An Integrated Intelligent System for IoT Device Selection and Placement in Opportunistic Networks Using Fuzzy Logic and Genetic Algorithm

Cuka, Miralda; Elmazi, Donald; Obukata, Ryoichiro; Ozera, Kesuke; Oda, Tetsuya; Barolli, Leonard

doi:10.1109/waina.2017.178

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…In [54], two cooperative techniques have been proposed using meta-heuristic algorithms, that is, hill climbing and random-search to improve security of physical layer by improving factors of path loss, phase fading and attenuation. In [55], the device selection and task allocation are effectively done using Fuzzy logic and genetic algorithm approaches by considering device's speed, remaining energy, density and selection decision.…”

Section: Device Layermentioning

confidence: 99%

IoT applications and challenges in smart cities and services

Rafiq

Mahmood

Razzaq

et al. 2023

The Journal of Engineering

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Internet of Things (IoT) is a revolutionary and novel platform where a smart network connects to the large number of electronic devices via internet through available communication systems for reliable and real time connectivity, sensing thus acquiring data from sensors, computing and actuating devices. A review of the current status of IoT features, architecture, communication infrastructure and applications is presented here. Number of IoT applications and challenges in the development of smart cities are covered about this innovative technological platform in this review along with communication protocols characteristics and applications. IoT aided smart city services, health applications, transport applications and smart grid improvements are also discussed. Many challenges and issues related to security, communication, applications and system architecture along with future directions of IoT technology are elaborated in detail.

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Section: Device Layermentioning

confidence: 99%

IoT applications and challenges in smart cities and services

Rafiq

Mahmood

Razzaq

et al. 2023

The Journal of Engineering

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“…Cuka et al [24] proposed a fuzzy-based mechanism for the smart selection of smart physical devices that are deployed in the SIoT environment by utilising a fuzzy logic model. They used device remaining energy, device interaction, contact time, device inter-distance, and device buffer occupancy as network parameters.…”

Section: Related Workmentioning

confidence: 99%

Development of a Model for Trust Management in the Social Internet of Things

et al. 2022

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The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.

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“…Despite the insightful contribution, the role of the Seed OppNet is (Not Defined). Placing Opportunistic Networks within Delay Tolerant Networks and the Internet of Things while acknowledging the challenges that come with OppNets, M. Cuka et al [91], to alleviate some of OppNets' challenges, proposed the design and implementation of an integrated intelligent system using fuzzy logic and genetic algorithm-based methods. For opportunistic Networks have particular characteristics, IoT device speed, IoT device density, IoT device remaining energy, IoT device selection decision, selection operator, crossover operator, and mutation operator, are the parameters required for the implementation of the integrated intelligent system.…”

Section: Oppnets Data Aggregation and Technologymentioning

confidence: 99%

Securing and Facilitating Communication Within Opportunistic Networks: A Holistic Survey

2021

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Opportunistic Networks is a new concept that is increasingly gaining ground since it appears as a concrete example of the Internet of Things, Internet of Vehicles, Industrial Internet of Things, and the Internet of Everything with Mobile ad hoc Networks' characteristics. An Opportunistic Network starts with a Seed OppNet that sets up the network; expands from the Seed OppNet to an extended Seed OppNet through devices' discovery. The characteristics of Opportunistic Networks make OppNets more challenging than any other networks. So, a deep understanding of OppNets' characteristics and demands is an unavoidable precondition before proposing any OppNets related scheme. However, under OppNets' constraints, the relevance of the Opportunistic Networks related articles in literature is yet to be established. Also, most surveys tackling Opportunistic Networks do not give a complete insight into what Opportunistic Networks stand for. This work reviews state of the art on Opportunistic Networks providing three main contributions. First, resorting to the primary definition of Opportunistic Networks, it elucidates what OppNets are, pointing out the particularities of an OppNet, its domains of applications, and challenges. Second, it provides a comprehensive review that encompasses most Opportunistic Networks' research areas: routing, intrusion detection, authentication, privacy protection, data aggregation, and the technology for OppNets, organising them in a taxonomy. Third, it evaluates the role of the Seed OppNet in Opportunistic Networks related schemes. Any proposed OppNets related scheme, to be relevant to OppNets' research, should include OppNets' characteristics and demands.

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An Integrated Intelligent System for IoT Device Selection and Placement in Opportunistic Networks Using Fuzzy Logic and Genetic Algorithm

Cited by 11 publications

References 17 publications

IoT applications and challenges in smart cities and services

IoT applications and challenges in smart cities and services

Development of a Model for Trust Management in the Social Internet of Things

Securing and Facilitating Communication Within Opportunistic Networks: A Holistic Survey

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