Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software -Define d Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technol ogical constraints of expanding capacity in the era of UAV & software - defined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section. ARTICLE HISTORY: Received : 21 December , 202 3. Revised : 12 February , 2024. Accepted : 07 March, 2024. Published: 31 March , 2024. KEYWORDS: Optical networks, Telemedicine Network, UAV, SDN, QOS, Network capacity . ARTICLE INCLUDES: Peer review DATA AVAILABILITY: On request from author(s) EDITORS: Ozoemena Anthony An i FUNDING: None --------------- ----- HOW TO CITE: Sadiq , B. O. , Zakariyya , O. S., Buhari , M. D. , and Shuaibu , A. N. “Maximizing Network Capacity, C ontrol a nd Management in Designing A Telemedicine Network: A Review a nd Recent Challenges ”, Nigerian Journal of Technology , 2024; 43(1 ), pp. 80 – 100; https://doi.org/10.4314 /njt.v43i1.11 © 2024 by the author(s). This article is open access under the CC BY - NC-ND license 1.0 INTRODUCTION Advancements in the telemedicine industry have been with the use of information and communication technology (ICT) and its tools. These ICT tools are used not on ly for visual communication but also for patient data transfer and collaborative learning. Nonetheless, achieving these uses needs a communication medium that can provide high spectral efficiency. As such, this review paper draws contribution from works th at delves into the historical evolution of traffic and capacity, both retrospectively and prospectively, papers that used UAV and SDN to improve telemedicine network by considering medical and image data communication protocol and WBAN framework, and those that focus on enhancing telemedicine network using QoS mechanism, machine learning and cloud infrastructure. This will therefore improve telemedicine network design capable of delivering performance, adaptability, and capacities essential for accommodati ng the anticipated surge in traffic in the upcoming decades. The need for high spectral efficiency to increase fiber utilization and the availability of channels with different bandwidth requirements have been the main drivers for Optical Networks (ONs). However, these Nigerian Journal of Technology (NIJOTECH) Vol. 43, No. 1 , March , 202 4, pp.80 - 100 www.nijotech.com Print ISSN: 0331 -8443 Electronic ISSN: 2467 -8821 https://doi.org/10.4314/njt.v43i1.1 1 81 Sadiq et al. (2024 ) © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/licenses/by -nc-nd/4.0/ drivers have been influenced by the nature of their management, capacity, and control. Telemedicine currently uses optical networks which are high - capacity networking in the era of searching novel dimensions to increase transport capacity [1,2] . This is due to the fact that o ptical netw orks have seen significant changes in their capacity, monitoring, management, and control framework during the previous two decades. Due to recent advancements in optical solutions that lead to real - time patient monitoring, better emergency care, secure data transmission, improved patient access, and collaboration between the medical team and members, the telemedicine industry has adopted the use of this network with advanced body sensors technologies such as an electrocardiogram (EKG), electroencep - halogra phy (EEG), heart rate, body temperature and monitoring and assistance to patients with chronic diseases. These are however achieved using wired and wireless infrastructures, real -time data processing, and interactive interfaces [3,4]. According to the me dical association that uses telehealth, rural health points have been able to connect using optical networks with the help of connected medical equipment ranging from simple scales, blood pressure monitors, and thermometers for primary parameters to medica l imaging equipment such as microscopes, scanners, magnetic resonance imaging, (MRI) and X -rays [5 -8]. The authors of [9] have discussed the prospects for the growth of e -health through the integration of optical networks. Telemedicine was originally intr oduced to the world in the early 1960s. It first emerged in metropolitan areas before making its way into the arena of emergency medicine. Telemedicine grew in popularity in rural areas, where people with limited or no access could now consult professional s from a distance. In 1967, the University of Miami School of Medicine was one of the first schools to collaborate with the local fire department to send electrocardiographic rhythms to Jackson Memorial Hospital through radio. Large -scale wired and wireles s networks, as well as mobile computing technologies such as optical networks, Wi-Fi mesh, WiMAX, and cellular 3G among others, allow healthcare personnel to access important information from inside healthcare network systems at any time and from any place . Presently, the coming of telemedicine with advancement in network research had made it more prominent in carrying out telemedicine key applications like store and forward, remote patient monitoring, and interactive telemedicine/telehealth [10 -12]. Patien ts and physicians can communicate directly via interactive telemedicine/telehealth. These sessions can take place at the patient's home or a medical kiosk. Video conferencing applications may be used in the interactions. The store and forward method, also known as asynchronous telemedicine, allows the healthcare provider to transmit patient data with another, such as lab results amongst others [13]. Remote patient monitoring, on the other hand, allows patients to be observed