Reservation Multiple Access in Underwater Sensor Networks Based on Compressed Sensing

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1 2013 8th International Conference on Communications and Networking in China (CHINACOM) Reservation Multiple Access in Underwater Sensor Networks Based on Compressed Sensing Shuo Shi, Xue Wang and Xuemai Gu School of Electronics and Information Engineering Harbin Institute of Technology Harbin, China Abstract Compared with terrestrial wireless sensor networks, underwater sensor network has a longer propagation delay. So many mature MAC protocols can t be directly applied in underwater environment. In this paper, the reservation multiple access method based on compressed sensing in underwater sensor network makes full use of the long propagation delay in the underwater environment and each node in the network can share the temporal and spatial resources of channel to achieve multiuser channel reservation simultaneously, improving the channel utilization. And if taking multiuser diversity into consideration, make the best user take advantage of the limited bandwidth for high-speed transmission, so that the throughput performance of the entire network can be improved. Keywords compressed sensing; underwater sensor network; reservation multiple access I. INTRODUCTION The ocean has rich resources and broad space. With the increasing development of ocean exploitation, underwater sensor network draws more attention it deserves. Considering extraordinary circumstances under water [1], acoustic wave has become the most effective means of communication in underwater sensor network. In the water, the acoustic propagation speed is about 1500m/s. Compared with the radio wave, there is less over five orders of magnitude. It brings greater propagation delay. As the underwater acoustic communication frequency is low and difficult to replace the battery under water, underwater sensor networks have more severe restrictions in both bandwidth and energy. So many mature MAC protocols can t be directly applied in underwater environment. In recent years, many improvement schemes for underwater sensor networks have been proposed. In order to solve the problems of hidden terminal and exposed terminal, a Slotted FAMA protocol which uses RTS/CTS mechanism in MAC layer is proposed [2]. Although using control packets such as RTS/CTS and ACK/NACK, can effectively solve the hidden terminal and exposed terminal problems, while reducing packet loss. The frequent use of control packets will increase the endto-end propagation delay, reduce the channel utilization, and aggravate the effect brought by the long propagation delay for the whole system throughput. It also consumes a lot of energy. An energy-efficient MAC protocol brings new ideas for energy limited underwater environment [3-4]. The protocol only needs local time synchronization, so it can solve the difficulty of time synchronization in the whole network. Avoid the underwater long propagation delay impact and awaken nodes receive data at the appropriate time. In [5], a propagation delay-aware opportunistic MAC protocol is put forward. Depending on delay table of each node, determine when to send data can avoid collision, but to maintain the time delay table requires continuous monitoring of peripheral node information and timely refreshes local delay table. It also consumes a lot of energy. It should be pointed out that, at present the improved methods mostly adopt control packets handshake mechanism to make sure packets transmit correctly. Each handshake adds a signal propagation delay, and all nodes in this period did not send the packet, thus for underwater sensor network with long propagation delay, there will be bigger transmission delay, severely limiting effective utilization of channel resources. So it is the bottleneck problem that restricts the performance of underwater sensor network. In recent years, a new method named compressed sensing [6-8] has been put forward, making break through the bottleneck possible. In the paper, a reservation multiple access method based on compressed sensing is proposed. During one period, only send reservation information in one direction and allow multiple nodes to reserve channel at the same time, then distribute channel resources. By reducing shake hands and avoiding collision, it is a good solution to low utilization rate of channel resources brought by long propagation delay in underwater sensor network. II. COMPRESSED SENSING In recent years, compressed sensing theory has brought new vigor and vitality in the field of signal sampling. Compared to Nyquist sampling theorem, it not only reduces the sampling rate, but also save the storage resources and improve the transmission efficiency. This theory points out: as long as the signal is compressible or sparse in a transform domain, it can be transformed from a high dimensional space onto a low one using one observation matrix which is not related with transform matrix. Information in the original signal can be reconstructed from such a small IEEE

