An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Memos, Vasileios A.; Psannis, Kostas E.; Ishibashi, Yutaka; Kim, Byung-Gyu; Gupta, Brij B.

doi:10.1016/j.future.2017.04.039

Cited by 285 publications

(97 citation statements)

References 28 publications

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“…(iii) Aerial surveillance intelligence: Over the last decades, there are a growing number of studies on using UAVs for aerial surveillance system (ASS) [86], [87], [132], in which UAVs mounted with the surveillance camera can employ controllable aerial photograph in a specified area. Different from fixed surveillance cameras that only offer fixed views on a limited region [205], UAV-enabled aerial surveillance can provide flexible views. Some studies investigate the autonomous capabilities of UAV-enabled surveillance, including regions detection [206], [207], objects positioning [149], and path planning [208]- [210].…”

Section: Intelligent Local Computing Servicesmentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

161

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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Section: Intelligent Local Computing Servicesmentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

161

View full text Add to dashboard Cite

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“…In this context, it is worth to note that energy efficiency and securing data transmission is a new challenging area in IoT applications. Several research efforts have been published in [17,18]. In a hybrid approach joining signature-and anomaly-based methods, a good example is a conceptual IDS called SVELTE described in details in [19].…”

Section: Ids For Internet Of Thingsmentioning

confidence: 99%

A distributed IDS architecture model for Smart Home systems

et al. 2017

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The common use of smart devices encourages potential attackers to violate privacy. Sometimes taking control of one device allows the attacker to obtain secret data (such as password for home WiFi network) or tools to carry out DoS attack, and this, despite the limited resources of such devices. One of the solutions for gaining users' confidence is to assign responsibility for detecting attacks to the service provider, particularly Internet Service Provider (ISP). It is possible, since ISP often provides also the Home Gateway (HG)-device that has multiple roles: residential router, entertainment center, and home's "command and control" center which allows to manage the Smart Home entities. The ISP may extend this set of functionalities by implementing an intrusion detection software in HG provisioned to their customers. In this article we propose an Intrusion Detection System (IDS) distributed between devices residing at user's and ISP's premises. The Home Gateway IDS and the ISP's IDS constitute together a distributed structure which allows spreading computations related to attacks against Smart Home ecosystem. On the other hand, it also leverages the operator's knowledge of security incidents across the customer premises. This distributed structure is supported by the ISP's expert system that helps to detect distributed attacks i.e., using botnets.

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“…A huge amount of data generated by the sensing devices in IoT is concentrated into the cloud computing through the network for further processing; cloud computing is the center of the data processing and storage. IoT is widely used in many scenarios as shown in Figure , such as smart homes, intelligent transportation, intelligent logistics, and so on; it provides a lot of convenience information . Cloud computing is the core of the smart city; it consists of hundreds of thousands of servers and allows users to access application services at any location using a variety of terminals.…”

Section: Introductionmentioning

confidence: 99%

“…IoT is widely used in many scenarios as shown in Figure 1, such as smart homes, intelligent transportation, intelligent logistics, and so on; it provides a lot of convenience information. 2 Cloud computing is the core of the smart city; it consists of hundreds of thousands of servers and allows users to access application services at any location using a variety of terminals. Cloud computing is a platform that can be customized to the requirement of applications.…”

Section: Introductionmentioning

confidence: 99%

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

Huo

Jiang

et al. 2019

Computational Intelligence

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Internet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine‐grained and accurate traffic information is important for IoT network management. Software‐defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine‐grained traffic information in SDN‐based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse‐grained traffic of flows and fine‐grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non‐deterministic polynomial‐hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine‐grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.

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An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Cited by 285 publications

References 28 publications

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

A distributed IDS architecture model for Smart Home systems

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

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