Internet of Things Testing Framework, Automation, Challenges, Solutions and Practices

The internet of things (IoT) is aimed at modifying the life of people by adopting the possible computing techniques to the physical world, and thus transforming the computing environment from centralized form to decentralized form. Most of the smart devices receive the data from other smart devices over the network and perform actions based on their implemented programs. Thus, testing becomes an intensive process in the IoT that will require some normalization too. The composite architecture of IoT systems and their distinctive characteristics require different variants of testing to be done on the components of IoT systems. This chapter will discuss the necessity for IoT testing in terms of various criteria of identifying and fixing the problems in the IoT systems. In addition, this chapter examines the core components to be focused on IoT testing and testing scope based on IoT device classification. It also elaborates the various types of testing applied on healthcare IoT applications, and finally, this chapter summarizes the various challenges faced during IoT testing.

Iot Device Classification in Cloud Organizations Using Deep Belief Networks

With the rapid increase in the usage of IoT devices, the cyber threats are increasing among the communication between the IoT devices. The challenges related to security surmounts with increasing number of IoT devices due to its functionality and heterogeneity. In recent times, deep learning algorithms are offered to resolve the constraints associated with detection of malicious devices among the networks. In this paper, we utilize deep belief network (DBN) to resolve the problems associated with identification, detection of anomaly IoT devices. Several features are extracted initially to find the malicious devices in the IoT device network that includes storage, computational resources and high dimensional features. These features extracted from the network traffic assists in achieving the classification of devices by DBN. The simulation is performed to test the accuracy and detection rate of the proposed deep learning classifier. The results show that the proposed method is effective in implementing the detection of malicious nodes in the network than existing methods.

Developing a Digital Solution for Remote Assessment in Multiple Sclerosis: From Concept to Software as a Medical Device

There is increasing interest in the development and deployment of digital solutions to improve patient care and facilitate monitoring in medical practice, e.g., by remote observation of disease symptoms in the patients’ home environment. Digital health solutions today range from non-regulated wellness applications and research-grade exploratory instruments to regulated software as a medical device (SaMD). This paper discusses the considerations and complexities in developing innovative, effective, and validated SaMD for multiple sclerosis (MS). The development of SaMD requires a formalised approach (design control), inclusive of technical verification and analytical validation to ensure reliability. SaMD must be clinically evaluated, characterised for benefit and risk, and must conform to regulatory requirements associated with device classification. Cybersecurity and data privacy are also critical. Careful consideration of patient and provider needs throughout the design and testing process help developers overcome challenges of adoption in medical practice. Here, we explore the development pathway for SaMD in MS, leveraging experiences from the development of Floodlight™ MS, a continually evolving bundled solution of SaMD for remote functional assessment of MS. The development process will be charted while reflecting on common challenges in the digital space, with a view to providing insights for future developers.