Advanced Healthcare Systems

Keywords :Internet of Things, healthcare, fog computing, cloud computing, security, privacy

IoT (Internet of Things) is very trending system consists of a variety of sensors, networking devices, microcomputer or microprocessor, optimized software, and different objects. To exchange data among each other, IoT devices, computers servers, and even cloud for data processing Apat et al . [24]. The demand of IoT devices is increasing day by day, and this will create a large opportunity for the IoT industry and researchers. Figure 2.1 illustrates healthcare using IoT. From the last few years, the sensor industry has evolved from making tiny sensors like photodetectors, temperature sensors, and hall sensors to more advanced sensors like blood glucose sensor, oxygen saturation level sensor, and ECG. With integration of fog computing and cloud, it facilitates the disease prediction of certain kinds and can get future insights of different diseases. The IoT system comprises various things like wearable IoT sensors, adaptive network interfaces, and optimized software integration module to cloud. Sensors are used to collect health-related data of sick patient with devices like oximeter for measuring oxygen saturation percentage in blood, blood pressure meter for measuring systolic blood pressure as well as diastolic blood pressure in arteries, blood glucose meter for measuring concentration of blood glucose using tiny drop of blood taken from human body, weight sensor for periodic measurement of patient weight for further analysis, and temperature sensor for monitoring real-time body temperature data.

Fall detection sensor for detection of accidental fall is using accelerometer and gyroscope and more advanced sensors like ECG for measuring electrical heart activity at rest. Adaptive networking interface provides support for a large number of networking protocols to easily integrate with different networking devices and software with enhanced algorithms to process health-related data efficiently and accurately Multag et al . [25]. There are a number of other uses of IoT devices. There are, like in fitness, a variety of fitness trackers available in the market for measuring daily activity like heart rate, sleep time, and running and walking time and also provide an interface for calling and messaging from right to the wrist. Companies offering these types of devices are Apple, Samsung, Fitbit, Xiaomi, etc. Next use of IoT devices is in smart homes, where the smart home Ghosh et al . [31], consists of a variety of a smart sensors like smart lock for keyless entry in car and homes, photo-director for turning garden light on at night and off at the day, sensor for detecting movement in room and triggering alarm when an intrusion happens, wireless camera with internet access to remotely monitor home and other premises, smart thermostat for controlling temperature according to human need, and smart fire alarm system for triggering alarm when a thing catches fire; IoT sensors can also be used in smart traffic management system for monitoring vehicle tracking on roads and switching traffic light according to traffic need. IoT sensors can also be used in warehouses for tracking workers activities. The increasing demand of smart devices in various fields creates huge amount of data and these data falls under various risk and challenges, if does not handled properly (especially health-related data). Patient data generated by smart heath sensors are very sensitive and critical, and management of these data is quite challenging Alihamidi et al . [32]. Every healthcare system must be provided security mechanism to handle five major factors in data handling: availability, integrity, confidentiality, authentication, and non-repudiation.

Figure 2.1Healthcare using IoT.

A paper proposed by Sun et al . [1] named “Security and Privacy in the Medical Internet of Things: A Review” was published in March 2018. In this paper, various security issues are discussed about health-related data which are traveling over the internet. Their main focus was data usability, integrity, and auditing.

They also talked about different existing techniques available like encryption and access control.

A survey paper by Zhang et al . [2] on “Security-Aware Measurement in Software-Defined Networking (SDN)” consists of SDN basic architecture, issue in security, performance analysis, bandwidth analysis, topologies, and future scope.

A paper named “Privacy-Preserving and Multifunctional Health Data Aggregation With Fault Tolerance for Cloud Assisted WBANs” written by Han et al . [3] discusses privacy preserving technique in wireless body area networks.

Abuwardih et al . [5] presented a paper on privacy preserving in healthcare data; in this paper, they discussed various types of attacks and privacy issues related to patient data. They also proposed some architectures and procedures to handle different type of attacks related to patient data. The paper was named “Privacy Preserving Data Mining on Published Data in Healthcare”, and it was proposed in 2016.

Anwar et al . [6] proposed a paper in 2015 named “Anytime Anywhere Access to Secure Privacy-Aware Healthcare Services: Issues Approaches and Challenges”; this paper consists of various approaches and challenges arises in healthcare industry for providing anytime and anywhere access of health-related resources. In this paper, they have shown approaches that are currently available and also discussed different policies made by government for information and technologies–related and international data–related security issues. The issues are generated by human, machine, and some other factors. All the security-related concern is discussed in detailed manner.

Rahman et al . [18] published a paper in ICOST (International Conference on Smart Homes and Health Telematics) naming “Inclusive Society: Health and Wellbeing in the Community and Care at Home”. In this paper, they have proposed a generic model “PriGen” for securing patient health-related data with the help of cloud storage. PriGen facilitates access of patient health-related data without involvement of any other party from the cloud as well as hiding highly sensitive data. PriGen uses homomorphic function for encryption of data that needs very high security and which is critical. This algorithm preserves the highly sensitive health-related personal data in public cloud environment and maintains confidentiality of the patient data.

Bindahman et al . [19] proposed a paper in 2011 ICIEIS (Informatics Engineering and Information Science: International Conference) in which they talked about general concept of privacy problem related to patient health data. They discussed various available security measures and its performance comparison related to healthcare data. Based on that, they also suggested some techniques for those security-related problems.

Next is the paper written by Dubovitskaya et al . [20] entitled “ICT Systems Security and Privacy Protection: 30th IFIP TC 11 International Conference SEC 2015”. In this paper, they have discussed various problems in building of health-related database for heterogeneous environment, where data are coming from various sources of different network environment of different hospitals. The integration of data comes from different locations. They introduced scaling and securing techniques for patient e-health data. They have used an algorithm called RSDB (Representative Protein Sequence Database) for collecting patient data efficiently and securely that are coming from various sources.