Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems

By Bharat Bhushan

Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems explores the various benefits and challenges associated with the integration of blockchain with IoT healthcare systems, focusing on designing cognitive-embedded data technologies to aid better decision-making, processing and analysis of large amounts of data collected through IoT. This book series targets the adaptation of decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures, as well as big data and Internet of Things (IoT) problems can be handled in practice.

Current Internet of Things (IoT) based healthcare systems are incapable of sharing data between platforms in an efficient manner and holding them securely at the logical and physical level. To this end, blockchain technology guarantees a fully autonomous and secure ecosystem by exploiting the combined advantages of smart contracts and global consensus. However, incorporating blockchain technology in IoT healthcare systems is not easy. Centralized networks in their current capacity will be incapable to meet the data storage demands of the incoming surge of IoT based healthcare wearables.

• Highlights the coming surge of IoT based healthcare wearables and predicts that centralized networks in their current capacity will be incapable to meet the data storage demands
• Outlines the major benefits and challenges associated with the integration of blockchain with IoT healthcare systems
• Investigates use-cases and the latest research on securing healthcare IoT systems using blockchain technology
• Discusses the evolution of blockchain technology, from fundamental theories to applications in healthcare systems
• Gathers and investigates the most recent research solutions that handle security and privacy threats while considering resource constrained IoT healthcare devices

• Language: English
• Publisher: Academic Press
• Release date: Jan 10, 2023
• ISBN: 9780323993913

Book preview

Preface

In the recent past, a huge range of connected wireless devices have emerged, thereby dramatically increasing the overall data generated by internet of things (IoT) systems. The IoT, or Industry 4.0, has led a digital revolution that has not bypassed the healthcare sector, making it necessary to switch to sophisticated medical sensors from the already prevalent wearable IoT e-health devices. Due to advances in precision medicine and the rise of genetic research, healthcare systems have witnessed a novel approach to disease treatment and prevention that incorporates an individual patient’s lifestyle, surroundings, and genetic makeup. Furthermore, recent advances in information technology (IT) have enabled the development of health data tracking tools, large health information databases, and the engagement of individuals in their own healthcare. Therefore, integration of IT and healthcare has fostered transformative and revolutionary change in the health IT field. Intelligent medical systems, or e-health, are among the most widely accepted applications of IoT, where the medical data is sensitive and highly vulnerable to any unauthorized access.

However, lightweight IoT devices, having shallow energy footprints and decentralized topology, are incapable of withstanding the challenges related to healthcare data privacy and security. As conventional security schemes are inapplicable for such systems, there is an urgent need for secured, distributed, lightweight, and scalable safeguards. Furthermore, the current IoT-based healthcare systems are incapable of sharing data between platforms in an efficient manner and holding them securely at the digital and physical levels. To this end, blockchain technology guarantees a fully autonomous secure ecosystem by exploiting the combined advantages of smart contracts and global consensus. Healthcare systems can be transformed by enhancing the accuracy of electronic health records (EHRs) and enhancing connections among heterogeneous systems. Blockchain technology supports EHR management, enables remote patient monitoring, and plays a vital role in sharing, storing, and retrieving remotely gathered health data. It promises to answer the data integrity dilemma and facilitates better data-level collaboration between providers and payers, adopting the principle of securely storing the electronic health records (EHRs). Furthermore, blockchain is useful in the pharmaceutical industry to solve issues related to counterfeit medications that might have serious consequences for patients. It can also benefit the field of health insurance claims in preserving data auditability, transparency, and immutability.

Based on the scope and diversity of the topics covered, this book will serve as a valuable and useful resource to scholars, researchers, a wide range of professionals, material developers, technology specialists, and methodologists dealing with the multifarious aspects of data privacy and security enhancement in blockchain-based IoT healthcare systems. This book is designed to be the first reference choice at research and development centers, academic institutions, university libraries, and for industries and institutions dealing with current research topics and challenging issues related to IoT healthcare systems.

Chapter 1: Integration of E-health and Internet of Things

Koduru Hajarathaiaha; Satish Anamalamudia; Murali Krishna Enduria; Abdur Rashid Sangib; H.M. Lavanyac    a Department of Computer Science and Engineering, SRM University-AP, Mangalagiri, Andhra Pradesh, India

b School of Artificial Intelligence and Big Data, Yibin University, Sichuan, China

c Department of Computer Science and Engineering, NITK (National Institute of Technology Karnataka) Surathkal, Mangaluru, Karnataka, India

Abstract

The proliferation of healthcare-specific Internet of Things (IoT) devices opens up huge opportunities in automated healthcare management systems. Integrating the healthcare system with IoT networks is crucial due to time-critical sensitive applications. State-of-the-art IoT networks transmit the application data through nondeterministic best effort traffic flows, whereas the data from different nodes used to be scheduled in a single shared channel. On the contrary, data from healthcare systems needs to be transmitted in predetermined per-flow deterministic traffic flows to guarantee the quality of service (QoS) in terms of transmission delay and packet drops. To achieve this, the current IoT protocol stack needs to be updated with the support of deterministic traffic flows to ensure the guaranteed QoS in healthcare and medical applications. Hence, this chapter proposes the protocol aspects (scheduling and routing protocol) to integrate E-health with IoT networks to ensure predetermined traffic flows with predictable end-to-end delays.

Keywords

Scheduling protocols; Reactive routing protocols; 6LoWPAN; IEEE 802.15.4

1: Introduction

State-of-the-art distributed networks have become ubiquitous and are able to touch almost every corner of the globe. This in turn affects human life in previously unimaginable ways through complete automation [1]. With these developments, the existing Internet architecture is going to enter into a new era with more pervasive connectivity where a very wide variety of applications can be connected to the World Wide Web (WWW). This concept has come to be known as the Internet of Things (IoT), which can be defined as an interaction between the physical and digital worlds through constrained capabilities. The digital world in general will interact with the physical world with the help of sensors and actuators. The Internet of Things (IoT) can be depicted as a combination of sensors and actuators that provide and receive the information that is being digitalized and placed into bidirectional networks to transmit user data for use in different services. In general, multiple different sensors can be attached to a single device in order to measure a broad range of physical variables or phenomena and then transmit the sensed data to either the cloud or a server for data analysis [2,3]. The sensing can be analyzed and is understandable as a service model that is shown in Fig. 1. With this service model, we can redefine the Internet of Things as computing and networking capabilities that are being embedded in any kind of object (healthcare, vehicles, industrial applications, etc.). These kinds of capabilities can be used to query the state of the object through sensing capabilities and to change its state of behavior.

Fig. 1

Fig. 1 Architecture of IoT systems.

With the IoT, a new kind of world can be created with interconnection of all the devices and appliances through distributed internetworking [4]. For the interconnection with different networks, IoT devices are designed to be equipped with embedded sensors, actuators, microprocessors, and transceivers. IoT is a combination of various technologies that work together to provide thing-to-thing communication. Sensors, along with the actuators, are constrained devices that help to interact with the physical domain. The application data that is collected by the sensors has to be routed, stored, and processed intelligently in order to trigger useful operations from the sensed information. It is noteworthy that the term sensor can be any mobile device or even a washing machine that count as a sensor node to provide the input from its current state. Actuator can be defined as a device which can be used to effect the change in the environment, such as the temperature monitor of a patient in a hospital. The processing and the storage of the application data that was sent by the generating sensors can occur at the edge of the network itself (gateways) or at the remote server.

The limitation of an IoT object is with its storage and processing capabilities due to constrained resources, which are often related to size, node energy, transmission power, and computational ability. Along with the constrained challenges, data collection and data handling need to be taken care of during network communication. The communication in between IoT devices is mainly with wireless (unguided medium) technology because, in general, they will be installed at geographically different locations, and narrow band radio channels are often highly unreliable with high rates of channel distortion. In such a scenario, assuring the reliable communication to the application data without too many packets drops and retransmissions is a significant issue in the communication technologies [5]. Currently, state-of-the-art IoT devices, such as automated blood pressure monitoring or other healthcare devices, consist of the combination of communication and sensing capabilities. Sensing capabilities are constantly increasing to incorporate better communication and sensing tools in automated healthcare systems.

