Internet Of Things Applications In Smart Cities Research Articles

Comprehensive Review of Smart Cities using IOT in Coud

The rapid growth of urbanization necessitates smarter and more sustainable city management. The convergence of Internet of Things (IoT) devices and cloud computing offers a transformative solution for building Smart Cities. This review explores the integration of IoT and cloud technologies in creating intelligent urban infrastructure. It examines how data collected from a network of sensors and devices across the city can be leveraged to improve various aspects of urban life, including traffic management, energy efficiency, public safety, waste management, and parking. The paper discusses the key benefits of cloud computing for Smart City IoT applications, such as scalability, centralized data management, cost-effectiveness, security, and advanced analytics capabilities. Additionally, it explores the challenges associated with implementing Smart City IoT solutions, such as data privacy concerns, cybersecurity risks, and the need for interoperability between different devices and platforms. Finally, the review concludes by highlighting the potential of Smart City IoT powered by cloud computing to create a more efficient, sustainable, and livable future for our cities.

Demystifying machine learning models of massive IoT attack detection with Explainable AI for sustainable and secure future smart cities

Smart cities rely heavily on Internet of Things (IoT) technology, which enables automation services through interconnected IoT devices. However, the widespread use of IoT applications in smart cities has resulted in security and privacy concerns that must be addressed to protect sensitive data. To safeguard smart cities from cyber attacks, learning theory-based automated attack-detection methods must be adopted. Various techniques have been proposed in the literature to create effective models for identifying IoT attacks. However, the majority of IoT detection algorithms have focused on only a few types of IoT attacks, and most IoT threat detection systems have used black-box deep learning models that lack interpretability to support their forecasts. This research aims to detect several types of large-scale attacks on IoT devices using the Extreme Gradient Boosting (XG-Boost) classifier and Explainable Artificial Intelligence (XAI) approaches. The proposed method not only improves the model’s performance but also increases trust in the model. The results of the experimental study on the IOTD20 dataset and XAI evaluation of each feature’s contribution to the model demonstrate that the proposed model can efficiently identify malicious attacks and threats, reducing IoT cybersecurity threats in smart cities.

An efficient edge computing management mechanism for sustainable smart cities

Since ancient times, humanity has envisioned a world where people and things connect and interact. The advent of generation mobile systems, the so-called 5 G in the early century, has realized this dream. 5 G can provide network services with extremely-high throughput and extremely-low delay and allows a huge device number to connect based on Internet infrastructure, forming the Internet of Things (IoT). In recent years, IoT has been applied in various fields serving humanity, such as smart cities, smart agriculture, healthcare, education, military, and IoT ecosystems. One of the main challenges of IoT applications is computing solutions to reduce service response times. This study proposes an efficient edge computing management mechanism for IoT applications in smart cities. Our mechanism proposes a small database (called an information map) that allows edge computing servers of smart cities to store edge service information. When the mobile end-users move to the new edge server' managed coverage, properties related to the EC service are exchanged between the edge servers. The experiment results have shown that our proposed mechanism improves service response time and energy consumption many times compared to the traditional mechanism. We hope that this mechanism will be strongly applied to sustainable smart cities in the future.

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into account IoT application requirements, available resource capacities, and geographical locations of servers, which would help optimize IoT application placement decisions, considering multiple objectives such as data transmission, power consumption, and cost. Simulation experiments were conducted with various IoT applications (e.g., augmented reality, infotainment, healthcare, and compute-intensive) to simulate realistic scenarios. The results showed that the proposed approach outperformed the existing cloud-based approach in terms of reducing data transmission by 64% and the associated processing and networking power consumption costs by up to 78%. Finally, a heuristic approach was developed to validate and imitate the presented approach. It showed comparable outcomes to the proposed model, with the gap between them reach to a maximum of 5.4% of the total power consumption.

