some mechanisms and homomorphic encryption technique controls the amount of data to be shared on the cloud. Since peripherals have limited memory and computational requirements, DL models must be optimized so that they can be deployed to IoT or peripherals efficiently. In terms of hardware optimization, the GPU provides low-power computation that reduces computation time. The FPGA and Google’s Tensor Processing Unit [40] are other DL devices that enhance DL network processing. In terms of memory optimization, algorithms such as shared memory allocation algorithms for DL models can be used. Dynamic scheduling [41] is one of the main processes used to improve performance on a cloud server.
At the heart of the IIoT are the WSNs, that include of multi-functional nodes, low-cost, along with sensing, have both communication and processing capabilities. In order to communicate wirelessly over short distances, these little, inexpensive sensor nodes have built-in transceivers and processors. They are densely exploited in an area of interest to collect sensory data, by coordinating and collaboratively exchanging information by training ad hoc wireless networks. Due to the small size and the batteries use, Sensor nodes are limited in processing, communication, and power. A unique feature of WSNs is their network processing attribute, whereby sensor nodes do not send raw sense data directly to the gateway but merge it locally to make it more consistent and save significant communication costs. Their application field is multiple and they are now ubiquitous components of intelligent environments, due to their unique attributes. Their various area of application covers home, surveillance, military, smart city, patient health monitoring, automation, etc. WSNs are used in telehealth applications in patient healthcare monitoring scenarios. As example, to monitor patients with chronic diseases and regularly check their various parameters such as heart rate, blood sugar and send this information wirelessly to a doctor remotely for further diagnosis. In order to help the elderly and disabled in their daily tasks, the WSNs are also used.
Indeed, they have seen major deployments in a diversity of applications, including agriculture, industrial process automation and control, transportation, and supply chain management over the past decades. Due to their ubiquitous presence and considering the potential benefits of these networks, such as simple deployment, cheap installation cost, no cabling cost, less complexity and mobility, they are increasingly used. in IIoT applications, which gave rise to IWSNs. WSNs can be used in an IIoT environment such as automation and control, process monitoring, and safety and emergency applications. In automation and process control applications, several tasks may require active nodes named actuators, which have the capability to act autonomously on the physical environment based on the detected measurements. For example, in the automation and control of feedback-based chemical processes, sensors measure temperature; if the temperature crosses a certain threshold value, they inform the actuators to reduce the temperature to a desired value so that the process remains in a stable state. Such applications place strict constraints on low latency and reliability because the sensor measurements must reach the actuator in a timely and reliable manner in order for the valve control action to be performed on time [42].
Today’s sensor nodes have more processing power, longer battery life and memory, due to recent technological improvements compared to the first resource-constrained sensor nodes. This allowed them to be used in IIoT applications and resulted in IWSN. IWSN makes processes independent and autonomous, especially in difficult areas, to get actuation and control information, sensory. Sensor nodes in the WSN field detect process variables (e.g. temperature, pressure, etc.) and pass them to the well or gateway. The sink then passes it to the process controller whose job is to control the process variable under some required value. The receiver is responsible for the sensor network management and is controlled and managed by the host application management. The Network and Security Manager is responsible for entire network monitoring and ensuring security against attacks. Therefore, WSN has the potential to improve production processes and quality of products without compromising the IIoT QoS. Actuation and control, and sensing, are also imperative in majority industrial applications. In these applications, the sensors detect the data and the actuators act on the data based on certain control decisions made by the process controller.
To implement FL’s full potential in IIoT and IWSN, there are still several fundamental challenges that need to be addressed. In this section, we describe the challenges followed by very promising opportunities to meet those challenges.
Indeed, the FL deployment on IIoT and IWSN networks relies deeply on the computational resources and memory of edge devices. Consequently, people often focus only on the IoT devices capabilities to gather data while ignoring their limited memory and compute resources, which makes it hard for most IoT and sensor devices to finish local computation with massive data or sophisticated models. In order to address this challenge, lightweight AI techniques have been explored, which can be implemented in resource-constrained FL-IoT and WSN environments, such as improved resource management approaches to accelerate FL training on devices.
In the multi-device settings, participants under the FL framework have various system resources, such as compute and memory resources. As the trend in machine learning is for larger and deeper models, the hardware heterogeneity within the IIoT and IWSN systems pose several challenges for the FL structure. They could easily train large models as devices with powerful memory and computing resources while other devices with limited resources could only train smaller models. Reader speed will also vary across devices, even for the same model size, which can trigger the problem of asynchronous communication discussed above. Due to the availability of the resources, an FL framework for IIoT and IWSN should provide a graceful adaptation of data and compute load on diverse devices.
