OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

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A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

Applied Sciences ◽

10.3390/app9010178 ◽

2019 ◽

Vol 9 (1) ◽

pp. 178 ◽

Cited By ~ 16

Author(s):

Belal Sudqi Khater ◽

Ainuddin Wahid Bin Abdul Wahab ◽

Mohd Yamani Idna Bin Idris ◽

Mohammed Abdulla Hussain ◽

Ashraf Ahmed Ibrahim

Keyword(s):

Intrusion Detection ◽

Detection System ◽

Fog Computing ◽

Raspberry Pi ◽

Space Representation ◽

Location Awareness ◽

Resource Limitations ◽

System Calls ◽

Iot Devices ◽

Hidden Layer

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

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Fog Computing: An Overview of Big IoT Data Analytics

Wireless Communications and Mobile Computing ◽

10.1155/2018/7157192 ◽

2018 ◽

Vol 2018 ◽

pp. 1-22 ◽

Cited By ~ 37

Author(s):

Muhammad Rizwan Anawar ◽

Shangguang Wang ◽

Muhammad Azam Zia ◽

Ahmer Khan Jadoon ◽

Umair Akram ◽

...

Keyword(s):

Data Analytics ◽

Smart Cities ◽

Fog Computing ◽

Computing Paradigm ◽

Key Characteristics ◽

Challenges And Opportunities ◽

Cloud Framework ◽

Iot Devices ◽

Health Care Monitoring ◽

Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

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Fog Computing: A Primer

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.165 ◽

2017 ◽

Vol 7 (7) ◽

pp. 405

Author(s):

Matthew N. O. Sadiku ◽

Mahamadou Tembely ◽

Sarhan M. Musa

Keyword(s):

Internet Of Things ◽

Real Time ◽

Fog Computing ◽

Resource Constrained ◽

Computing Model ◽

Computing Services ◽

Iot Devices ◽

Real Time Computing ◽

And Storage ◽

The Ideal

Fog computing (FC) was proposed in 2012 by Cisco as the ideal computing model for providing real-time computing services and storage to support the resource-constrained Internet of Things (IoT) devices. Thus, FC may be regarded as the convergence of the IoT and the Cloud, combining the data-centric IoT services and pay-as-you-go characteristics of clouds. This paper provides a brief introduction of fog computing.

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S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices

ACM Transactions on Sensor Networks ◽

10.1145/3480462 ◽

2022 ◽

Vol 18 (1) ◽

pp. 1-51

Author(s):

Alberto Giaretta ◽

Nicola Dragoni ◽

Fabio Massacci

Keyword(s):

Fog Computing ◽

Life Cycles ◽

Information Flows ◽

Storage Space ◽

Proof Of Concept ◽

Human Beings ◽

The Past ◽

Iot Devices ◽

And Storage ◽

The Internet Of Things

The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.

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

PeerJ Computer Science ◽

10.7717/peerj-cs.787 ◽

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.

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Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.1.29 ◽

2021 ◽

pp. 308-318

Author(s):

Hadeel T. Rajab ◽

Manal F. Younis

Keyword(s):

Fault Tolerance ◽

Fog Computing ◽

Middle Layer ◽

Massive Data ◽

Data Filtering ◽

Iot Devices ◽

Processing And Storage ◽

Cloud Network ◽

And Storage

Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.

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State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

Sustainability ◽

10.3390/su13169463 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9463

Author(s):

Ritika Raj Krishna ◽

Aanchal Priyadarshini ◽

Amitkumar V. Jha ◽

Bhargav Appasani ◽

Avireni Srinivasulu ◽

...

Keyword(s):

