Science Foundation Ireland (SFI) - co-funded under the European Regional Development Fund (Grant Number 16/RC/3835 – VISTAMILK and 13/RC/2077 - CONNECT)
Smoking remains one of the top 3 causes of illness in the US; it is one of top 5 causes of fire hazards in a home and is the single most preventable cause of illness and premature death in the US. The use of Deep Neural Networks (DNN) is demonstrated to detect cigarette smoke much sooner and with much higher accuracy than conventional smoke/carbon monoxide detectors used today. The hardware demonstration and prototype engages machine learning to not only discriminate cigarettes from other sources of smoke and carbon monoxide such as burning coal, wood or food – typically not possible with conventional smoke detectors, but also to accurately detect cigarette smoke produced in a room from a single cigarette when concentrations of component gases of cigarette smoke are extremely low. Our prototype also demonstrates the opportunity to classify and discriminate different levels of toxicity and flammability for spaces used by
Mobile Edge Computing (MEC) in cellular networks aims to bring computational capabilities close to end-users to reduce the latency of applications on the Internet of Things (IoT). This is particularly crucial to computation-intensive IoT broadband applications (e.g., video analytics, augmented reality, etc.) demanding a data processing task to be performed within a given time threshold. In this regard, the task offloading problem has been investigated in the literature in order to achieve an appropriate trade-off between energy and latency. However, there is a need for the joint design of task offloading mechanisms and cell selection algorithm as a mean to select the most appropriate to each device in order to meet delay requirements and fulfill resource constraints at the MEC server site. In this paper, we present the foreseen framework to tackle such a challenging problem. Keywords–Delay-sensitive applications, internet of things, mobile edge computing, offloading.
Wireless Sensor Networks (WSN) are becoming widely adopted in many industries including health care, building energy management and conditional monitoring. As the scale of low-power sensor network deployments increases, the cost and complexity of battery replacement and disposal have become more significant and in time may become a barrier to adoption. Harvesting ambient energies provides a pathway to reducing dependence on batteries and for many application scenarios, may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device (node) were modelled including sensors, transceiver and microcontroller as well as the energy source components (batteries, solar (PV) cells, Thermoelectric Generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre-characterized devices as well as add user defined devices. The goal of this simulation tool is to provide a WSN installer with a methodology to deploy systems with optimum battery lifetime by scaling battery and energy harvesting component sizes appropriately for a given scenario. It also allows a component designer to examine trade-offs in system level performance versus device specifications for optimum battery lifetime.
A method for an inductive communication and localization system for wireless sensors in internally illuminated photobioreactors is presented here. The communication is implemented through an on-off switched hartley-oscillator where its inductance is used as transmitting coil for the wireless sensor data. As modulation technique, the on-off keying is used. The magnetic field of the transmitting coil is sensed from outside the reactor with special designed receivers in order to evaluate the magnetic field components of the transmitting coil in all three spatial directions at one position. This enables the localization of the transmitting coil and thus the localization of the wireless sensor. A prototype has been implemented and test measurements performed with a two receiver setup. Additionally simulations were performed in order to see the accuracy improvement of the localization by using more receivers. Keywords–wireless sensors; inductive localization; inductive communication.
The demand for Wireless Sensor Networks (WSN) applied to industrial process monitoring and control is increasing as the forth industrial revolution (Industry 4.0) gathers pace. Flexibility and low cost make WSN the perfect choice for these modern 21st century manufacturing plants. Small and Medium Sized Enterprises (SMEs) have an important role in the growth of developing economies as they account for approximately 60% of all private sector employment, but are currently finding it difficult to take advantage of new sensor technologies. This paper describes the tests carried out with a WSN in order to ascertain the relationship between the Received Signal Strength Indicator (RSSI) and the Packet Error Rate (PER). Subsequently, a new RSSI based network management strategy is presented; it includes two RSSI tracking indices that guarantee an early warning in case the radio signal deteriorates. Both indices are generated in real time; the first estimates the RSSI tendency allowing for the mapping of a sample position in the set and its value, while the second compares the current RSSI to a preconfigured reference value. This article ends with the implementation and testing of the strategy on the ScadaBR Supervisory System. Keywords-Wireless Sensor Networks; WSN; PER; RSSI; Network Management.
