How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

5G Smart and innovative Healthcare services: opportunities, challenges and prospective solutions

Due to its abilities to boost productivity, reduce costs and enhance user experiences, smart healthcare is widely recognised as a potential solution to reduce pressures on existing health systems. Since the new era of 5G will unite enhanced connectivity, improved cloud-based storage and interconnection of an array of devices and services, a massive boost in the digital transformation of healthcare is expected. In this transformation process, healthcare services such as medical diagnosis, treatment and remote surgery will be facilitated by a range of technologies such as Internet of Things, Robotics and Artificial Intelligence, among others, that will advance further under 5G. Moreover, real-time health services will become a reality and will offer people with quality care and improved experiences. On the other hand, different challenges can hinder the proliferation of 5G smart and innovative healthcare solutions, including security and heterogeneous devices. This chapter presents how 5G will boost digital transformation of healthcare through delivery and consumption of smart and innovative healthcare services, while probing into key hurdles in the process as well as prospective solutions

The Evolution of 5G: Delineating the Impact and Limitations across Transportation, Education, Healthcare, Agriculture, and Manufacturing

A world previously only thought possible in science fiction is about to become a reality. A world where doctors can operate on patients thousands of miles away. A world where students can experience ancient cities and distant galalike they are physically there. A world of fully self-driving cars. A world where every step of the supply chain is automated. A world where factories have a handful of employees overseeing robots handling the entire manufacturing process. A world of fully autonomous farms, where one farmer can manage an entire farm from seed to harvest from their smartphone. The development, implementation, and adoption of 5G cellular networks will make this world a possibility. 5G is the fifth generation of cellular networks. It is the successor of the current fourth generation (4G) networks. 5G technology is characterized by ultra-low latency, massive data rates, near perfect reliability, extreme density of connection, and wide coverage areas. 5G is not just another “G”, it has the potential to completely disrupt the way we work and live (Binney, 2020). 5G networks will impact the world in an almost infinite number of ways. Business models will change, the way people work will change, the way students learn will change, the way patients get health care will change, the way people get their food will change, the way people drive will change. In this thesis, I examine the development, applications, benefits, and socioeconomic impacts of 5G technology, as well as current limitations facing the industry and ways to address them

The Evolution of 5G: Delineating the Impact and Limitations across Transportation, Education, Healthcare, Agriculture, and Manufacturing

A world previously only thought possible in science fiction is about to become a reality. A world where doctors can operate on patients thousands of miles away. A world where students can experience ancient cities and distant galalike they are physically there. A world of fully self-driving cars. A world where every step of the supply chain is automated. A world where factories have a handful of employees overseeing robots handling the entire manufacturing process. A world of fully autonomous farms, where one farmer can manage an entire farm from seed to harvest from their smartphone. The development, implementation, and adoption of 5G cellular networks will make this world a possibility. 5G is the fifth generation of cellular networks. It is the successor of the current fourth generation (4G) networks. 5G technology is characterized by ultra-low latency, massive data rates, near perfect reliability, extreme density of connection, and wide coverage areas. 5G is not just another “G”, it has the potential to completely disrupt the way we work and live (Binney, 2020). 5G networks will impact the world in an almost infinite number of ways. Business models will change, the way people work will change, the way students learn will change, the way patients get health care will change, the way people get their food will change, the way people drive will change. In this thesis, I examine the development, applications, benefits, and socioeconomic impacts of 5G technology, as well as current limitations facing the industry and ways to address them