The basic architecture of IoT systems is categorized into four layers, namely:

1.Object sensing layer

2.Data exchange layer

3.Information integration layer

4.Application data generation layer

Consumers of the sensor data can communicate with the sensor network via the information integration layer, which is responsible for all the communication and transactions. In addition, new requirements and challenges to exchange the data along with information filtering and data integration is significant in the network architecture [6,7]. The use of distributed cloud technologies continues to grow exponentially due to the increase in deployment of constrained IoT devices. New infrastructure platforms, along with the software applications, have begun to be deployed in the IoT networks. With the Internet of Things (IoT), an integrated communication within the interconnected devices and platforms can be engaged in both the virtual and physical world. Remote digital healthcare-based IoT systems makes the transmission of medical data as a routine daily task [8,9]. Hence, it is significant to develop efficient scheduling and routing protocols to efficiently transmit and receive the patient’s diagnostic data within the IoT environment. Smart IoT devices can be interconnected with the traditional Internet through wired or wireless networks [10].

In this work, the existing constrained protocol stack of the sensor network is being amended with the IETF 6LoWPAN adaptation layer to compress the IPv6 address along with other header fields of the transport and network layer. In addition, a point-to-point reactive AODV-RPL routing protocol is being proposed to support asymmetric bidirectional constrained links. It is crucial to implement the asymmetric links to share the network load, especially in the constrained IoT networks. This will help to reduce packet drops and enhance the performance of the overall network. For medical applications, the network throughput plays a prominent role in transmitting the patient data to the destination within predetermined timeslots.

2: Overview of protocol stack in IoT networks

The IETF (Internet Engineering Task Force) propose 6LoWPAN (IPv6 over low-power wireless personal area network) as a standard communication protocol stack for low power radio devices. 6LoWPAN support in traditional sensor networks enables routing to public switched networks through an IPv6 address. However, without modifications in the IP protocol stack, it is difficult to enable routing in sensor networks to operate in the heterogeneous networks. This is because the traditional IP protocol stack is mainly designed with the assumption of operating with high data supportable rates, whereas in IoT networks it is completely constrained in terms of radio and network resources. One approach to integrate the traditional wireless networks with the IPv6-enabled sensor network is with the support of tunneling. However, the implementation of tunneling in IPv6 transition is difficult to interoperate the heterogeneous networks because IPv6 is mainly used for 6LoWPAN protocol, which runs over the IPv6 network. This will not provide a way to utilize an existing IPv4 routing infrastructure to carry IPv6 traffic that was generated through IoT networks.

IETF 6LoWPAN is a constrained protocol specification proposed to enable the IPv6 standards to be used in low-power constrained wireless networks, specifically with the Institute of Electrical and Electronics Engineers IEEE 802.15.4 standard. It is being managed and maintained by the IETF 6LoWPAN working group. The reason for introducing 6LoWPAN protocol stack to IoT networks is that the existing IPv6 is too bulky (in terms of header formats and packet sizes) for IPv6-enabled wireless sensor networks. In 6LoWPAN networks, a new layer, called the adaptation layer, is introduced in between the network and MAC/PHY layer without disturbing the main functionality of the IPv6 protocol [11]. In traditional wireless (infrastructure or ad hoc) networks, the IPv6 maximum transmission unit (MTU) is 1280 bytes, which can be supported by 2.4 GHz or 5 GHz spectrum bands through IEEE 802.11 a/b/g/n standards. However, the transmission of the same 1280 bytes with IPv6 maximum transmission unit (MTU) is difficult to achieve in constrained narrow bands over IEEE 802.15.4 standard. To deal with this, IETF 6loWPAN group propose a new layer called adaptation layer, which performs fragmentation and reassembly to transmit/receive the minimal packets over IEEE 802.15.4 standard. The detailed overview and the operation of the adaptation layer in 6LoWPAN networks is explained in IETF standard RFC4944 [12].

The IETF proposed 6LoWPAN protocol stack consists of low-power wireless area networks (LoWPANs), which are collection of IPv6 subnetworks. In the sense, 6LoWPAN contains a collection of 6LoWPAN nodes that share a similar IPv6 address prefix (first 64-bits of a 128-bit IPv6 address). Like nodes in wireless ad hoc networks, 6LoWPAN nodes can play the dual role of host or router along with one or more gateway routers. In general, we have three types of 6LoWPANs networks, namely simple LoWPANs, extended LoWPANs, and ad hoc LoWPANs [13], as shown in Fig. 2. The first type, simple LoWPAN, will be connected through one LoWPAN edge router to another wired or wireless IP(IPv6 or IPv4) network. The second type of 6LoWPAN, extended LoWPAN, contains multiple edge/gateway routers along with a high speed backbone link to interconnect them [12]. The third type of 6LoWPAN, ad hoc LoWPAN, in general won’t be connected to the Internet and only operates without an infrastructure.

