1 EnviroStore: A Cooperative Storage System for Disconnected Operation in Sensor Networks Liqian Luo, Chengdu Huang, Tarek Abdelzaher John Stankovic INFOCOM 2007

2 Outline Introduction Design Implementation Evaluation Conclusion

3 Introduction (1/3) Data Collection  A sensor network is connected to the base station that collects the data.  near-real-time information is desirable  object tracking, event notification…etc Some applications do not require real-time information  environmental monitoring  temperature, light variation  Disconnected Network Model

4 Introduction (2/3) Disconnected Network Model  No need to maintain a base station in the field.  No need to connect every node as well as the base station. But, not preclude contact with a base station.  opportunistic data upload  via data mules Primary Concern  to maximize effective storage capacity  to minimize data loss  flash memory overflow and power consumption

5 Introduction (3/3) EnviroStore EnviroStore  a cooperative storage system  employ data redistribution scheme  also consider the rate of energy consumption  can delay the onset of data loss with large input data imbalance Communication- centric Storage- centric path routing, data aggregation, …etc maximize effective storage capacity

6 Design System Model Sensory data must be buffered until an upload opportunity arises. partitioned network island Share data across partitioned networks through mobile mules. disruption-tolerant!

7 Design In-network Data Redistribution Data Redistribution  to balance storage utilization…  Offloading data from nodes that are highly loaded to nodes that are not. Perfectly balanced system is not energy- efficient.  excessive and unnecessary data dissemination  Not to start offloading data too early!  lazy-offload scheme

8 Design In-network Data Redistribution Lazy-offload Scheme  to postpone data balancing until the latest possible time  allow certain imbalance between neighboring nodes  use local information only Node i decides to offload data when…  : remaining storage size : threshold value : average remaining storage of the neighbor : level of local imbalance

9 Design In-network Data Redistribution Node i should select the destination node from underloaded neighbors.  whose remaining storage size is above  should prevent data ping-pong  choosing under loaded neighbor with probability (proportional to its remaining storage)  amount of data to be transferred… When to offload data Who to offload data How Much to offload

10 Design In-network Data Redistribution Node i selects node j as the redistribution destination.  Not to reverse the direction of imbalance.  further avoid data ping-pong  So, the amount of data to be transferred: : node advertisement threshold : amount of data transferred

11 Design In-network Data Redistribution Our algorithm keep track of...  remaining free storage  remaining node energy Node i could invoke or accept data redistribution when… remaining energy of node i initial energy of node i remaining storage of node i initial storage of node i

12 Design Cross-partition Data Redistribution A B partitioned network island mobile data mules calculate mule advertisement message (high frequency) node advertisement message (low frequency) Upload or Download? : average remaining storage : available storage on the mule

13 Design Cross-partition Data Redistribution State transition of a sensor node in cross-partition data redistribution. Upload data to the mules. Download data from the mules. use back-off timers frequency difference between nodes and mules! proportional to current occupancy ratios

14 Implementation System architecture for sensor nodes Reading and Writing log items. Send advertisement messages and maintain neighbor table. Determine whether the current node should offload data to the neighbor or the mule. Start data transfer towards a selected destination. Provide reliable unicast for nodes to transfer log items.

15 Implementation Local Storage Structure Circular Buffer  containing continuous log items Random Access is not required. Not need for any complex space management Prolong flash lifetime by balancing write access.

16 Implementation User Interface EnviroStore supports two types of log files. Log-array Files  simultaneously written by different nodes  attributes of an environmental event that is independently monitored by multiple nodes Log-sequence Files  one writer at a time  Multiple nodes should coordinate with each other.  useful for tracking moving objects

17 Implementation User Interface Example: Different Types of Log Files to obtain the temporal and spatial distribution of the temperature in Room 303 to track the position of a vehicle Hand off leadership from node to node!

18 Evaluation EnviroStore is implemented in nesC on TinyOS.  Use TOSSIM to experimentally evaluate the performance. Deployment Configuration  Field:  36 nodes (6x6 Grid) Data Mules  The movement of mules follow a constraint random walk model.  Speed: 5 ft/s  Turning Angle: random between and

19 Evaluation Pentium4 1.7 GHz machine with 1G RAM. Storage Capacity  Sensor Nodes: 16 KB  Data mules: 64 KB Parameter Settings  : 0.95*S  : 0.05*S  : 0.01*S to accelerate heavy-weight simulation…

20 Evaluation I. Single Disconnected Sensor Network Scenario 1: Single Disconnected Sensor Network Deployment Configuration Node 4 non-zero input rate

21 Evaluation I. Single Disconnected Sensor Network Without EnviroStore data loss caused by insufficient local storage 256 sec Data storing rate at different time Drop below the input rate!

22 Evaluation I. Single Disconnected Sensor Network Data storing rate at different time 1900 sec With EnviroStore Drop below the input rate! drop gradually…

23 Evaluation I. Single Disconnected Sensor Network To investigate the effects of on the energy consumption Number of data messages sent per second 540 sec 180 sec 1200 sec significant energy due to lazy offload!

24 Evaluation I. Single Disconnected Sensor Network We explore a more general scenario.  Data rates are uniformly distributed among nodes.  The input rates are random samples from an exponential distribution.  Mean = 16 B/s Example: input rates at different nodes

25 Evaluation I. Single Disconnected Sensor Network Data storing rate at different time 500 sec 800 sec 60% improvement!

26 Evaluation II. Partitioned Sensor Network with Mules Scenario 2: Partitioned Sensor Network with Data Mules Deployment Configuration 64 B/s 32 B/s 0 B/s

27 Evaluation II. Partitioned Sensor Network with Mules Data storing rate at different time 96 B/s 512 sec 256 sec 1900 sec 2400 sec 2800 sec Delay data loss by a factor of more than 10.

28 Evaluation II. Partitioned Sensor Network with Mules Distribution of total stored data after 3600 sec Without mules Without one mule 64 B/s 32 B/s 0 B/s 64 B/s 32 B/s 0 B/s overloaded underloaded more balanced storage occupancy!

29 Evaluation II. Partitioned Sensor Network with Mules Number of data messages per second sec 2400 sec drop

30 Evaluation II. Partitioned Sensor Network with Mules Deployment Configuration Scenario 2: Add base station and extra nodes Base Station Extra Nodes

31 Evaluation II. Partitioned Sensor Network with Mules Data storing rate of the base station over time 0 Ensure the connectivity between sensor nodes and the base station.

32 Evaluation II. Partitioned Sensor Network with Mules Total stored data of the base station over time Disconnect the network partitions from the base station and add some data mules. More mules can increase the rate of uploading data to the base station.

33 Conclusion EnviroStore  cooperative storage system  maximize network storage capacity  in-network and cross-partition data redistribution  opportunistic data offload Plan to further extend the work.  controllable data mules  data replacement policies  Evaluation on real hardware platforms.