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You- Chiun Wang, Member, IEEE, Yao-Yu Hsieh, and Yu- Chee Tseng, Senior Member, IEEE

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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You- Chiun Wang, Member, IEEE, Yao-Yu Hsieh, and Yu- Chee Tseng, Senior Member, IEEE

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  1. 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

  2. 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

  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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. Layer-i compression

  13. 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

  14. 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.

  15. 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”.

  16. Temporal Compression Algorithm(1/2) : complete reporting intervals : partial reporting intervals, where is a multiple of .

  17. 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.

  18. 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.

  19. 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 .

  20. 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.

  21. 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.

  22. 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

  23. 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.

  24. Prototyping Experience(2/3) The maximum error is 0.189

  25. PrototypingExperience(3/3)

  26. 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.

  27. [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.

  28. 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,].

  29. 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.

  30. 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.

  31. Effect of the Spatial Compression Algorithm(1/2) Set = = 0 to eliminate the effect of the temporal compression algorithm.

  32. Effect of the Spatial Compression Algorithm(2/2)

  33. Effect of the Temporal Compression Algorithm Set to fix the effect of the spatial compression algorithm and set = =.

  34. 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

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