Ultra-low power wireless sensor networks: distributed signal processing and dynamic resources management

Transcription

1 Ultra-low power wireless sensor networks: distributed signal processing and dynamic resources management Candidate: Carlo Caione Tutor: Prof. Luca Benini

2 Compressive Sensing The issue of data gathering in a large-scale wireless sensor network is a very challenging problem and the successful deployment of such networks is strictly connected to their lifetime In this scenario data gathering techniques are developed to ensure data collection with the minimum energy expenditure Saving on communication costs is obtained through in-network compression The global communication and energy cost reduction is obtained through innetwork decentralized compression to increase the energy efficiency of data gathering techniques

3 Compressive Sensing The powerful idea behind CS is that we are able to reconstruct a spatial (or temporal or multidimensional) signal from a compressed version The compression is performed by nodes that are able to compress each own data without any prior information about the signal When all the data is gathered at the sink, the original signal can be reconstructed through an optimization problem

4 Compressive Sensing CS is a novel data compression technique that can overcome some of the limitations of the classical compression scheme CS is used to compress data to obtain a representation for the signal that is smaller than the original one If the signal is sparse in one basis it can be recovered from a small number of projections onto a second basis incoherent with the first with high probability through an optimization problem sample compress transmit receive decompress

5 Compressive Sensing Compression: Gaussian or Bernoulli/Rademacher matrices = If the original signal is sparse in a matrix, the two matrices and are incoherent and is large enough, CS theory assures that we can recover the original signal from compressed measurements. Decompression:

6 Compressive Sensing for WSN Using CS each node compresses the sampled signal and transmits this shorter version of the signal to its parent, saving on energy for communication The parent node aggregates the contribution of the children nodes to its own compressed signal and again forwards the result toward its parent toward the sink The compression and the in-network aggregation mechanism guarantees a longer lifetime to the network

7 Compressive Sensing for WSN In our research 1 we have considered a big sized ZigBee network Our goal was to investigate the performance of both CS and a classical data gathering technique with increasing number of nodes The main contribution was the design of a new adaptive mixed algorithm for data transmission designed to minimize the power consumption and extending lifetime sink GPS location Temperature field from real data set 1 Carlo Caione, Davide Brunelli, Luca Benini. Compressive sensing optimization over ZigBee networks. In Proceedings of SIES'2010

8 Average number of transmitted packets per node CS optimization For data gathering in the network we have investigated three different techniques: 1. PF: a very simple energy-saving strategy in which each node tries to encapsulate data in the smallest possible number of packets 2. CS: when it is used for in-network compression the number of packets sent is constant for the entire network and depending only on the dimension of the random matrix DCS PF Mixed Algorithm Number of nodes 3. Mixed algorithm (MA): each node autonomously chooses whether compress data with CS or not, trying to minimize number of packets to send out

9 Lifetime [days] Lifetime [days] CS optimization PF: B =1byte data MA: B data =1byte PF: B =2bytes data MA: B =2bytes data PF: B data =3bytes MA: B data =3bytes Simulations are performed using real hardware specification and modifying the size of the packet fields PF: B =4bytes ID MA: B ID =4bytes PF: B =6bytes ID MA: B =6bytes ID PF: B ID =8bytes MA: B ID =8bytes Number of nodes The mixed algorithm proposed always performs better than PF and CS extending lifetime and preserving the reconstruction quality Number of nodes

10 CS optimization Although great part of the energy is spent in packets transmission, a notnegligible fraction is also used for data compression especially impacting the lifetime of the nodes at the boundary between PF and CS In our research 1 we also tried to prolong the nodes lifetime, reducing the computational work of the boundary nodes slightly modifying the original algorithm PF CS Boundary node between PF and CS regions Energy for compression not negligible E comp /E TXRX B ID =0 bytes B ID =log 2 (N) bytes B ID =2 bytes B ID =4 bytes Number of nodes (N) 1 Carlo Caione, Davide Brunelli, Luca Benini. Distributed compressive sampling for lifetime optimization in dense wireless sensor networks. In IEEE Transactions on Industrial Informatics

11 DLifetime [days] CS optimization A new parameter is added to the algorithm. This parameter influences the node about the choice to adopt CS instead of PF even if the number of outgoing packet is higher Simulations show how an optimal value for the parameter does exist for the network that is able to prolong the network lifetime, reducing the contribution of the energy spent in compression N=625 N=729 N=841 N= B off

12 Multidimensional CS Climate, habitat and infrastructure monitoring are among the most important applications of WSN that are inherently multidimensional In cases where sensors are near each other, the signals are quite similar and thus nodes produce correlated outputs, we can then expect that the ensemble of signals have a certain kind of joint structure The two most prominent frameworks dealing with sparsity and compressibility of multidimensional signals and signal ensembles are Distributed Compressive Sensing (DCS) and Kronecker Compressive Sensing (KCS) Under the right conditions the decoder at the sink can jointly reconstructs all of the signals precisely. DCS and KCS differs in the conditions under which the reconstruction is optimal and achievable and in the recovery procedures as well

