Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.

Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs

The current state of knowledge of our lived world is in constant confrontation of two environments that does not yet fully converge. On the one hand stands a person with his or her own cultural traditions and historical devel‐ opment, and on the other hand stands the technology per se, in its relatively rapid development phase. These two parts constantly roam without bringing us a significant breakthrough in knowledge, which is the needed human cognitive reference point when interacting with computers. Our aim is to find out certain nodes of understanding between these two worlds, and propose a hypothesis for their possible approximation.

Design, User Experience, and Usability: Design Discourse

In this position paper a perspective on how movies and science fiction are currently shaping the design of robots is presented. This analysis includes both behaviour and embodiment in robots. We discuss popular movies that involve robots as characters in their storyline, and how people’s beliefs and expectations are affected by what they see in robot movies. A mismatch or contradiction emerges in what the robots of today can accomplish and what the movies portray. In order to overcome this mismatch we present design implications that may be of benefit to HRI designers.

Human robot interaction and fiction: A contradiction

The Internet of Medical Things (IoMT) is set to create a revolutionary shift in healthcare by transforming traditional methods of disease diagnosis to a data-centered, technology-driven analysis. However, data security and privacy issues are significant hurdles that need to be addressed. Blockchain technology has radically transformed data storage mechanisms using decentralization and cryptographic principles, making it a better choice for sensitive medical data than cloud storage services. A majority of the existing research focus on implementing blockchain in IoT devices which have much higher processing capabilities than IoMT devices such as pacemakers. Most of the existing blockchain frameworks are unsuitable for miniature IoMT devices due to high computational and storage requirements. This work aims to overcome this challenge by proposing a private blockchain framework in which different stakeholders of a medical system such as patients, doctors, IoMT devices, etc. act as nodes, creating a decentralized network in which physiological data output by IoMT devices can be stored securely and tamper-free forever. The proposed design is implemented in a Raspberry Pi network, using Proof of Authority (PoA) consensus mechanism which has minimal computational requirements. Data confidentiality is ensured using a double-encryption mechanism by means of Elliptic Curve Integrated Encryption Scheme. It is observed to perform well in comparison with existing approaches with a transaction speed of a minimum of 25 transactions per second and is easily scalable to accommodate different personnel of the healthcare sector. Further development on this design could potentially result in a major innovation in the IoMT industry in the form of a new array of real-time health monitoring devices with high security and data privacy.

A private Ethereum blockchain implementation for secure data handling in Internet of Medical Things

Aiming the problems that the clinical data of different patients is difficult for reasonable representation and the time interval between medical events is different, which lead to the difficulty of clinical prediction, a clinical prediction model based on the long short-term memory (LSTM) network optimized by fruit fly optimization algorithm in health time series data is proposed. First, FastText method is used to represent the interpretable vector of medical events, which can extract the concept relationship rich in medical information more effectively. Then, considering the strong dependence of clinical data on time stamp, LSTM network is used to model clinical events for better extraction of long-term and short-term information, so as to improve the prediction performance of the model. Finally, the fruit fly optimization algorithm is used to find the optimal super parameters of LSTM network, which can improve the training efficiency and prediction precision of the network. Experimental results on MIMIC datasets show that the prediction precision, Recall@k and MAP@k of the proposed model are better than those of other models. The validity of the model is proved.

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A Clinical Prediction Model in Health Time Series Data Based on Long Short-Term Memory Network Optimized by Fruit Fly Optimization Algorithm

An Ethics-Informed Approach to the Development of Social Robotics

Nowadays we are witnessing the formation of a massive Internet-of-Things (IoT) ecosystem that integrates a variety of wireless-enabled devices ranging from smartphones, wearables, and virtual reality facilities to sensors, drones, and connected vehicles. As IoT is penetrating every aspect of people’s life, work, and entertainment, an increasing number of IoT devices and the emerging IoT applications are driving exponential growth in wireless traffic in the foreseeable future. As a result, current IoT system architectures are facing significant challenges to handle millions of devices; thousands of servers; the transmission and processing of large volume of data, etc.

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Special Issue on Deep Reinforcement Learning for Emerging IoT Systems

Much research has focused recently on the development of smart environments and services for human-centered applications for personalized care and improved quality of life. This is especially relevant to support the elderly to lead an active and independent life. Recent efforts exploited the state of art development in the Internet of Things, Smart Sensors grid, Embedded and Wearable systems as well as Cloud Computing to build mathematical models of personal behavior and lifestyle largely driven by big data analytics. In order to overcome the range of challenges associated with the size and heterogeneity of the related data, hardware and software, as well as of the human and social factors involved, a context-aware architecture appropriate for smart environments is needed. This paper describes ACTiVAGE (ACTiVe AGeing sErvices), a conceptual framework for developing Personalized Elderly Care services that leverage big data analytics for context-awareness in smart environments.

A Context-Aware Architecture for Personalized Elderly Care in Smart Environments

The idea of cross-boundary clinical decision support has the potential to transform the design of open data and decision support platforms through a connected system that allows for harnessing of information and peer opinion across geographical and organizational boundaries for more effective decision making. In health care, cross-boundary clinical decision systems pose a major challenge from the perspective of e-security design. When clinical decision support systems, which essentially enable the transfer and storage of patient data, become cross-boundary systems, the protection of this data at different storage locations and in transit becomes more challenging. In this paper, we present a model of awareness for cross-boundary clinical decision support, which takes account of the concept of work practice as a design feature for enabling context-aware information sharing and secured health data management in cross-boundary clinical decision support. The proposed model is based on the practice theoretic paradigm and draws from a notion of context awareness as an interaction problem with a view to representing work practices as a context parameter for the design of computational systems for cross-boundary decision support. We illustrate how the approach addresses key security and privacy challenges in clinical decision support systems for cross-boundary support.

Obinna Anya

Papers

An Ethics-Informed Approach to the Development of Social Robotics

Special Issue on Deep Reinforcement Learning for Emerging IoT Systems

A Context-Aware Architecture for Personalized Elderly Care in Smart Environments

Cybersecurity design considerations for cross-boundary clinical decision support