Enabling LPWANs for Coexistence and Diverse IoT Applications in Smart Cities Using Lightweight Heterogenous Multihomed Network Model

Smart cities have been envisioned to provide smartness in managing internet of things (IoT) application domains, such as transport and mobility, health care, natural resources, electricity and energy, homes and buildings, commerce and retail, society and workplace, industry, agriculture, and the environment. The growth trajectory in usage of these IoT domains has led to a heterogeneous dense network in a smart city environment. The heterogeneous dense network in smart cities has led to challenges, such as difficulties in the management of LPWAN coexistence, interference, spectrum insufficiency, QoS, and scalability issues. The existing LPWAN technologies cannot support the heterogeneous dense network challenges in smart cities. Further, it cannot support diverse IoT, including medium- to high-bandwidth applications, due to the power, complexity, and resource constraints of the LPWAN devices. Hence, this paper addresses high data rate IoT applications and heterogeneous dense networks. This paper proposes a lightweight heterogenous multihomed network (LHM-N) model for diverse smart city applications that will address dense heterogeneity network challenges in a smart city. The work aims to advocate and integrate a manageable license-free LPWAN that will coexist with 5G private and public cellular networks in the LHM-N model. This will help to provide a cost-effective solution model in a heterogeneous dense smart city environment. Further, a secured lightweight energy-efficient packet-size forwarding engine (PSFE) algorithm is presented using the discrete event simulation (DES) methodological approach in MATLAB for complexity evaluation. In addition, a 5G reduced capability (RedCap) IoT device is integrated into the (LHM-N) model to support smart city. Finally, the results show that the LHM-N model outperforms the conventional quadrature amplitude modulation (QAM) protocol scheme in terms of error rate, latency, and data throughput with reduced energy costs for medium- to high-bandwidth industrial IoT applications. This validates the suitability of the LHM-N model for high data rate IoT applications.

Design and Analysis of a RFID Reader Microstrip Array antenna for IoT Applications in Smart Cities

This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate. The dielectric constant εr and substrate thickness h are 4.4 and 1.6mm, respectively. The 2*1 and 4*1 array antennas present a gain improvement of 27.3% and 61.9%, respectively. The single,2*1 and 4*1 array antennas were performed with CADFEKO.

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Drones, the Internet of Things (IoT), and Artificial Intelligence (AI) could be used to create extraordinary responses to today’s difficulties in smart city challenges. A drone, which would be effectively a data-gathering device, could approach regions that become complicated, dangerous, or even impossible to achieve for individuals. In addition to interacting with one another, drones must maintain touch with some other ground-based entities, including IoT sensors, robotics, and people. Throughout this study, an intelligent approach for predicting the signal power from a drone to IoT applications in smart cities is presented in terms of maintaining internet connectivity, offering the necessary quality of service (QoS), and determining the drone’s transmission range offered. Predicting signal power and fading channel circumstances enables the adaptable transmission of data, which improves QoS for endpoint users/devices while lowering transmitting data power usage. Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. The signal strength estimations are also utilized to forecast the drone’s flight patterns. The results demonstrate that the proposed ANN approach has an excellent correlation with the verification data collected through computations, with the determination of coefficient R2 values of 0.97 and 0.98, correspondingly, for changes in drone height and distances from a drone. Furthermore, the finding shows that signal distortions could be considerably decreased and strengthened.

Design and Analysis of a RFID Reader Microstrip Array Antenna for IoT Applications in Smart Cities

This paper presents the design of 2*1 and 4*1 RFID reader microstrip array antenna at 2.4GHz for the Internet of things (IoT) networks which are Zigbee, Bluetooth and WIFI. The proposed antenna is composed of identical circular shapes radiating patches printed in FR4 substrate. The dielectric constant εr and substrate thickness h are 4.4 and 1.6mm, respectively. The 2*1 and 4*1 array antennas present a gain improvement of 27.3% and 61.9%, respectively. The single,2*1 and 4*1 array antennas were performed with CADFEKO.

