RFID Localisation for Internet of Things Smart Homes : A Survey

Alsinglawi, Belal; Elkhodr, Mahmoud; Nguyễn, Quang Vinh; Gunawardana, Upul; Maeder, Anthony; Simoff, Simeon

doi:10.5121/ijcnc.2017.9107

Cited by 38 publications

(8 citation statements)

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“…Henceforth, current techniques are insufficient to support trust establishment in the IoT . In IoT healthcare applications, for example, the information would be supplied by a range of heterogeneous devices.…”

Section: Related Workmentioning

confidence: 99%

Data provenance and trust establishment in the Internet of Things

Elkhodr

Alsinglawi

2019

Security and Privacy

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The Internet of Things (IoT) is a network of heterogeneous networks encompassing various forms of communications form the current traditional communication models to ubiquitous and pervasive machine to machine communications. In such an ever expanding, dynamic, and complex environment, it becomes vital to know the origin or the source of data and whether this data can be trusted or no. This requires not only accurate, secure, and correct data collection processes; but also provisioning of data provenance throughout the life-cycle of an IoT device and the data it produces. To this end, this work introduces a provenance-based trust management solution, which provides a trust establishment mechanism amongst communicating devices in the IoT. It provides a way to quantitatively assess the trustworthiness of data supplied by a given IoT device. Thus, providing a novel solution to preserve data provenance of IoT devices. This IoT data provenance solution extends the capabilities of our previously proposed IoT platform known as the IoT-management platform, which aimed at preserving the privacy of users in the IoT. K E Y W O R D SInternet of Things, data provenance, privacy, trust 1 Security Privacy. 2020;3:e99.wileyonlinelibrary.com/journal/spy2

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Section: Related Workmentioning

confidence: 99%

Data provenance and trust establishment in the Internet of Things

Elkhodr

Alsinglawi

2019

Security and Privacy

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“…Many localization algorithms have been designed for wireless sensor networks for both indoor and outdoor environments [24]. For example, Alsinglawi et al [15] have identified Radio Frequency Identification (RFID) technologies as a key enabler technology for the IoT due to its cost-effectiveness, high readability rates, automatic identification and energy efficiency characteristics. However, one of the major limitations of RFID technologies is that it needs to equip the environment with a set of pre-identified markers and thus limits the openness of the system.…”

Section: B Localization In the Iotmentioning

confidence: 99%

An Ontology Based Context-Aware Architecture for Smart Campus Applications

Verstaevel

Garzone

Monteil

et al. 2018

2018 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Ubiquitous Computing &Amp; Communications, Big

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With an estimated number of more than 15 billion, objects, the management of architectures for the Internet of Things is a veritable challenge. The inherent mobility (in terms of devices and users) of the IoT means that the architecture has to be resilient to appearance and disappearance of devices. In this paper, we address the problem of autonomic management of IoT architecture by the means of ontologies. The problem we address is that given a dynamic system which is built upon a multitude of entities abstracted as services and characterized by their inputs and outputs, evolving targets that aim to provide services in terms of data or in terms of control, our goal is to enable autonomic management of these kinds of systems to cope with changes and evolutions so that the specified targets are fulfilled throughout the execution according to the specifics and dynamic needs of the system's users. We propose an innovative architecture that relies on ontologies to enable context-aware application to self-compose on demand. This architecture is being deployed to two smart campuses in two universities from Toulouse, France and Wollongong, Australia.

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Section: Introductionmentioning

confidence: 99%

“…Compared to the conventional backscatter communication scheme in radio frequency identification (RFID) systems, AB communication does not require a dedicated energy-emitting reader, and relies solely on external energy sources in the ambient environment, such as WiFi, public radio, and cellular transmit power. As such, the application of AB communication can effectively reduce the deployment cost of large-size IoT network, such as smart homes, smart cities, and environment monitoring [3], [4], [5].There has been tremendous research interests recently on ambient backscatter communications [6], [7]. For instance, [8] analyzed the bit error rate of an AB communication link when the receiver uses an energy detector to detect the 1bit information transmitted per channel use.…”

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confidence: 99%

Throughput Maximization for Ambient Backscatter Communication: A Reinforcement Learning Approach

Wen

Lin

et al. 2019

2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

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Ambient backscatter (AB) communication is an emerging wireless communication technology that enables wireless devices (WDs) to communicate without requiring active radio transmission. In an AB communication system, a WD switches between communication and energy harvesting modes. The harvested energy is used to power the devices operations, e.g., circuit power consumption and sensing operation. In this paper, we focus on maximizing the throughput performance of AB communication system by adaptively selecting the operating mode under fading channel environment. We model the problem as an infinite-horizon Markov Decision Process (MDP) and accordingly obtain the optimal mode switching policy by the value iteration algorithm given the channel distributions. Meanwhile, when the knowledge of channel distribution is absent, a Q-learning (QL) method is applied to explore a suboptimal strategy through device repeated interaction with the environment. Finally, our simulations show that the proposed QL method can achieve closeto-optimal throughput performance and significantly outperforms the other than representative benchmark methods.Index Terms-Ambient backscatter communication, Markov decision process, reinforcement learning, Q-learning. I. INTRODUCTIONThe future Internet of thing (IoT) technology interconnects numerous sensing devices with communications capability for a wide range of applications, e.g., remote monitoring, automatic control, diagnosis and maintenance [1]. Recently, a new communication paradigm named ambient backscatter (AB) communication is widely studied as an energy-efficient method applicable in IoT system [2]. In particular, a tag transmitter in AB communication system communicates with its receiver by backscattering its ambient radio frequency (RF) signals. Specifically, a transmitter tag transmits '0' or '1' by switching its antenna to non-reflecting or reflecting mode, respectively. Compared to the conventional backscatter communication scheme in radio frequency identification (RFID) systems, AB communication does not require a dedicated energy-emitting reader, and relies solely on external energy sources in the ambient environment, such as WiFi, public radio, and cellular transmit power. As such, the application of AB communication can effectively reduce the deployment cost of large-size IoT network, such as smart homes, smart cities, and environment monitoring [3], [4], [5].There has been tremendous research interests recently on ambient backscatter communications [6], [7]. For instance, [8] analyzed the bit error rate of an AB communication link when the receiver uses an energy detector to detect the 1bit information transmitted per channel use. [9] integrates the AB communication with conventional harvest-then-transmit (HTT) protocol in the radio frequency-powered cognitive radio networks, where the backscatter tag can choose to backscatter the ambient RF signal to the receiver or harvest energy for later active transmissions. To achieve the optimal throughput performance, the authors assume ...

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RFID Localisation for Internet of Things Smart Homes : A Survey

Abstract: The Internet of Things (IoT)

Cited by 38 publications

References 40 publications

Data provenance and trust establishment in the Internet of Things

Data provenance and trust establishment in the Internet of Things

An Ontology Based Context-Aware Architecture for Smart Campus Applications

Throughput Maximization for Ambient Backscatter Communication: A Reinforcement Learning Approach

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