Internet of things (IoT) is pushing the integration of physical and virtual worlds. Sensor devices provide rich sensory information to context-aware services. Actuators implement in the physical world application needs. The role of software is increasing as more and more resources become virtualized and software-defined. Smart environment is an emerging concept that has the potential to address many of the humanity problems. In this context, what are the promising tools for building the smart environments of the future? This paper provides a review of platforms, middleware, and frameworks that can help in this big challenge, discussing their architectures, service life-cycling, digital twins, cloud-based operation, virtualization, security, privacy, communication model, support for AI and machine learning, among other aspects. The proposed revision innovates by employing previous work on future Internet key enablers as parameters for qualitative comparisons. The idea is to determine the degree of alignment among current initiatives for smart environments and the ones emerging from future Internet research. Among the main conclusions are: (i) heterogeneity come to stay; (ii) many contemporary proposals do not cover important aspects raised in future Internet research; (iii) publish/subscribe model is largely employed; (iv) many proposals are stuck to the limitations of current Internet model (another reason to explore the relationship among current platforms and previous future Internet research for IoT); (v) devices interoperability is a problem solved; (vi) ingredients from future Internet research, such as SDN/NFV, ICN and SCN are systematically being adopted for smart environments design. Many others are to come; (vii) AI, machine learning, and big data support are missing not only in TCP/IP-based approaches, but also in future Internet-based.

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Platforms for Smart Environments and Future Internet Design: A Survey

https://iopscience.iop.org/article/10.1088/1742-6596/1500/1/012104/pdf

Use of Haversine Formula in Finding Distance Between Temporary Shelter and Waste End Processing Sites

It's getting more difficult to find available parking spaces with the rapid growth of the vehicles, especially in big cities. The parking guidance system (PGS) which powered by real time data or historical data could reduce the time spent on looking for parking spaces and alleviate the heavy traffic around the parking lots. However, the PGS is completely ineffective when the parking lots missing data or even have no parking data. This paper makes an exploration on data repairing by digging geospatial data and historical data. First, this paper proposes a method to verify the possibility of parking data similarity when parking lots have spatial similarity. Then take the known data of parking lots as samples and generate reparative data through the Recurrent GANs. The experiment shows, when parking lots have similar spatial features, the parking data, in a high probability, have some similarity with each other, too. And the data generated by Recurrent GANs have the same distribution with real data. In fact, this paper provides a new idea to solve the problems of timeseries data repairing, to a certain extent, this method can help to save the cost of equipment and time.

Exploration on Spatiotemporal Data Repairing of Parking Lots Based on Recurrent GANs

Nowadays, it's difficult to find available parking spaces in big cities within China due to the rapid growth of the vehicles. Parking guidance system(PGS) would play an important role to reduce the time spent on looking for parking spaces. An intuitional idea is to collect the real-time occupancy information of city-wide parking lots and guide the vehicles to the proper one on demands. But the idea is hardly implemented practically because of the huge financial and time costs on linking all city-wide parking lots in early deployment stage. Unlike the expensive real-time data, the public information of parking lots is easily obtained with little costs. What if we dig these free public data and use them in parking recommendation? Inspired by the classic Page rank algorithm used in web searching, this paper propose a novel method, named Parking rank, to sort the parking lots based on the public information totally, such as price, location, total spaces, etc. And a driving-cost sensitive recommendation method is presented in this paper. The simulation shows, when working with the Parking rank algorithm, the recommendation algorithm can help vehicles find the proper parking lots efficiently and reasonably, even in the urban district lack of any real-time information. In fact, Parking rank algorithm can be used as pre-processing of static ranking of city-wide parking lots, and can work with other recommendation algorithm related to real-time occupancy information, or work alone if there is only the public information about parking lots.

Parking rank: A novel method of parking lots sorting and recommendation based on public information

Smart parking system as a part of smart city development has been widely proposed with several research. In this research, proposed a system of parking management application that functions to monitor and control the location of parking slot that can be used by the parking management and parking users. The web application connected to ultrasonic sensor and GPS using MQTT protocol and real-time database. The research used modify algorithm of the SSGA, to optimize the allocation of empty parking slot and MQTT protocol to obtain the faster response time of the system when many users are accessing the website application. The results obtain a variation of sending delays from the client publish to firebase at 4 seconds. Meanwhile, for the sending delay from the broker to firebase the variation was at 2 seconds for each time of data sending.

Smart parking management system using SSGA MQTT and real-time database

In recent years due to globalization and increase in competition to capture market share, the customer is having many options of cars at much affordable prices. This has led to tremendous amount of cars on the road. The big cities in India are normally crowded and the city's existing infrastructure falls short to satisfy the growing needs. This problem is also combined with the inefficient use of parking space and uneven distribution of traffic which leads to congestion in vehicular traffic. This paper introduces a novel technique based on Internet of Things platform to improve efficiency of the existing parking systems. It guides driver to find nearest parking slots. The prototype uses cloud based architecture of Internet of things. There is minimum delay in finding an optimal parking slot which helps to ease the traffic congestion. The proposed system also helps users automatically find a free parking space at the smallest distance based on Haversine formula if nearest parking region is full.

Harshal V. Jaurkar

Papers

Parking guidance system using Internet of Things