Estimates and measurements of radiofrequency exposures in smart-connected homes

Abstract

Estimates and measurements of radiofrequency exposures in smart-connected homes Joyner K, Milligan M, Knipe P. Estimates and measurements of radiofrequency exposures in smart- connected homes. Bioelectromagnetics. 2024 Jul 18. doi: 10.1002/bem.22518. Abstract The aim of this research was to quantify the levels of radiofrequency electromagnetic energy (RF-EME) in a residential home/apartment equipped with a range of wireless devices, often referred to as internet of things (IoT) devices or smart devices and subsequently develop a tool that could be useful for estimating the levels of RF-EME in a domestic environment. Over the course of 3 years measurements were performed in peoples' homes on a total of 43 devices across 16 device categories. Another 12 devices were measured in detail in a laboratory setup. In all a total of 55 individual devices across 23 device categories were measured. Based on this measurement data we developed predictive software that showed that even with a single device in 23 device categories operating near maximum they would, in total, produce exposures at a distance of 1 m of 0.17% of the ICNIRP (2020) public exposure limits. Measurements were also made in two separate smart apartments—one contained over 50 IoT devices and a second with over 100 IoT devices with the devices driven as hard as could reasonably be achieved. The respective 6-min average exposure level recorded were 0.0077% and 0.44% of the ICNIRP (2020) 30- min average public exposure limit. Excerpts .... Based on all of these measurements, we have developed predictive software that can be used to estimate exposure levels on a conservative basis by incorporating a 3 dB enhancement to produce a realistic upper bound for the exposure estimation. The RF estimator tool, which is available from the Mobile & Wireless Forum (MWF) website (mwfai.org), has a drop-down menu that allows the selection of multiple IoT devices and separation distances and returns an estimate of the exposure level that could be expected in the home environment. Devices in neighboring rooms can be included in the software by selecting the appropriate device and distance. However, because of the inverse square dependence with distance and the attenuation through walls, the majority contribution in a particular room is determined by the proximity of devices in that room. Future work will include the expansion of the device categories and the inclusion of wall/window attenuation to account for neighboring homes and apartments. CONFLICT OF INTEREST STATEMENT This work was fully funded by the Mobile and Wireless Forum (mwfai.org) and that KJ and MM are contractored by the MWF. Open access paper: onlinelibrary.wiley.com

AI evidence extraction

Main findings

Over 3 years, RF-EME measurements were performed in people’s homes on 43 devices across 16 device categories, and 12 additional devices were measured in a laboratory setup (55 devices across 23 categories total). Predictive software based on these measurements estimated that 23 device categories operating near maximum would produce exposures at 1 m totaling 0.17% of the ICNIRP (2020) public exposure limits. In two smart apartments (>50 IoT devices and >100 IoT devices, driven as hard as reasonably achievable), the recorded 6-min average exposure levels were 0.0077% and 0.44% of the ICNIRP (2020) 30-min average public exposure limit, respectively.

Outcomes measured

• Measured RF-EME exposure levels in homes/apartments with IoT devices
• Predicted/estimated RF-EME exposure levels using developed software tool
• Exposure levels expressed as % of ICNIRP (2020) public exposure limits

Limitations

• Frequencies and specific RF technologies used by devices are not stated in the abstract.
• Exposure metrics are reported only relative to ICNIRP (2020) limits; absolute field levels are not provided in the abstract.
• Predictive tool uses a conservative 3 dB enhancement; details of model validation are not described in the abstract.
• Potential conflict of interest: work funded by the Mobile & Wireless Forum; two authors are contractors.

Suggested hubs

• smart-meters (0.25)
  Study concerns RF exposures from smart/IoT devices in homes; not specific to smart meters but related to smart-connected home RF sources.
• who-icnirp (0.7)
  Results are framed as percentages of ICNIRP (2020) public exposure limits and reference averaging times.

{
    "study_type": "exposure_assessment",
    "exposure": {
        "band": "RF",
        "source": "smart-connected homes / IoT (wireless devices)",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": "6-min average (smart apartments); 30-min average limit referenced"
    },
    "population": "Residential homes/apartments (people's homes; smart apartments)",
    "sample_size": null,
    "outcomes": [
        "Measured RF-EME exposure levels in homes/apartments with IoT devices",
        "Predicted/estimated RF-EME exposure levels using developed software tool",
        "Exposure levels expressed as % of ICNIRP (2020) public exposure limits"
    ],
    "main_findings": "Over 3 years, RF-EME measurements were performed in people’s homes on 43 devices across 16 device categories, and 12 additional devices were measured in a laboratory setup (55 devices across 23 categories total). Predictive software based on these measurements estimated that 23 device categories operating near maximum would produce exposures at 1 m totaling 0.17% of the ICNIRP (2020) public exposure limits. In two smart apartments (>50 IoT devices and >100 IoT devices, driven as hard as reasonably achievable), the recorded 6-min average exposure levels were 0.0077% and 0.44% of the ICNIRP (2020) 30-min average public exposure limit, respectively.",
    "effect_direction": "no_effect",
    "limitations": [
        "Frequencies and specific RF technologies used by devices are not stated in the abstract.",
        "Exposure metrics are reported only relative to ICNIRP (2020) limits; absolute field levels are not provided in the abstract.",
        "Predictive tool uses a conservative 3 dB enhancement; details of model validation are not described in the abstract.",
        "Potential conflict of interest: work funded by the Mobile & Wireless Forum; two authors are contractors."
    ],
    "evidence_strength": "moderate",
    "confidence": 0.7800000000000000266453525910037569701671600341796875,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "RF-EME",
        "radiofrequency exposure",
        "smart home",
        "smart-connected homes",
        "IoT",
        "wireless devices",
        "exposure assessment",
        "ICNIRP 2020",
        "predictive software",
        "Mobile & Wireless Forum"
    ],
    "suggested_hubs": [
        {
            "slug": "smart-meters",
            "weight": 0.25,
            "reason": "Study concerns RF exposures from smart/IoT devices in homes; not specific to smart meters but related to smart-connected home RF sources."
        },
        {
            "slug": "who-icnirp",
            "weight": 0.6999999999999999555910790149937383830547332763671875,
            "reason": "Results are framed as percentages of ICNIRP (2020) public exposure limits and reference averaging times."
        }
    ]
}

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