Communication overload is considered to be one of the main challenges in FL-based IIoT and IWSN environments. Currently, most IoT and WSN devices communicate using wireless networks that have a much lower bandwidth than the wired network bandwidth. As more and more devices join the system, the communication problem arises when the clients have different resource allocations. The limited network bandwidth not only makes the communication between clients and the server inefficient, but also triggers the presence of late clients, which fail to share their local update with the server during the communication cycle. To meet this challenge, some key ideas can be used, such as decentralized training, data compression and participant selection.
The IoT devices prevalence also poses an attractive target in the real-world deployment for adversaries seeking to launch attacks, such as identity theft, phishing, and distributed denial of service (DDoS). Many IoT and ISN devices do not have the compute resources to do so, although these attacks can be easily defended by installing security patches. It is critical for the IIoT andd IWSN systems to detect the malicious or broken IoT devices that will ruin the model training with limited resource. To address this challenge, one of the promising directions is to implement a lightweight security protocol in the IIoT andd IWSN systems for the detection of broken and malicious devices.
Undeniably, the IIoT and IWSN ecosystems continue to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes are expected to be predominant as the number of IoT devices grows toward the trillions to connect the unconnected world and things. However, their future in the IIoT and IWSN ecosystems still seems foggy, where several challenges, such as device’s connectivity, artificial intelligence (AI) at the edge, security and privacy concerns, growing energy needs, the right technologies to be used and keep pulling in opposite directions. To address these issues, which are caused by the complexity and variability of the environment, advanced computing related technologies are widely applied. However, the edge computing is limited by cost, volume, power consumption, and other conditions, so the capacity of edge computing cannot be fully exploited. So that edge computing fully exploits its characteristics of flexible management, federated and collaborative execution and heterogeneous environment, the reconfigurable real-time computer system based on FPGA SoC is strongly recommended. The system, as depicted in Figure 3 can be built in real time as needed, by the characteristics of the FPGA SoC, including its reconfigurability, partial and total and precise clock control.
Reconfigurable edge computing system based on FPGA SoC.
A multi-threaded huge number, computing requirements, and parallel heterogeneous data processing are persistently proposed in many environments of manufacturing. However, depending on the multi-environment’s requirements with different multiple tasks and several scenes, a single algorithm can no longer face the requirements so that numerous complicated tasks require the algorithm to be reconfigured and replaced. Without a doubt, the FPGA employment gratifies this multitude of requirements. However, it can rebuild the logic of the chip by means of configuration and reconfiguration of the resources inside the chip to form hardware with different functions by means of software. Therefore, in addition to the programmability and flexibility of the software, the FPGA also exhibits high throughput, low power, and low latency characteristics. In addition, due to its rich In-Output, FPGA SoCs are also very relevant for use On-chip protocols applications and interface conversion. The main benefits from employing FPGA for the edge computing are as follows:
A constant throughput can be provided by the FPGA with a constant load size-based application, so that can integrate multiple service requests from several sensors in the IoT.
Large-scale temporal and spatial parallelism is provided by the FPGA with fine granularity, so that ensures a high concurrency and high dependency algorithm with high acceleration performance.
Compared with the processor, the FPGA has the lower power consumption and faster computing speed, which can provide the stability and lower task energy consumption.
The 5G network infrastructure design should focus on attentive consideration of software control, hardware infrastructure, and interconnection between them. In this context, we consider a network slicing architecture consisting of a set of IIoT slices
Deep federated RL-based network slicing architecture.
This slice set
where
however,
We assume that agents, by sharing its self models based on the Q-network experiences, collaborate to receive global rewards from the federated orchestrator. While the orchestrator collects these models to builds a global network model that provides an optimal actions, on LoRa parameters, that maximize QoS revenue [43, 44].
The considered network consists of two agents, called as agent
where
At this stage, the Mean Square Error (MSE), is defined for agents
while
Training and testing phases.
The proposed framework has been implemented in Python language using TensorFlow-gpu package on Intel Xeon E5-2620 v4 2x 8-Core with 64 GB RAM. Also, the NVIDIA GK110BGL [Tesla K40c] is used to improve speed during the training phase.
