scholarly journals Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities

2021 ◽  
Vol 13 (16) ◽  
pp. 9092
Author(s):  
Amjad Rehman ◽  
Khalid Haseeb ◽  
Tanzila Saba ◽  
Jaime Lloret ◽  
Zara Ahmed
Keyword(s):  
Real Time ◽  
Mobile Networks ◽  
Data Privacy ◽  
Smart Cities ◽  
Mobile Sink ◽  
Public Healthcare ◽  
Daily Routine ◽  
Mobility Support ◽  
5G Communication ◽  
Iot Devices

The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.

scholarly journals Lightweight MIPv6 with IPSec Support

2014 ◽  
Vol 10 (1) ◽  
pp. 37-77 ◽  
Author(s):  
Antonio J. Jara ◽  
David Fernandez ◽  
Pablo Lopez ◽  
Miguel A. Zamora ◽  
Antonio F. Skarmeta
Keyword(s):  
Smart Cities ◽  
Mobile Ipv6 ◽  
Security And Privacy ◽  
Mobility Support ◽  
Memory Processing ◽  
Constrained Environments ◽  
Iot Devices ◽  
The One ◽  
New Generation ◽  
Constrained Devices

Mobility management is a desired feature for the emerging Internet of Things (IoT). Mobility aware solutions increase the connectivity and enhance adaptability to changes of the location and infrastructure. IoT is enabling a new generation of dynamic ecosystems in environments such as smart cities and hospitals. Dynamic ecosystems require ubiquitous access to Internet, seamless handover, flexible roaming policies, and an interoperable mobility protocol with existing Internet infrastructure. These features are challenges for IoT devices, which are usually constrained devices with low memory, processing, communication and energy capabilities. This work presents an analysis of the requirements and desirable features for the mobility support in the IoT, and proposes an efficient solution for constrained environments based on Mobile IPv6 and IPSec. Compatibility with IPv6-existing protocols has been considered a major requirement in order to offer scalable and inter-domain solutions that were not limited to specific application domains in order to enable a new generation of application and services over Internet-enabled dynamic ecosystems, and security support based on IPSec has been also considered, since dynamic ecosystems present several challenges in terms of security and privacy. This work has, on the one hand, analysed suitability of Mobile IPv6 and IPSec for constrained devices, and on the other hand, analysed, designed, developed and evaluated a lightweight version of Mobile IPv6 and IPSec. The proposed solution of lightweight Mobile IPv6 with IPSec is aware of the requirements of the IoT and presents the best solution for dynamic ecosystems in terms of efficiency and security adapted to IoT-devices capabilities. This presents concerns in terms of higher overhead and memory requirements. But, it is proofed and concluded that even when higher memory is required and major overhead is presented, the integration of Mobile IPv6 and IPSec for constrained devices is feasible.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Tran Anh Khoa ◽  
Le Mai Bao Nhu ◽  
Hoang Hai Son ◽  
Nguyen Minh Trong ◽  
Cao Hoang Phuc ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Internet Of Things ◽  
Real Time ◽  
Mobile Networks ◽  
Smart Home ◽  
Smart Cities ◽  
Smart Homes ◽  
Authentication Mechanism

Smart homes are an element of developing smart cities. In recent years, countries around the world have spared no effort in promoting smart cities. Smart homes are an interesting technological advancement that can make people’s lives much more convenient. The development of smart homes involves multiple technological aspects, which include big data, mobile networks, cloud computing, Internet of Things, and even artificial intelligence. Digital information is the main component of signal control and flow in a smart home, while information security is another important aspect. In the event of equipment failure, the task of safeguarding the system’s information is of the utmost importance. Since smart homes are automatically controlled, the problem of mobile network security must be taken seriously. To address these issues, this paper focuses on information security, big data, mobile networks, cloud computing, and the Internet of Things. Security efficiency can be enhanced by using a Secure Hash Algorithm 256 (SHA-256), which is an authentication mechanism that, with the help of the user, can authenticate each interaction of a given device with a WebServer by using an encrypted username, password, and token. This framework could be used for an automated burglar alarm system, guest attendance monitoring, and light switches, all of which are easily integrated with any smart city base. In this way, IoT solutions can allow real-time monitoring and connection with central systems for automated burglar alarms. The monitoring framework is developed on the strength of the web application to obtain real-time display, storage, and warning functions for local or remote monitoring control. The monitoring system is stable and reliable when applying SHA-256.

Realizing a Ultra-Low Latency M-CORD Model for Real-Time Traffic Settings in Smart Cities

2021 ◽  
pp. 93-105
Author(s):  
Kathiravan Srinivasan ◽  
Aswani Kumar Cherukuri ◽  
Senthil Kumaran S. ◽  
Tapan Kumar Das
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Mobile Networks ◽  
High Speed ◽  
Smart Cities ◽  
Low Latency ◽  
Mobile Service ◽  
Core Network ◽  
Real Time Traffic ◽  
Device User

At present, the need for an ultra-high speed and efficient communication through mobile and wireless devices is gaining significant popularity. The users are expecting their network to offer real-time streaming without much latency. In turn, this will result in a considerable rise in network bandwidth utilization. The live streaming has to reach the end users mobile devices after traveling through the base station nodes, core network, routers, switches, and other equipment. Further, this will lead to a scenario of content latency and thereby causing the rejection of the mobile devices users' request due to congestion of the network and mobile service providers' core network witnessing an extreme load. In order to overcome such problems in the contemporary 5G mobile networks, an architectural framework is essential, which offers instantaneous, ultra-low latency, high-bandwidth access to applications that are available at the network edge and also making the task processing in close proximity with the mobile device user.