at home using handheld apps or remote sensors that collect data on temperature, blood sugar levels, blood pressure, and other vital indicators. Telemonitoring is the term for this [14]. Optical networks have been used in telemedicine as one of the most promising types of networks [15]. Previous research, on the other hand, relied on a wireless mesh network. Wireless mesh networks are utilized to provide a stable and reliable communications infrastructure backbone for telemedicine systems. They are chosen over other wireless networks beca use the broadband services they provide are stable. in addition, they are also robust because if one or more mesh nodes fail, the other mesh nodes may still route the data. Wireless mesh networks not only extend coverage as well as save costs on wiring and personnel management. Interference and fluctuating load, however, diminish their performance [16]. In wireless mesh networks, interference is a major problem. Because of the overcrowded unlicensed radio frequency spectrum, interference arises. A solution to this is the use of Optical Mesh Networks (OMN). In mesh network design, an optical mesh network is a type of optical telecommunications network that uses either wired fiber -optic transmission or wireless free -space optical communication. In wired optica l networks, light signals go through a fiber, but in free -space optical communication, light signals travel over the air [17,18]. Figure 1: Different aspects of Optical Network Capacity Trends and Management in Telemedicine MAXIMIZING NETWORK CAPACITY, CONTROL AND MANAGEMENT IN DESI… 82 © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/license s/by-nc-nd/4.0/ A graphical view of the various aspects of research that contributes to the increase capacity and management of optical networks when applied to telemedicine applications is presented in Figure 1. Significant research has been done on how to increase the capacity, control, and management of optical networks to serve bandwidth -dependent applications like telemedicine. The earlier approaches involved the continuous manufacturing of optical fibers that can carry more traffic [19]. The process of the manufactu red fiber was subsequently improved using different usage optimization techniques in several ways like routing protocol design [20], Quality of Service (QoS) [21], software -defined networking [22], machine learning [23], etc. These optimization techniques served as tools to improve capacity in terms of reducing delay, preventing packet loss amongst others, whilst managing and controlling the optical networks with a view to increasing their capacities. Another area of research is cloud -based optical networks . This type of network combines the power of optical network technologies and cloud computing. This type of network is utilized to provide telemedicine applications with high -speed local networks [24,25]. For high -speed, high -quality, and high-capacity int ernet traffic, [26] proposes an impairment -aware reactive defragmentation strategy for dynamic elastic optical networks servicing telehealth care -oriented traffic. The suggested technique was designed to provide high -priority data on the shortest path avai lable between the source and destination nodes, minimizing latency. There are several research efforts on Internet of Things (IoT) applications with telemedicine [27,28], with a particular emphasis on high -speed connectivity, with the progress of sensor technology and connectivity. The application of artificial intelligence (AI) and machine learning (ML) techniques in this field allows for the gathering, analysis, and real -time decision -making process of patient data [29,30]. For example, in the study [31 ], the authors looked at the movement of heart rate in the context of internet of things, telemedicine and mobile health. Optimization of a remote public medical emergency management system based on the internet of things [32,33] has also been studied in a number of studies recently with little latency. However, the aforementioned applications are fraught with difficulties. Dealing with a variety of protocols necessitates specialist tools (scalability), and internet disruption can kill a patient, implying t hat network failure is not acceptable, necessitating a high - speed capacity network. Other strategic concerns include, for example, government policies on patient record security and environmental hazards. Optical network capacity and trends are highlight ed in many review articles as such as [31] as a promising subject for future research. The highlighted subject follows the typical telemedicine center framework presented in Figure 2. 2(a): Internal Structure 2(b): Connecting Infrastructure Figure 2: A typical Telemedicine Center A well -written review article describes the past and current state of the topic, offers future directions, and summarizes the previous development of the issue. Several studies on the optical network, control, and managemen t have been written in various domains, according to the literature. Researchers give review articles on the use of optical networks in UAV networks, agricultural modernization, disaster management, remote sensing, communication networks, and power transmi ssion line monitoring, among other topics. The following are the main features of this paper:  It is the first review study based on capacity trends, control, and management of the usage of optical networks in designing a telemedicine network. 