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Software Development for Atom based Safe and Sustainable BSN
IMPACT LAB
Project Report on “Safe, Secure and Sustainable Body Area
Networks using Intel Atom” funded by Intel
Impact Lab Research GoalEnvironmentally Aware
(physical), Performance aware (cyber),
Criticality Aware, Safe and Secure
Cyber-Physical Systems
Body-Area Sensor NetworksObjectives Minimize energy consumption Minimize health risks (safety) Ensure security and privacy Allow complex applications
Approach Infuse energy awareness Introduce high computation AADL modeling
Taxi Cab SchedulingObjectives Compute optimal route
with targets Minimize fuel
consumption Evaluate solutions
Approach Design proactive, spatio-
temporal schedules AADL modeling
Data CentersObjectives Minimize Energy
Consumption Maintain Performance
Approach Integrated Management Infuse Proactive Spatio-
Temporal Scheduling AADL Modeling
Project Goal
Body Sensor Networks (BSNs) – network of medical devices on human body are small scale cyber-physical system Critical infrastructures – used in medical applications Require to support life saving applications Involvement of human users require BSNs to be safe (reduce medical hazards)
and sustainable (provide seamless operation)
Complex application requirements (especially security protocols) demand powerful processors in BSN nodes
Atom is used as the BSN node processor to provide required computational capabilities
However, higher power dissipation of Atom, hampers the safe and sustainable operation of BSN nodes
Software Design of Computationally capable Safe and Sustainable Atom based BSN
Traditional Body Sensor Network
Present
Salient Features
Computationally incapable set of nodes
Heterogeneous hardware and software configuration
Constrained in energy – battery operated
No energy scavenging
Application requirements
Monitoring and Feedback Online detection of freezing of gait [1] in
Parkinson’s patients from on-body sensors Feedback through on-body actuators
Requirements Response within a small time window Fast Computation of windowed FFT and
associated signal processing
Security Physiological Value based Security [3] Combines signal processing with security
algorithms
Requirements
Hogs up 80 % of total RAM
Continuous Monitoring Seamless 24 hrs medical monitoring [2]
Requirements Increased lifetime of the sensors Battery less non-intrusive operation
Figure explains PVS Implementation
References1. M. B¨achlin et al. Online Detection of Freezing of Gait in Parkinson’s Disease Patients: A Performance Characterization. In Proc. of the 4th Intl. Conference on Body Area Networks, Apr. 20092. K. Venkatasubramanian et al. Ayushman: A Wireless Sensor Network Based Health Monitoring Infrastructure and Testbed. In Distributed Computing in Sensor Systems, pages 406–407, July 20053. K. Venkatasubramanian et al. Plethysmogram-based secure inter-sensor communication in body area networks. Military Communications Conference, 2008. IEEE, pages 1–7, Nov.
Proposed BSN System
Future
Computationally capable sensors Use Intel Atom as the sensor processor Addresses the computational requirements
of the present day applications
Homogeneous hardware and software platform Sensors running intel atom can have
stripped down versions of the same OS kernel
Resolves software compatibility issues
Energy Scavenging Incorporate energy scavenging hardware in
the network to sustain operation of the sensors
Supplement battery power Makes the BSN system greener
Challenges of Atom based BSNAtom can provide a uniform platform with highly capable BSN processors
Challenges with Atom Energy Efficiency
Relatively higher power footprint of Atom
Thermal Safety Possible high thermal footprint of Atom
Sustainability How long can energy scavenged from human sources sustain Atom operation ?
Atom background
Ultra low power processor for embedded applications However, order of magnitude higher power dissipation than the state-of-art BSN
node
IA-32 microarchitecture helps in easy application development Can use high level programming languages to develop applications
Six low power sleep states with ultra low power deep sleep state Sleep scheduling can be employed to reduce power consumption
Intel Speed Step technology enables seven different operating frequency levels Clock frequency control to reduce operating power
Sleep state and frequency control performed through easy ACPI support (through Model Specific Register (MSR) accesses)
Ayushman [2] health monitoring application is considered as the workload Ayushman has three phases of operation –
Sensing Phase – Sensing of physiological values (Plethysmogram signals) from the sensors and storing it in the local memory
Transmission Phase – Send the stored data to the base station in a single burst Security Phase – Perform network wide key agreement for secure inter-sensor communication using
Physiological value based Key Agreement Scheme (PKA) [3].
The Security phase occurs once in a day The Sensing phase and Transmission phase alternate forming a sleep cycle
Typical BSN Workload
Ayushman Phase Rate of Execution
Sensing 60 samples per second
Data Transmission every 5 seconds
PKA execution Once in 24 hrs
(the processor can sleep during sensing phase while it can be active during the transmission phase)
1/s sf t
Txt
Sensor CPU Utilization
Time
Sensing Phase
Transmission Phase
Security PhaseSleep Cycle
Ayushman Workload
Enables Sleep Scheduling
Frequency Throttling during security phase
4. K. Venkatasubramanian et al. Green and sustainable cyber-physicalsecurity solutions for body area networks. In BSN ’09: Proc. of the SixthIntl. Workshop on Wearable and Implantable Body Sensor Networks,pages 240–245, Washington, DC, USA
Management Strategies for Safety and Sustainability Challenge - Atom’s high TDP (2.2 W) with respect to present day
sensor nodes (~ 80 mW [4]) Remedy – Power budgeting through sleep scheduling and clock frequency control Road Blocks –
In a sleep mode the processor cannot compute Decrease in clock frequency increases computation time
Challenge - Increase lifetime of operation Remedy – include scavenging nodes in the BSN that will charge the Atom nodes
wirelessly and supplement battery Road Blocks –
The operation of scavenging sources are intermittent depending on the stochastic behavior of the host Often the scavenging nodes fail to provide appropriate power levels to the nodes
Intelligent design is required to achieve safety and sustainability while respecting the real time requirements of the applications
The strategies are closely related to the applications real time requirements.
Software Design Methodology
BSN Hardware model
Base Station
BSN Node BSN node Intel N270 single core processor
1.6 GHz clock frequency, 1 GB RAM Intel SpeedStep frequency control technology – useful for power
management 6 sleep states including one ultra low power sleep state (C6) – sleep
scheduling
Chipcon 2420 radio 2.45 GHz, 802.15.4 wireless standard Maximum Power dissipation (58 mW [4])
Scavenging Sources Body Heat, Ambulation, Respiration and Sun Light Wireless charging of BSN nodes from scavenging sources is
assumed Each source has a specified range upto which it can charge
nodesScavenging Sources
Wireless Charging
Base Station Atom based mobile phone
BSN node Software
The power consumption of Atom processor depends on the Operating System used Mobile Intel 945 GMCH board power consumption
Open Suse Linux = 11.7 W Moblin OS = 10.4 W
ACPI support required for accessing Intel SpeedStep frequency control and sleep states Moblin provides ACPI through which one can write to or read appropriate MSR
registers to – Control clock frequency Sleep States Measure core temperature
The BSN workload considered is the Ayushman application
Profiling Requirements
Thermal Safety – The maximum temperature of the skin in contact with the node should not exceed 39 ºC for 24 Hrs of operation
Thermal behavior of Atom under the given workload has to be evaluated
Sustainability – The available power from the scavenging sources should be able to meet the power demands of Atom node under the given workload
Power profiling of Atom processors during execution of Ayushman
Thermal Profiling Requires core temperature measurements for different operating points
of the Atom processor The Mobile Intel 945 GSE development platform (GMCH) provided by
Intel has digital thermal sensors The board thermal sensors were read from Model Specific Registers
The maximum core temperature (43 ºC) was observed during PKA execution
C6 Sleep State Run Ayushman
Turn On GMCH board
Read MSR
Log Temperature Data
Set OperatingFrequency
Thermal profiling methodology
PKA is the most power consuming computation in Ayushman [3] The difference between idle power and power during PKA execution
was measured using the GMCH board Idle power of Atom N270 processor was added to it to obtain PKA
power consumption
Power Profiling
AC Mains
Power Meter
Intel Atom N270 on Mobile Intel Chipset 945 GSE
Operating Mode (Percent throttling)
Power (W)
0 0.191
13 0.1864
25 0.17
37, 50, 62, 75 0.167
87 0.164
Power Measurement Set up Table showing Atom power consumption for PKA execution at different operating frequencies
Board Power Lead
Resource Consumption
Platform RAM Usage
Power (mW)
Computation Time (ms)
TelosB 80% 66 2186
Atom 0.006% 164 41
Resource Consumption for PKA execution
PKA computation in Ayushman involves signal processing of physiological signals as well as execution of security algorithms
Resource footprint of PKA is evaluated in terms of – RAM usage, Power Consumption, and Computation Time
Atom compared to TelosB provides very low RAM usage and computation time
However as expected it has around thrice the power consumption
Modeling Phase Industry Standard AADL language is used for modeling
Safety Analysis The temperature rise of human skin due to contact with Atom based
BSN node has to be evaluated The temperature rise occurs due to several physical phenomenon
and is modeled using the Penne’s bioheat equation -
2 4 4( ) ( )p b c r
dTC K T b T T SAR P A T Tdt
Sustainability Analysis Duty Cycling of Atom operation during Ayushman execution
Sleep mode (C6) during Sensing Phase Power consumption = Psleep for time ts
Active mode during data transmission phase Power consumption = Pactive + radio power Pradio for data transmission time ttx
Active mode during PKA execution Power Consumption = Pactive + PKA execution power PPKA for time tPKA
PKA involves transmission of security related information (vault) between two sensors The Atom processor must be in active state with the radio on. Power Consumption = Pactive + Pradio for
time tvault
Total energy consumption for n BSN nodes
x is the number of sleep cycles required in a day
[{( ) ( )} ( 1) / 2 ( )( 1) / 2]BSN s sleep Tx radio active PKA PKA Vault active radioE n t P t P P x t P n t P P n
Total EnergySensing Energy Transmission Energy PKA communication Energy (Pair wise
PKA)
PKA Computation Energy (Pair wise
PKA)
( ) ( 1) / 2 24 3600s Tx PKA Vaultt x t nx t t n n Total Sleep Cycle Time Pair wise PKA Execution Time
Sustainability Analysis Results
Four energy scavenging sources were considered Body Heat, Ambulation, Respiration and Sun Light
PM – Processor and radio
sleep scheduling NPM – Radio sleep sche-
ling NPNM – no sleep schedule
Scavenging Source
Available Power (W)
Scavenge Time (Hrs)
Body Heat 0.1 – 0.15 24
Ambulation 1.5 2
Respiration 0.42 6
Sun Light 0.1 3
PM NPM NPNM0
10
20
30
40
50
60
70
80
90
100
110
120Number of nodes sustained through scavenging for different design decisions
All FourBody Heat + Ambulation (Long term monitoring)Respiration + Ambulation(Athletes in training)Body Heat + Respiration(Patient Monitoring in Hospital)Ambulation + Sunlight(Perfomance Monitoring for outdoor sports)
Conclusions
Proper Sleep scheduling and Frequency throttling can be used to bring Atom’s power consumption to safe and sustainable levels
Atom based BSNs with upto 25 nodes can be sustained using scavenged energy from body heat and respiration
A model based engineering tool has also been developed in this process It uses industry standard AADL to model Analysis of Model is performed through an eclipse interface by developing java
based plug-ins
Problems Faced
Inaccuracies in Thermal Profiling Presence of heat sink on the Atom processor can cause additional thermal effects
which are not accounted for in the analysis Reliability of thermal sensors not known
Inaccuracies in Power Profiling Available board was used to determine the power consumption of Atom The power consumption may include several other components in the board The sense resistors across which power can be measured were not found due to
lack of documentation
Require stripped down version of Atom based development boards
Options to turn off components of the board has to evaluated
Thank You