Bayesian Sensor Aggregation

bayesian.items

Switch proxySwitch "I am ... [MAP(sleeping.map):%s]"
Number proxyNumber "Probability [%.5f %%]"

Group gBAYESIAN "Bayesian Sleep Sensor" {
    bayesian="proxySwitch" [ prior=0.5, threshold=0.85, state_given_true="ON", state_given_false="OFF" ]
}

Switch testSwitch1 "I am at home ... [MAP(yes-no.map):%s]" <presence> (gBAYESIAN) {
    bayesian="ON" [ prob_given_true=0.99, prob_given_false=0.5 ]
}
Switch testSwitch2 "It is after sunset ... [MAP(yes-no.map):%s]" <sunset> (gBAYESIAN) {
    bayesian="ON" [ prob_given_true=0.75, prob_given_false=0.3 ]
}
String testString1 "The time of day is ... [%s]" <sunset> (gBAYESIAN) {
    bayesian="NIGHT" [ prob_given_true=0.75, prob_given_false=0.3 ]
}
Switch testSwitch3 "The lights are ... [%s]" <light> (gBAYESIAN) {
    bayesian="OFF" [ prob_given_true=0.9, prob_given_false=0.4 ]
}
Switch testSwitch4 "My phone is muted ... [MAP(yes-no.map):%s]" <soundvolume-muted> (gBAYESIAN) {
    bayesian="ON" [ prob_given_true=0.95, prob_given_false=0.5 ]
}

bayesian.py

from core.rules import rule
from core.triggers import when

from core.actions import LogAction

from core.metadata import get_metadata

RULE_NAME = "bayesian"
GROUP_NAME = "gBAYESIAN"

def update_probability(prior, prob_given_true, prob_given_false):
    numerator = prob_given_true * prior
    denominator = numerator + prob_given_false * (1 - prior)
    return numerator / denominator

@rule("Bayesian Sensor Aggregation")
@when("Member of " + GROUP_NAME + " changed")
def bayesianSensorAggregation(event):
    LogAction.logDebug(RULE_NAME, "Item '{}' changed from '{}' to '{}'.", event.itemName, event.oldItemState, event.itemState)

    group = ir.getItem(GROUP_NAME)
    if group is None:
        LogAction.logError(RULE_NAME, "Group '{}' is missing.", GROUP_NAME)
        return

    groupMetadata = get_metadata(GROUP_NAME, "bayesian")
    LogAction.logDebug(RULE_NAME, "Group '{}': metadata = {}", GROUP_NAME, groupMetadata)
    if groupMetadata is None:
        LogAction.logError(RULE_NAME, "Group '{}': metadata is missing.", GROUP_NAME)
        return

    if ir.getItem(groupMetadata.value) is None:
        LogAction.logError(RULE_NAME, "Group '{}': targetItem is missing.", GROUP_NAME)
        return

    if groupMetadata.configuration["prior"] is None:
        LogAction.logError(RULE_NAME, "Group '{}': prior is missing.", GROUP_NAME)
        return
    prior = groupMetadata.configuration["prior"].floatValue()
    LogAction.logDebug(RULE_NAME, "Group '{}': prior = {}", GROUP_NAME, prior)

    threshold = groupMetadata.configuration["threshold"]
    if threshold is None:
        LogAction.logError(RULE_NAME, "Group '{}': threshold is missing.", GROUP_NAME)
        return

    # iterate group members
    for item in ir.getItem(GROUP_NAME).members:
        itemMetadata = get_metadata(item.name, "bayesian")
        LogAction.logDebug(RULE_NAME, "Item '{}': metadata = {}", item.name, itemMetadata)
        if itemMetadata is None:
            LogAction.logError(RULE_NAME, "Item '{}': metadata is missing.", item.name)
            continue

        prob_given_true = itemMetadata.configuration["prob_given_true"]
        if prob_given_true is None:
            LogAction.logError(RULE_NAME, "Item '{}': prob_given_true is missing.", item.name)
            continue

        prob_given_false = itemMetadata.configuration["prob_given_false"]
        if prob_given_false is None:
            LogAction.logDebug(RULE_NAME, "Item '{}': prob_given_false is missing - using default: (1 - prob_given_true)", item.name)
            prob_given_false = 1 - prob_given_true.floatValue()
        else:
            prob_given_false = prob_given_false.floatValue()

        # apply bayesian rule
        if str(item.state) == itemMetadata.value:
            prior = update_probability(prior, prob_given_true.floatValue(), prob_given_false)
            LogAction.logDebug(RULE_NAME, "Group '{}': posterior = {}", GROUP_NAME, prior)

    if not ir.getItem("proxyNumber") is None:
        # update posterior probability
        events.sendCommand("proxyNumber", str(prior * 100))

    state_given_true = groupMetadata.configuration["state_given_true"]
    if state_given_true is None:
        LogAction.logDebug(RULE_NAME, "Group '{}': state_given_true is missing - using default: ON", GROUP_NAME)
        state_given_true = "ON"

    state_given_false = groupMetadata.configuration["state_given_false"]
    if state_given_false is None:
        LogAction.logDebug(RULE_NAME, "Group '{}': state_given_false is missing - using default: OFF", GROUP_NAME)
        state_given_false = "OFF"

    # update proxy item
    events.sendCommand(groupMetadata.value, str(state_given_true if prior >= threshold.floatValue() else state_given_false))

bayesian.sitemap

sitemap bayesian label="Bayesian Sensor Aggregation" {
        Frame label="Bayesian Sleep Sensor" {
                Switch item=proxySwitch
                Text item=proxyNumber
        }
        Frame label="Presence Sensor" {
                Switch item=testSwitch1
        }
        Frame label="Time Of Day Sensor" {
                Switch item=testSwitch2
                Selection item=testString1 mappings=["DAY"="Day", "NIGHT"="Night"]
        }
        Frame label="Lights" {
                Switch item=testSwitch3
        }
        Frame label="Phone" {
                Switch item=testSwitch4
        }
}

sleeping.map

ON=sleeping
OFF=not sleeping
NULL=-
UNDEF=-
-=-

yes-no.map

ON=YES
OFF=NO
NULL=-
UNDEF=-
-=-

Hi Christoph i've found your presentation very intresting. Forgive my ignorance...may I ask you how to use the bayesian.pi file? I've an OH installation up and running on a ARMBIAN based hardware. I'd like to replicate your sensor aggregation idea!

Hi @robitasmo,

Thanks for your interest in my demo. To use it you have to install OH 2.5.x and the following requirements: Map Transformation and Jython. It should be possible to use the demo setup out-of-the-box by dropping it into your OPENHAB_CONF/ folder.