83 Sadiq et al. (2024 ) © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/licenses/by -nc-nd/4.0/  It examines several domains of telemedicine where optical networks are employed, as well as a comprehensive literature review on the design issue.  It emphasizes the larger features as well as the parallel research directions employed to improve telemedicine network ca pacity.  It outlines the research shortcomings in the discipline and the areas where more research is required.  It identifies the many types of issues that the telemedicine field faces and lists the remedies suggested by various researchers. Therefore, the evolution of optical network capacity and management to support telemedicine networks, software -defined networks as support to telemedicine, machine learning in optical networks, UAV -based telemedicine network, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. Figure 3: The Detailed structure of the review process. The paper's organization is as follows: it commences with a broad introduction to the subject, encompassing recent advancements in optical networks within the telemedicine sector, the diverse range of applications in telemedicine networks, and the imperative for employing high -capacity optical networks. The subsequ ent subsections delve deeper. The first subsection delves into the historical evolution of traffic and capacity, both retrospectively and prospectively. In the second section, a medical and image data communication protocol for telemedicine is introduced, scrutinizing the technological limitations of capacity expansion in the context of software -defined networking. Moving forward, the third section explores strategies for optimizing network capacity, considering the challenges of maintaining quality of serv ice (QoS). Ultimately, the paper investigates the construction of a telemedicine network capable of delivering performance, adaptability, and capacities essential for accommodating the anticipated surge in traffic in the upcoming decades. A visual represen tation detailing the comprehensive structure of the review process is illustrated in Figure 3. 2.0 UAV AND SOFTWARE -DEFINED NET - WORKS AS SUPPORT TO TELEMEDICINE By offering a dependable and secure way to transfer medical data and supplies to remote or di saster - affected locations, Unmanned Aerial Vehicles (UAVs) [34,35] and Software -defined Networks (SDNs) [36 -37] can enhance telemedicine in a significant way. UAVs can be used to deliver medical supplies and equipment to far -flung locations that are challe nging to access by conventional transportation. Also, they can be utilized to deliver medical samples from these regions back to labs for examination and analysis. Additionally, by enabling doctors to remotely monitor vital signs and converse with patients via video conferencing technology, UAVs can be utilized to deliver real -time medical help to patients in remote locations. On the other hand, SDNs can promote telemedicine by giving healthcare practitioners and patients a safe and dependable way to transf er medical information. SDNs enable the development of virtual networks that may be dynamically rearranged to satisfy the varying requirements of telemedicine applications [37]. So, even in remote or disaster -affected locations, healthcare professionals ca n give patients more individualized care. UAVs and SDNs can work together to create a potent technology stack that will assist telemedicine and enhance patient outcomes in underserved areas. Yet, it's critical to make sure that these technologies are used responsibly and ethically, considering issues with effective communication, security, privacy, and other moral issues. The issues of effective communication, security, and privacy amongst others can be achieved via the development of an efficient commun ication protocol and framework. Therefore, some research works such as [38,39] have presented communication protocols that consider effective medical and image data communication. Therefore, the success of increasing the capacity, control, and management o f optical networks in telemedicine networks can also be dependent on the communication protocol and connectivity framework. A conceptual diagram of how UAVs can enhance the telemedicine network [40] is depicted in Figure 4. Nonetheless, the success of the framework is dependent on the communication protocol used. MAXIMIZING NETWORK CAPACITY, CONTROL AND MANAGEMENT IN DESI… 84 © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/license s/by-nc-nd/4.0/ Figure 4: UAV -based telemedicine network 2.1 Medical data and image communication protocol With a view to maximizing the capacity of a telemedicine network, some authors such as the work [41,43] have developed medical data and image communication protocols as well as discussed, the communications requirements for image data in telemedicine. Based on the gathered perception, text, pictures, and sound must be sent and received for telemedical a pplications. The text contains patient data, laboratory results, and ECG (heart tracing) results. Still, photos and full -motion videos are both examples of images. Further to voice and chest sounds, radiological pictures, slides, and graphics may also be c ommunicated. As presented by [41], up to two or three simultaneous video bitstreams could be needed for telemedicine systems: two low -rate bitstreams for teleconferencing and an optional high - rate bitstream for diagnostic video. To achieve communication, u se H.261 (64 kbps to 1.92 Mbps), H.263 (15 -34 kb/s), or MPEG -1 (1.2 -2 Mb/s). Nevertheless, the jitter and latency of real -time video streams are always important and should be reduced. The authors in the work of [42] also showed the capability of deliverin g medical ultrasound images over a wide area network in real -time. [43] from their work have stressed that the communication of images in telemedicine networks required much bandwidth. Therefore, there should be an efficient and reliable control mechanism [44]. As an alternative, a network of satellite ground stations with symmetric bandwidth connected by satellite is the architecture that was suggested. Other suggestions by earlier authors of telemedicine with respect to the topic of the presentation are p resented in Table 1. Table 1: Comparison of the articles on medical data and image communication protocol to improve telemedicine network Topic Reference Year of publication Digital Imaging and Communications in Medicine (DICOM) [45] 2010 Health Level 7 (HL7) Protocol [46] 1993 Picture Archiving and Communication Systems (PACS) [47] 1994 Simple Object Access Protocol (SOAP): [48] 2010 Network capacity, control, and management protocol in Telemedicine No record In the work of [45], the use of Digital Imaging and Communications in Medicine (DICOM) as a standard in medical imaging was presented. The benefits of employing DICOM, such as enhanced workflow, improved interoperability, and increased efficiency in the sh aring of patient and medical picture data, were also discussed. The authors also explain a number of real-world uses for DICOM in medical imaging, such as radiology, cardiology, and ophthalmology. The study [46] outlines the benefits of HL7, such as enhanc ed workflow, improved interoperability, and increased effectiveness in the interchange of medical data. The work also discusses the different parts of the HL7 protocol, including messages, segments, fields, and tables. Also, the author talks about how to u se HL7 in healthcare contexts for things like clinical documentation, reporting test findings, and billing and claims processing. An overview of PACS technology, including its design, parts, and uses, was given in the work by [47]. The author analyzes the many PACS options, including standalone PACS, mini -PACS, and integrated PACS, as well as their benefits and drawbacks. The author also discusses how PACS is used in radiology, cardiology, and other fields of medicine. The study also covers the difficulti es and restrictions of putting PACS into practice, such as the requirement for standardization of data formats and protocols, security and privacy issues, and the high expense of putting PACS into practice and maintaining it. An overview of the SOAP protoc ol and how it is used to secure data transmission over the internet can be found in [48]. The use of SOAP in healthcare information systems is examined by the authors, along with its benefits and drawbacks, including its capacity to offer a secure and depe ndable means of data transfer. Additionally, the paper discusses the security prerequisites for EMR databases, such as confidentiality, integrity, and availability, and looks at how SOAP might be utilized to satisfy these prerequisites. The research argues that the SOAP protocol is appropriate for safeguarding the transmission of EMR databases since it offers a 85 Sadiq et al. (2024 ) © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/licenses/by -nc-nd/4.0/ dependable and secure means of data transfer. Nevertheless, they also point out that adopting SOAP has several drawbacks, including its complexity a nd the requirement for additional security precautions to guarantee data confidentiality and integrity. Generally, the summary of what each of the protocols represents is discussed as follows: Healthcare providers frequently exchange clinical and administrative data using the messaging protocol HL7. It is commonly utilized in healthcare IT applications and electronic health record (EHR) systems [49]. HL7 can be used in a range of he althcare situations, including telemedicine, and facilitates the sharing of numerous sorts of data, including medical pictures. The complexity and lack of capability for real-time data sharing are just two of HL7's drawbacks. In hospitals and clinics, PACS is a medical imaging technology that is frequently employed. It enables digital medical picture storage and retrieval and can be utilized in telemedicine applications to streamline the exchange of medical images between healthcare professionals [50]. PACS is generally easier to use than DICOM (digital Imaging and Communications in Medicine) and is accessible through regular web browsers. However, PACS can be expensive and may require extensive IT infrastructure to implement. On the other hand, a common p rotocol used in telemedicine called DICOM is used to exchange medical images and related data [51]. It can be utilized in telemedicine applications that include the transmission of medical pictures and is commonly employed in radiology and other medical im aging disciplines. DICOM supports a wide range of image types, including X -rays, CT scans, and MRIs, and allows for the exchange of image data in a standardized format. Yet to understand and show pictures via DICOM, specialist software is needed. Another t ype of common protocol used in the telemedicine field to transfer medical data from source to destination is SOAP [52]. To make the interchange of medical data, including clinical data and medical imaging, easier, it can be employed in telemedicine applica tions. Several programming languages support SOAP, which is rather easy to implement. However, SOAP might not be as effective as other protocols, and it might need more bandwidth to send big volumes of data. Based on