The Internet of Things (IoT) technology allows physical objects integrated with sensors to monitor and communicate with the outside world. The paper develops an Intelligent Shopping Trolley (IST) system to provide IoT service in a hypermarket. Each trolley is equipped with sensing modules to cooperatively monitor its customer’s behavior. The sensing data is sent to a server through the ZigBee network to analyze the customer’s preference. Then, the retailer can notify the customer of sales information about interesting products in real time. Also, a customer can query the trolley for product data such as where it is. Then, the system schedules an obstacle-free shortest path to guide that customer. The paper reports both system design and implementation experience. A prototype is also deployed in a real-life hypermarket to verify the feasibility of IST system. Keywords–customer behavior; indoor navigation; Internet of Things (IoT); shopping trolley; ZigBee.
Wireless Sensor Network gateways are key components to connect smart environments with Internet of Things and to support Wireless Sensor Network applications. With regard to the increasing amount of smart devices, communicating with each other in Home area networks or over the Internet, low cost and low power devices are desirable. In this paper, we present such a low cost and low power gateway and system architecture with an example application in the Smart Home environment to control smart devices with a mobile application. We show an easy way to use low cost and low power hardware to expand the existing home network just with an USB-Stick without any extra power supply. The gateway we developed needs only 0.3 % of the average power consumption of the used network router. Keywords–Sensor Networks; Sensor Network Architecture; Sensor Network Gateway; Low-Power Devices; Internet of Things.
Within this work we aim to assess the structural integrity of buildings in the case of catastrophic events using several off-the shelf smart phones featuring vibration sensors. In order to compare the vibration samples obtained from different devices, precisely synchronized clocks are needed. In this article, we suggest how to align clocks based on sound beacons to mutually take clock drift and skew into account, in a precision which can be expected from traditional synchronization approaches like Network Time Protocol (NTP). Keywords–sensor networks; time synchronization; clock skew; clock drift
The work reported in this paper is funded by the MAROFF project (Norwegian Research Council) no. 195153/160 in collaboration with Faroe Petroleum.
This paper presents a proposal to develop and implement analytical and technology tools for estimating the level of vulnerability of existing buildings. The proposed platform includes the design and implementation of a comprehensive structural monitoring platform based on wireless sensor networks. This platform is a low cost instrument capable of providing the necessary information to implement methods of response analysis and consequently improve structural damage detection. With the development of the system, we will be able to obtain practical criteria and automated functions, in order to estimate seismic structural vulnerability of existing buildings in a preventive way. Keywords-structural vulnerability; seismic events; wireless sensor networks; acceleration sensors.
Wireless Sensor Network (WSN) is a network with limited power sensing devices with a communications infrastructure for monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. Among the concerns of these networks is prolonging the lifetime by saving nodes energy. There are several protocols specially designed for WSNs based on energy consumption and network lifetime. However, many WSNs applications require QoS (Quality of Service) criteria, such as latency and throughput. In this paper, we will compare three routing protocols for wireless network sensors LEACH (Low Energy Adaptive Clustering Hierarchy), AODV (Ad hoc on demand Distance Vector) and LABILE (Link Quality-Based Lexical Routing) using Castalia simulator in terms of energy consumption, number of nodes alive and stability period, throughput and latency time of packets received by the base station under various conditions. The results prove that LEACH had the longest network stability period, consumes the least energy and had the least latency time, while the LABILE and AODV protocols have the highest throughput. Keywords-WSNs; Quality of Service; LEACH; AODV; LABILE.
