Smart Signs: Showing the way in Smart Surroundings

This paper presents a context-aware guidance and messaging system for large buildings and surrounding venues. Smart Signs are a new type of electronic door- and way-sign based on wireless sensor networks. Smart Signs present in-situ personalized guidance and messages, are ubiquitous, and easy to understand. They combine the easiness of use of traditional static signs with the flexibility and reactiveness of navigation systems. The Smart Signs system uses context information such as user’s mobility limitations, the weather, and possible emergency situations to improve guidance and messaging. Minimal infrastructure requirements and a simple deployment tool make it feasible to easily deploy a Smart Signs system on demand. An important design issue of the Smart Signs system is privacy: the system secures communication links, does not track users, allow almost complete anonymous use, and prevent the system to be used as a tool for spying on users

HOG, LBP and SVM based Traffic Density Estimation at Intersection

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and can run efficiently on raspberry pi board. Code is released at https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201

On possibilities of smart meters switching at low voltage level for emergency grid management

Smart Meter (SM) is an advanced remotely readable energy meter with two-way communication capability which measures the electrical energy in real-time or near-real-time and securely sends data to Distribution System Operator (DSO). A smart metering system is an application of SMs on a larger scale, i.e. the application of a general principle on a system rather than on individual appliance. The European Commission (EC) has included ten common minimum functional requirements for electricity smart metering systems. One functionality requirement among these functional requirements is that the SM should allow remote ON/OFF switch to control the supply. Some DSOs who have installed remote ON/OFF switch are currently applying this technique for customers typically one by one when customers are changing addresses, or when contracts are terminated, or have defaulted on their payments. The switching functionalities of the SMs could be used for multiple customers, thereby opening up new possibilities for emergency electrical grid management by excluding prioritized customers. There is an interest to investigate if the multiple SMs switching might have some impacts on the Power Quality (PQ) of the electrical grid and also the challenges in implementing this technique on the existing smart metering system during emergency situation. In this thesis work, three field tests have been performed on multiple SMs switching focusing on the impact of the SMs switching on the PQ of the grid. A risk analysis was carried out before conducting the field tests. The PQ measurements were done by Power Quality Meters (PQMs) during the multiple SMs switching. Voltage variations and PQ events were recorded in the PQMs. Waveform data of the PQ events were recorded at 12.8 kHz sampling frequency. The test results are then evaluated based on PQ standards. Moreover, performance of the existing smart metering system was investigated during the multiple SMs switching to identify the challenges and possibilities of using multiple SMs switching. The analysis of the test results show that there were no other PQ events or voltage variations except some transient events which were recorded at some customer level during the reconnection of the SMs. However, the duration of the transient events was only fractions of a millisecond and deviation of the voltage transients were below +/-50% except for few transient events which have deviations of more than +/- 50% but less than +/-60%. This type of transient events may not be able to create damage to sensitive customers’ loads. The multiple SMs switching may not have impact on the PQ if the number of customers is low. However, SMs switching for large number of customers might have impact on the PQ which needs to be investigated. Moreover, the performance of the existing smart metering system during multiple SMs switching shows some limitations on implementing the switching technique for large scale of customers. The identified limitations are e.g., long time requirement for SMs switching and errors in the real-time status update report during SMs switching. Furthermore, the findings show that more research is needed to identify required functions for future smart metering system to implement multiple SMs switching during emergency grid management

smart Emergency Response System (smartERS) – the Oil Spill use case

Thanks to the huge progress within the last 50 years in Earth Observation, Geospatial science and ICT technology, mankind is facing, for the first time, the opportunity to effectively respond to natural and artificial emergencies such as: earthquake, flood, oil spill, etc. Responding to an emergency requires to find, access, exchange, and of course understand many types of geospatial information provided by several types of sensors. Majors oil spills emergencies as, the Gulf of Mexico (Macondo/Deepwater Horizon) in 2010, the sinking of the oil tanker Prestige in 2002, have offered lessons learned and identified challenges to be addressed. Interoperability provides the principles and technologies to address those challenges. Since years interoperability has been developing based on traditional Service Oriented Architecture, request/response communication style, and implemented through Spatial Data Infrastructures. The experience handling oil spill responses shows that emergency services based on SDIs have some limitations, mainly due to their real-time peculiarity. Moreover despite the effort that Private Sector and Public Administration have been putting since years, the goal to provide an exhaustive picture of the situation during an Emergency Response is still far to be reached. We argue that to achieve this goal, we have to frame the problem in a different way. Emergency Response is not just sensing; it should be smart enough to encompass intelligent actions such as, automatically and dynamically acquire context driven information. The gaol of this paper is to define what a “smart Emergency Response System” (smartERS) should be.JRC.G.3-Maritime affair

From knowledge co-creation to value co-creation and beyond: challenging global emergency in smart service systems.

