Power saving techniques for underwater communication networks. Arnau Porto Dolc Advisor: Milica Stojanovic

Transcription

1 Power saving techniques for underwater communication networks Arnau Porto Dolc Advisor: Milica Stojanovic June 2007

2 Contents 1 Underwater Wireless Communications Acoustic signal Noise Attenuation Bandwidth and equalization Multi-path and Doppler effect Interference Transmission area vs Interference area Connectivity Limitations of acoustic transducers Limitations of AUVs The underwater network Introduction Scenario Surface size and initial node location Mobility Positioning Frequency, attenuation and noise level Transmission range and scalability MAC protocols in underwater networks Related work MAC protocols DACAP protocol Waiting time Back offs and fairness Comparative performance Limitations of DACAP Conclusion Simulations Simulation strategy Packet generation

3 4.1.2 Results Simulation parameters Physical parameters Protocol parameters Scenario parameters Adaptive power control Introduction Design Distance estimation in power control mode Cross effect: interference area Simulation results Conclusion Optimal transmission range Introduction Design Simulation results Optimal transmission range calculation Density and inter-node distance Standar deviation and histogram Conclusion Dynamic routing Introduction Design Simulation results Conclusion and further work 48 2

4 List of Figures 1.1 Power spectral density of the ambient noise Absorption coefficient Narrow-band SNR Diagram of transmission area and interference area of a given transmission Connectivity levels Example of deployment positions of the nodes of the network Example of acceleration of an AUV Example AUV avoiding to leave the scenario Example of movements Throughput when t min = T D/c = T without ACKs Throughput and power waste when t min = 2T without ACKs Power waste and delay in terms of the transmission range Uncertainty in the position of an AUV Transmission and interference areas with power control Transmission and interference areas with and without power control Total energy with and without power control - without ACKs Throughput for with and without power control in a maximally connected network and in network with minimal connectivity Total energy consumption with and without power control for a maximally connected network with ACKs Optimal transmission range Energy Throughput Homogeneous scenario Scenario than needs maximal connectivity Scenario with uniform distribution of relays over a grid Optimal transmission range Optimal transmission power Standard deviation of the optimum transmission range Distribution of Rcon

5 7.1 Example of the transmission of a RCP - step Example of the transmission of a RCP - step Hysteresis cycle Total energy for static and dynamic routing - wo ACKs Throughput for static and dynamic routing - wo ACKs

6 Abstract This thesis proposes three techniques to reduce the power consumption in underwater acoustic networks with randomly placed fixed and mobile nodes. The transmission range is optimized so as to minimize the power consumption and/or to maximize the throughput. It is shown that the optimal transmission range is the one required in minimally connected networks.

7 Acknowledgments I would not have been able to complete this thesis without the help of all the people who I have met during these months. First, I want to thank Milica for her energetic attitude, for the freedom she has given me to develop my ideas, and for the patience she showed in the reviewing process of this work. Without Paolo s help I would not have learned quickly enough the use of Matlab and Latex. His advise has been very valuable thorough all my research. I also want to thank Borja for sharing his simulation files and for his suggestions. I would have find much harder to get started in Simulink without his s. A lot of people deserve my gratitude because they made me feel comfortable in Boston. My family deserves a special mention because I owe them everything I have achieved. The friendships I have built in these months have been the emotional support that I needed to work here. Thanks Alba, Alberto, Carla, Claudia, Cris, Dan, Edu, Fernando, Javier, Jordan, Nacho, Paolo, Peter, Taylor, Tere and Turan. I also want to thank you Mariola, my girlfriend, for everything. Our Skype sessions made me feel very close despite of the geographical distance between Boston and Dunedin. Finally, working at the MIT Sea Grant College has been a pleasure thanks to the people that research here. They helped me to improve my work with their comments and I learned from their experience. 1

