SAFER: An IoT-Based Perpetual Safe Community Awarenessand Alerting Network

Nailah Saleh Alhassoun Md Yusuf Sarwar Uddin Nalini VenkatasubramanianDepartment of Computer Science, University of California, Irvinenailah@uci.edu yusuf.sarwar@uci.edu nalini@ics.uci.edu

Abstract— Perpetual awareness systems are sens-ing systems characterized by continuous monitoringand ubiquitous sensing; they are essential to manysafety and mission-critical applications, e.g. assistedliving, healthcare and public safety. In this paper,we present SAFER, a perpetual heterogeneous IoTsystem; deployed in homes to detect critical events(injury, hazardous-environment) that must triggerimmediate action and response. A key challenge hereis the energy consumption associated with perpet-ual operations. We propose a novel energy-awareperpetual home IoT system where battery-operatedand wall-powered IoT devices co-execute to ensuresafety of occupants. We use a semantic approach thatextracts activities-of-daily-living from device data todrive energy-optimized sensor activations. To validateour approach, we developed an elderly fall detectionsystem using multi-personal and in-situ sensing de-vices. Using initial measurements to drive larger sim-ulations, we show that our Cost-Function-Gradientalgorithm can achieve greater than 4X reductions inenergy dissipation without loss of sensing accuracy.

I. MotivationRecent advances in device, communication and pro-

cessing technologies have created the Internet of Things(IoT) revolution; this is considered to be a key techno-logical enabler that can embed intelligence into variousaspects of our daily lives by creating smart homes, com-munities, and city level infrastructures worldwide. Forinstance, smart buildings, today, contain heterogeneousIoT devices, that manage control systems to improve thesafety and comfort of building occupants by enabling arange of applications such as surveillance/security andfire protection while reducing energy footprints to loweroperational cost.

Similarly, in the past decade, smart home sensingsystems have created the capability to automaticallyand unobtrusively collect information about a resident’severyday behavior and provide value-added services de-rived from this information. Such efforts have usedsensing technology to monitor the resident’s activitiesin their home environment [1]. The new generation ofpersonal sensing devices has also created the ability tosense personal health factors (e.g. heart rate, activity)and is making possible always connected health-care IoTsolutions for elder care and care for individuals withdisabilities. Such IoT awareness systems are comprised

of diverse heterogeneous IoT devices that interact andcooperate to deliver a service and trigger a crucial event.

One relevant use case of an IoT-based perpetual aware-ness application is that of elderly fall detection. Falldetection is a major challenge in the public health caredomain - the Center for Disease Control reports over 2million falls annually in the United States. The designof reliable systems to quickly detect and mitigate theeffects of falls will help improve outcomes significantly.Similarly, a smart home security system can assist thosewith disabilities (with vision impairments, deaf and hardof hearing) by capturing anomalous events and intrusionsand providing alerts in the event of suspicious movement.

While IoT deployments hold significant promises toimprove the quality of life of citizens, several limitationsarise in operating IoT deployments in a scalable, re-silient manner over time. First, mission-critical awarenesssystems are expected to operate 24/7, i.e. perpetually,to monitor and detect any critical event. This raisesissues of the cost of operation and continuous energyconsumption. Second, IoT devices typically are small insize with restricted resources including limited computepower, battery and storage capability. Third, the needfor low cost and mass-scale production further enhancesthe likelihood of component variability and structuralfailures. Lastly, the diversity of settings and deploymentsplay an important role in both the accuracy and cost ofthe applications deployed.

In this paper, we aim to handle the energy limitationscaused by perpetual operation. Execution lifetimes ofIoT devices rely heavily on limited on-board batterycapacity; this has an impact on service availability andin turn affects the quality of service delivered by thesesolutions. Our key idea is to exploit heterogeneity of IoTdevices and knowledge of the activities of daily living tocreate an energy-efficient perpetual IoT system withoutloss of service quality. Using a multistage approach thatmodels the behavior of humans in a space, we developintelligent device activation techniques that can enhancethe effectiveness of the IoT awareness system, in termsof energy consumption and accuracy.

Our IoT setting is dynamic - it includes devices withvarying capabilities in terms of computation, sensing,energy source, energy consumption, mobility and com-

978-1-5386-3470-7/17$31.00 ©2017 IEEE

munication; these devices use diverse communicationprotocols and direct connections to cloud platforms.Perpetual IoT applications incur high communicationand energy costs; managing the number of active devicesto reduce network overhead is critical. We aim to designa system model that alters the state of IoT devices andcommunication by utilizing real time semantic knowledgeon user activities. We also exploit the heterogeneity ofIoT devices, multiple communication networks, currentenvironment conditions and the activity patterns beinginferred to enable energy efficiency. To lend focus, wedevelop our techniques in a target application domain- our IoT-based assisted living system aims to provideoccupant safety while ensuring energy efficiency. Notethat accuracy of event detection is a primary goal -missing a critical event (i.e. injury or fall) as a resultof energy optimizations is not acceptable.

Key contributions of this paper include:• Formalizing energy efficiency of perpetual Iot-based

awareness systems as a constrained optimizationproblem, which we show to be NP-hard.

• Design a three-phase framework and associated al-gorithms for smart homes that combines a floor-plansegmentation algorithm [2], an activity recognitiontechnique [3], and energy optimization algorithmswith heterogeneous IoT devices.

• Development and validation of a prototype heteroge-neous IoT system,”SAFER: an elderly fall detectionIoT system” in a real world testbed.

• Extensive evaluations to study the scalability andeffectiveness of our algorithms and approach usingsimulation studies.

II. Energy Efficiency and Perpetual IoT

In this section, we describe the unique aspects ofenergy optimization in the perpetual IoT setting anddesign a phased approach to address this concern.

Related Work: In recent years, novel techniques havebeen proposed to reduce energy consumption in sensornetworks at the device, communication and networklevels. At the device level, vendors and engineers lookinto circuit and hardware optimization to reduce powerconsumption through energy harvesting methods andimproved duty-cycling methods [4, 5]. At the communi-cation layer, standardization bodies and industry asso-ciations (IETF, IEEE, Bluetooth SIG, Zigbee Alliance,etc.) have specified and developed protocols to enableenergy efficient IoT by reducing communication over-heads. This research has focused on the optimizationof access technologies (802.15.4 radio, BLE, Wi-Fi lowpower, etc.), on adaptation of IP protocols (6LoWPAN,RPL, and CoAP) to extend the web architecture to themost constrained sensors, and on developing lightweightprotocols enabling the connection of almost everythingto the cloud (MQTT, etc.). Literature on wireless sensornetworks (WSNs) for network level energy optimization

can be classified into three main approaches duty cycling,data-driven and mobility [6].

Duty cycling approaches [7]–[10] focus on optimizingthe networking subsystem by exploiting node redun-dancy. As sensor nodes perform a cooperative task, nodesalternate between active and sleep states. Early work induty cycling generally assume that the sensor nodes inthe wireless sensor networks are homogeneous, and con-nected via homogeneous transmission methods which areenergy constrained (battery, energy harvesting). Tech-niques such as Low-Energy Adaptive Clustering Hier-archy (LEACH) [8], applied in homogeneous networks,aims to distribute energy dissipation evenly, so doublingthe useful system lifetime. Data-driven approaches [11,12] focus on reducing data sampling and transmission byexploiting data aggregation, compression, or prediction.Mobility [6] is focused in having mobile entities, whichcan be the sink or the whole network nodes.

In comparison, the IoT ecosystem is characterized by alarger level of heterogeneity. Clustering algorithms usedin heterogeneous networks include techniques such asthe Stable Election Protocol (SEP) [13], where nodeshave two different energy levels, normal and advancedand the Energy efficient heterogeneous clustered (EEHC)technique [14] which assumes that a percentage of thepopulation of static sensors are equipped with addi-tional energy. Similarly, the Energy Efficient Cluster-ing Scheme (EECS) [15], uses cluster heads, i.e. nodeswith more residual energy as gathering points. Energyefficient cluster head election protocols [16] have beendesigned for heterogeneous networks. A distributed ex-tension, Distributed Energy Efficient Clustering (DEEC)[17], assumes that all nodes in the sensor network areequipped with different levels of energy. In [18], theyexplore placing static nodes that have unlimited energyresources, so they have two levels, nodes that consumethe most energy should be line powered and the leafnodes that consume less power.

A. Problem DescriptionThe energy efficiency problem is characterized in a

assisted living context where heterogeneous IoT devices(wearable, ambient, and vision), Figure 5, are scatteredin a home. These IoT devices have varying capabilities(power source, battery lifetime, connectivity, reliabilityand accuracy) are used as a part of an elderly falldetection system (SAFER). In general, they can be usedfor any critical event detection task. Through the elderlyfall detection system, we recognized and identified prob-lems and opportunities for improved operations. Battery-powered devices such as mobile and wearable sensorsdissipate power quickly and need to be recharged. Fur-thermore, one can designate areas in the floor plan thatare used in patterns; i.e. not all wall powered IoT devicesare utilized all the time. Knowledge of the activities ofdaily living (ADLs) of a resident can provide us withinformation about the location and activity type; this can

Fig. 1: The SAFER Architecture illustrating the Three Phase Approachbe utilized intelligently to minimize energy dissipation inthe integrated system. Knowledge of device capabilitiescan be also used to activate an adequate subset of IoTdevices to meet the accuracy demand levels.

Given the above observations, our goal is to minimizeenergy consumption of the integrated IoT deployment(battery and wall powered) to enable long-term operationwhile meeting accuracy threshold demands. We formalizethe energy efficiency problem for heterogeneous IoT de-vices as a constrained optimization problem (proven tobe NP-hard). Note that this optimization problem canbe applied to any heterogeneous IoT deployment setting(independent of layout and instrumentation) and can beconfigured to preserve the desired accuracy thresholds.B. SAFER: The Three Phase Approach

Developing an optimal energy efficient system forperpetual and heterogeneous IoT operation needs com-prehensive knowledge about the floor-plan architecture,individual’s activity patterns and IoT device status. Tohandle this complexity that arises due to dynamic natureand diversity of the underlying ADLs and IoT devices,we propose a three-phase system framework (Figure 1).

In the first phase, i.e. the learning phase, we aim tocapture the deployment setting. Specifically, we computea floor-plan segmentation [2] of the space being instru-mented and monitored, the IoT device profiles, includingstatus and different configurations given infrastructureinformation; we also leverage an elementary activitypattern that is provided by the user. In the secondactivity recognition phase, we utilize the infrastructureknowledge to develop a sliding window based approach[3] to perform activity recognition in a streaming fashion;recognizing activities as and when new occupancy sensorevents are recorded, such as motion sensors. In the thirdphase, the configuration phase, the status of participatingIoT devices is adjusted at run-time based on the currentactivity location and type. We design a dynamic configu-ration algorithm that is executed on the local controllerto control the IoT network and to compute and realizethe optimal overall energy configuration.

III. The Energy Optimization Problem forPerpetual IoT Systems

In this section, we discuss our assumptions and definefrequently used terms and notations. With the assump-tions, we formulate the heterogeneous IoT devices energyproblem as an NP-hard optimization problem.

A. AssumptionsWe assume only one resident at home, because our

approach targets independent assisted living systems;also, our dataset and recognition techniques are appliedfor one resident [19]. Moreover, we assume that in eachhome there is a local controller, which is a fixed devicethat has the ability to connect with all IoT devicesthrough different protocols, such as Wi-Fi, Bluetooth,Ethernet, and ZigBee, to track and manage the IoTdevices’ status. Also, we assumed that all IoT devices areoperating independently in terms of detecting the criticalevent they are monitoring.B. Terms and Notations

The Activities of daily living (ADLs) are routine activ-ities that people tend to do every day without needingassistance, such as cooking. Additionally, we define acritical event to be an incident that has a high conse-quence such as fall of an elderly person in an assistedliving home. All IoT devices deployed in the scene areintended to monitor the person and detect the criticalevent if it’s ever happened. In addition, the floor-planis divided into several segments, where each segment isa sub area of the home, such as the living room, thatconsists of a set of IoT devices.

We classify IoT devices in two groups in terms of theirsource of energy/power: unconstrained wall-powered de-vices that are connected to the energy grid all the time;constrained battery-powered devices that rely on theirown limited battery or energy harvesting throughouttheir lifetime. Each IoT device can operate in differ-ent configurations in terms of choosing values for theirdifferent operating parameters, such as communicationintervals, sampling rate and computation frequency. Thevariation of these values results in different amount ofenergy consumption rate and varying degree of accuracylevel across the different configurations of a particularIoT device. We define accuracy of an IoT device fora given configuration to be the probability of detect-ing the critical event when the device operates in thatconfiguration for a certain amount of time (referred toas the operation cycle). Obviously, there is a trade-off between the energy consumed by a device at itsdifferent configurations and their accuracy levels. Higheraccuracy is desired but only at the cost of higher energyconsumption, which leads to shorter system lifetime (itdrains out the battery-powered devices). We are required

to choose configurations for the devices so that systemlasts long.

C. Problem FormulationWe formulate energy optimization for hetero-

geneous IoT devices problem as a constrained opti-mization problem as follows. We have a set of n heteroge-neous IoT devices in a certain segment, i = 1, · · · , n. Eachdevice can be described by a profile, which consists of dif-ferent configurations the device can operate. Let device ihave li configurations, and eik and aik denote the rate ofenergy consumption and accuracy level respectively forconfiguration k of device i (1 ≤ k ≤ li). Each device hasa remaining battery capacity, denoted by ri, at a certaintime. Note that for wall-powered devices ri is not definedor assumed to be ∞. Once a configuration is chosen for adevice, it operates for a certain amount of time before thenext configuration is chosen. This duration, the operationcycle, is denoted by T .