The study seeks to investigate the impact of pandemic on teaching and learning processes involved in Higher Education (HE) by analysing the way in which knowledge exchange and value co-creation are reframed through ICTs and technology. The adoption of the interpretative lens of Service Science permits to reread HE as a smart service system. The empirical research, based on content analysis as an inquiry, analyses: 1) the transformations introduced in technology adoption, information sharing, knowledge and value co-creation to comply with the disruption “imposed” by the the sanitary emergency; 2) the way in which this transformation can introduce novelties in Higher education system. The results identify the different drivers for value and knowledge co-creation that can be implemented in technology-enhanced teaching and learning and the different novelties that can be generated from the emergence of innovation

Modeling of Dynamic Pricing of Energy for a Smart Grid Using a Multi-agent Framework

The use of smart grids is being promoted to address issues such as energy independence, global warming and emergency resilience. A smart grid is a digitized form of the power grid and is comprised of an intelligent monitoring system that keeps track of the two-way digital communications in the system. A multi-agent system is a collection of interacting intelligent agents that can be used in problem solving for systems that are difficult or impossible to be solved by an individual agent. Applications of multi-agent systems can range from transportation, logistics, graphics, networking and mobile technologies to modeling real world scenarios to achieve automatic and dynamic load balancing, pricing, and disaster response. The goal of this project was to design and implement a multi-agent system to model dynamic pricing of electricity in a smart grid, thereby improving the overall efficiency of electricity consumption in a real world scenario. This project was accomplished by devising and implementing a multi-agent system for regulating automatic and dynamic pricing of electricity by monitoring power consumption periods and rising or falling prices accordingly. The system developed has the capability of rising and lowering the prices of electricity based on the availability of electricity from energy sources. This system will depict how much energy the consumers are using and how much it is actually costing them. We believe that the logistics analyzed above will help energy-consumption utilities and consumers to make better energy-efficient decisions

SMART TRAFFIC CONTROL SYSTEM FOR PREVENTION AND CONTROL OF BLOCKADE, EMERGENCY VEHICLE CLEARANCE AND STOLEN VEHICLE IDENTIFICATION

This paper presents a smart traffic control system to avoid traffic congestion and to allow the emergency vehicles to pass with high priority. The main aim of this project is to implement the smart traffic control system to prevent and control of blockade, emergency vehicle detection and identification of stolen vehicle. An RFID tag is implanted on every vehicle such that it is impossible to remove or destroy the tag. These RFID tags are scanned by using the RFID scanner, NSK EDK-125-TTL and ARM7 LPC2148 microcontroller. The number of vehicles that cross the particular predefined path in certain duration will be specified and hence congestion is prevented or controlled by determining the necessary green light duration. If the RFID tag (Identification number) belonging to any stolen vehicle is identified by the RFID scanner at traffic signal junction, then the respective way will be blocked and also a SMS will be sent to the owner as well as the police control room by using the GSM SIM900 regarding the location of identified stolen vehicle. The emergency vehicle approaching the signal will be identified using RF modules on ARM7 LPC248 microcontroller which establish the wireless communication between the emergency vehicle and the controller unit at the traffic signal junction. The model is developed in such a way that if two emergency vehicles are approaching the junction from two different directions, then the emergency vehicle which is at shortest distance and first established the communication with master kit at the signal is allowed to cross the junction first by giving the green signal where the second one is given with green signalimmediately after the first vehicles crosses the junction

Density Based Traffic Control System with Smart Sensing Of Emergency Vehicles

Present Traffic Light Controller (TLC) relies upon micro-controller and microchip. These TLC have restrictions as they are depend on pre-portrayed gear, which is filling in with respect  to the program that doesn't have the versatility of adjustment on continuous reason. Owing to fixed time spans, orange and red signal’s holding up time is more and vehicle uses more fuel. To make traffic light leadership progressively beneficial, we abuse the advancement of new procedure called as “Density based traffic control system with smart sensing of emergency vehicles”. It is constructed mainly by using Magnetic Sensors for real world environment and by using IR modules for Model. The main objective of our project is to clear traffic efficiently by effective usage of the green signal time. In this system the density of the vehicle in a particular lane is obtained by the number of magnetic sensors kept in the road side which produces output signal with respect to the density of the traffic. Thus produced output signal is further processed by ARM microcontroller and according to the density obtained by the magnetic sensors the countdown time of the green signal is varied by the microcontroller and hence the usage of green signal even after all the vehicle pass by are prevented. In addition to this system our system also senses the emergency vehicle like ambulance that approaches the signal by detecting the RF signal transmitted by the Ambulance or other emergency vehicle with the help of RF receivers that kept at the road side and halts all the vehicles by putting red signal for all the four sides of road and puts special ‘green jeep signal’ for the emergency vehicle to pass by hence our system provide way for emergency vehicle. It can also prioritize the emergency vehicle with the help of RF transmitter and receiver. As the signalling board receives the RF signal, it turns the Corresponding lane ON, thus clearing the route for the emergency vehicle. DSS also analyses the pollution levels by placing a check over the vehicle emissions at the junctions. When the priorities of any two lanes clash, pollution levels are taken into account to provide the signals for them in turns. The gas sensors are fitted onto the signalling boards which help in calculating the pollutant levels