13 Distributed Compressive Sensing DCS In the DCS model all signals can be represented by two components: a common sparse and an innovation component. The DCS theory proposes three different models for jointly sparse signals, each one suitable for a different classes of signals. 1. JSM-1: global phenomena affect all sensors while local phenomena affect individual sensors 2. JSM-2: all the sensors acquire a replica of the same sparse signal but with phase shifts and attenuations caused by signal propagation 3. JSM-3: model well suited for video reconstruction problems

14 time node1 node2 node3 node4 Kronecker Compressive Sensing KCS In KCS we consider the ensemble of the signals as expressed in a multidimensional matrix where each column of the matrix represents the signal evolution in time and each rows is a snapshot in space at the same time for all the nodes. If the multidimensional signal presents different sparsity properties along each of its dimension it is possible to obtain a single sparsity and measurement basis for the entire multidimensional signal to jointly compress and recover the original components (dimensions) of the signal. space

15 DCS and KCS In our research 1 we compare the different techniques against a common set of artificial signals properly built to embody the main characteristics of natural signals. DCS always performs better than KCS and independent reconstruction and JSM- 2 reconstruction algorithm has a lower complexity as the reconstruction problem goes through a fast greedy algorithm 1 Carlo Caione, Davide Brunelli, Luca Benini. Compressive Sensing Optimization for Signal Ensembles in WSNs Proposed to IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS)

16 SNR (db) Distributed Compressive Sensing To investigate the performance of DCS with JSM-2 signals ensembles we consider three different sets of real data: temperature, humidity and light In the plot is reported the reconstruction quality of the signals for a 1:10 compression ratio varying the window size for the low-pass filter The DCS outperforms the independent reconstruction: it is possible to obtain a good reconstruction of the original signal with fewer bits for data saving on communication costs Temperature Temperature (DCS) Humidity Humidity (DCS) Light Light (DCS) Span of the moving average

17 # CPU cycles Energy [mj] Energy-efficient DCS CS data compression goes through a matrix-vector multiplication that is not efficient on medium/small microcontrollers Hardware: STM32W108 (32bit 24MHz, ARM Cortex-M3 microprocessor, 128Kbyte Flash and 8K- byte RAM memory, IEEE compliant transceiver) 18 x CS no compression Comparison between the energy spent in compression and transmission for CS and the energy for transmission when no compression is applied DCS ok DCS too expensive The intersection point is function of the measurement matrix used for compression Compressed vector length (M) 0

18 # CPU cycles Energy [mj] Energy-efficient DCS CS theory claims that for CS compression it is possible to use several kind of random compression matrices created from random matrices ensembles Gaussian matrix Binary sparse matrix T1 T2 T3 T4 T5 T TG TB HG HB LG LB NC SNR (db) Compressed vector length (M)

19 SNR (db) DCS with sparse random matrices DCS with sparse binary matrices permits a near-optimal reconstruction quality with less energy than that one required by both Gaussian matrices and transmission of data without compression 45 Temperature Comparison between reconstruction performance of Gaussian matrix and binary sparse matrix for temperature. DCS and independent reconstruction using CS are also compared Gaussian Gaussian (DCS) Binary sparse Binary sparse (DCS) Compressed vector length (M)

20 Platform Based Design The increasing complexity, heterogeneity and reliability requirements of wireless sensor networks is posing major challenges to the capability of developing effective designs The Platform Based Design (PBD) methodology was originally developed for classical embedded systems, and it has been revisited and applied to the wireless sensor networks domain According to PBD, a design is obtained as a sequence of refining steps that take guide the designer from the initial specification all the way down to a physical implementation. The starting point is to separate the functional and architectural models and connect them by mapping

21 PtolemyII and MetroII The ongoing research is focused on trying to map the functional description and the architectural model that are in different modeling environment Example: mapping actors and events in PtolemyII to an architecture modeled in SystemC enabling performance estimation and architecture exploration

22 Publications Carlo Caione, Davide Brunelli, Luca Benini. Rapid and efficient application design using a signal processing framework for WSN. In Proceedings of ISCC'2010 Carlo Caione, Davide Brunelli, Luca Benini. Compressive sensing optimization over ZigBee networks. In Proceedings of SIES'2010 Carlo Caione, Davide Brunelli, Luca Benini. Distributed compressive sampling for lifetime optimization in dense wireless sensor networks. To appear in IEEE Transactions on Industrial Informatics Carlo Caione, Davide Brunelli, Luca Benini. Compressive Sensing Optimization for Signal Ensembles in WSNs. Proposed to IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS)

23 Projects and Research CHIRON (Cyclic and Person-centric Health Management) ARTEMIS The Project intends to combine state-of-the art technologies and innovative solutions into an integrated framework designed for an effective and person-centric health management along the complete care cycle 3ENCULT (Efficient Energy for EU Cultural Heritage) FP7 The project wants to investigate technical solutions for the energy enhancement as well as smart monitoring and control GENESI (Green Sensor Networks for Structural Monitoring) FP7 Develop long lasting sensor nodes by combining cutting edge technologies for energy generation from the environment (energy harvesting) and green energy supply (small factor fuel cells). MuSyC (Multiscale System Center) with University of California, Berkeley The goal of the Multiscale Systems Center is to create a comprehensive and systematic solution to the distributed multi-scale system design challenge.

24 Thank You