Privacy Preserving in Smart Cities Using Various Computing Technologies

2022 ◽  
pp. 47-54
Author(s):  
T. N. Gayathri ◽  
M. Rajasekharababu
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Privacy Preserving ◽  
Synthetic Dataset ◽  
Smart Environment ◽  
Sensitive Data ◽  
Daily Lives ◽  
Simulation Results ◽  
Iot Devices ◽  
High Possibility

IoT has influenced our daily lives through various applications. The high possibility of sensing and publishing sensitive data in the smart environment leads to significant issues: (1) privacy-preserving and (2) real-time services. Privacy is a complex and a subjective notion as its understanding and perception differ among individuals, hence the observation that current studies lack addressing these challenges. This chapter proposes a new privacy-preserving method for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, the authors find that the solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, the proposed solution can be widely used for smart cities.

scholarly journals Detecting security attacks in cyber-physical systems: a comparison of Mule and WSO2 intelligent IoT architectures

2021 ◽  
Vol 7 ◽  
pp. e787
Author(s):  
José Roldán-Gómez ◽  
Juan Boubeta-Puig ◽  
Gabriela Pachacama-Castillo ◽  
Guadalupe Ortiz ◽  
Jose Luis Martínez
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
Security Attacks ◽  
Physical Systems ◽  
The Real ◽  
Real Time Detection ◽  
Iot Devices ◽  
Definition Of ◽  
The Internet Of Things

The Internet of Things (IoT) paradigm keeps growing, and many different IoT devices, such as smartphones and smart appliances, are extensively used in smart industries and smart cities. The benefits of this paradigm are obvious, but these IoT environments have brought with them new challenges, such as detecting and combating cybersecurity attacks against cyber-physical systems. This paper addresses the real-time detection of security attacks in these IoT systems through the combined used of Machine Learning (ML) techniques and Complex Event Processing (CEP). In this regard, in the past we proposed an intelligent architecture that integrates ML with CEP, and which permits the definition of event patterns for the real-time detection of not only specific IoT security attacks, but also novel attacks that have not previously been defined. Our current concern, and the main objective of this paper, is to ensure that the architecture is not necessarily linked to specific vendor technologies and that it can be implemented with other vendor technologies while maintaining its correct functionality. We also set out to evaluate and compare the performance and benefits of alternative implementations. This is why the proposed architecture has been implemented by using technologies from different vendors: firstly, the Mule Enterprise Service Bus (ESB) together with the Esper CEP engine; and secondly, the WSO2 ESB with the Siddhi CEP engine. Both implementations have been tested in terms of performance and stress, and they are compared and discussed in this paper. The results obtained demonstrate that both implementations are suitable and effective, but also that there are notable differences between them: the Mule-based architecture is faster when the architecture makes use of two message broker topics and compares different types of events, while the WSO2-based one is faster when there is a single topic and one event type, and the system has a heavy workload.

scholarly journals A Conceptual Framework for Intelligent Power Distribution Transformers

2020 ◽  
Vol 9 (4S2) ◽  
pp. 97-99
Keyword(s):  
Conceptual Framework ◽  
Real Time ◽  
Health Monitoring ◽  
Power Supply ◽  
Power Distribution ◽  
Smart Cities ◽  
Distribution Transformers ◽  
Iot Devices ◽  
Reliable Power Supply ◽  
Continuous Power

With the agenda of developing smart cities there is huge demand for continuous power supply. Power distribution transformers play avital role in providing a reliable power supply. Failure of a transformer will lead to interruptions in power supply. Many parameters lead to transformer failures. Health monitoring of transformer using IoT technology may help take proactive maintenance steps instead of reactive maintenance. When we combine IoT with AI it will more effective and IoT devices will take decision on their own. This paper presents a conceptual framework of this concept which makes the IoT devices in the transformers to make real-time decisions with the use of AI.

scholarly journals The PMIPv6-Based Group Binding Update for IoT Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Guan ◽  
Ilsun You ◽  
Changqiao Xu ◽  
Hongke Zhang
Keyword(s):  
Mobile Networks ◽  
Mobility Management ◽  
Network Mobility ◽  
Embedded Devices ◽  
Mobility Support ◽  
Signaling Cost ◽  
Complex Functions ◽  
Mobility Management Protocols ◽  
Iot Devices ◽  
Binding Update

Internet of Things (IoT) has been booming with rapid increase of the various wearable devices, vehicle embedded devices, and so on, and providing the effective mobility management for these IoT devices becomes a challenge due to the different application scenarios as well as the limited energy and bandwidth. Recently, lots of researchers have focused on this topic and proposed several solutions based on the combination of IoT features and traditional mobility management protocols, in which most of these schemes take the IoT devices as mobile networks and adopt the NEtwork MObility (NEMO) and its variants to provide the mobility support. However, these solutions are in face of the heavy signaling cost problem. Since IoT devices are generally combined to realize the complex functions, these devices may have similar movement behaviors. Clearly analyzing these characters and using them in the mobility management will reduce the signaling cost and improve the scalability. Motivated by this, we propose a PMIPv6-based group binding update method. In particular, we describe its group creation procedure, analyze its impact on the mobility management, and derive its reduction ratio in terms of signaling cost. The final results show that the introduction of group binding update can remarkably reduce the signaling cost.

scholarly journals Towards Secure Fitness Framework Based on IoT-Enabled Blockchain Network Integrated with Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1640
Author(s):  
Faisal Jamil ◽  
Hyun Kook Kahng ◽  
Suyeon Kim ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Privacy ◽  
Inference Engine ◽  
Machine Learning Algorithms ◽  
Easy Access ◽  
Network Computing ◽  
Useful Knowledge ◽  
Smart Contract ◽  
Iot Devices

Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee’s health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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