the review of the medical data and ima ge communication protocol, it can be concluded that the telemedicine network can be categorized into three different network layers which are the transportation protocol layer, the medical device layer, and the application layer. Most of the previously use d telemedicine protocols were adopted rather than created as an integral part of medical data. It was determined that two types of protocol exist which are: a. The Store -and-forward type of protocol b. The real -time protocol The earlier works in medicine use the store and forward type of protocol to control and manage the telemedicine applications. However, with recent advancements in the use of UAV and software - defined networking as support for telemedicine, the adopted proto cols are Real -time. These Realtime protocols are typically used under the coined name of Wireless Body Area Network (WBAN) framework for telemedicine. 3.0 TELEMEDICINE -BASED WBAN FRAME WORK This subsection presented the telemedicine based WBAN framework design as a contribution to improving the network capacity, control, and management in a telemedicine network. By enabling real-time remote patient monitoring, Wireless Body Area Networks (WBANs) are a promising technology for enhancing telemedicine networ ks. A number of wireless sensors are attached to the patient's body as part of the WBAN architecture, and these sensors gather information about the patient's temperature, blood pressure, and heart rate [53]. Then, a central hub, which may be situated in t he patient's home or a healthcare facility, receives this data wirelessly. The healthcare provider can then use the data to monitor the patient's health status and deliver the necessary care after the hub sends it to them in real -time [54]. WBANs are subje ct to a number of standards, including IEEE 802.15.6 and ISO/IEEE 11073 [55,56]. These guidelines aid in ensuring interoperability among various hardware and software systems by defining the communication protocols and data formats used in WBANs. Standards -compliant WBANs can assist improve the dependability and security of telemedicine networks. WBANs can be used in telemedicine for a variety of purposes, such as the remote monitoring of chronic conditions like diabetes and hypertension, the home monitorin g of elderly patients, and the monitoring of patients in intensive care units. WBANs can provide real -time patient monitoring, allowing medical professionals to react to changes in the patient's health status immediately. WBANs have a few issues that nee d to be fixed despite any potential benefits they may have. The limited range and bandwidth of wireless MAXIMIZING NETWORK CAPACITY, CONTROL AND MANAGEMENT IN DESI… 86 © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/license s/by-nc-nd/4.0/ communications, the requirement for frequent sensor battery replacement, and the possibility of interference from other wireless devices are a few of th ese drawbacks. furthermore, there are problems with the safety and confidentiality of patient information transferred over a wireless connection [57]. Some pertinent works that have used the WBAN framework to improve the telemedicine network are presente d in Table 2 . Table 2: Summary of the articles on improving telemedicine network using WBAN framework Topic Reference Year of publication Wireless body area network for telemedicine [58] 2020 E-health beyond telemedicine [59] 2019 Health telemonitoring [60] 2019 WBAN -based remote monitoring system [61] 2023 time framework for -Real patient monitoring [62] 2020 Telemedicine framework in the 19 pandemic- COVID [63] 2022 Network capacity, control, and management protocol in Telemedicine No record The use of Wireless Body Area Network (WBAN) technology in telemedicine for emergency care is covered in the study [58]. WBANs are wireless networks of medical sensors that track vital signs and other health -related information when they are worn on or imp lanted into a person. The benefits and drawbacks of adopting WBANs for emergency treatment are discussed in the study, including its capacity to deliver real -time monitoring and diagnosis, lower hospitalization expenses, and enhance patient outcomes. The a uthors also address the technological and legal concerns that must be resolved before WBAN -based telemedicine systems can be put in place. The report concludes that although WBAN -based telemedicine offers significant potential to enhance emergency care and save healthcare costs, more study and development are required to address the technological and legal issues. In [59], Wireless Body Area Networks (WBANs) are introduced and their potential to advance e -healthcare beyond telemedicine to remote health moni toring is explored. Also, the article provides a review of the literature on WBANs and healthcare applications, such as remote patient monitoring and the management of chronic diseases. The authors also discuss the technological difficulties and solutio ns related to the implementation of WBAN -based healthcare systems, including energy -efficient protocols and security procedures. The report ends with a discussion of the potential implications for e -healthcare of the future paths of WBAN research concernin g telemedicine. In [60], a novel method for secure patient data transmission across Wireless Body Area Networks (WBANs) for remote monitoring of health is proposed. To ensure a high level of data security during transmission, the authors advise employing a hybrid encrypti on strategy that combines symmetric and asymmetric encryption algorithms. To