Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities

The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

/pdf/green-iot-for-eco-friendly-and-sustainable-smart-cities-2gn566r87v.pdf

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Tremendous technological developments in the field of internet of things (IoT) have changed the way we live and work. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also contributes to toxic pollution, consumes energy and generates e-waste. These persistent issues place new stress on the smart world and environments. To enhance the benefits and reduce the harmful effects of IoT, there is an increasingly desired to move towards green IoT. Green IoT is seen as the environmentally friendly future of IoT. Therefore, it is necessary to put different desired measures to conserve environmental resources, reduce carbon footprints and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, sensors, communications, clouds, and internet operate in synergy towards the common goal of increased energy efficiency and reduced carbon emissions. This work presents a thorough survey of the current ongoing research and potential technologies of green IoT with an intention to provide some directions for future green IoT research.

Greening internet of things for greener and smarter cities: a survey and future prospects

It is expected that next-generation wireless networks will provide a plethora of mobile wireless services to users and ubiquitous network coverage at all times. Meeting these expected goals requires that new and existing networks be seamlessly integrated together to form Heterogeneous Wireless Networks (HWNs). Thus, seamless and efficient handover mechanisms are pertinent for optimal network performance in HWNs; so that the mobile user can switch from one access network to another, in search of the best connection for the demanded services. The HWNs' performance can be reduced, if efficient network selection is not achieved. In HWNs, network-selection decisions can be evaluated by using multi-criteria, or a single criterion. However, network selection and decision-making in HWNs often involves taking into account a large number of complex and conflicting network-decision factors, or criteria. Thus multi-criteria decision-making techniques are more efficient than single-criterion techniques. Multi-Criteria Decision-Making (MCDM) techniques comprise of a developed branch of operational research for assisting in the resolution of complex decision-making problems. MCDM is an important tool that has been used to model and analyze handover-decisions and network-selection problems in HWNs. This paper reviews and classifies the most significant MCDM algorithms that have been used to address the network decision-making problems in HWNs in terms of algorithmic approach, the type of calls, the cardinality of decision criteria employed, handover-control points and the types of network utilities. Comprehensive step-wise mathematical implementations of the reviewed MCDM schemes are presented, while pointing out their strengths and limitations. This paper review fills a research gap in the investigation on network-selection criteria's interdependence and interactions, and their effects on criteria's weight of importance. It then provides an insight into the importance of network-criteria weighting and the current research trend in the application of MCDM algorithms to network-selection problems in HWNs.

Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review

Bluetooth low energy (BLE) has received much attention from researchers as one of the prominent solutions for short-range communication The applications of BLE are growing rapidly and there are many issues to be resolved for establishing the deployment of it Practically, BLE devices may experience a lot of collisions during discovery process due to contention among devices, particularly in a crowded environment With an increasing number of BLE devices, discovery latency and energy consumption rise exponentially, which could degrade the quality of service required by user applications In this study, the authors propose an enhanced discovery mechanism by employing a backoff strategy for BLE devices to reduce collisions during advertisement process Through experimental and mathematical analysis, the proposed mechanism has shown its effectiveness in terms of discovery latency and energy consumption in crowded BLE networks

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2016.0462

Backoff scheme for crowded Bluetooth low energy networks

The concept of smart city is widely favored, as it enhances the quality of urban citizens’ life, involving multiple disciplines. Consequent to the complex urban networks, data processing complexity has increased significantly. Thus, it creates a crucial demand to facilitate autonomous decision-making and real-time data processing and analysis of smart cities. Therefore, in this paper we propose a smart city framework based on Big Data analytics. The proposed framework operates in three levels 1) Data generation and acquisition level, 2) Data management and processing level, and 3) Application level. Moreover, we analyzed the water consumption, traffic congestion, and air pollution data of Surrey (Canada) and Aarhus (Denmark) cities to determine the threshold values for data filtering. The analysis shows that the proposed architecture offers useful insights to the community development authorities to improve the existing smart city architecture. Keywords— Smart city, big data, urban planning.

/pdf/planning-of-smart-cities-performance-improvement-using-big-4g359uoflb.pdf

Planning of smart cities Performance improvement using big data analytics approach

Bluetooth Low Energy (BLE) is one of the optimal candidate technology used in short range wireless communications. The features of BLE such as low-cost and low-power dominates it over other technologies such ZigBee, etc. The BLE 4.0 standard provides the in-depth analysis of the BLE architecture. But still many aspects regarding the discovery process of the BLE needs refinement. The performance evaluation of the classical Bluetooth analysis is widely studied, but it cannot apply to the BLE 4.0 standard. Several schemes have been proposed in this regard, but none of them provides the complete discovery process analysis. Therefore, in this paper we provide a complete, new, and an accurate discovery process of BLE standard. The intensive analytical and simulation study provide a basis for the modeling of the BLE discovery process. Moreover, the proposed analysis investigates discovery probability as well as expected discovery latency in a wireless sensor network based on BLE, which are then validated via extensive experiments. We also investigate different parameters which directly affect the performance of the BLE discovery process. The parameters settings are examined, and an optimal setting is also proposed for low-power consumption during the discovery process.

Modeling and analysis of performance based on Bluetooth Low Energy

Recently, wireless sensor network (WSN) has become a formidable force in almost all areas of our everyday life. However, there are still many issues with wireless sensor networks among which is the energy problem. One of the numerous techniques that have been introduced as a way of solving the problem of energy deficit inherent in a WSN is sleep scheduling. Sleep scheduling allows sensor nodes to periodically take turns to sleep in order to minimize energy cost in a WSN. Apparently, Overemission in a WSN is one of the main causes of energy drainage in sensor nodes. Traditional schemes fail to take into consideration the sleeping timetable of other nodes; hence they let transmitter nodes repeatedly send RTS preamble packets and similar control packets to sleeping nodes. Our proposed sleep scheduling scheme addresses this problem from a whole new perspective using a system called Designated Sensor Node (DSN) mechanism. The DSN scheme significantly reduces flooding of the network with unnecessary control packets. The effect in turn leads to the minimization of unnecessary energy waste in a WSN.

Energy Efficient Sleep Scheduling for Wireless Sensor Networks

The speed and the efficiency of short range communication has increased with the evolution of wireless technologies i.e. Wireless Local Area Network (WLAN), ZigBee, and Bluetooth Low Energy (BLE). Especially, the BLE shows promising results in different fields such as Internet of Things (IoT), health-care, body area networks, etc. As a result of dramatic increase in the wireless device, the frequency interference in 2.4 GHz Industrial Scientific Medical (ISM) band has raised, resulting a higher packet loss. In this paper, we propose Fast Adaptive Frequency Hopping Scheme (FAFH) to mitigate the effects of interference in BLE. The proposed FAFH scheme immediately hops to a new channel without waiting for completion of the current connection event when the channel quality becomes worse by interference. The simulation results show that the FAFH performs better in dense wireless networks as well as improved performances comparing to AFH scheme.

A Fast Adaptive Frequency Hopping Scheme Mitigating the Effect of Interference in Bluetooth Low Energy Networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jinbae Kim

Author Tools

Chat about Author