Chapter

Exploiting LoRa, edge, and fog computing for traffic monitoring in smart cities

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Smart cities are already a reality, with hyperconnected urban areas and increasing ubiquity of Internet of things (IoT) devices. These connected devices generate and transmit a vast amount of data about the city’s environment, traffic, and any other aspects that define the interaction between the cities and their citizens. The appearance and rising penetration of new computing paradigms and low-power wireless area network technologies are turning traditional cloud architectures that gather data from Wi-Fi, Bluetooth, or GSM/LTE connected devices into more distributed platforms with computation at different network layers. At the same time, low-power wide-area network (LPWAN) technology has opened a whole new world of opportunities in the IoT. More concretely, LPWAN solutions that operate on unlicensed radio bands have enabled low-power and long-distance communication to industry, researchers, public organizations, and individuals equally. Together with these, the increasing penetration of the edge and fog computing paradigms is allowing for smarter IoT solutions that do not necessarily rely on cloud servers. In this chapter, we present a hybrid edge-fog-cloud computing architecture for monitoring environment parameters and traffic flow in a city with a very small footprint in terms of installed infrastructure. In particular, we put an emphasis on traffic monitoring and propose a lightweight image processing algorithm that estimates traffic density.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Internet of Things (IoT) applications based on smart and distributed cameras have gained considerable attention in the last decades and become very popular. There are numerous applications that require the integration of Wireless Vision Sensor Networks (WVSN), such as traffic monitoring in smart cities [1], security and inspection during production in Industry 4.0 [2,3], the diagnosis of diseases in healthcare [4] and self-driving cars in the automotive sector [5]. The continuous advancement in the different sectors means that more and more applications require greater speed, greater autonomy, more data and a smaller system size. ...
Article
Full-text available
Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.
... Examples of such algorithms have been proposed [70,71]. Furthermore, in these types of applications, fog computing services can be used to provide local traffic monitoring and management [72], while cloud computing services can be used to offer global city monitoring and management [3]. ...
Article
Full-text available
Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.
... Active research areas in TIERS include multi-robot coordination [1], [2], [3], [4], [5], swarm design [6], [7], [8], [9], UWB-based localization [10], [11], [12], [13], [14], [15], localization and navigation in unstructured environments [16], [17], [18], lightweight AI at the edge [19], [20], [21], [22], [23], distributed ledger technologies at the edge [24], [25], [26], [27], [28], [29], edge architectures [30], [31], [32], [33], [34], [35], offloading for mobile robots [36], [37], [38], [39], [40], [41], [42], LPWAN networks [43], [44], [45], [46], sensor fusion algorithms [47], [48], [49], and reinforcement and federated learning for multi-robot systems [50], [51], [52], [53]. ...
... In terms of communication-constrained devices, low-power wide area networks (LPWANs) have emerged in recent years as a solution for extending the range of applications, with LoRa and LoRaWAN being the most prominent radio and network technologies [7,114]. Edge computing is a natural paradigm to be integrated with LPWAN networks owing to the low-bandwidth available and thus the need to preprocess large amounts of raw data [115,116,117]. However, the integration of blockchain into LPWAN networks is not direct [118]. ...
Preprint
Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in a inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.
Article
With the rapid industrialization and urbanization worldwide, air quality levels are deteriorating at an unprecedented rate and posing a substantial threat to humans and the environment. This brings the concern to effectively monitor and forecast air quality levels in real-time. Conventional air quality monitoring stations are built based on centralized architectures involving high latency, communication technologies demanding high power, sensors involving high costs and decision making with moderate accuracy. To address the limitations of the existing systems, we propose a smart and distinct Air Quality Monitoring and Forecasting system embracing Fog Computing with IoT and Deep Learning (DL). The system is a three-layered architecture with the Sensing layer first, Fog Computing layer in between, and Cloud Computing layer at the end. Fog Computing is a powerful new generation paradigm that brings storage, computation, and networking at the edge of the IoT network and reduce network latency. A DL based BiLSTM (Bidirectional Long Short-Term Memory) model is deployed in the Fog Computing layer. The proposed system aims at real-time monitoring and accurate air quality forecasting to support decision making and aid timely prevention and control of pollutant emissions by alerting the stakeholders when a dangerous Air Quality Index (AQI) is expected. Experimental results show that the BiLSTM model has a better predictive performance considering the meteorological parameters than the baseline models in terms of MAE and RMSE. A proof of concept realizing the proposed system is elaborated in the paper.
Chapter
The development of mobile user equipment progresses cooperatively with the advancement of the latest mobile applications. Still, the limited battery capacity prevents users from running computationally intensive applications on their gadgets. This one stimulated the evolution of Mobile cloud computing (MCC). Instead of its ample data storage and processing capability, MCC suffers from high latency. To deal with the latency problem a novel promising concept known as mobile edge computing has been introduced. Mobile edge computing (MEC) and wireless sensor networks (WSN) are two ever-promising research domains of the wireless network. The integration of MEC with WSN has given birth to Sensor Mobile Edge Computing (SMEC). However, sensor mobile edge computing is an emerging field, and energy-efficiency is one of the major challenges of this field. In MEC, services are provided at the edge of the mobile network for reducing the latency that in turn can improve the quality of user experience. Previously MEC focused on the use of base stations for offloading computations from mobile devices. However, after the arrival of fog computing, the definition of edge devices becomes broader. SMEC is a fusion of mobile edge computing and wireless sensor network. SMEC is an architecture where the sensor nodes capture the status of environmental objects and the collected data are sent to the cloud through the edge devices which participate in data processing also. This chapter discusses sensor mobile edge computing, its architecture, and its applications. The future scopes and challenges of SMEC are also addressed in this chapter.
Chapter
Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in an inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.
Article
Full-text available
With the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB-IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery-powered IoT objects that enable long-distance communication in low-power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.
Chapter
In this chapter, authors analyzes how fog computing can be efficiently utilized to improve the productivity in the industry 4.0 and smart city applications. The main aim of industry 4.0 applications is to improve the efficiency of manufacturing process through the incorporation of latest technologies. This environment can be improved by incorporating the fog computing paradigm. High energy consumption and abundance of data to be processed at the data nodes are some of the challenges that need to be addressed in industry 4.0 and smart city implementations. A fog computing-enabled architecture helps to reduce some of these challenges by working as a low complexity computational layer between cloud and internet of things (IoT) layers. By introducing this fog layer computationally intensive data processing tasks can be moved from the cloud layer to the fog layer and this fog layer can also act a gateway to the other upper layers. In smart city applications also fog computing can be effectively utilized. In the fog computing environment the data analytics tasks can be pushed to the edge of the network which leads to better efficiency. In fog computing paradigm major functionalities are moved near to the local nodes. Since most of the computations are happening locally the need of transferring data to the cloud servers is significantly reduced. The benefits of this architecture can be utilized to improve the efficiency of smart city and industry 4.0 implementations. In this chapters, authors analyze how fog computing can be effectively utilized to improve industry 4.0 and smart city applications.
Article
Low Power Wide Area Network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an Adaptive Data Rate (ADR) algorithm to allocate proper data rates to end devices. However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to end devices one-by-one in an independent way. In this paper we propose a novel and more efficient ADR algorithm, denoted as Collision-Aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of end devices in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation-experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement w.r.t. the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.
ResearchGate has not been able to resolve any references for this publication.