2 amount of projection with a high probability by solving an optimization problem. It means this projection contains enough information for reconstruction of signal. Under the theoretical framework, the sampling rate no longer depends on signal bandwidth, but depends on the structure and content of information in the signal. Here two conditions must be met, namely sparseness and Restricted Isometry Property (RIP), which restricts to the sampled signal and observation matrix. B. System model In Fig.2, it describes a simple communication scenario. All user nodes are deployed in a circular area which sink node is as a center and radius is r, communicating with sink node in single hop. T Θ = Ψ X Y = ΦΘ T min Ψ X CS s. t. A X = Y 0 Y = A Figure 1. Compressed sensing theory framework CS X Ν 1 One signal X R, if there is an orthogonal transform Ν Ν Ψ R and the projection of the signal Θ in the transform Μ 1 domain is sparse, obtain an observation vector Y R by a Μ Ν linear measurement process Φ R and Μ Ν. The measurement vector Y is the compressed sampling value of X, apparently Y dimension is far smaller than X, achieving low speed sampling and compression. There signal reconstruction is seemed as to seek the optimal solution of the problem under the constraint condition, can be expressed as: min Θ s. t. ΦΨ X = Y (1) 0 The signal can be accurately reconstructed by solving the optimization problem. Due to the non-zero data and its position of Θ can completely express X and Y dimension is enough to ensure that it can contain the data, realize to obtain information directly from the signal. III. SYSTEM MODEL A. Multiuser Diversity in Underwater Channel Underwater acoustic channel is a very complex channel, having large propagation loss, multipath effect and dispersion effect, but also impacted by water temperature, salinity, water depth (atmospheric pressure) and so on. Due to various factors, channels between two nodes have large difference. When the quality of communication link is bad, it seems broken or not existence. As the channel quality changes randomly, when the channel gain is above a certain threshold value, it is considered that there must be communication between two nodes. Multiuser diversity was first used by Knopp and Humbler [9], in order to obtain the desired capacity, at any given moment, allowing only one or a few users which have excellent channel environment to make full use of the limited bandwidth to transmit with high speed, so as to improve the throughput performance of the whole system. In the fading channel with multiple users, different users experience their respective channel gain peak in different time. More users there are; more probability that there is a best user at every moment will be [10]. In a large-scale underwater sensor network, we will use the compressed sensing technology to realize the multiuser diversity and complete the channel reservation of the best user in a round trip time (Round Trip Time, RTT). Figure 2. Communication scenario In the reservation stage, sink node only receives reservation packets and not reply. At the last moment, sink node broadcast all subscriber answers, as well as publish channel allocation result, achieving multiple user reservation at the same time. In a transmission reservation packet process, sink node will always be in the receiving state. The existence of underwater long propagation delay provides a temporal and spatial multiplexing opportunity for sending the reservation packets. Not only that, a reservation multiple access method based on compressed sensing can avoid a situation of sending multiple RTS control packets without responding because of bad channel quality by considering channel diversity, making the best use of limited communication resources, to further improve the utilization rate of channel. IV. RESERVATION MULTIPLE ACCESS METHOD A working cycle is divided into two stages, respectively used for channel reservation and data transmission, further will make the reservation time divided into several time slots. In the reservation stage, making multiuser simultaneous reservation comes true based on compressed sensing [11], reducing the average reservation time of each user and improve the utilization rate of channel effectively. In a certain correlation time, there is reciprocity between uplink and downlink. At the beginning of a working cycle, sink node broadcasts a pilot signal to all nodes in communication coverage area and each node continues monitoring the channel after a period of time. If not receiving the pilot signal, node automatically enter sleep state until the next cycle start, and others estimate their own channel quality by measuring signalto-noise ratio of the received pilot signal. According to the information in the pilot signal, each node determines whether its channel quality meets requirements, if not, they will enter sleep state until the next cycle start. The nodes which meet requirements send a reservation packet with content 1 and 364

