List of Titles

Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing

- Yousafzai, Abdullah, Yaqoob, Ibrar, Imran, Muhammad, Gani, Abdullah, Md Noor, Rafidah

Authors: Yousafzai, Abdullah , Yaqoob, Ibrar , Imran, Muhammad , Gani, Abdullah , Md Noor, Rafidah
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4171-4182
Full Text: false
Reviewed:
Description: Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC. © 2014 IEEE.

Quick View

Decision behavior based private vehicle trajectory generation towards smart cities

- Chen, Qiao, Ma, Kai, Hou, Mingliang, Kong, Xiangjie, Xia, Feng

Authors: Chen, Qiao , Ma, Kai , Hou, Mingliang , Kong, Xiangjie , Xia, Feng
Date: 2021
Type: Text , Conference paper
Relation: 18th International Conference on Web Information Systems and Applications, WISA 2021 Vol. 12999 LNCS, p. 109-120
Full Text:
Reviewed:
Description: In contrast with the condition that the trajectory dataset of floating cars (taxis) can be easily obtained from the Internet, it is hard to get the trajectory data of social vehicles (private vehicles) because of personal privacy and government policies. This paper absorbs the idea of game theory, considers the influence of individuals in the group, and proposes a decision behavior based dataset generation (DBDG) model of vehicles to predict future inter-regional traffic. In addition, we adopt simulation tools and generative adversarial networks to train the trajectory prediction model so that the private vehicle trajectory dataset conforming to social rules (e.g., collisionless) is generated. Finally, we construct from macroscopic and microscopic perspectives to verify dataset generation methods proposed in this paper. The results show that the generated data not only has high accuracy and is valuable but can provide strong data support for the Internet of Vehicles and transportation research work. © 2021, Springer Nature Switzerland AG.

Quick View

Obfuscated memory malware detection in resource-constrained iot devices for smart city applications

- Shafin, Sakib, Karmakar, Gour, Mareels, Iven

Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven
Date: 2023
Type: Text , Journal article
Relation: Sensors Vol. 23, no. 11 (2023), p. 5348
Full Text:
Reviewed:
Description: Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.

A survey on big multimedia data processing and management in smart cities

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
Date: 2019
Type: Text , Journal article
Relation: ACM computing surveys Vol. 52, no. 3 (2019), p. 1-29
Full Text: false
Reviewed:
Description: Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.

Quick View

Historic urban landscapes and visualising Ballarat : Citizen participation for sustainable urban planning and design

- Murphy, Angela, Dahlhaus, Peter, Thompson, Helen

Authors: Murphy, Angela , Dahlhaus, Peter , Thompson, Helen
Date: 2016
Type: Text , Conference proceedings , Conference paper
Relation: 3rd Annual Conference of Research@Locate, R@Loc 2016; Melbourne, Australia; 12th-14th April 2016; published in CEUR Workshop Proceedings
Full Text:
Description: Technological innovation has provided enhanced capacity for knowledge building, for connection and for improved infrastructure planning in the development of the modern city. In parallel to the building of technology supported urban planning and design capacity, a debate has emerged around the need to maximise citizen participation in urban planning. The role of identity, culture and social context has been assessed as being as integral to sustainability in urban planning as is infrastructure management. In 2011 UNESCO, through the mechanism of the recommendation for Historic Urban Landscapes (HUL), created an imperative for the overt recognition of the role of culture, place and identity in sustainable urban planning. The City of Ballarat, Victoria, was the first of a series of international cities to pilot HUL and commit to inclusive citizen based collaboration in urban planning. Through online technology, a platform for partnership building was established. Developed and supported through the Centre for eResearch and Digital Innovation at Federation University Australia, the HUL and Visualising Ballarat portals track the diversity of urban landscapes-from built environment to geomorphology to cultural identity-and facilitate their inclusion in planning and resource allocation. Crowdsourcing was promoted as pivotal in this process, while spatial innovation provided a means through which to bring to life the notion of distinctiveness, identity and place. Through mapping intangibles across complex and diverse groups within community, the potential for improving the quality and management of the planning process was found to be enhanced. Local Area Planning provided a mechanism for a conceptual alignment of past and present and the voice of community has gained a stronger (and more disruptive) voice in determining what communities' value within their lived environment. This shift was assessed as playing an important, and increasingly recognised, role in sustainable urban planning and design.
Description: CEUR Workshop Proceedings

Quick View

Wireless underground sensor communication using acoustic technology

- Al Moshi, Md Adnan, Hardie, Marcus, Choudhury, Tanveer, Kamruzzaman, Joarder

Authors: Al Moshi, Md Adnan , Hardie, Marcus , Choudhury, Tanveer , Kamruzzaman, Joarder
Date: 2024
Type: Text , Journal article
Relation: Sensors Vol. 24, no. 10 (2024), p.
Full Text:
Reviewed:
Description: The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin–Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs. © 2024 by the authors.

Showing items 1 - 6 of 6