26 research outputs found
A systematic review on energy efficiency in the Internet of Underwater Things (IoUT): recent approaches and research gaps
Due to the advancement of wireless communications, Internet of Things (IoT) becomes a promising technology in
today’s digital world. For the enhancement of underwater applications such as ocean exploration, deep-sea
monitoring, underwater surveillance, diver network monitoring, location and object tracking, etc., Internet of
underwater things (IoUT) has been introduced. However, underwater communication suffers from energy consumption due to fluctuations of the underwater environment and operational factors according to the distributions of objects or vehicles in shallow and deep water. The IoT quality of service (QoS) in underwater
communication networks is critically affected by the different energy factors related to networking and the
physical layer. Network topology and routing protocol are two important major factors affecting the power
consumption of IoUT nodes and vehicles. The clustering approach is considered the best choice for IoUT,
however it may suffer from various influences related to the underwater environment. The optimisation-based AI
technologies in clustering approaches enable to achieve of energy efficiency for IoUT applications. This paper
provides a systematic review of different energy efficiency methodologies for IoUT, and classified them according
to the strategies used, in addition to the research gaps in clustering-based approaches, and future directions
Performance evaluation of uplink shared channel for cooperative relay based narrow band internet of things network
– Low Power Wide Area Network (LPWAN) is one of
the fastest growing network techniques provides efficient
communciations for smart cities, e-Health, industry 4.0 and
other applications. LPWAN enables long-rang communcaitons
for M2M and cellular IoT networks. Narrowband-IoT (NB-IoT)
is a type of LPWAN developed by 3GPP to connect a wide stream
of IoT services and devices. NB-IoT systems rely on the
mechanism of repeating the same signal every specified period of
time in order to improve radio coverage better than it is in LTE
systems. Repetition process is used to enhance the coverage of
NB-IoT and for upgrade throughput as well. However, increasing
the repetition of the signal significantly may give a negative result
relative to the bandwidth limits. A cooperative relay (CoR) can
be used beside repetition mechanism to helps reduce bandwidth
stress. Moreover, the use of CoR for NB-IoT in physical uplink
shared channel with repetitions will enhance the throughput.
This paper will evaluate the performance of the CoR to enhance
physical uplink shared channel in NB-IoT. The NB-IoT system
model is simulated bu MATLAB to demonstrate the use of
Cooperative relay (CoR) scheme in NPUSCH for NB-IoT for
performance evaluation and comparison of using CoR scheme by
considering metrics like data rate, throughput, and delay. The
results conclude that in using CoR in NB-IoT gives high
performance in overall NoT network throughput
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Abstract
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms