Idea: A System for Efficient Failure Management in Smart IoT Environments Palanivel Kodeswaran, Ravi Kokku, Sayandeep Sen, Mudhakar Srivatsa MobiSys 2016 Presented by: Zaeem Hussain CS 3720 Feb 20, 2018

Basic Idea Sensors fail over time, needing replacement or some other form of failure management Cost of failure management increases with the scale of deployment Inherent sensor redundancy in detecting Activities of Daily Living (ADLs) Leverage this redundancy to increase the time between repairs

Setup

Motivation ADLs generally trigger multiple sensors e.g. Cooking {Fridge, Microwave, Cupboard, Stove} Not all sensors may be necessary to detect an activity Failure of a sensor may or may not significantly degrade ADL detection System can monitor sensors and delay failure management measures if detection performance does not fall below acceptable level

Architecture of Idea Online Offline ADL Detection Application ADL Signature Generation Sensor Failure Detection ADL Rules Maintenance Scheduling Sensor Impact Estimation

Datasets Name # User Duration (days) # ADL (# Activity) # Sensor (# Event) KasterenA 1 25 16 (283) 14 (2006) KasterenB 15 25 (172) 27 (22595) KasterenC 19 27 (254) 23 (39861) Aruba 219 11 (6477) 39 (805268) Twor9-10 2 249 25 (3745) 100 (711421) Twor2009 57 14 (499) 100 (136504) TworSmr 63 8 (1016) 100 (366075) AdlNorm 24 84 5 (120) 39 (6425)

Signature Extraction Identify frequently occurring subsets of sensors for each activity Extract rules of the form: (Fridge,Groceries_Cbrd,Plates_Cbrd) ⇒ Prepare_Breakfast <support:0.05,confidence:0.7> Depending on the number of sensors triggered, an ADL may have multiple rules Augment the rules with average Time of Day and Duration

ADL Detection Assume sensor trigger sequence partitioned by activity Matching rule: Set of sensors in the rule is a subset of sensors in the activity Sensor score of ADLk: Sum (confidence value of all matching rules of ADLk) x Prob(ADLk) Temporal matching: Activity’s temporal feature lies within 1.5 standard deviations of the rule’s temporal feature Temporal score: Sum (confidence value of temporally matching rules) Final score: Weighted sum of temporal and sensor scores

Failure Detection For periodic sensors, generate alert if time elapsed since last report from the sensor exceeds some predetermined threshold For 100% impact sensors, if time elapsed since last detection of ADL for which the sensor is critical exceeds the threshold, generate alert For rest of the sensors, keep track of rarity score ρ Rarity score: Probability that observed sequence of activities happens without triggering the sensor in consideration Once rarity score for a sensor falls below some threshold, generate alert Choice of threshold for rarity score represents tradeoff between false alerts and detection latency

Maintenance Scheduling Cloud collects following information for each home Impact of failed sensor Location of home Degradation in ADL detection accuracy Optimize for labor cost and minimum distance travelled by mechanic(s)

Experiments Split data into 80% train and 20% test sets Longer duration traces generated by replaying the original sequence multiple times Failures emulated by removing the failed sensor’s trigger events from the dataset, starting from the time of failure Sensor failures follow Weibull distribution with MTBF of 1 year

Questions?