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Energy Efficient Data Management in Health Care
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Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.
The extensive use of sensor technology in every sphere of life, along with the continuous digitization of society, makes it realistic to anticipate that the planet will soon be patched with small-sized devices all over the place in the not-too-distant future. These devices give monitoring and surveillance capabilities, as well as access to a vast digital universe of information, among other things. Finding data and information, as well as processing enquiries, is made much easier thanks to the seamless transmission of information over wireless media, which is based on the “anywhere, anytime, everywhere” paradigm that allows information to be sent anywhere, at any time. Sensing networks came into existence as a consequence of the downsizing of wireless devices that are capable of receiving information from a source, transferring it, and processing it. Sensor networks, although they share many of the features, uses, and limits of ad hoc networks, have their own set of capabilities that are unique to them. While performing their responsibilities, sensor networks must contend with a variety of security issues, including unsecured wireless channels, physical compromise, and reprogramming. Because of the small size and ubiquitous availability of wireless sensor networks, compromise attacks are the most severe kind of attack in this environment (WSNs). With the proliferation of wireless sensor networks (WSNs), it is becoming more difficult to rely only on machine learning techniques. We sought to tackle the security challenge by developing a key management system as well as a secure routing mechanism. We are building scalable key management approaches that are resistant to node compromise and node replication attacks, which we will demonstrate in our proposed study, by using deployment-driven localization of security information and leveraging distributed key management. Using a security-aware selection of the next hop on the route to the destination, we were able to design safe routing algorithms that were effective.
The internet of things (IoT) is an ecosystem of connected objects that are accessible and available through the internet. This "thing" in the IoT could be a sensor such as a heart monitor, temperature, and oxygen rate in the blood. These sensors produce huge amounts of information that lead to congestion and an effect on bandwidth in the IoT network. In this paper, the proposed system is based on the Zstandard compression algorithm to compress the sensor data to minimize the amount of data transmitted from the IoT level to the fog level and decrease network overloading. The proposed system was evaluated using compression ratio, throughput, and latency time for healthcare applications. The result showed better calculation through decreased response time and increased throughput for transmitted data compared with the case of non-compressed data. It showed the compression data ratio about 70% of orignial data, maximum number of IoT sensor reads as 100, throughput is 85.43 B/ms, and fog processing delay is 6.25 ms.
The advances of 5G, sensors, and information technologies enabled proliferation of smart pervasive sensor networks. 5G mobile networks provide low-power, high-availability, high density, and high-throughput data capturing by sensor networks and continuous streaming of multiple measured variables. Rapid progress in sensors that can measure vital signs, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. The increase in size and complexity of wireless sensor networks and expansion into multiple areas of health monitoring creates challenges for system design and software engineering practices. In this paper, we highlight some of the key software engineering and data-processing issues, along with addressing emerging ethical issues of data management. The challenges associated with ensuring high dependability of sensor network systems can be addressed by metamorphic testing. The proposed conceptual solution combines data streaming, filtering, cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. Integration of blockchain technologies and artificial intelligence offers a solution to the increasing needs for higher accuracy of measurements of vital signs, high-quality decision-making, and dependability, including key medical and ethical requirements of safety and security of the data.
The use of wireless sensor networks, which are the key ingredient in the growing Internet of Things (IoT), has surged over the past few years with a widening range of applications in the industry, healthcare, agriculture, with a special attention to monitoring and tracking, often tied with security issues. In some applications, sensors can be deployed in remote, large unpopulated areas, whereas in others, they serve to monitor confined busy spaces. In either case, clustering the sensor network’s nodes into several clusters is of fundamental benefit for obvious scalability reasons, and also for helping to devise maintenance or usage schedules that might greatly improve the network’s lifetime. In the present paper, we survey and compare popular and advanced clustering schemes and provide a detailed analysis of their performance as a function of scale, type of collected data or their heterogeneity, and noise level. The testing is performed on real sensor data provided by the UCI Machine Learning Repository, using various external validation metrics.
Internet of Things (IoT) has the idea of receiving data and sending them for each object within the communication network. One of the main issues in this type of networks is handling a growing volume of data with various data sources and data types to satisfy the performance requirements of applications. In this regard, data management in the IoT plays an important role in its efficient operations and has become a major research topic. However, the data management has a crucial role in the IoT, there is not any comprehensive and systematic work to analyze its approaches. Thus, main propose of this paper is to systematically review and study the existing data management approaches in the IoT. The data management approaches are classified into three main classes, including SQL database, NoSQL database, and graph database. In addition, the detailed comparison of the important mechanisms in each category brings a recommendation for further works.
Wireless sensor networks consist of a large number of small, low-power sensors that communicate through wireless links. Wireless sensor networks for healthcare have emerged in recent years as a result of the need to collect data about patients’ physical, physiological, and vital signs in the spaces ranging from personal to hospital and availability of the low cost sensors that enables this data collection. One of the major challenges in these networks is to mitigate congestion. In healthcare applications, such as medical emergencies or monitoring vital signs of patients, because of the importance and criticality of transmitted data, it is essential to avoid congestion as much as possible (and in cases when congestion avoidance is not possible, to control the congestion). In this paper, a data centric congestion management protocol using AQM (Active Queue Managements) is proposed for healthcare applications with respect to the inherent characteristics of these applications. This study deals with end to end delay, energy consumption, lifetime and fairness. The proposed protocol which is called HOCA avoids congestion in the first step (routing phase) using multipath and QoS (Quality of Service) aware routing. And in cases where congestion cannot be avoided, it will be mitigated via an optimized congestion control algorithm. The efficiency of HOCA was evaluated using the OPNET simulator. Simulation results indicated that HOCA was able to achieve its goals.