Scalable and Efficient Hardware Architectures for Authenticated Encryption in IoT Applications

Internet of Things (IoT) is a key enabling technology, wherein sensors are placed ubiquitously to collect and exchange information with their surrounding nodes. Due to the inherent interconnectivity, IoT devices are vulnerable to cybersecurity attacks. To mitigate these vulnerabilities, cryptographic primitives can be employed, but they require significant computation, which restricts their adoption in IoT. Moreover, IoT systems have diverse requirements, ranging from high-throughput (TP) to the area constrained. This makes it hard to deploy appropriate security measures in a systematic manner. To address these issues, three generic implementation strategies (unrolled, round-based, and serialized) are proposed for developing highly efficient hardware architectures. They are applicable to all authenticated encryption schemes and are lightweight and fast, compared to conventional public key encryption. In this article, Ascon is implemented as an example based on those three strategies: 1) the unrolled architecture achieves TP of 766.9 Mb/s (Ascon-128) and 1389.2 Mb/s (Ascon-128a), which are suitable for high-throughput IoT applications; 2) the round-based architecture achieves 0.153 (Ascon-128) and 0.244 (Ascon-128a) TP-to-area ratio, which are, respectively, 73.8% and 40.2% better than state-of-the-art results; and 3) a novel serialized implementation technique is proposed wherein the substitution-box (S-box) is processed in multiple-bit-per-cycle, in contrast to the conventional one-bit-per-cycle approach. The TP of the two-bits-per-clock-cycle implementation is increased by 230.8% with only 36.8% additional hardware area. The proposed strategies allow us to scale the number of rounds (round-based) and bits-per-clock-cycle (serialized) to meet differing requirements in TP and area which are demonstrated for smart city IoT applications.

LPGA: Location Privacy-Guaranteed Offloading Algorithm in Cache-Enabled Edge Clouds

The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39 percent compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.

Guest Editorial Special Issue on Trust-Oriented Designs of Internet of Things for Smart Cities

The Internet of Things (IoT) offers new opportunities for cities to make citizens live and work in more sustainable, healthy, and safe places. Since IoT applications in smart cities are characterized by different devices, networking standards, and data management strategies, trust becomes a fundamental issue in the IoT ecosystem. The explosion of IoT devices, along with their decentralized deployment, constraint resources, limited computational and cryptographic capabilities, brings challenges to trust management in IoT. The coexistence of multiple IoT domains also raises challenges, for example, how to evaluate and maintain trust across domain boundaries. This special issue aims at bringing the researchers from both academia and industry together to disseminate their recent advances related to the challenges and solutions in building trustful IoT for smart cities.

MicroServices Suite for Smart City Applications.

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform.

Sampling Sensor Nodes to Extend the Longevity of Wireless Sensor Networks

Background and Objective: Wireless Sensor Networks (WSN) are one of the most important elements in the Internet of Things (IoT) paradigm. It is envisaged that WSNs will seamlessly bridge the physical world with the Internet resulting in countless IoT applications in smart cities, wearable devices, smart grids, smart retails amongst others. It is necessary, however, to consider that sensing, processing and communicating large amounts of sensor data is an energy-demanding tasks. Recharging or replacing those battery-powered sensor nodes deployed in inaccessible locations is generally a tedious and time-consuming task. As a result, energy efficient approaches for WSN need to be devised in order to prolong the longevity of the network. Methods: In this paper, we present an approach that reduces energy consumption by controlling the sampling rate and the number of actively communicating nodes. The proposed approach applies compressive sensing to reduce the sampling rate and a statistical approach to decrease the sample size of sensor nodes. Results and Conclusion: The proposed approach is expected to significantly increase the lifetime of the network whilst maintaining the event detection accuracy.

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

The widespread growth of Big Data and the evolution of Internet of Things (IoT) technologies enable cities to obtain valuable intelligence from a large amount of real-time produced data. In a Smart City, various IoT devices generate streams of data continuously which need to be analyzed within a short period of time; using some Big Data technique. Distributed stream processing frameworks (DSPFs) have the capacity to handle real-time data processing for Smart Cities. In this paper, we examine the applicability of employing distributed stream processing frameworks at the data processing layer of Smart City and appraising the current state of their adoption and maturity among the IoT applications. Our experiments focus on evaluating the performance of three DSPFs, namely Apache Storm, Apache Spark Streaming, and Apache Flink. According to our obtained results, choosing a proper framework at the data analytics layer of a Smart City requires enough knowledge about the characteristics of target applications. Finally, we conclude each of the frameworks studied here have their advantages and disadvantages. Our experiments show Storm and Flink have very similar performance, and Spark Streaming, has much higher latency, while it provides higher throughput.

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications.

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.