Infocom’07 Authors:Liqian Luo, Chengdu Huang, Tarek Abdelzaher John Stankovic Presented By Rohini Kurkal Under Guidance of Dr.Bin Tang
Contents Introduction System Design Implementation Performance Evaluation Possible Extensions
Introduction Applications: -Environmental monitoring/data logging No real time communication b/w sensor and sink Disconnected system EnviroStore - cooperative storage system for sensor networks suitable for disconnected operation It is storage-centric rather than communication-centric Major concern -maximizing effective storage capacity Implemented in nesC for TinyOS and evaluated in TOSSIM
Storage centric paradigm Should be simple and lightweight Micaz has only an 8MHz 8-bit processor and a 4KB RAM, Iris has 8KB RAM, 512KB Measurement Flash Memory Sensor nodes do not maintain files but just writes data to collection station and never read the data they write Data redistribution–must improve overall storage utilization
System Design Sink – process that runs on a user’s PC, identified by a regular IP address and a well known TCP port Data mule-collects data wirelessly from encountered nodes and dumps these data later to the base station Two types of data mules: - Intentionally relay data b/w the sink and the sensor nodes - Opportunistic data upload
System Model
System Design Data redistribution is used to maximize the effective storage space of the sensor network In-network Data Redistribution: Sensor nodes are in a single network Overloaded nodes offload data to neighboring empty nodes Cross-partition Data Redistribution: Disconnected network Overloaded nodes upload to data mules Data Mules offload to under-loaded nodes
In-network data redistribution Uses lazy-offload scheme to save energy – postpones data balancing until the storage overflows Overloaded nodes should satisfy below conditions: R i R imbalance Where, R i = Remaining Storage size R TH = Threshold to delay data transfer R imbalance = Parameter to allow local imbalances R i ’ = Average remaining storage
Contd.. Bad Idea: Selecting neighbor with largest remaining free space This can cause data ping-pong Prevent data ping pong - bound the amount of data transfer Remaining storage & remaining node energy must be checked Node should not invoke or accept data redistribution unless its estimated energy lifetime > estimated storage lifetime
Cross-partition data redistribution Uses data mules Discriminate nodes (overloaded and under-loaded): Data mule calculates its free storage value R’m as the weighted sum αR ’ +(1-α)R m. α = 1 : mule favors redistribution to neighborhood α =0 : emphasizes upload Conserve power and reduce message collision, nodes use back-off timers
Transition state of sensor node
System Architecture of sensor nodes
Implementation Implemented using nesC in Tinyos Local storage space of nodes is organized into a circular buffers Uses: It consumes minimum code and data memory organizes space as continuous data chunk eliminates the need for free space management mechanisms prolongs flash lifetime by balancing write access to different locations
Log Files Log-array file : Simultaneously written by different nodes Generates a sequence of log items Logs attributes of environmental events independently monitored by multiple nodes Log-sequence file: one writer at a time Multiple nodes must coordinate with each other Maintains unique & continuous serial numbers Used in EnviroSuite
Log-Array File
Log-Sequence File
PerformanceEvaluation Basic deployment configuration: field of 80 × 80 ft 2, 36 nodes were deployed Two Scenarios: Scenarios 1: Single Disconnected sensor network Scenario 2 :Partitioned sensor network with data mules
Comparison of data storing rate at different time
Why EnviroStore is different ? Used for disconnected sensor networks Extra constraint of limited energy – use lazy offload Resource limitation of individual nodes Load balancing must be dependent only on local information Has additional challenge of redistributing data between entities that are disconnected
Possible Extensions use of controllable data mules to optimize data redistribution and upload data replacement policies to maximize the total amount of information instead of just the amount of stored data performance evaluation of EnviroStore on real hardware platform
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