Evaluation of classical machine learning techniques towards urban sound recognition embedded systems

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

HPC as a Service: A naive model

Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is out of range from the majority. Nowadays Supercomputers are actually clusters of computers usually made-up of commodity hardware. Such clusters are called Beowulf Clusters. The history of which goes back to 1994 when NASA built a Supercomputer by creating a cluster of commodity hardware. In recent times a lot of effort has been done in making HPC Clusters of even single board computers (SBCs). Although the creation of clusters of commodity hardware is possible but is a cumbersome task. Moreover, the maintenance of such systems is also difficult and requires special expertise and time. The concept of cloud is to provide on-demand resources that can be services, platform or even infrastructure and this is done by sharing a big resource pool. Cloud computing has resolved problems like maintenance of hardware and requirement of having expertise in networking etc. An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud. The main target is to create a system which can develop a capability of providing computing power as a service which to further be referred to as Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B and 3B+ Single Board Computers. The reason for using RPi boards was increasing popularity of ARM processors in the field of HPCComment: 2019 8th International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan, 201

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

A Lightweight Network-Controlled Power Strip for Low-Cost Cluster Systems

Low-cost clusters are not equipped with costly, sophisticated tools and cannot be controlled remotely. This work aims at addressing this issue and develops a lightweight network-controlled power strip, which enables administrators to monitor the cluster and perform operation via remote. The power strip is controlled via a web interface and a RESTful web service, which are implemented with the programming language Python and the web framework Flask. The solution is inexpensive and easy to implement and use. In this paper, we describe in detail the development and construction of the prototype of the solution and discuss its purchase cost and power consumption

Cyber security investigation for Raspberry Pi devices

Big Data on Cloud application is growing rapidly. When the cloud is attacked, the investigation relies on digital forensics evidence. This paper proposed the data collection via Raspberry Pi devices, in a healthcare situation. The significance of this work is that could be expanded into a digital device array that takes big data security issues into account. There are many potential impacts in health area. The field of Digital Forensics Science has been tagged as a reactive science by some who believe research and study in the field often arise as a result of the need to respond to event which brought about the needs for investigation; this work was carried as a proactive research that will add knowledge to the field of Digital Forensic Science. The Raspberry Pi is a cost-effective, pocket sized computer that has gained global recognition since its development in 2008; with the wide spread usage of the device for different computing purposes. Raspberry Pi can potentially be a cyber security device, which can relate with forensics investigation in the near future. This work has used a systematic approach to study the structure and operation of the device and has established security issues that the widespread usage of the device can pose, such as health or smart city. Furthermore, its evidential information applied in security will be useful in the event that the device becomes a subject of digital forensic investigation in the foreseeable future. In healthcare system, PII (personal identifiable information) is a very important issue. When Raspberry Pi plays a processor role, its security is vital; consequently, digital forensics investigation on the Raspberry Pies becomes necessary