The study of human movement effect on Signal Strength for indoor WSN deployment

Turner, J. S. C.; Ramli, Mohammad Fadzli; Kamarudin, Kamarulzaman; Zakaria, A.; Shakaff, Ali Yeon Md; Ndzi, David; Nor, C. M.; Nornikman, H.; Zakaria, Syed Muhammad Mamduh Syed

doi:10.1109/icwise.2013.6728775

Cited by 47 publications

(21 citation statements)

References 9 publications

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“…If the phone is located very close to the position of a Raspberry Pi, that Pi receives a high RSSI ranging between -51 dBm and -40 dBm if the phone is on the tripod and between -51dBm and -48dBm if the phone is held in the user's hand. This confirms Farragher's results [3] that the body of a user acts as an absorber of the signal, as the human body is made up of more than 70% water [10] [1]. This effect of the body can be noticed in figure 4, as there are more points in the grid with relatively strong signals in the region nearest the Raspberry Pi in the case when the phone is on the tripod than in the case when the phone is in the user's hand.…”

Section: Set Up Of Experiments and Data Collectionsupporting

confidence: 90%

Using BLE to Pre-empt Transitions between Smart Indoor Spaces

Rifaï¹,

Velasco²,

Kelly³

et al. 2018

Electronic Workshops in Computing

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One of the abiding concerns of relatives of people with intellectual disability who are approaching adulthood, and those who care for senior citizens with cognitive impairment, relates to scheduling and organisation of daily activities such as commuting, medication plans and appointments. With appropriate software, information can be collected and harvested to support ID service users or aging people who have lost part of their cognitive skill. One key enabler in this system is the smartphone that has become part of modern life. Although the GPS capabilities of smartphones work well for outside locations, indoor positioning is still problematic and indoor location is a vital awareness component in ambient assistance. This study considers the use of Bluetooth Low Energy (BLE) Received Signal Strength Indicators (RSSI) from either a smart phone or beacon to assist with user embarkation and transition between spaces. The full scope of the study considers the impact of phone orientation, whether a phone is in the hand of the user, how performance can be impacted by presence of typical office furniture and the impact of different phone models. In this preliminary version we focus on differences between whether the phone is in the hand of the user or on a table top and how RSSI information can be used to develop services that predict and assist with behaviour of users as they move between locations.

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Section: Set Up Of Experiments and Data Collectionsupporting

confidence: 90%

Using BLE to Pre-empt Transitions between Smart Indoor Spaces

Rifaï¹,

Velasco²,

Kelly³

et al. 2018

Electronic Workshops in Computing

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“…According to the designed algorithm, the PSO algorithm will work on checking continuously the performance at the Rxs, and adapting the diffusion spots' locations and intensities according to the detected performance at receivers. By looking at the fast pace movement speed in indoor environments (about 0.5 m/second [30]), the PSO algorithm should need relaxed conditions, such as checking the performance at the receivers every 0.25 second, for adapting the diffusion spots according to the performance for the users, which is achievable using the considered number of PSO evaluations. For increasing the speed of PSO convergence, the last achieved optimum configuration for the diffusion spots could be forwarded as an initial condition for PSO.…”

Section: Parametermentioning

confidence: 99%

Optimization of locations of diffusion spots in indoor optical wireless local area networks

Eltokhey

Mahmoud

Ghassemlooy

et al. 2018

Optics Communications

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In this paper, optimization of diffuse spots' parameters in indoor optical wireless communications (OWC) system is carried out by using the particle swarm optimization (PSO) algorithm. By simultaneously optimizing the diffusion spots' locations and intensities, we show an improvement in the signal-to noise-ratio (SNR) and the delay spread at the receivers, while considering both the background noise and the multipath dispersion. A comparison is made between different optimization scenarios, to illustrate the effect of varying the parameters that are being optimized. We show that the optimization of both intensities and locations of diffuse spots resulted in improvement up to 42% and 23% in the average delay spread and the average SNR, respectively, compared with the centrally located position of diffuse spots' distribution, with respect to the receivers' locations, which has a uniform distribution of power.

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“…Both of them may experience a significant impact of the human presence, as well as carried wirelessequipped devices, on the signal propagation [20]. Usually, building hosting offices, museums or malls, are regularly structured, and sensors are installed for surveillance in the offices of a company, or inside other public place such as a museum.…”

Section: State-of-the-art Of Wsn-based Environmental Monitoring Systemsmentioning

confidence: 99%

Wireless Real-Time Monitoring System for the Implementation of Intelligent Control in Subways

Carbonari¹,

Vaccarini²,

Valta³

et al. 2016

Real-Time Systems

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This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a modelbased predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported.

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The study of human movement effect on Signal Strength for indoor WSN deployment

Cited by 47 publications

References 9 publications

Using BLE to Pre-empt Transitions between Smart Indoor Spaces

Using BLE to Pre-empt Transitions between Smart Indoor Spaces

Optimization of locations of diffusion spots in indoor optical wireless local area networks

Wireless Real-Time Monitoring System for the Implementation of Intelligent Control in Subways

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