Fig. 2

Fig. 2 Architecture of 6LoWPAN networks (simple 6LoWPAN, extended 6LoWPAN, and ad hoc 6LoWPAN).

Fig. 3 shows the 6LoWPAN protocol stack in comparison with the traditional IP protocol stack. It is noteworthy that 6LoWPAN is almost identical to a traditional IPv6 implementation, but with two major differences [13,14]:

•The proposed 6LoWPAN stack, in general, only supports IPv6 addresses, because of which, a new adaptation layer (LoWPAN) has been implemented in the protocol stack to optimize and compress the IPv6 over constrained narrow bank link layers.

•6LoWPAN is specifically designed to work with IEEE 802.15.4 standard at the data link and physical layer.

Fig. 3

Fig. 3 6LoWPAN adaptation layer in the TCP/IP protocol stack.

The rest of this chapter is organized as follows. Section 3 briefly describes the existing scheduling protocols for IoT networks; following which, a new scheduling protocol is proposed for the end-to-end traffic flows with resource reservation protocols. Section 4 explains about the reactive routing protocols along with a detailed explanation of implementing the asymmetric bidirectional data links. The support of asymmetric bidirectional data links is of the utmost importance in machine-critical applications. Section 5 explains about the simulation/experimental analysis for the proposed AODV-RPL with asymmetric bidirectional links. Finally, Section 6 presents the conclusion and future work.

3: Scheduling protocols for best effort IoT networks

In constrained IoT networks, designing an optimal distributed scheduling algorithm is significant to maximize the utilization of CPUs and enhance the performance of the IoT networks in terms of throughput, and to minimize the end-to-end latency along with the power consumption. In general, the applications of IoT networks include repeated tasks that, in general, will be executed in various constrained sensor nodes, which may result in higher sensing cost with reduced node and network lifetime. This kind of issue can be resolved by simply assigning similar tasks within the specific region in a single network system. However, in reality, selecting the single network system for execution is always a challenging task. In addition, the selection of an efficient scheduling algorithm will avoid the repeated execution of the same distributed process, which may lead to unnecessary internode IoT communication.

The data flow of 6LoWPAN-IoT networks is usually depicted as a directed acyclic graph (DAG) whose vertex in the given network graph represents the constrained nodes and its communication edges depict the communication in between two IoT nodes for the application data transmission. In addition, the operation of the best effort scheduling protocols can be separated into two categories, namely, application task selection and node/network resource allocation. In first case, the application task selection scenario is proposed to determine the sequence of multiple traffic flows from different applications. These tasks, in general, can be divided in different ways as long as the successive dependencies cannot be violated [15]. Hence, the task execution phase may be implemented in different ways. Furthermore, the resource allocation phase is divided into two different traffic flows, namely, best-effort traffic flows and per-flow deterministic traffic flows. Scheduling protocols in the constrained distributed systems have been widely investigated by many researchers to efficiently utilize the shared medium with reduced collision rate due to channel contention. Brandt et al. [16] propose a low-rate cost-based scheduling algorithm for smart utility based IoT grids. The proposed algorithm aims to minimize the end-to-end delay with minimal packet drops along with consideration of the node channel utilization. Over the last few years, active research has been initiated in terms of the evaluation of the IEEE 802.15.4 wireless standard for constrained data transmission through scheduling protocols [17].

The proposed idea is to apply the GTS allocation mechanism to enhance and efficiently utilize the channel bandwidth through the scheduling concept with the assistance of IEEE 802.15.4 standard [18]. The 6TiSCH architecture within the 6LOWPAN networks is shown in the Fig. 4. It represents a reference protocol stack that is being implemented and tested within open-source simulators and supported by IETF and ETSI efforts. One of the major goals is to assist other network bodies to easily adopt the protocol stack as a whole in the IPv6-based IoT protocol stack. RPL is a standard routing protocol that is being proposed by IETF to route the data in constrained IoT networks. Until now, there is no need was identified to propose a specific Objective Function for 6TiSCH network [19]. The existing Minimal 6TiSCH Configuration describes the operation of proactive based RPL routing protocol (routing protocol for low-power and lossy networks) over a static schedule used in a slotted aloha fashion. This works well for active slots that may be used for transmission or reception of both one-to-one (unicast) and one-to-many (multicast frames)