The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

Research Challenges ◽

Network Load ◽

And Control ◽

Edge Layer ◽

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

10.36227/techrxiv.12657182 ◽

2020 ◽

Author(s):

Baha Rababah ◽

Tanweer Alam ◽

Rasit Eskicioglu

Keyword(s):

Internet Of Things ◽

The Internet ◽

Raw Data ◽

Next Generation Internet ◽

Research Challenges ◽

Network Load ◽

And Control ◽

Edge Layer ◽

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

THE INTERNET OF THINGS: A SURVEY

Tạp chí Nghiên cứu dân tộc ◽

10.25073/0866-773x/64 ◽

2018 ◽

Cited By ~ 1

Author(s):

Luigi Atzori ◽

Antonio Lera ◽

Giacomo Morabito

Keyword(s):

Internet Of Things ◽

Social Science ◽

Communication Protocols ◽

Research Community ◽

Wireless Sensor ◽

The Internet ◽

Next Generation Internet ◽

Complex Scenario ◽

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.

Research Challenges for the Internet of Things: What Role Can OR Play?

Systems ◽

10.3390/systems5010024 ◽

2017 ◽

Vol 5 (1) ◽

pp. 24 ◽

Cited By ~ 16

Author(s):

Peter Ryan ◽

Richard Watson

Keyword(s):

Internet Of Things ◽

The Internet ◽

Research Challenges ◽

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS) ◽

Strategic research challenges in the Internet of Things

10.1109/rcis.2015.7128856 ◽

2015 ◽

Cited By ~ 3

Author(s):

John Favaro

Keyword(s):

Internet Of Things ◽

The Internet ◽

Strategic Research ◽

Research Challenges ◽

Cryptographic Security Solutions for the Internet of Things - Advances in Information Security, Privacy, and Ethics ◽

A Review of Cryptographic Algorithms for the Internet of Things

10.4018/978-1-5225-5742-5.ch003 ◽

2019 ◽

pp. 67-93 ◽

Cited By ~ 1

Author(s):

Issmat Shah Masoodi ◽

Bisma Javid

Keyword(s):

Internet Of Things ◽

Power Consumption ◽

The Internet ◽

Reference Architecture ◽

Memory Space ◽

Security Issues ◽

Research Challenges ◽

Short Period ◽

Cryptographic Algorithms ◽

There are various emerging areas in which profoundly constrained interconnected devices connect to accomplish specific tasks. Nowadays, internet of things (IoT) enables many low-resource and constrained devices to communicate, do computations, and make smarter decisions within a short period. However, there are many challenges and issues in such devices like power consumption, limited battery, memory space, performance, cost, and security. This chapter presents the security issues in such a constrained environment, where the traditional cryptographic algorithms cannot be used and, thus, discusses various lightweight cryptographic algorithms in detail and present a comparison between these algorithms. Further, the chapter also discusses the power awakening scheme and reference architecture in IoT for constrained device environment with a focus on research challenges, issues, and their solutions.

Towards a Scalable IOTA Tangle-Based Distributed Intelligence Approach for the Internet of Things

Advances in Intelligent Systems and Computing - Intelligent Computing ◽

10.1007/978-3-030-52246-9_35 ◽

2020 ◽

pp. 487-501

Author(s):

Tariq Alsboui ◽

Yongrui Qin ◽

Richard Hill ◽

Hussain Al-Aqrabi

Keyword(s):

Internet Of Things ◽

The Internet ◽

Intelligence Approach ◽

A Data Acquisition and Control System in Smart Home Based on the Internet of Things

International Journal of Simulation Systems Science & Technology ◽

10.5013/ijssst.a.17.07.17 ◽

2016 ◽

Author(s):

Special Issues Editor

Keyword(s):

Control System ◽

Internet Of Things ◽

Data Acquisition ◽

Smart Home ◽

The Internet ◽

Home Based ◽

And Control ◽

Management of Software-Defined Networking Powered by Artificial Intelligence

10.5772/intechopen.97197 ◽

2021 ◽

Author(s):

Jehad Ali ◽

Byeong-hee Roh

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Internet Of Things ◽

Heterogeneous Networks ◽

Software Defined Networking ◽

Machine Learning Algorithms ◽

The Internet ◽

Underlying Network ◽

And Control ◽

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

The spectrum of control: A social theory of the smart city

First Monday ◽

10.5210/fm.v20i7.5903 ◽

2015 ◽

Cited By ~ 55

Author(s):

Jathan Sadowski ◽

Frank Pasquale

Keyword(s):

Internet Of Things ◽

Social Theory ◽

Smart City ◽

Critical Infrastructure ◽

The Internet ◽

Control Mechanisms ◽

Physical Objects ◽

Smart Technologies ◽

And Control ◽

There is a certain allure to the idea that cities allow a person to both feel at home and like a stranger in the same place. That one can know the streets and shops, avenues and alleys, while also going days without being recognized. But as elites fill cities with “smart” technologies — turning them into platforms for the “Internet of Things” (IoT): sensors and computation embedded within physical objects that then connect, communicate, and/or transmit information with or between each other through the Internet — there is little escape from a seamless web of surveillance and power. This paper will outline a social theory of the “smart city” by developing our Deleuzian concept of the “spectrum of control.” We present two illustrative examples: biometric surveillance as a form of monitoring, and automated policing as a particularly brutal and exacting form of manipulation. We conclude by offering normative guidelines for governance of the pervasive surveillance and control mechanisms that constitute an emerging critical infrastructure of the “smart city.”