Applying deep learning models to large-scale IoT data is a compute-intensive task and needs significant computational resources. Existing approaches transfer this big data from IoT devices to a central cloud where inference is performed using a machine learning model. However, the network connecting the data capture source and the cloud platform can become a bottleneck. We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources. The basic processing stages and trained models are distributed towards the edge of the network and on in-transit and cloud resources. The proposed approach performs initial processing of the data close to the data source at edge and fog nodes, resulting in significant reduction in the data that is transferred and stored in the cloud. Results on an object recognition scenario show 71% efficiency gain in the throughput of the system by employing a combination of edge, in-transit and cloud resources when compared to a cloud-only approach. Video Sources Stream Preprocessing Edge Node1 Stream Preprocessing Edge Node2 Stream Preprocessing Edge Node3

With increasing availability and use of Internet of Things (IoT) devices such as sensors and video cameras, large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance, augmented reality and autonomous vehicles demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning based analytics on this data. This centralized approach has limited ability to support analysis of real-time, large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The stages are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. We demonstrate that for a 10K element data streams, with a frame rate of 15-100 per second, the job completion in the proposed system takes 49% less time and saves 99% bandwidth compared to a centralized cloud-only based approach.

The Constitution of Pakistan (1973) and our various educational policies have given clear direction on aims of our education system, which should be based on Islamic principles. However, since independence, Pakistan is still unable to devise a system of education, the aims of which are derived from Qur'an and Sunnah as mandated by the constitution. As such a dire need is felt to understand the aims and objectives education from our ideological perspective. It has been generally agreed that Al-Ghazali's thoughts comply with the Qur'anic principles, he is well accepted by vast strata of Muslims in Pakistan and appreciated by west as well. Accordingly, this qualitative study explored educational aims and objectives from Al-Ghazali's thoughts and philosophy, using content analysis of various writings of and on Al-Ghazali. Based on this research, we can conclude that the aim of Islamic education should be associated with the aim of a person's life. Based on this fundamental assumption and guideline provided by Al-Ghazali, we can develop aims and objectives of Islamic education as directed in the

Human activity recognition from sensor data is a fundamental research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed at enabling people's capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Moreover, new technological approaches to support AAL within the home or community setting offers people the prospect of more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A twolayer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is implemented. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart homes and smart health and offers a pervasive sensing environment for the elderly, persons with disability and chronic illness.

The automatic oscillometric monitor provides a useful, accurate, objective, and quantitative method for postoperative bypass graft surveillance.

The use of Information and Communications Technology (ICTs) in healthcare has the potential of minimizing medical errors, reducing healthcare cost and improving collaboration between healthcare systems which can dramatically improve the healthcare service quality. However interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a lack of collaboration and exchange of healthcare information. To solve this problem, cross healthcare system collaboration is required. This paper proposes a conceptual semantic based healthcare collaboration framework based on Internet of Things (IoT) infrastructure that is able to offer a secure cross system information and knowledge exchange between different healthcare systems seamlessly that is readable by both machines and humans. In the proposed framework, an intelligent semantic gateway is introduced where a web application with restful Application Programming Interface (API) is used to expose the healthcare information of each system for collaboration. A case study that exposed the patient's data between two different healthcare systems was practically demonstrated where a pharmacist can access the patient's electronic prescription from the clinic.

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage.

In this paper, we study the statistical properties of the connectivity of Vehicular Ad hoc NETworks (VANETs) with user mobility at the steady state. It is assumed that the nodes travel along a single dimensional strip with finite length. The nodes arrive at the service strip through one of the traffic entry points following a Poisson process and move along the strip in the same direction according to a user mobility model until they reach their exit point. The service strip allows the users with different speeds to pass each other. The nodes, which are within the service strip, are able to participate in communications. We derive the probability distribution of the user population size within the service strip and node's location distribution. Then, we determine the mean cluster size, fraction of nodes within the cluster and probability that nodes will form a single cluster. This work shows significance of mobility on the connectivity of ad hoc networks. The results of the paper may be used to avoid traffic congestion in the highways.