We have a set of predefined ADLs each of which hasits own demand accuracy, denoted by τ. The demandaccuracy is the level of accuracy that all active devicesshould at least produce. We argue that when multipledevices monitor the critical event, the combined accuracyincreases. For example, if two devices independentlydetect the critical event with accuracy, which is the prob-ability of detecting the critical event, equals to a1 and a2,then the combined accuracy will be the probability thatat least one of them is detecting the event. That is:

combined accuracy = 1 − (1 − a1)(1 − a2)

The demand accuracy is a variable that changes de-pending on the daily activity performed. For example,the demand accuracy will be higher if the individual iscooking versus if he is sleeping. The more the activityis crucial, the higher the demand accuracy should be.Therefore, the demanded accuracy τ for each ADL canbe defined in many ways. It can be defined based onlabeled activity pre-training phase model. Another wayis to increase the demand accuracy when the intensity ofoccupancy sensors’ readings are increased. In addition, itcan be prescribed by the supervising physician which weconsidered in this paper. We want to select the optimalIoT devices subset with their appropriate configurations,so that the total energy expenditure remains as low aspossible, while keeping the expected level of accuracyfrom the selected configurations above the demandedactivity’s accuracy level τ.

This subset selection minimizes the overall energyconsumption and maximizes the battery-powered de-vices lifetime. In order to minimize the overall energyconsumption, we should consider the device’s energyconsumption rate at its different configurations and thenumber of active devices (depending on the demandaccuracy). We also need to extend the lifetime of battery-powered devices, so we should consider the remainingbattery capacity of those devices. Considering all these

issues, we define a cost function, denoted by cik , for eachconfiguration of an IoT device as follows:

cik = ηi · eik · T (1)

The cost function captures the “cost” of operating devicei in configuration k. The cost is directly proportional tothe amount of energy consumed during the cycle, whichis eik .T . The cost also takes into account the fact thatoperating a battery-operated device is costlier than anequivalent wall-powered device when they both consumethe same amount of energy. This is because battery-powered device runs on battery and their life dependson the remaining battery capacity. The operation ar-guably gets costlier when the remaining battery capacitybecomes low. To reflect this, we multiply the base energyconsumption with an adjustment factor, ηi, which isgiven by:

ηi = 1 + β · exp(− ri

r0i

)(2)

where r0i denotes the initial battery capacity of thedevice and β is a tunable parameter to adjust the effect.Obviously, for wall-powered device, we have r0i = ∞,hence ηi = 1. For battery-operated devices, ηi > 1.Particularly, at the beginning (when ri and r0i are equal),ηi = 1+ β. 1e , and then ηi progressively takes higher value(as the ri declines) until it reaches to 1 + β when thereis no battery power left (i.e., ri = 0).

We obtain an optimization formulation that choosesthe configurations minimizing the overall cost of opera-tion subject to the constraint that the combined accuracylevel remains equal or above the demand accuracy. Forthe ease of exposition, we introduce an idle configuration(configuration 1) for each device that has zero accuracyat zero or low cost. This allows all devices to be operatingexactly one configuration. By denoting xik to be the bi-nary variable indicating whether we choose configurationk of device i, we have the following optimization problem:

minimizen∑

i=1

li∑k=1

xik · cik (3)

subject to 1 −n∏

i=1

li∏k=1

(1 − xik · aik) ≥ τ (4)

li∑k=1

xik = 1, ∀i (5)

∀xik ∈ {0, 1}, ∀i = 1, · · · n, ∀k = 1, · · · liWe can simplify constraint (4) as follows:

1 −n∏

i=1

li∏k=1

(1 − xik · aik) ≥ τ

lnn∏

i=1

li∏k=1

(1 − xik · aik) ≤ ln(1 − τ)

n∑i=1

li∑k=1

xik · ln(1 − aik) ≤ ln(1 − τ) (6)

Consequently, we obtain:

minimizen∑

i=1

li∑k=1

xik · cik

subject ton∑

i=1

li∑k=1

xik · ln(1 − aik) ≤ ln(1 − τ)

li∑k=1

xik = 1, ∀i (7)

The energy optimization for heterogeneous IoTdevices problem is an np-hard problem that can bereduced from the Minimum Multiple Choice KnapsackProblem. The knapsack problem is known to be a well-studied np-hard problem and a special case of the multi-ple choice knapsack problem with the feature that eachitem is in a group of its own [20].

In the minimum multiple choice knapsack prob-lem there is a set of items which are partitioned intogroups and each item has a benefit and a weight. Theobjective of the MMKP is to find the least profitable setof items such that the total weight of the selected itemsis at least the weight limit [21].

Similarly, in the heterogeneous IoT devices en-ergy optimization problem, the goal is to select aset of IoT devices that minimize the total cost withexceeding the activity’s accuracy threshold. Each IoTdevice has a set of configurations, including the optionof not selecting it. Therefore, each IoT device defines aclass from which we are selecting at most one option.

IV. Algorithms and Heuristics for EnergyOptimization in Perpetual IoT Systems

One naive approach we can consider is to activateall the IoT devices all the time. Obviously, that woulddeplete energy from all devices without raising system ac-curacy much. In this section, we propose a set of feasibletechniques to energy efficient perpetual IoT operation.

A. The Priority Algorithms1) Based on location: In this approach, we activate the

IoT devices that are present in the segment area wherethe user is currently in.

2) Based on power supply: Since our goal is to keepthe battery-powered devices alive for longer time, weimplement a second approach that gives priority to thewall-powered devices and activates them first until thecombined accuracy exceeds the demand accuracy for thecurrent activity.

B. Greedy Algorithms1) Balanced Remaining Battery Lifetime (BRBL):

In this approach, we activate the wall-powered devicesfirst and then choose the battery-operated devices indescending order of their remaining battery capacityuntil we exceed the current activity’s accuracy threshold.

2) Cost Function Gradient (CFG): This solution isthe greedy heuristic solution to our formulated MCKPproblem. It is known that linear relaxation of MCKP [22],in which the indicator variables xik can be assigned realvalues instead of binary 0, 1, that can be optimally solvedby the greedy algorithm. As we cannot add a fractionalIoT device, we will use the greedy integral algorithmwhich stops short of the last selection that results ina fractional placement. The algorithm starts with anempty set with 0 overall benefit and 0 cost. It then makesa sequence of changes in which the selected IoT devices’configurations are upgraded to more accurate option andmore overall benefits. The process continues until thedemanded accuracy is achieved.

The pseudo-code for CFG is given in Algorithm 1.Algorithm 1: IoTSelectionBasedonCFG(τ, s)

1 CombinedAccuracy = 0;2 Initialize list(i, k) ← all the available IoT devices

with its configurations k in this segment;3 Enable selectedIoT(i, k) ← the lowest configuration

k for all available IoT devices4 while (CombinedAccuracy ≤ τ) do

5 (for all list(i,k))← 1 + β · exp (−rir0i)

6 foreach list(i, k) do7 deltaA = accur(i, knext) − accur(i, kcur)8 deltaC = cost(i, knext) − cost(i, kcur)9 Calculate slope(i, k)←deltaA/deltaC

10 end11 Select ik with the largest slope;12 selectedIoT(i, k) =1 ;13 CalculateCombinedAccuracy;14 end

V. SAFER: A Prototype Platform and TestbedSAFER is a low-cost elderly fall detection system,

it was motivated by the SCALE smart communityproject [23]; an initial version was deployed at a seniorliving facility in Montgomery County, MD. SAFER isimplemented to validate our approach and explore chal-lenges that arise in real world deployments.

A. Elderly Fall Detection SystemAccording to the U.S. Department of Health and

Human Services, the senior population (65+) represented13.3% of the U.S. population in 2011 with an increasefrom 35 million in 2000 to 41.4 million in 2011. About28% (11.8 million) of all non-institutionalized seniors in2012 lived alone. In addition, according to the Centersfor Disease Control and Prevention, one out of threeolder people fall each year and 2.5 million elderly peopleare treated in emergency departments for fall injuries.For the elderly who experience serious fall injuries, theamount of time spent immobile often affects their healthoutcome. Muscle cell breakdown starts within 30-60minutes of compression due to falling. Consequently, one

of the most important personal sensing systems in thesafe communities for elderly people is the fall detectionsystem.

The testbed for SAFER, as shown in Figure 2, includes(a) a smart pressure pad which is a 4x6 matrix consistingof 24 Square Force-Sensitive Resistor sensors; (b) wear-able sensor (CC2541 Ti SensorTag); (c) mobile sensor(accelerometer); (d) a camera (INSTEON). A local bro-ker (Raspberry Pi B) supports the interconnections ofmultiple networks and sensors, and publishes data to theback-end SCALE server using the MQTT protocol.

(a) Smart pad (b) Ti Tag (c) Accelerometer

Fig. 2: SAFER Prototype’s IoT Devices

B. Initial Measurements on Real TestbedsWe measure the energy consumption and accuracy

of fall detection for each participating IoT device indifferent configurations. The values are shown in Table I.

