Attack resistance-based topology robustness of scale-free internet of things for smart cities

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

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Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

ACM Transactions on Sensor Networks ◽

10.1145/3446937 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-23

Author(s):

Ning Chen ◽

Tie Qiu ◽

Mahmoud Daneshmand ◽

Dapeng Oliver Wu

Keyword(s):

Internet Of Things ◽

Smart Cities ◽

Evolutionary Process ◽

Small Error ◽

Cyber Attacks ◽

Communication Capacity ◽

Error Ratio ◽

The Internet Of Things ◽

Evolution Learning

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

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M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities

Environmental Technology & Innovation ◽

10.1016/j.eti.2021.101802 ◽

2021 ◽

pp. 101802

Author(s):

Amjad Rehman ◽

Khalid Haseeb ◽

Tanzila Saba ◽

Hoshang Kolivand

Keyword(s):

Internet Of Things ◽

Data Management ◽

Smart Cities ◽

Security Measures

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State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

Sensors ◽

10.3390/s21051598 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1598

Author(s):

Sigurd Frej Joel Jørgensen Ankergård ◽

Edlira Dushku ◽

Nicola Dragoni

Keyword(s):

Internet Of Things ◽

Smart Cities ◽

Cyber Attacks ◽

Resource Constrained ◽

Remote Attestation ◽

Comprehensive Overview ◽

Research Issues ◽

Advantages And Disadvantages ◽

Iot Devices ◽

Specialized Hardware

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.

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Graph-based service recommendation in Social Internet of Things

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211009047 ◽

2021 ◽

Vol 17 (4) ◽

pp. 155014772110090

Author(s):

Yuanyi Chen ◽

Yanyun Tao ◽

Zengwei Zheng ◽

Dan Chen

Keyword(s):

Internet Of Things ◽

Social Relationships ◽

Latent Variable ◽

Smart Cities ◽

Knowledge Graph ◽

Service Recommendation ◽

Variable Model ◽

Completion Problem ◽

Social Internet Of Things ◽

Object Usage

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

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The Internet of Things: Making Cities — AndThe Way They Use Technology — Smarter

Cross Americas: Probing Disglobal Networks- Papers ◽

10.35483/acsa.intl.2016.49 ◽

2016 ◽

Author(s):

Wendy W. Fok ◽

Keyword(s):

New York ◽

Internet Of Things ◽

Energy Use ◽

Smart Cities ◽

Traffic Monitoring ◽

Urban Life ◽

The Internet ◽

Time Data ◽

Heating And Cooling ◽

The Internet Of Things

Minerva Tantoco was named New York City’s first chief technology officer last year, charged with developing a coordinated citywide strategy on technology and innovation. We’re likely to see more of that as cities around the country, and around the world, consider how best to use innovation and technology to operate as “smart cities.”The work has major implications for energy use and sustainability, as cities take advantage of available, real-time data – from ‘smart’ phones, computers, traffic monitoring, and even weather patterns — to shift the way in which heating and cooling systems, landscaping, flow of people through cities, and other pieces of urban life are controlled. But harnessing Open Innovation and the Internet of Things can promote sustainability on a much broader and deeper scale. The question is, how do you use all the available data to create a more environmentally sound future? The term “Internet of Things” was coined in 1999 by Kevin Ashton, who at the time was a brand manager trying to find a better way to track inventory. His idea? Put a microchip on the packaging to let stores know what was on the shelves.

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