guard against unwanted access to and tampering with patient data, the proposed system additionally incorporates a method for patient authentication and data integrity verification . The authors presented a technique of using a wireless sensor network to track patient health information in real -time, such as vital signs and activity levels. The suggested method was said to increase the effectiveness and precision of remote health mon itoring while simultaneously guaranteeing the privacy and security of patient data. The author of [61] proposed a Wireless Body Area Network (WBAN) -based remote monitoring system for healthcare services using machine learning techniques. The system is in tended to gather data from numerous WBAN sensors worn by patients, analyze that data using machine learning algorithms, and then deliver insights into the health status of those patients. A centralized server for data processing and storage is part of the suggested system, along with a user interface for viewing and analyzing patient data for healthcare professionals. It explored the system's technical difficulties, such as data security, privacy, and data processing capabilities. They also emphasized the p otential advantages of the suggested method, such as enhanced diagnostic precision, lower healthcare expenses, and better patient outcomes. The research by [62] proposes a wireless body area network -based real -time platform for patient monitoring systems ( WBAN). The proposed framework attempts to address the requirement for ongoing patient monitoring in medical facilities and at home. The WBAN, which consists of a set of wearable sensors placed on the patient's body to collect physiological data, and a re mote monitoring system that receives and interprets the acquired data, make up the framework's two primary parts. The ZigBee and Bluetooth communication protocol were suggested to be used by the authors to wirelessly transfer data from sensors to the remot e monitoring system. The authors used a machine learning algorithm to categorize and forecast patient health status to deal with the massive amounts of data supplied by the WBAN. The results demonstrate that the proposed framework is capable of accurately detecting and classifying various 87 Sadiq et al. (2024 ) © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/licenses/by -nc-nd/4.0/ medical conditions in real -time using a set of physiological signals collected from real patients. The authors of [63] suggested a telemedicine framework for use in the COVID -19 pandemic. Due to the risk of infection, th e framework is intended to offer remote healthcare services to patients who are unable or unwilling to visit hospitals or clinics. The authors talk about the difficulties experienced by healthcare professionals during the pandemic, such as the requirement to preserve social distance and lower the danger of virus exposure. They contend that telemedicine can be a practical means of delivering healthcare remotely while lessening the strain on medical institutions. A web -based platform for teleconsultations, re mote monitoring tools, and electronic medical records are some of the components of the proposed system. The authors also examine the technological and legal difficulties that come with putting the framework into practice Eventually, they draw the followin g conclusions: Telemedicine can be crucial in the control of COVID - 19 and other pandemics, and the suggested framework can serve as a useful guide for politicians and healthcare professionals. Based on these works, it is evident that the WBAN - based framew ork for telemedicine has contributed to the efficient delivery of medical care over network infrastructure. However, to achieve seamless communication between patients, medical practitioners and other assisted telemedicine support such as the UAV and softw are-defined networks amongst others, there is a need to use a communication medium that guarantees the quality of service (QoS) and bandwidth utilization. 4.0 OPTICAL NETWORKING Before now, certain telemedicine applications have been implemented using both basic telephone lines and wired communications technologies like ISDN and ADSL. However, mobile and ambulatory telemedicine systems can now be used alongside contemporary technologies like WLAN, Bluetooth, UMTS, GPRS, and Edge as well as satell ite connectivity . The communication networks used with the telecommunication systems are presented in Table 3. Table 3: Communications networks for telemedicine Network Type Connection Data Transfer and Frequency Band Telemedicine Services Satellite Skybridge , Celestri ICO Global Star , amongst others . Ku band (16Kb/s -2MB/s), Ka band (155MB/s) amongst others . The different satellite has different bands and data transfer rates voice, data and video transmission GSM and UMTS (3G) GSM -1900 (1900 MHz)/ UMTS 9.6-43.4KB/s / 2Mbps GSM supports Emergency telemedicine patient monitoring in Adhoc locations while UMTS can support high bandwidth mobile telemedicine applications Wireless LAN Bluetooth Home RF 1.0 Home RF 2.0 IEEE 802.11a./b/g/n 2.4GHz 1Mbps and 1Mbps and 2.4GHz 10Mbps and 2.4GHz IEEE 802.11a: 54Mbps and 5GHz IEEE 802.11b: 11Mbps and 2.4GHz IEEE 802.11g: 54Mbps and 2.4GHz IEEE 802.11n: 600Mbps and 2.4GHz & 5GHz Home hospital patient tele monitoring and access to medical data in the hospital environment LTE(4G) and 5G Broadband 150Mbps and 20Gbps High bandwidth applications and scenarios 6G Broadband High bandwidth applications and scenario It is important to note that various telemedicine applications have different communications technology needs in terms of complexity and range. There are no theoretical bandwidth requirements for the transmission of medical data. Longer transmission times are assumed when there is insufficient bandwidth. As such, there is a need to minimize longer transmission times. The telecommunication infrastr - ucture in recent times that supports telemedicine applications makes use of an optical network as a backbone [6 4,66] in addition to linking different hospital centers in different locations [67]. This can be attributed to the high availability of bandwidth they possess. As such, there is a need to maximize its capacity. Conclusively, we can argue that optical netw orks are crucial to telemedicine because they provide fast, dependable, and secure communication between healthcare professionals and patients. 