A use case of low power wide area networks in future 5G healthcare applications

Abstract. The trend in all cellular evolution to the Long-Term Evolution (LTE) has always been to offer users continuously increasing data rates. However, the next leap forwards towards the 5th Generation Mobile Networks (5G) will be mainly addressing the needs of devices. Machines communicating with each other, sensors reporting to a server, or even machines communicating with humans, these are all different aspects of the same technology; the Internet of Things (IoT). The key differentiator between Machine-to-Machine (M2M) communications and IoT will be the added -feature of connecting devices and sensors not only to themselves, but also to the internet. The appropriate communications network is the key to allow this connectivity. Local Area Networks (LANs) and Wide Area Networks (WANs) have been thought of as enablers for IoT, but since they both suffered from limitations in IoT aspects, the need for a new enabling technology was evident. LPWANs are networks dedicated to catering for the needs of IoT such as providing low energy consumption for wireless devices. LPWANs can be categorized into proprietary LPWANs and cellular LPWANs. Proprietary LPWANs are created by an alliance of companies working together on creating a communications standard operating in unlicensed frequency bands. An example of proprietary LPWANs is LoRa. Whereas cellular LPWANs are standardized by the 3rd Partnership Project (3GPP) and they are basically versions of the LTE standard especially designed for machine communications. An example of cellular LPWANs is Narrowband IoT (NB IoT). This diploma thesis documents the usage of LoRa and NB IoT in a healthcare use case of IoT. It describes the steps and challenges of deploying an LTE network at a target site, which will be used by the LoRa and NB IoT sensors to transmit data through the 5G test network (5GTN) to a desired server location for storing and later analysis.Matalan tehonkulutuksen ja pitkänkantaman teknologian käyttötapaus tulevaisuuden 5G:tä hyödyntävissä terveydenhoidon sovelluksissa. Tiivistelmä. Pitemmän aikavälin tarkastelussa matkaviestintäteknologian kehittyminen nykyisin käytössä olevaan Long-Term Evolution (LTE) teknologiaan on tarkoittanut käyttäjille yhä suurempia datanopeuksia. Seuraavassa askeleessa kohti 5. sukupolven matkaviestintäverkkoja (5G) lähestytään kehitystä myös laitteiden tarpeiden lähtökohdista. Toistensa kanssa kommunikoivat koneet, palvelimille dataa lähettävät anturit tai jopa ihmisten kanssa kommunikoivat koneet ovat kaikki eri puolia samasta teknologisesta käsitteestä; esineiden internetistä (IoT). Oleellisin ero koneiden välisessä kommunikoinnissa (M2M) ja IoT:ssä on, että erinäiset laitteet tulevat olemaan yhdistettyinä paitsi toisiinsa myös internettiin. Tätä kytkentäisyyttä varten tarvitaan tarkoitukseen kehitetty matkaviestinverkko. Sekä lähiverkkoja (LAN) että suuralueverkkoja (WAN) on pidetty mahdollisina IoT mahdollistajina, mutta näiden molempien käsitteiden alle kuuluvissa teknologioissa on rajoitteita IoT:n vaatimusten lähtökohdista, joten uuden teknologian kehittäminen oli tarpeellista. Matalan tehonkulutuksen suuralueverkko (LP-WAN) on käsite, johon luokitellaan eri teknologioita, joita on kehitetty erityisesti IoT:n tarpeista lähtien. LP-WAN voidaan jaotella ainakin itse kehitettyihin ja matkaviestinverkkoihin perustuviin teknologisiin ratkaisuihin. Itse kehitetyt ratkaisut on luotu lukuisten yritysten yhteenliittymissä eli alliansseissa ja nämä ratkaisut keskittyvät lisensoimattomilla taajuuksilla toimiviin langattomiin ratkaisuihin, joista esimerkkinä laajasti käytössä oleva LoRa. Matkaviestinverkkoihin perustuvat lisensoiduilla taajuuksilla toimivat ratkaisut on puolestaan erikseen standardoitu 3GPP-nimisessä yhteenliittymässä, joka nykyisellään vastaa 2G, 3G ja LTE:n standardoiduista päätöksistä. Esimerkki 3GPP:n alaisesta LPWAN-luokkaan kuuluvasta teknologiasta on kapea kaistainen IoT-teknologia, NB-IoT. Tässä diplomityössä keskitytään terveydenhoidon käyttötapaukseen, missä antureiden mittaamaa tietoa siirretään langattomasti käyttäen sekä LoRa että NB-IoT teknologioita. Työssä kuvataan eri vaiheet ja haasteet, joita liittyi kun rakennetaan erikseen tiettyyn kohteeseen LTE-verkon radiopeitto, jotta LoRa:a ja NB-IoT:a käyttävät anturit saadaan välittämään mitattua dataa halutulle palvelimelle säilytykseen ja myöhempää analysointia varten. LTE-radiopeiton rakensi Oulun yliopiston omistama 5G testiverkko, jonka tarkoitus on tukea sekä tutkimusta että ympäröivää ekosysteemiä tulevaisuuden 5G:n kehityksessä

Hardware, Heartware, or Nightmare: Smart-City Technology and the Concomitant Erosion of Privacy

Smart city technology is being adopted in cities all around the world to simplify our lives, save us time, ease traffic, improve education, reduce energy usage and keep us safe. This article discusses smart city projects being utilized in crime prevention and investigations. Specifically, this article highlights examples of gunshot detection devices and surveillance that have led to improvements in public safety in Cape Town, Chicago and Atlanta, and discusses their impacts to privacy

Hardware, Heartware, or Nightmare: Smart-City Technology and the Concomitant Erosion of Privacy

Smart city technology is being adopted in cities all around the world to simplify our lives, save us time, ease traffic, improve education, reduce energy usage and keep us safe. This article discusses smart city projects being utilized in crime prevention and investigations. Specifically, this article highlights examples of gunshot detection devices and surveillance that have led to improvements in public safety in Cape Town, Chicago and Atlanta, and discusses their impacts to privacy

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques