Embedded Systems for Wireless Sensor Network Rabi Mahapatra

Mahapatra-Texas A&M-Spring'072 Background Advancement of integration between “tiny embedded processors, wireless interfaces, and “micro-sensors” based on MEMS led to emergence of wireless sensor network. Characterized by their ability to monitor the physical environment through ad-hoc deployment of numerous tiny, intelligent, wirelessly networked sensor nodes.

Mahapatra-Texas A&M-Spring'073 Large number of heterogeneous sensor devices –Ad Hoc Network complex sensor nodes with –communication, processing, storage capabilities What is Wireless Sensor Networks

Mahapatra-Texas A&M-Spring'074 Emerging applications Indoor Settings: condition based maintenance of equipment in factory Outdoor environment: –Monitor natural habitats –Remote ecosystems –Forest fires –Disaster sites –Defense armaments –Spy microsats

Mahapatra-Texas A&M-Spring'075 Challenges of WSN Requirements: small size, large number, tetherless and low cost. Hence constrained by –Energy, computation and communication Small form factors => prohibits large long lasting batteries Cost & energy => low power processors, small radios with minimum bandwidth & small transmission ranges. Ad-hoc deployment => no maintenance and battery replacement Increase NW lifetime => No raw data to gateway for computation

Mahapatra-Texas A&M-Spring'076 Topics to be discussed Simulation tools on WSN A simulation architecture overview Sensor node model & framework of SN Battery model Case studies Bonus points

Mahapatra-Texas A&M-Spring'077 Existing Simulators JavaSim: Pros –Very modular –Easy to use Cons –Geared for wired inter-networks –No wireless support, not efficient due to overhead SSFNet: a parallel simulator for wireless GlomoSim: Specific for mobile wireless networks. Built as a set of libraries. The libraries are built in Parsec( a C-based discrete event simulation language). Layered architecture with easy plug-in capability. SSFNet and Glomosim are not better than NS-2 in terms of design and extensibility.

Mahapatra-Texas A&M-Spring'078 Existing Simulators NS-2: De facto standard for network simulations –Does support wireless simulations –A primitive energy model is present. –Object oriented design and Lots of documentation. –Uses Tcl to specify the Components, and Otcl to glue them together. Cons: –Difficult to use and learn –Interdependency among modules pose difficult to implement new protocols. –Originally built for wired networks, later extended for wireless. –Supposedly, does not work well for large topologies.

Mahapatra-Texas A&M-Spring'079 More Sensor Network Simulator WSNS –Based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol developed by Dr. Wendi Heinzelman –Has included Network Preserving Protocol (NPP) for better performance along with LEACH –Not completed for robustness

Mahapatra-Texas A&M-Spring'0710 Sensor Network Simulator SENSE ( Component Features: (2004) –Battery Model: Linear Battery, Discharge Rate Dependent and/or Relaxation Battery –Application Layer : Random Neighbor; Constant Bit Rate –Network Layer: Simple Flooding; a simplified verion of ADOV without route repairing, a simplified version of DSR without route repairing –MAC Layer: NullMAC; IEEE –Physical Layer: Duplex Transceiver; Wireless Channel –Simulation Engine: CostSimEng (sequential)

Mahapatra-Texas A&M-Spring'0711 Status of simulators Other simulator: OpNET All these tools are not equipped to capture all the aspects of interests in sensor networks.

Mahapatra-Texas A&M-Spring'0712 Simulator: SensorSim from UCLA Extension to NS - 2. Provides battery models, radio propagation models and sensor channel models. Provides a lightweight protocol stack. Has support for hybrid simulation. Must be integrated with NS - 2.

Mahapatra-Texas A&M-Spring'0713 SensorSim Architecture monitor and control hybrid network (local or remote) Simulation Machine Gateway Machine ns modified event scheduler V R V V V GUI app R real sensor apps on virtual sensor nodes gateway socket comm serial comm HS Interface Ethernet RS232 Proxies for real sensor nodes GUI Interface app

Mahapatra-Texas A&M-Spring'0714 SensorSim Architecture Overview Sensor NW has three types of nodes: –Sensor nodes: monitor immediate environment, with many transducers –Target nodes: generates various stimuli for sensor nodes –User nodes: client and administration of sensor network Separate channels: –Sensor channels: communication among sensor nodes and target –Network channels: to user node or gateways and onward transmission to other network. –Concurrent transmission possible –Easier to model complex behavior of sensor nodes, reaction to multiple sensor signals.

Mahapatra-Texas A&M-Spring'0715 Sensor Network Model architecture Sensor channel Wireless channel sensor Target user

Mahapatra-Texas A&M-Spring'0716 SensorSim Model Sensor node => one wireless NW protocol stack, one or more sensor stack corresponds to as many transducers –Sensor stack detects stimuli, process it and forward them to application layer, which in turn process and send them to user node through wireless channel –A power model corresponding to energy producing-consuming hardware components is also provided. These component can stay at different power saving and performance states. –The algorithm in both the stacks control the mode of power states of hardware components. Also, performance of the algorithm depends on the mode.

Mahapatra-Texas A&M-Spring'0717 Sensor Node Model in SensorSim Node Function Model Network Layer Sensor Node Applications Power Model (Energy Consumers and Providers) Battery Model Radio Model CPU Model Sensor #1 Model Sensor #2 Model MAC Layer Physical Layer Sensor Layer Wireless Channel Sensor Channel 1 Network Protocol Stack Sensor Protocol Stack Middleware Physical Layer State Change Status Check Sensor Stack 1 Sensor Layer Physical Layer Sensor Channel 2

Mahapatra-Texas A&M-Spring'0718 Network Protocol Stack Network Layer MAC Layer Physical Layer Wireless Channel User Application User Node Sensor Stack Sensor Layer Physical Layer Target Application Sensor channel Target Node

Mahapatra-Texas A&M-Spring'0719 Framework of Sensor Network Simulation Node Placement & traffic generation –Performance of WSN is affected when topology of node distribution changes –Application requires a typical distribution (uniform for forest fire, Gaussian for perimeter defense) – Three types of traffics: user-to-sensor (command & queries), sensor-to-user (sensor reporting to user) and sensor-to-sensor (collaborative signal processing before reporting)

Mahapatra-Texas A&M-Spring'0720 Sensor Stack & Channel Sensor stack is a signal sink that is responsible for triggering the application layer every time a sensing event occurs Simple sensing scheme to elaborate signal processing can be implemented on sensor stack Sensor stack acts as a signal source in Target Node and contains signatures unique to the model Sensor channel model the medium of signal transmission (e.g. ground to carry seismic events). A good simulation tool should model varieties of mediums and type of sensors ( acoustic, infra red, ultrasonic)

Mahapatra-Texas A&M-Spring'0721 Battery Model Goal: increase the battery life time Need to study how different aspects of real battery behavior can affect the energy efficiency of applications T = C/I, C is capacity in Ah. I is discharge current Linear Model: –Linear storage of current. Assumes the maximum capacity is unaffected by discharge rate. –Allows user to see efficiency of user application by providing how much capacity is consumed. The remaining capacity after t d can be expressed as C = C’ -  I(t)dt integral taken over period t = 0 to t d –It assumes that the I(t) will stay same during the period, if operation mode does not change (radio switching from Tx toRx) –Remaining capacity is computed when discharge rate is changed

Mahapatra-Texas A&M-Spring'0722 Battery Model Discharge rate dependent model: –Considers the effect of battery discharge rate on maximum capacity –Battery capacity efficiency factor K is introduced. K = C eff /C max –Capacity C = K.C’ – I. T d –K varies with current I and is close to 1 when discharge rate is low and approaches 0 when discharge rate is high. A Popular Battery Model: Dual Foil from UC Berkeley.

Mahapatra-Texas A&M-Spring'0723 Battery Model Relaxation Model: –Real-life battery exhibit a phenomenon called “relaxation”.(Fuller 94, Linden 95, Chiasserini 99) –When battery discharge rate is high, diffusion rate of active ingredients through the electrode & electrolyte falls behind. If high discharge is sustained, the battery reaches its end even if the active materials are still available. –However, if discharge current is either cut-off or reduced during the discharge, active materials catches up with depletion of the materials. It gives battery to recover the capacity lost at high discharge rate. –An analytical model has been used for SensorSim

Mahapatra-Texas A&M-Spring'0724 Case Studies Low rate/low power vs. high rate/ high power (Fig. 7 in the reference) Monitoring a moving vehicle in a sensor field –Study the effect of traffic on the sensing and communication traffic and evaluate the power management.

Mahapatra-Texas A&M-Spring'0725 Power Management Model Transmit Receive Off Idle BZR event receive done transmit done Without Power Management Transmit Receive Sleep Off Idle transmit timeout(3 sec) BZR event transmit done BZR event receive done receive timeout With Power Management

Mahapatra-Texas A&M-Spring'0726 Some Important Studies Utility-based decision-making in WSN. Upper bound on network life-time Impact of mobility on capacity and life-time Coverage and Density –Criticality threshold, scalability, integration etc. Security Ease of Deployment Synchronization

Mahapatra-Texas A&M-Spring'0727 Conclusion We have looked at some of the issues with SN and discussed the Sensor Network model developed at UCLA. Assignments: –Read the reference papers and look for more titles –Prepare a bibliography on each topic mentioned in the previous page. –Consider one topic as assigned to you in the class and read apex papers on that topic. –Summarize and comment on the contributions and shortcomings. –Due Tuesday morning by with file name as (your name- sensor.doc)

Mahapatra-Texas A&M-Spring'0728 Reference Sung Park, A savvides & M B Srivastava, “Simulating Networks of Wireless Sensors”, Proceedings of the 2001 Winter Simulation Conference.