3 others which have entered sleep state send nothing, in other words, it is considered that they send a reservation packet with content 0. Thus, there is an original information vector using multiuser diversity: [ ] X= x1x2 xν (2) Where xi ( i = 1,2,, Ν) is the reservation packet content from N nodes. Because the number of nodes which meet the channel condition is very few, namely there are only a few 1 in X, so X can be seen as a sparse signal. According to the compressed sensing theory, it is known that the observation matrix can be adaptive, not along with the signal changes. There, we use stochastic Bernoulli matrix as the observation matrix: a11 a12 a1 Ν a21 a22 a 2Ν Φ= = [ α1α2 αν ] (3) a a a α = is the feature vector of ith node. Μ1 Μ2 ΜΝ i a1 i a2i aμi Where [ ] Each node must be initialized before deployed in the water, and input its own feature vector. Sink node maintains a complete observation matrix. When the old node deleted or new node added, only update the observation matrix in sink node and no influence on other nodes. Using stochastic Bernoulli matrix as the observation matrix observes the original signal X for M times to obtain the observation vector Y: Namely, [ ] Y =Φ X= y y y (4) 1 2 y1 a11 a12 a1 Ν x1 y 2 a21 a22 a 2Ν x = 2 (5) yμ aμ1 aμ2 aμν xν For each observation result yi = ai 1x1+ ai2x2+ + aiνxν, when aij 0 and x j 0, y i adds one. The reservation nodes send M packets to sink node and each packet only has its own serial number. In the sink node, accumulate all the same serial number packets as an observation result yj ( j = 1,2,, Μ), completing a signal sampling and compression in the space. At the beginning, sink node broadcasts a pilot signal which contains the requirements for channel gain. As the distance between sink node and other nodes is different, the moment the broadcast signal arrives is different. Each node demodulates the signal, judging whether they can reserve channel. If they meet requirements, send the reservation packets, such as nodes A, B, C, D, E in Fig. 2. The maximum propagation delay T d is determined by communication range r. To set each reservation slot length is equal to the transmission time of reservation packet T 0, thus there are T d /T 0 slots in period of T 0. Actually, the number of Μ reservation packets is determined by its feature vector (the number of 1 ). The transmission time is selected randomly in period of T d, as shown in Fig. 3. After received the broadcast signal, reservation nodes send their own packets to sink node in period of T d. Figure 3.Channel reservation Although the time range of each node reservation packets arrived in sink node is overlap, the distances from sink node are different, leading to different starting moment to transmit packets, as well packets are sent randomly in a while, and it can reduce the collision of reservation packets greatly. At the same time, compressed sensing reconstruction algorithms, such as orthogonal matching pursuit (OMP), has a certain tolerance for observation error due to the collisions. It can recover the whole network channel reservation information only from a small number of observations. The above process don t need handshake for many times between sink node and others. In given channel reservation period (such as 5 T d ), sink node confirms multiusers who have good channel condition, to reduce the average reservation time of each user, allowing more time to perform efficient data transmission, to improve the system throughput performance. Below are given two procedures. One is reservation multiple access method based on compressed sensing (referred to as CS scheme) and as a comparison, the other is multiple access scheme based on RTS/CTS mechanism (referred to as RTS/CTS scheme), as shown in Fig. 4 and Fig. 5. Figure 4.CS scheme procedure Figure 5.RTS/CTS scheme procedure V. SIMULATION RESULTS In the section, we present a numerical study that compares the network performance of the reservation multiple access method based on compressed sensing (CS scheme) with other multiple access scheme based on RTS/CTS mechanism. The 365

4 main simulation parameters are set as follows: total number of nodes (or users) in the network is 100, the length of control packet is 8bit, the length of the data packet is 256bit, data transmission rate is 2kbps, communication radius is 600m and the velocity of acoustic is 1500m/s. Fig. 6 shows the total time for completing the same number user information transmission under two schemes. data transmission efficiency, as shown in Fig. 7. It gives the curve of data transmission efficiency as communication area radius changing with different number users and different length of data packet. As can be seen, as communication radius increases, data transmission efficiency drops. This is because the maximum propagation delay Td grows longer with the increase of communication radius, leading to reservation time adding and the proportion of transmission time in one work cycle reducing. On the other hand, as the number of reservation users grows, the data transmission efficiency improves. Moreover, different communication radius corresponds to different data transmission efficiency, so data transmission efficiency can be controlled by changing the communication radius. As shown in Fig. 8 and Fig. 9, the system throughput is achieved by data transmission. It can be seen in Fig. 8, the throughput increases as the bandwidth grows. The performance of CS scheme is better than the other, especially with the increasing of reservation users, the throughput performance of CS scheme will be further improved. In Fig. 9, as the packet length increases, the system throughput performance also can be improved. The reason is that the increasing of packet length makes the proportion of data transmission add. Figure 6.The total delay of multiusers transmission CS scheme allow more than one user to transmit at the same time. RTS/CTS scheme requires single user to reserve channel and each data transmission includes a propagation delay. Thus, the time CS scheme needs is far less than that in RTS/CTS scheme as the number of reservation users grows. We just need to point out, when the number of reservation users is few (for example, there is only one reservation user), the time CS scheme required is higher than that in RTS/CTS scheme. In the simulation, the reservation time in CS scheme is 5 T d. There will be higher time delay for fewer reservation users. Figure 8.System throughput performance Figure 7.Data transmission efficiency The meaningful part of the whole communication process for us is data transmission, so we hope reservation time is shorter and data transmission time becomes longer, and the proportion of transmission time in one work cycle determines Figure 9.System throughput performance 366

5 VI. CONCLUSIONS A reservation multiple access method based on compressed sensing is proposed for underwater sensor network which has long propagation delay. Multiusers can share the opportunity of reusing in time and space brought by long propagation delay. The average reservation time for each user is reduced and data transmission efficiency is improved, to further improve the system throughput. Meanwhile, we can get the satisfying data transmission efficiency by changing the communication radius. Compared with RTS/CTS scheme, CS scheme has more efficient channel reservation capacity, higher data transmission efficiency, and better throughput performance. At the same time, the data transmission efficiency can be manually controlled. ACKNOWLEDGMENT The work was sponsored by the National Science and Technology Major Project of China (2010ZX ) and the National Natural Science Foundation Project of China ( ). REFERENCES [1] J. Heidemann, Ye Wei, J. Wills, A. Syed, Li Yuan, Research challenges and applications for underwater sensor networking, Wireless Communications and Networking Conference, WCNC IEEE, vol.1, pp , 3-6 April [2] M.J Molins, M. Stojanovic, Slotted FAMA: a MAC protocol for underwater acoustic networks, OCEANS Asia Pacific, pp.1-7, May [3] V. Rodoplu and Min Kyoung Park, An energy-efficient MAC protocol for underwater wireless acoustic networks, OCEANS, Proceedings of MTS/IEEE, pp Vol. 2, Sept [4] Min Kyoung Park and V. Rodoplu, UWAN-MAC: An Energy-Efficient MAC Protocol for Underwater Acoustic Wireless Sensor Networks, Oceanic Engineering, IEEE Journal of, vol.32, no.3, pp , July [5] Y. Noh, P. Wang, Lee Uichin, D. Torres, M. Gerla, DOTS: A propagation Delay-aware Opportunistic MAC protocol for underwater sensor networks, Network Protocols (ICNP), th IEEE International Conference on, pp , 5-8 Oct [6] D.L. Donoho, Compressed sensing, Information Theory, IEEE Transactions on, vol.52, no.4, pp , April [7] E.J. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information Information Theory, IEEE Transactions on, vol.52, no.2, pp , Feb [8] E.J. Candes and T. Tao, Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? Information Theory, IEEE Transactions on, vol.52, no.12, pp , Dec [9] R. Knopp and P.A. Humblet, Information capacity and power control in single-cell multiuser communications, Communications, ICC '95 Seattle, 'Gateway to Globalization', 1995 IEEE International Conference on, vol.1, pp , Jun [10] X. Qin and R.A. Berry, Distributed approaches for exploiting multiuser diversity in wireless networks, Information Theory, IEEE Transactions on, vol.52, no.2, pp , Feb [11] S.T. Qaseem, T.Y. Al-Naffouri, T.M. Al-Murad, Compressive sensing based opportunistic protocol for exploiting multiuser diversity in wireless networks, Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on, pp , Sept