For the mobile phone experiments, we used an An-droid Nexus 4 mobile with BLE support and an In-vensense MPU-6050 accelerometer, which functions asan accelerometer and a gyroscope. The Nexus 4 systemprovides four default sampling rates: FASTEST (5ms),GAME (20ms), UI (66ms), and Normal (200ms). Ahigher sampling rate delivers more frequent updatesleading to more accurate results but also consumes moreenergy. The wearable device we used in the experimentsis Ti Sensor Tag, a BLE (Bluetooth Low Energy) dongle,that transmits its sensor data to the local controller atfixed intervals, the fastest interval being 100ms. Thetag uses KXT J9 accelerometer and a standard CR2032coin battery, which has the typical capacity of 240mAh.Therefore, we do not expect the Ti Sensor Tag to worklonger than 48 hours in its maximum mode. The smartpad consists of 24 FSR pressure sensors with the fastsampling rate of 5 system snapshots at every 2.4 sec.

We also measure the energy consumption of occupancysensors as we use them in the activity recognition phase.For many years, occupancy sensors, such as motion,door and temperature sensors, have been used in smartbuildings, often for lighting and HVAC control. Studieshave shown that adding occupancy sensors to controllighting consumption can reduce lighting energy use from10% to 90% or more depending on the use of the space.One study conducted on a university campus found thatinstalling occupancy sensors to control lighting in morethan 200 rooms in 10 buildings provided an annual costsavings of about $14,000 with a simple payback of 4.2years.

In our large setup, as shown in Figure 5, we consider39 occupancy sensors that have been used by CASASdataset [19, 24] for daily activity recognition. It com-prises of 31 motion, 4 door, and 4 temperature sensors.These sensors consume power at a rate less than 8 wattsand can be wired or wireless with a slow-battery-drain. Incontrast, in-home fall detection systems use IoT devices,such as cameras, Microsoft Kinect, and mobile phonesthat consume considerably higher amount of energy. Thesystem also contains mobile and wearable sensors thatare power restricted and are fast-battery-drain devices.

IoT Device Battery Configurations Power Performancecapacity consumption accuracy

Mobile phone 8Wh Fastest 421 mW 70%Game 392 mW 65%

UI 334 mW 50%Normal 287 mW 40%

Idle 28 mW 0%Wearable device 0.72Wh 100 ms sample rate 16.5 mW 63%

500 ms sample rate 12.48 mW 41%Idle 1.32 mW 0%

Smart pad ∞ Fast sample rate 2.1 W 70%4x6 Slow sample rate 1.85 W 50%

Idle 1.1 W 0%Camera ∞ High image resolution 3 W 84%[25] [26] Low image resolution 2 W ≃ 50%

Idle 1 W 0%Microsoft Kinect ∞ High resolution 12 W 91%

[27] Low resolution 7 W 83%Idle 1 W 0%

TABLE I: SAFER IoT Devices’ ConfigurationsVI. Performance Evaluation and results

A. Experimental Setup - Simulation StudiesTo conduct further experiments, we developed a

discrete-event simulator and created two test cases atdifferent scales based on real-world elderly living options.The first case is of medium scale and corresponds toan assisted living community. The average floor-planincludes a bedroom, living room and a bathroom with anaverage space of 350 ft2. Figure 4 shows the floor-planwith the associated IoT devices. We assume an instru-mentation density of 1 IoT device for every 50 ft2 - themedium scale, therefore, has 7 IoT devices: 5 static wall-powered devices and 2 mobile battery-powered devices.The second test case is an independent living community,which is a large scale deployment. Here, the averagefloor-plan includes 2 bedrooms, a living room, kitchen,office and 2 bathrooms with an average space of 900 ft2(Figure 5). In the large scale setup, we considered 18IoT devices: 15 static wall-powered devices and 3 mobilebattery-powered devices. We execute our simulationson the CASAS trace dataset obtained from [19] thatcontains the activities of daily living of an individual inan assisted living setting for a week.Performance Evaluation Metrics:

• Cumulative Energy Consumption The total energyconsumption can be used as a benchmark to evaluatethe energy optimization algorithms.

0 25 50 75 100 125 150 175Time(h)

0

5000

10000

15000

20000

25000

Total E

nergy Co

nsum

ption(kJ)

All DevicesPriority based on locationPriority based on power supplyBRBLCFG

(a) Energy consumption in ’medium setting’

0 25 50 75 100 125 150 175Time(h)

0

2

4

6

8

10

Numbe

r of A

live De

vice

s

All DevicesPriority based on locationPriority based on power supplyBRBLCFG

(b) Number of alive devices in ’medium scale’

29.0 29.5 30.0 30.5 31.0 31.5 32.0Time(h)

0

10

20

30

40

Powe

r Con

sumption(J)

All DevicesPriority based on locationBRBLCFG

(c) Power consumption comparison

0 25 50 75 100 125 150 175Time(h)

0

10000

20000

30000

40000

50000

60000

Total E

nergy Co

nsum

ption(kJ)

All DevicesPriority based on locationPriority based on power supplyBRBLCFG

(d) Energy consumption in ’large setting’

0 25 50 75 100 125 150 175Time(h)

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Numbe

r of A

live De

vice

s

All DevicesPriority based on locationPriority based on power supplyBRBLCFG

(e) Number of alive devices in ’large setting’

29.0 29.5 30.0 30.5 31.0 31.5 32.0Time(h)

0

20

40

60

80

100

Accuracy(%

)

Demanded AccuracyPriority based on locationBRBLCFG

(f) Accuracy comparison

Fig. 3: Simulation Results

Fig. 4: Floor-plan of the “medium” scale setting

Fig. 5: Floor-plan of the “large” scale setting

• Number of alive battery-powered devices Intuitively,an optimized algorithm should maximize the lifetimeof battery-powered devices by increasing their idletime and utilizing the wall-powered devices.

B. Experimental Results1) Cumulative energy consumption comparison: Fig-

ure 3a shows the total energy consumption in different

algorithms. As we can observe, activating sensors basedon locations reduces energy consumption by half com-pared to the case when all IoT devices are running. TheCFG algorithm saves nearly 80% to 90% energy. On theother hand, the BRBL algorithm consumes little moreenergy than CFG because of its high reliance on the wall-powered devices, which consume more energy than theirbattery counterpart.

2) Battery lifetime of IoT devices: In our experiments,we used at least two battery-powered devices: the mobilephone, and wearable devices. Figure 3b shows powerdrain patterns of the battery-powered IoT devices. Assuch, we do not expect any wearable IoT device, such asthe Ti Sensor Tag, and the mobile phone to work longerthen 48 hours, 20 hours respectively. Note that battery-powered devices may be occasionally recharged, but forconsistent comparison we show the results for only onefull cycle (from a fully charged battery to zero-power).

The CFG algorithm extends the battery capacity 2–4 times by reducing the energy consumption of theIoT devices by choosing the best configurations for thedevices. As we can see, the first IoT device in the CFGalgorithm drains out after 80 hours, while the secondIoT battery capacity extends to more than 175 hours.The BRBL algorithm extends the battery capacity of theconstrained IoT devices up to 175 hours because it keepsthem on the lowest configurations at all time, except inlocations where wall-powered devices are not available,such as in the bathroom in the medium scale deployment.

3) Power consumption and accuracy comparison:Figure 3c and Figure 3f show the comparison amongdifferent algorithms in terms of power consumption andaccuracy respectively. Specifically, we zoomed a 4-hourwindow after 29 hours to observe the difference amongthe different algorithms. We notice that CFG has thelowest power consumption and it remains closest to thedemanded accuracy. This is because of its utilization ofdifferent configurations in different devices.

0 5 10 15 20 25

IoT Devices Density0

2

4

6

8

10

Total E

nergy Co

nsum

ption(MJ)

Fig. 6: Density of IoT devices and energy consumptionusing the CFG algorithm in the medium scale setting

4) Scalability: We examine the effect of device densityin energy consumption using the CFG algorithm. Inour setting, we deploy one IoT device per 50 ft2. Wedouble and triple this number to see the effect on energyconsumption. Results in Figure 6 show that CFG workswell when we increase the device density. As the densitygrows, the energy consumption also slightly rises, whichis due to the energy consumption of the added IoTdevices in their idle configurations.

VII. ConclusionIn our approach we uniquely leveraged the concept

of activities of daily living (ADLs) for energy-optimizedsensor activations to create SAFER, a perpetual IoTawareness platform. We developed and deployed an el-derly fall detection system; testbed measurements wereused to drive larger scale simulation studies. Experi-mental studies with real world trace datasets indicatedthat the proposed Cost-Function-Gradient algorithm wasable to achieve more than 80% reductions in energyconsumption, doubling the system-lifetime. We believethat such techniques are essential to creating deployableIoT for mission-critical societal applications that requireperpetual operations such as healthcare and assistedliving. More broadly, we aim to enable and ensure themultiple functional and non-functional needs of societalscale applications by leveraging new emerging technolo-gies - this will require an in-depth understanding of howthese requirements interact.

References[1] E. Kim, S. Helal, and D. Cook, “Human activity recognition

and pattern discovery,” IEEE Pervasive Computing, vol. 9,no. 1, pp. 48–53, Jan 2010.

[2] R. Bormann et al., “Room segmentation: Survey, implemen-tation, and analysis,” in IEEE ICRA, 2016, pp. 1019–1026.

[3] N. C. Krishnan and D. J. Cook, “Activity recognition onstreaming sensor data,” Pervasive and Mobile Computing, vol.10, Part B, pp. 138–154, 2014.

[4] P. Kamalinejad et al., “Wireless energy harvesting for theinternet of things,” IEEE Communications Magazine, vol. 53,no. 6, pp. 102–108, June 2015.

[5] M. Gorlatova et al., “Movers and shakers: Kinetic energy har-vesting for the internet of things,” IEEE Journal on SelectedAreas in Communications, vol. 33, no. 8, pp. 1624–1639, 2015.

[6] G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella,“Energy conservation in wireless sensor networks: A survey,”Ad Hoc Netw., vol. 7, no. 3, pp. 537–568, May 2009.

[7] D. Ganesan et al., “Networking issues in wireless sensor net-works,” J. Parallel Distrib. Comput., vol. 64, no. 7, pp. 799–814, 2004.

[8] W. R. Heinzelman et al., “Energy-efficient communication pro-tocol for wireless microsensor networks,” in System sciences,2000. Proceedings of the 33rd annual Hawaii internationalconference on. IEEE, 2000, pp. 10–pp.

[9] A. Warrier et al., “How much energy saving does topologycontrol offer for wireless sensor networks? - a practical study,”Comput. Commun., vol. 30, no. 14-15, pp. 2867–2879, 2007.

[10] A. Mainwaring et al., “Wireless sensor networks for habitatmonitoring,” in Proceedings of the 1st ACM InternationalWorkshop on Wireless Sensor Networks and Applications, ser.WSNA ’02. New York, NY, USA: ACM, 2002, pp. 88–97.

[11] C. Tang and C. S. Raghavendra, “Wireless sensor networks.”Kluwer Academic Publishers, 2004, ch. Compression Tech-niques for Wireless Sensor Networks, pp. 207–231.

[12] E. Fasolo et al., “In-network aggregation techniques for wire-less sensor networks: a survey,” IEEE Wireless Communica-tions, vol. 14, no. 2, pp. 70–87, April 2007.

[13] A. B. G. Smaragdakis, I. Matta, “Sep: A stable election pro-tocol for clustered heterogeneous wireless sensor networks,” inSecond International Workshop on Sensor and Actor NetworkProtocols and Applications (SANPA), 2004.

[14] D. Kumar, T. C. Aseri, and R. Patel, “Eehc: Energy efficientheterogeneous clustered scheme for wireless sensor networks,”Comput. Commun., vol. 32, no. 4, pp. 662–667, Mar. 2009.

[15] M. Ye, C. Li, G. Chen, and J. Wu, “Eecs: an energy efficientclustering scheme in wireless sensor networks,” in PCCC 2005.24th IEEE International Performance, Computing, and Com-munications Conference, 2005., April 2005, pp. 535–540.

[16] H. Li, “An energy efficient routing algorithm for heterogeneouswireless sensor networks,” in 2010 International Conferenceon Computer Application and System Modeling (ICCASM2010), vol. 3, Oct 2010, pp. V3–612–V3–616.

[17] L. Qing, Q. Zhu, and M. Wang, “Design of a distributedenergy-efficient clustering algorithm for heterogeneous wirelesssensor networks,” Computer communications, vol. 29, no. 12,pp. 2230–2237, 2006.

[18] M. Yarvis et al., “Exploiting heterogeneity in sensor net-works,” in Proceedings IEEE 24th Annual Joint Conferenceof the IEEE ComSoc., vol. 2, 2005, pp. 878–890.

[19] D. Cook, “Learning setting-generalized activity mdoels forsmart spaces,” IEEE Intelligent Systems, 2011.

[20] H. Kellerer, U. Pferschy, and D. Pisinger, Knapsack problems.Springer, 2004.

[21] M. T. Islam et al., “Approximation algorithms for minimumknapsack problem,” Ph.D. dissertation, Lethbridge, Alta.:University of Lethbridge, 2009.

[22] P. Sinha and A. A. Zoltners, “The Multiple-Choice KnapsackProblem,” Operations Research, vol. 27, no. 3, pp. 503–515,1979.

[23] M. Y. S. Uddin et al., “The scale2 multi-network architecturefor iot-based resilient communities,” in IEEE InternationalConference on Smart Computing, 2016, pp. 1–8.

[24] A. S. Crandall et al., “Casas: A smart home in a box,”Computer, vol. 46, no. undefined, pp. 62–69, 2013.

[25] C.-L. Liu, C.-H. Lee, and P.-M. Lin, “A fall detection systemusing k-nearest neighbor classifier,” Expert Systems with Ap-plications, vol. 37, no. 10, pp. 7174 – 7181, 2010.

[26] R. Igual, C. Medrano, and I. Plaza, “Challenges, issues andtrends in fall detection systems,” BioMedical Engineering,vol. 12, no. 1, p. 66, 2013.

[27] E. E. Stone and M. Skubic, “Fall detection in homes of olderadults using the microsoft kinect,” IEEE J. of Biomedical andHealth Informatics, vol. 19, no. 1, pp. 290–301, 2015.