We provide, in this section, the mean percentage of Packet Loss Rate (PLR) for IIoT devices, as denoted in Figures 6–8, and compare it to the PLR within MBGD scheme [7].
PLR of UCLE.
PLR of HCLE.
PLR of LCLE.
However, by increasing the devices, PLR will increase subsequently. This return to the data rate, that when increase, the number of successful transmitted packet increase accordingly. While it is not the case when throughput is low. We remark also, in Figures 6–8, that UCLE and HCLE slices have a reduced PLR compared to LCLE. However, this due to the reliability and efficiency constraints dedicated for this slice which is not the case in LCLE slice that consider only the load. Compared with the slice results using the MBGD technique, we could obviously note the efficiency of the proposed federated scheme in supporting dynamic slicing strategy by reducing PLR over than 9
This prospective chapter presents a future outlook on low-end motes in the IIoT and IWSN eras. Following a detailed discussion of the trends and challenges posed by the IIoT and IWSN paradigm to low-end devices, it discusses how modern reconfigurable platforms are the perfect candidate to meet the ever-evolving industrial environments. Indeed, in this chapter, we proposed a federated network slicing based on deep reinforcement learning techniques for channels and bandwidth management based LoRa promising technology that meet IIoT and IWSN network service requirements based on the SDN, NFV, network slicing, and deep reinforcement learning techniques. Each LoRa GW plays an agent role, in the environment, and profits from the learning experience provided by the other coexist agents via the global federated model.
In the case of future studies, this chapter introduced comprehensive review and several research lines, especially one attractive future line is related to the integration of FPGA SoC at the edge to build a smart factory as well as IIoT and IWSN environments with environmentally friendly capabilities and functionalities. In addition, future research is needed to fully embrace cloud services and new ways of connectivity in order to get the full benefits of the new Edge FPGA SoC technology.
J=1…j | IIoT network slices set |
K=1…k | LoRa gateways (agents) set |
I=1…i | IIoT devices set associated to each slice |
B=1…b | channel bandwidth set |
C=1…c | LoRa-GW’s channels set |
αi∈01,∀i∈Ij,k | device’s admission and association index to slice |
∀i∈Ij,k | device i assigned to slice j on gateway k |
TP | transmission power |
SF | spreading factor |
ϕi,∀i∈Ij,k | throughput of device i |
di,∀i∈Ij,k | delay of device i |
uQoSj,k | quality of service metric for slice j on GW k |
pit,∀i∈Ij,k | the power allocated for each device i |
uEEj,k | energy efficiency metric for slice j on GW k |
pir | the received power |
uRELj,k | reliability metric for slice j on GW k |
Urmj,k,∀k∈K,∀j∈J | the global slice utility revenues metric |
SATℛ | state, action, transition function, reward |
α and β | agent α and agent β |
γ | discount factor |
θα,θβ | DQL network parameters (weights) |
θg | DNN network parameters (weights) |
Dα,Dβ | reply memories to store transitions |
IoT | internet of things |
IIoT | industrial IoT |
IWSN | industrial wireless sensor network |
AI | artificial intelligence |
ML | machine learning |
DL | deep learning |
QoS | quality of service |
RL | reinforcement learning |
DRL | deep reinforcement learning |
FL | federated learning |
DFL | deep federated learning |
DFRL | deep federated reinforcement learning |
CPIToS | cyber-physical internet of thing systems |
5G | fifth generation network |
SDN | software defined network |
NFV | network function virtualization |
NS | network slicing |
DQL | deep Q-learning |
GD | gradient descent |
GMM | gaussian mixture model |
SP | service provide |
MEC | mobile edge computing |
GPU | graphic processor unit |
FPGA | field programmable gate array |
UCLE | ultra critical of latency and efficiency |
HCLE | high critical of latency and efficiency |
LCLE | low critical of latency and efficiency |
GW | gateway |
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\n\nCSIC affiliated authors can also take advantage of a central Open Access fund (amounting to 10,000 EUR) to cover up to 50% of the rest of the OAPF until it expires. Effective for chapters accepted from January 1, 2020.
\n\nCorresponding authors will receive a 25% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters. A 20% discount for publishing a long-form monographs, 25% for compacts and 23% for short-form monographs.
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\n\nThe University of Surrey is pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
\n\nCorresponding authors will receive a 10% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
\n\nMonographs Only
\n\n\n\nImportant: You must be a member or grantee of the above listed institutions in order to apply for their Open Access publication funds.
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