State Of The Art ◽

Personal Information ◽

Fog Computing ◽

Vital Role ◽

The Internet ◽

Resource Limitations ◽

Three Layer Architecture ◽

Research Problems ◽

Iot Devices ◽

The Impact

The Internet of Things (IoT) plays a vital role in interconnecting physical and virtual objects that are embedded with sensors, software, and other technologies intending to connect and exchange data with devices and systems around the globe over the Internet. With a multitude of features to offer, IoT is a boon to mankind, but just as two sides of a coin, the technology, with its lack of securing information, may result in a big bane. It is estimated that by the year 2030, there will be nearly 25.44 billion IoT devices connected worldwide. Due to the unprecedented growth, IoT is endangered by numerous attacks, impairments, and misuses due to challenges such as resource limitations, heterogeneity, lack of standardization, architecture, etc. It is known that almost 98% of IoT traffic is not encrypted, exposing confidential and personal information on the network. To implement such a technology in the near future, a comprehensive implementation of security, privacy, authentication, and recovery is required. Therefore, in this paper, the comprehensive taxonomy of security and threats within the IoT paradigm is discussed. We also provide insightful findings, presumptions, and outcomes of the challenges to assist IoT developers to address risks and security flaws for better protection. A five-layer and a seven-layer IoT architecture are presented in addition to the existing three-layer architecture. The communication standards and the protocols, along with the threats and attacks corresponding to these three architectures, are discussed. In addition, the impact of different threats and attacks along with their detection, mitigation, and prevention are comprehensively presented. The state-of-the-art solutions to enhance security features in IoT devices are proposed based on Blockchain (BC) technology, Fog Computing (FC), Edge Computing (EC), and Machine Learning (ML), along with some open research problems.

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IoT Registration and Authentication in Smart City Applications with Blockchain

Sensors ◽

10.3390/s21041323 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1323

Author(s):

Célio Márcio Soares Ferreira ◽

Charles Tim Batista Garrocho ◽

Ricardo Augusto Rabelo Oliveira ◽

Jorge Sá Silva ◽

Carlos Frederico Marcelo da Cunha Cavalcanti

Keyword(s):

Time Management ◽

Smart Cities ◽

Fog Computing ◽

Denial Of Service ◽

Urban Environments ◽

Edge Computing ◽

Primary Data ◽

Security Challenges ◽

Security Information ◽

Iot Devices

The advent of 5G will bring a massive adoption of IoT devices across our society. IoT Applications (IoT Apps) will be the primary data collection base. This scenario leads to unprecedented scalability and security challenges, with one of the first areas for these applications being Smart Cities (SC). IoT devices in new network paradigms, such as Edge Computing and Fog Computing, will collect data from urban environments, providing real-time management information. One of these challenges is ensuring that the data sent from Edge Computing are reliable. Blockchain has been a technology that has gained the spotlight in recent years, due to its robust security in fintech and cryptocurrencies. Its strong encryption and distributed and decentralized network make it potential for this challenge. Using Blockchain with IoT makes it possible for SC applications to have security information distributed, which makes it possible to shield against Distributed Denial of Service (DDOS). IoT devices in an SC can have a long life, which increases the chance of having security holes caused by outdated firmware. Adding a layer of identification and verification of attributes and signature of messages coming from IoT devices by Smart Contracts can bring confidence in the content. SC Apps that extract data from legacy and outdated appliances, installed in inaccessible, unknown, and often untrusted urban environments can benefit from this work. Our work’s main contribution is the development of API Gateways to be used in IoT devices and network gateway to sign, identify, and authorize messages. For this, keys and essential characteristics of the devices previously registered in Blockchain are used. We will discuss the importance of this implementation while considering the SC and present a testbed that is composed of Blockchain Ethereum and real IoT devices. We analyze the transfer time, memory, and CPU impacts during the sending and processing of these messages. The messages are signed, identified, and validated by our API Gateways and only then collected for an IoT data management application.

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PADL: A Modeling and Deployment Language for Advanced Analytical Services

Sensors ◽

10.3390/s20236712 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6712

Author(s):

Josu Díaz-de-Arcaya ◽

Raúl Miñón ◽

Ana I. Torre-Bastida ◽

Javier Del Ser ◽

Aitor Almeida

Keyword(s):

Smart City ◽

Flood Control ◽

Smart Cities ◽

Big Data Analytics ◽

Heterogeneous Environments ◽

Iot Devices ◽

Definition Of ◽

Definition Of Information ◽

Analytical Services

In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.

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Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

Journal of Information and Data Management ◽

10.5753/jidm.2021.1798 ◽

2021 ◽

Vol 12 (4) ◽

Author(s):

João Paulo Clarindo ◽

João Pedro C. Castro ◽

Cristina D. Aguiar

Keyword(s):

Data Warehouse ◽

Spatial Data ◽

Smart Cities ◽

Fog Computing ◽

Real Data ◽

Distributed Data ◽

Spatial Analytics ◽

Spatial Data Warehouse ◽

Iot Devices

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

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