Wearable sensors and forms of social media are very helpful in
providing a new way to collect medical data for effective healthcare
monitoring. Technological solutions have benefited people's daily
lives practically in every industry. Sensing devices that detect and
gather signals from the sources such as human, environment, and
machines, design the response accordingly. Innovative sensor
techniques are used in a variety of applications in healthcare,
production, lifestyle, fitness, and everyday life. These sensors can
be used as wrist watch, implanted under the body or skin and can be
attached with the treads of clothes. Wearable sensors which have
transmission, computing, storage, and sensing capabilities are
known as a wireless body sensor network (WBSN). The Internet of
Things (IoT) is an emerging information technology that enables
performance enhancement and improved solutions in the health
sector. This review gives a brief outline of the various types of
sensors that are used for healthcare monitoring, WBSN components
and its applications.
The rapid advancement in Internet of Things (IoT) associated with biosensors provides many benefits to the humans, such as smart healthcare systems (SHS). SHS offer plenty of opportunities to the healthcare professionals and hospitals to monitor the patients’ health in a remote basis. The combination of IoT devices with the increasing networked nature of the healthcare environment permit the healthcare professionals to deliver emergency and precautionary medical services to their patients more efficiently and effectively. SHS-collected health data are highly sensitive in nature. However, SHS are rendering the patients’ health data vulnerable to various attacks. Maintaining the security and privacy of the patients’ health data are the biggest challenges in smart healthcare systems. This chapter investigates in detail the privacy of health data and security threats of IoT healthcare, as well as rules and policies involved in developing an IoT-based smart healthcare system. This chapter also explains the various ubiquitous legislation and attacks exposed to corporate digital properties and patients’ health information along with the essential existing solutions such as a cryptographic function and a communication protocol for overcoming the existing challenges.
In recent times, networking, information, and communication technologies are widely employed and getting spread in the area of medical applications. In a short period, real-time applications for healthcare monitoring produce a huge amount of data. Data is to be bursting in the case of critical applications; hence there is a greater need to enable reliable communication methods that ensure energy efficiency. A large amount of data results in high data transmission, network congestion, and latency, which in turn cause a hop increment between the IoT and cloud servers, and thus data might be unprocessed and insufficient for end-users. Since Fog computing is a distributed intermediate layer between the edge network and the cloud environment, latency can be reduced to a remarkable level, and the reliable communication can be achieved with the help of fog computing on IoT assisted wearable sensor platform. This research proposes a dynamic model for analysis and the Heuristic Hybrid Time Slot Fuzzy-Allocation Algorithm (HHTSF-AA), which improves health monitoring by indulging IoT assisted wearable sensor platform. Fog computing assisted wearable sensor platform is the most suitable and reliable platform for robust life-critical applications that are likely not to be delayed in communication. Besides, routing data packets are equipped with a low-cost energy minimum selection algorithm incorporated to improve the overall network performance. Dynamic slot assignment reduces time in a network and allows high levels of network capacity channel utilization.
In recent times, networking, information, and communication technologies are widely employed and getting spread in the area of medical applications. In a short period, real-time applications for healthcare monitoring produce a huge amount of data. Data is to be bursting in the case of critical applications; hence there is a greater need to enable reliable communication methods that ensure energy efficiency. A large amount of data results in high data transmission, network congestion, and latency, which in turn cause a hop increment between the IoT and cloud servers, and thus data might be unprocessed and insufficient for end-users. Since Fog computing is a distributed intermediate layer between the edge network and the cloud environment, latency can be reduced to a remarkable level, and the reliable communication can be achieved with the help of fog computing on IoT assisted wearable sensor platform. This research proposes a dynamic model for analysis and the Heuristic Hybrid Time Slot Fuzzy-Allocation Algorithm (HHTSF-AA), which improves health monitoring by indulging IoT assisted wearable sensor platform. Fog computing assisted wearable sensor platform is the most suitable and reliable platform for robust life-critical applications that are likely not to be delayed in communication. Besides, routing data packets are equipped with a low-cost energy minimum selection algorithm incorporated to improve the overall network performance. Dynamic slot assignment reduces time in a network and allows high levels of network capacity channel utilization.
Smart pervasive sensor networks are becoming an important part of our daily lives. Low-power, high-availability and high-throughput 5G mobile networks provide the necessary communication means for highly pervasive sensor networks, introducing a technological disruption to health monitoring. The meaningful use of large concurrent sensor networks in healthcare requires multi-level health knowledge integration with sensor data streams. In this paper, we highlight some software engineering and data-processing issues that can be addressed by metamorphic testing. The proposed solution combines data streaming with filtering and cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. © 2019 IEEE.
Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.
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