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Ambient assisted living is good way to look after ageing population that enables us to detect human’s activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms. [ABSTRACT FROM AUTHOR]

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%. [ABSTRACT FROM AUTHOR]

The zero-wait (ZW) policy is widely held to achieve maximum information “freshness,” i.e., to achieve minimum peak age (PA) and maximum throughput, for real-time Internet-of-Things applications. In this letter, it was shown through a series of experiments that the ZW policy is not necessarily the optimum policy for freshness or throughput in all real-world scenarios. First, the effect of delay on the ZW policy was shown on a local area network (LAN). Afterward, the server was located on the Internet, and it was shown that the ZW policy incurred a two-fold PA and throughput performance degradation compared with continuously sending status updates. [ABSTRACT FROM AUTHOR]

Carrier aggregation (CA) functionality introduces new challenges for radio resource management (RRM) function of network. In this paper, an optimum and efficient RRM algorithm is proposed for Long Term Evolution-Advanced with reduced complexity and compared with the state-of-the-art (SOTA) solutions. The proposed algorithm optimally assigns component carriers, resource blocks, and modulation and coding scheme values to users, based on their channel state information and CA capabilities. Most of the SOTA solutions are based on approximate approaches that have much inferior performance compared to the performance of our proposed optimal solution. Furthermore, performance of the proposed solution has been evaluated using system-level simulations, and results show clearly that the proposed algorithm outperforms the SOTA algorithms in terms of fairness throughput and average cell throughput. [ABSTRACT FROM PUBLISHER]

Energy-efficient resource allocation can significantly reduce power consumption of network and, consequently, operating expenses. In this letter, we study the energy efficiency of long-term evolution (LTE) networks with carrier aggregation, where user equipments are able to utilize multiple component carriers simultaneously. Furthermore, an LTE-compliant optimal resource block allocation algorithm to maximize throughput while minimizing energy consumption is proposed. Single-cell multi-user simulation results show that the proposed algorithm offers significant gains in terms of cell throughput and energy consumption over the state-of-the-art solutions. [ABSTRACT FROM PUBLISHER]