4.1 Optical Network Capacity M aximization To fulfill the rising demand for high -bandwidth applications and services, network operators must be MAXIMIZING NETWORK CAPACITY, CONTROL AND MANAGEMENT IN DESI… 88 © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/license s/by-nc-nd/4.0/ able to maximize optical network capacity, which is a crucial component of network design. Some of the general techniques that can or have been used to maximize the network capacity of optical networks in telemedicine are discussed as follows: a. Quality of Service (QoS) To increase network performance, reliability, and user satisfaction in optical networks, quality of service (QoS ) is a crucial approach. With the help of QoS, the network can give some traffic types priority over others, ensuring that vital applications and services have access to the bandwidth, latency, and dependability they need to operate at their best. Traffic Classification and Prioritization, Bandwidth Reservation, Traffic Shaping, Network Slicing, and Monitoring and Management are some of the uses of the Quality -of-service functions in optical network capacity maximization [68,69]. b. Cloud -based Solution Cloud -based solutions have become a potent method for telemedicine networks to operate as efficiently as possible. Telemedicine providers may increase patient care, save costs, and increase access to healthcare services by utilizing the power of the cloud throu gh the optical telecommunications infrastructure. These are several strategies for telemedicine networks that can employ cloud -based solutions to increase efficiency [70]. Cloud -based solution for telemedicine infrastructure is depicted in Figure 5. Figure 5 : Cloud -based solution for t elemedicine infrastructure Scalable resources provided by cloud -based technologies allow telemedicine operators to swiftly and simply expand or decrease resources in response to demand. This guarantees that medical facilities can satisfy the rising demand for telemedicine services without sacrificing quality or performance. Moreover, cloud -based solutions let telemedicine service providers combine several telemedicine platforms, like messaging, video conferencing, and remote monitoring [71]. This increases efficiency and lowers costs by enabling healthcare practitioners to offer a variety of telemedicine services from a single platform. c. Machine Learning (ML) approach Machine learning and artificial intelligence (AI) algorithms can be used with cloud -based systems to evaluate vast vo lumes of patient data and offer in -the- moment insights into patient health. By doing so, healthcare professionals can better understand potential health hazards and treat patients. By reducing latency and optimizing network efficiency, ML and AI algorithms can raise the quality of telemedicine services. Predictive algorithms that adapt network settings in real -time based on the type of data being transmitted can help with this. Critical telemedicine applications like video conferencing and remote monitoring can be given the bandwidth and low latency they require to operate at their best using ML and AI to prioritize network traffic [72]. Moreover, it can be used to dynamically assign bandwidth based on network demand, ensuring that resources are used effecti vely and efficiently. This can aid telemedicine providers in cutting expenses and enhancing network efficiency. Some other salient techniques that have been used to improve the optical network capacity for telemedicine are the use of Wavelength Division M ultiplexing (WDM) [73], Optical Amplifiers [74], Dynamic Optical Networks (DON) achieved with the use of multiplexers and SDN [75]. These discussed considerations are important to consider whilst designing a telemedicine network. However, the physical laye r, the network layer, as well as the operational and commercial requirements of the network must all be taken into account to maximize optical network capacity for telemedicine. This may entail a number of technologies and methods as well as continuing net work maintenance and optimization. 5.0 DESIGNING A TELEMEDICINE NETW - ORK The designing of a telemedicine network in this subsection focused on a design that can improve network capacity, control, and management. The Internet of Things (IoT) technology ha s been the subject of numerous studies with the aim of making telemedicine smart. These study fields include cognitive radio network communications, artificial intelligence methodologies, and techniques, Internet of Things wearable sensors and hardware dev ices, 89 Sadiq et al. (2024 ) © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/licenses/by -nc-nd/4.0/ smartphones, and cloud computing, which all aim to improve telemedicine architecture and design. The following subsections will discuss different telemedicine network architecture design approaches. 