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Multi-Resolution Spatial and Temporal Coding in a Wireless Sensor Network for Long-Term Monitoring Applications. You- Chiun Wang, Member, IEEE, Yao-Yu Hsieh, and Yu- Chee Tseng, Senior Member, IEEE. IEEE TRANSACTIONS ON COMPUTERS, TC-2008-08-0420. Outline. Introduction

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slide1

Multi-Resolution Spatial and Temporal Coding in a Wireless Sensor Network for Long-Term Monitoring Applications

You-Chiun Wang, Member, IEEE, Yao-Yu Hsieh, and

Yu-Chee Tseng, Senior Member, IEEE

IEEE TRANSACTIONS ON COMPUTERS, TC-2008-08-0420

outline
Outline
  • Introduction
  • Comparison with prior works
  • Multi-Resolution Compression and Query(MRCQ) Framework
    • Spatial Compression Algorithm
    • Temporal Compression Algorithm
    • Reverse-Exponential Storage Algorithm
  • Prototyping Experience
  • Simulation Studies
    • Comparison with DIMESIONS
    • Effect of the Spatial Compression Algorithm
    • Effect of the Temporal Compression Algorithm
  • Conclusions
introduction 1 3
Introduction(1/3)
  • A WSN is composed of numerous sensor nodes, each being a tiny wireless device that can continuously collect environment information and report to a remote sink through a multi hop ad hoc network [4].
    • for example, habitat monitoring, health care, smart home, and surveillance
  • Because sensor nodes are typically operated by batteries and recharging is usually infeasible, it is a critical issue to extend the network lifetime by conserving their energy.

[4] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Comm. Magazine, vol. 40, no. 8, pp. 102–114, 2002.

introduction 2 3
Introduction(2/3)
  • We consider WSNs with the following characteristics:
    • 1) The communication overhead will dominate their energy consumption. Thus, it is important to reduce the amount of transmissions of regular reporting of sensor nodes.
    • 2) Sensing data often exhibits a certain degree of correlation. (Fig.1)
    • 3) The WSN should be designed to allow periodical lower-resolution reports to be sent to the sink as well as to keep higher-resolution data inside the network for further queries.
    • 4) The proposed compression and storage schemes cannot be too complicated to fit into sensor nodes.
introduction 3 3
Introduction(3/3)
  • Main idea:
    • To reduce communication cost, only lower-resolution summaries are sent to the sink.On the other hand, higher resolutiondata is kept in the network to allow occasional queries from users.
  • Method:
    • Organize sensor nodes hierarchically and then establish multi-resolution summaries of sensing data, via spatial and temporal coding techniques.
    • We develop a reverse-exponential storage algorithm to keep historical summaries inside the network.
comparison with prior works
Comparison with prior works

The simulation results show that MRCQ incurs a lower amount of data transmissions and renders more accurate reports compared to DIMENSIONS [11].

[11] D. Ganesan, B. Greenstein, D. Estrin, J. Heidemann, and R. Govindan, “Multiresolution storage and search in sensor networks,” ACM Trans. Storage, vol. 1, no. 3, pp. 277–315, 2005.

multi resolution compression and query mrcq framework
Multi-Resolution Compression and Query(MRCQ) Framework
  • Spatial compression algorithm modifies the popular DCTmethod.
  • Temporal compression algorithm adopts a differential coding to transmit continuous data.
  • Use reverse-exponentialconcept to store historical data.
slide8

The network is recursively divided into (> 1) blocks

We select a nod in each blocks as PN to collect and compress sensing reports from lower-layer blocks

spatial compression algorithm
Spatial Compression Algorithm
  • The spatial compression algorithm is performed by each PN to compress sensing data from its lower layer.
  • A compression ratio can be specified.
  • There are 3 components:
    • Layer-1 compression
    • Layer-i compression (i>1)
    • Decompression
layer 1 compression 1 2
Layer-1 compression(1/2)
  • A layer-1 PN collects the sensing data from its local LNs and stores them in a kk matrix M=, where is the value of the local pixel ( i , j ).
  • Apply the 2D-DCT on M to generate a new matrix M’=, where
  • Since k is a small integer, we can maintain a small table in each PN to record the results of cosine operations.
layer 1 compression 2 2
Layer-1 compression(2/2)
  • A reduced zigzag scan (RZS) is performed to translate M’ into an one-dimensional array D until elements are scanned.
  • Due to the property of 2D-DCT, array D keeps most significant values of M.
  • Then, the array D is transmitted to its layer-2 PN.
layer i compression1
Layer-i compression
  • The size of each packet transmitted by a layer-1 PN is
    • bits.
  • For each layer-2 PN, after including a packet header,
    • bits will be sent to its parent.
  • The size of each packet transmitted by layer-i PN is
    • bits.
  • A smaller incurs less amount of data transmission and thus preserves more energy of PNs, but it also reduces data accuracy.

h : the size of packet header

: the size of a pixel

decompression 1 2
Decompression(1/2)
  • Case 1:
    • The sink based on the reduced layer-1 matrices collected from its children, each with pixels , where d is the number of layers.
    • Each reduced matrix will be expanded to a matrix M’=by appending sufficient 0’s at the end.
    • Perform the inverse 2D-DCT to transform M’ to a matrix M=, where
    • The sink then puts all these recovered matrices together to form a large matrix of approximate sensing data.
decompression 2 2
Decompression(2/2)
  • Case 2:
    • query a certain layer i.
    • each layer-iPN will send the discarded part( pixels, as shown in Fig. 4) to its layer-(i+1) PN.
    • Such operation is repeated until the sink receives all discarded pixels of all PNs from layers ito d.
    • The sink can have the “complete” matrices seen by all layer-iPNs and then recover the sensing data with a resolution of “layer i”.
temporal compression algorithm 1 2
Temporal Compression Algorithm(1/2)

: complete reporting intervals

: partial reporting intervals, where is a multiple of .

temporal compression algorithm 2 2
Temporal Compression Algorithm(2/2)
  • The compression between LNs and layer-1 PNs will be controlled by a small update threshold .
  • The compression between layer-1 PNs and layer-2 PNs will be controlled by a threshold .
    • Current matrix
    • Previously report matrix
  • All layer-iPNs, , will not conduct temporal compression, because computing the difference of two compressed matrices is time-consuming.
reverse exponential storage algorithm 1 5
Reverse-Exponential Storage Algorithm(1/5)
  • The users can query different resolutions of sensing data on the time domain.

1. : the frame stored by a LN/PN at timestamp i, where a frame is the unit of sensing data for the node.

reverse exponential storage algorithm 2 5
Reverse-Exponential Storage Algorithm(2/5)
  • When a query of a past frame () arrives at a node:
  • Case A: The node is a LN. The response includes three possibilities:
    • directly reply to the sink if is stored in the node’s local memory.
    • The sink applies a linear interpolation to calculate

if .

    • replies a FAIL message to the sink if .
reverse exponential storage algorithm 3 5
Reverse-Exponential Storage Algorithm(3/5)
  • Case B: The node is a PN. The query should specify a certain layer i.
    • The sink will flood the query to all layer-iPNs.
    • Each layer-i PN will send its frame(s) or a FAIL message according to the above three cases to its layer-(i+1) PN.
    • Such operation is repeated until the sink receives all frames from all layer-i to layer-d PNs.
    • The sink can combine these frames and recover the historical data via the inverse 2D-DCT method and a linear interpolation
  • There are two properties in the reverse-exponential storage scheme:
    • a long history of data can be stored with small buffers.
    • finer resolutions are available for more recent data, while coarser resolutions are available for older data.
reverse exponential storage algorithm 4 5
Reverse-Exponential Storage Algorithm(4/5)
  • How to maintain historical frames in a node’s memory as time moves on: (Suppose that the current time is t.)
  • Case A: The node is a LN.

For each frame , we can apply the linear interpolation in Eq.(3) on the frames andto approximate its value.

reverse exponential storage algorithm 5 5
Reverse-Exponential Storage Algorithm(5/5)
  • Case B: The node is a PN.
    • For each remaining frame,,we need to select one frame that has the closest timestamp to it to represent this frame.
    • For each place , the actual frame stored in that place always satisfies a timestamp
prototyping experience 1 3
Prototyping Experience(1/3)
  • We use the MICAz Motes [29] to build a 2-tier, 16 nodes WSN.
  • Each Mote’s radio is a 2.4 GHz, IEEE 802.15.4-compatible module allowing low-power operations and offering a data rate of 250 Kbps.
  • Set and k=2, so there are 4 layer-1 PNs in our prototype, each being responsible for collecting and compressing pieces of data.
  • For each LN, a sensing report is 15 bytes, which contains 11 bytes of header and trailer and 4 bytes of payload.
  • The size of packets reported from a PN is 19 bytes with 8 bytes of payload.
  • We use this network to collect the indoor temperatures during 45 hours.
  • = 10 min.
  • The compression ratio = 0.75.
  • = = 0.5

[29] Crossbow, “MOTE-KIT2400 - MICAz Developer’s Kit,”

http://www.xbow.com.

prototyping experience 2 3
Prototyping Experience(2/3)

The maximum error is 0.189

simulation studies
Simulation Studies
  • sensing filed divided to grids.
  • 1000 sensor nodes are randomly deployed.
  • Designate 20 and 4 nodes as layer-1 and layer-2 PNs.
  • The transmission range of each sensor node is set to 30 m.
  • Total simulation time is 100 min.
  • During every minute, the temperature of each grid may be changed and a number of events will arbitrarily occur in the sensing field.
  • Define the average error as

where K = 32.

comparison with dimesions 11 1 4

[11] D. Ganesan, B. Greenstein, D. Estrin, J. Heidemann, and R. Govindan,“Multiresolution storage and search in sensor networks,” ACM Trans. Storage, vol. 1, no. 3, pp. 277–315, 2005.

Comparison with DIMESIONS [11](1/4)

1. Observe the effect of the ranges of grid temperatures.

1. = = 0.5

2. The average temperature of each grid is randomly picked from[(25-x),(25+x)].

3. There are 5 events arbitrarily occurring in the sensing field, each increasing [1,3] in its vicinity.

comparison with dimesions 2 4
Comparison with DIMESIONS(2/4)

2. Observe the effect of the increasing temperatures of events.

There are 20 events arbitrarily appearing in the sensing field, each increasing y in its vicinity.

The average temperature of each grid is randomly picked from[24.8,].

comparison with dimesions 3 4
Comparison with DIMESIONS(3/4)

3. Observe the effect of the number of events.

The average temperature of each grid is randomly picked from[24,26].

There are 0-100 events arbitrarily occurring in the sensing field, each increasing [1,3] in its vicinity.

comparison with dimesions 4 4
Comparison with DIMESIONS(4/4)
  • In summary, MRCQ can compresses more data whenthe environment is stable, and preserve more accuracyon reports by transmitting more data when the environmentchanges drastically.
effect of the spatial compression algorithm 1 2
Effect of the Spatial Compression Algorithm(1/2)

Set = = 0 to eliminate the effect of the temporal compression algorithm.

effect of the temporal compression algorithm
Effect of the Temporal Compression Algorithm

Set to fix the effect of the spatial compression algorithm and set = =.

conclusions
Conclusions
  • The proposed multi-resolution idea can significantly extend a WSN’s lifetime, especially in long-term monitoring applications with a slowly changed environment.
  • Our in-network compression algorithms adopt the concepts of DCT and differential coding to reduce data redundancy.
  • Our storage algorithm helps sensor nodes to store historical data in their small memories by a reverse-exponential solution.
  • The simulations and prototyping system show that our MRCQ framework can flexibly adjust the amount of data transmissions according to the environmental stability and preserve important characteristics of sensing reports