5.1 Cognitive Radio -Based Telemedicine Netw - ork Design Researchers' interest in the wireless transfer of patient medical data via diverse networks has lately increased. Yet, challenges like efficient frequency spectrum usage and longer gadget lifespans are thought to be the most urgent ones right now [ 76]. MBANs are now being used in unlicensed frequency bands, where there is a high risk of cross -interference with other electronic devices [77]. Feng, et al., colleagues (2010) investigated the effectiveness of enabling telemedicine traffic in a CRN to ad dress the aforementioned issue. Procedures and performance for enabling urgent and real -time periodic telemonitoring were proven after the installation of an infrastructure -based CRN for telemedicine. Traffic on the network suggests that telemedicine traff ic can obtain sufficient real -time performance [78]. [77] suggests a workable architecture for an MBAN based on UWB radio technology with useful CR capabilities. In this scenario, cognitive capacities are developed via multiband orthogonal frequency - divisi on multiplexing and impulse radio (IR). The key technological contribution of the work is the design of an MBAN with frequency agility and frequency domain spectrum shaping capabilities. When multiple devices are operating in the same frequency segments and are placed close to one another, as may occur in busy medical facilities, these qualities make it simpler to prevent interference. A Bio Cog algorithm was also developed to set up cognitive networks for disseminating medical data. Using a range of wire less technologies, such as XBee, Wi-Fi, and Bluetooth [76], it uses an efficient frequency spectrum allocation approach that takes network heterogeneity into account. [79] design a three -tiered telemedicine platform to offer prompt medical services. Body s ensors serve as the first layer of the design, while a UWB communication -enabled gateway serves as the second tier. In the third tier, the hospital and the mobility vehicle were connected via cognitive radio -enabled technology. Network connections are often irregular and occasionally unimpressive in a remote or rural place in terms of bandwidth availability. For the e - healthcare system to be able to adjust its transmission process based on the status of the communication network, an adaptive network architecture must be developed. In order to address network congestion and irregularity, as well as to provide priority -based health services to outlying primary health care facilities, a dependable wireless telemedicine network for e - health applications was proposed and assessed using a cognitive radio network technology. A MAC protocol that handles emergency data was created to order patient data in accordance with their medical conditions. By switching the data transmission process t o any of the available networks (GSM, 3G - UMTS, WiMAX, and 4G -LTE), the framework was able to address network congestion and consistency difficulties [80]. [81] suggested a particle swarm optimization approach for essential medical wireless application networks employing cognitive radio -enabled WBAN for effective battery use and uninterrupted data transfer in BAN. For the conveyance of patient data inside hospitals and telemedicine, [82] suggests an overlay cognitive radio method. In this case, the primary user is a set of telemedicine transmitters and receivers, and the secondary user is an in -hospital patient monitor. The bit error rate and spectral efficiency can both be improved with this technique. 5.2 Artificial Intelligent Based Telemedicine Networ k Design Two recent technology developments, artificial intelligence, and robots, may be helpful in telemedicine, in enhancing the human ability to respond to pandemics and other catastrophes, and in supporting home -based care. While developing decision s upport systems, more care must be given to applications in the medical industry [83]. In order to avoid cancer misdiagnosis caused by human error or improper data interpretation, [83] presents a reliable computer -aided diagnosis method supported by intelli gence learning models. To improve prediction performance, a feature modeling approach based on machine learning is suggested. The investigations made use of datasets relevant to breast, cervical, and lung cancer. Using supervised learning methods, the best characteristics minimized by the proposed system are trained and validated [83]. [84] recommend a logical -centered software design for popular health monitoring apps. To swiftly connect third -party sensor devices in BAN, PAN, and NAN Area Networks, the system features a transparent, smartphone -based sensing architecture with adaptable wireless interfaces and plug -and-play capability. By combining different components and scripting the application logic, the new visual Inference Engine Editor enables mach ine learning MAXIMIZING NETWORK CAPACITY, CONTROL AND MANAGEMENT IN DESI… 90 © 202 4 by the author(s). Licensee NIJOTECH. Vol. 4 3, No. 1, March 2024 This article is open access under the CC BY -NC-ND license. https://doi.org/10.4314/njt.v43i1.11 http://creativecommons.org/license s/by-nc-nd/4.0/ specialists and medical specialists to build data processing models. [85] gives a prediction model for a mobile phone - based video streaming system that was developed and tested for dementia patients using a number of carefully selected data mi ning approaches for categorization. The aptitude, living conditions, and p