8 Introduction Underwater wireless communication is a practically unexplored field if compared with terrestrial wireless communications. However, interest in underwater communications has been growing in the last years because of the increasing number of commercial and military applications that require the use of autonomous underwater vehicles (AUVs). The state of the art of the required technology is mature for the development of applications for basic underwater wireless networks. Experimental research has focused on characterizing the physical medium and on implementing single carrier modulations [1]. Currently, multi-carrier modulations for underwater communications are being designed. There is enough basic knowledge about the physical layer to attempt the design of protocols for the medium access control (MAC) layer. In this thesis we will focus on the design and analysis of techniques aimed to reduce the power consumption of MAC protocols. In particular DACAP (Distance aware collision avoidance protocol) [2] was specifically designed for underwater networks and has been shown in simulations to offer better overall performance in terms of throughput and energy consumption than other protocols. It avoids collisions and minimizes handshake duration with unsynchronized nodes based on receiver s tolerance to interference when the nodes are closer than the maximal transmission range. Reducing the power consumption is a key design objective due to the high attenuation of the UAC and the limited autonomy of the AUVs. Increasing the battery lifetime is crucial due to the high deployment cost of the nodes. The main idea is to develop energy saving techniques based on using transmissions with lower power. This can be done through several techniques. For instance, we could adapt the transmitted power to the length of the hop. Another option would be to find the optimal transmission range that minimizes the energy consumption. Reducing the transmission power will reduce the power consumption and will allow to scale up the network, but it will require multi-hoping. Finally, dynamic routing could solve the routing inefficiencies caused by the movement of the nodes. In each of these three techniques simulation results are used to evaluate the cross-effects on performance and interference reduction of these options. The thesis is organized as follows: in Chapter 1 the basic characteristics of the underwater acoustic channel are presented. In Chapter 2 we will present the scenario that we are going to simulate. In Chapter 3 we will discuss the work of other authors 2

9 in the field and the MAC protocol that we have chosen. In Chapter 4 we explain the simulation strategy that we have used to achieve the results that we present in the following chapters. The adaptive power control, the optimal transmission range and the dynamic routing are developed and their simulation results analyzed in Chapters 5, 6 and 7, respectively. In the last chapter conclusions and further work proposals are summarized. 3

10 Chapter 1 Underwater Wireless Communications The Underwater acoustic channel has some inherent characteristics that make it difficult to implement the same designs that were successfully implemented in terrestrial applications. 1.1 Acoustic signal Electromagnetic waves do not propagate well in water. As a result, acoustic signals are used. Sound propagates at about 1500 m/s in water, about 4 times faster than its speed in the air. However, it is still times slower than radio waves. This implies that any underwater wireless communication is subject to a long propagation delay that causes high latency. Therefore, the use of bi-directional real-time applications is severely restricted. Nevertheless, it is not an impediment for a lot of applications that simply require lower data rates. 1.2 Noise In an underwater environment the ambient noise is not white [1]. Its power spectral density decreases with the increase in frequency. Experimental results show that wind and shipping activity also affect the ambient noise. In Fig.1.1. (from [1]) we can see the pattern of ambient noise and an approximation for f < 10 5 Hz: 10 log(n(f)) = logf (1.1) The power figures are expressed db re µpa, which are used to measure the strength of the acoustic waves. The approximation conversion rate is 170 db re µ Pa 1 acoustic Watt. 4

11 noise p.s.d. [db re micro Pa] wind at 10 m/s wind at 0 m/s shipping activity 0, 0.5 and 1 (bottom to top) f [Hz] Figure 1.1: Power spectral density of the ambient noise, N(f) [db re µ Pa] from [1]. The dashed line shows an approximation 10 logn(f) = logf. 1.3 Attenuation The attenuation of underwater acoustic channels is given by: A(l, f) = l k a(f) l (1.2) where k is the spreading factor and a(f) is the absorption coefficient. The spreading factor describes the geometry of propagation of the acoustic waves. The most common values used are k = 1 for cylindrical spreading, k = 1.5 for practical spreading and k = 2 for spherical spreading. The absorption coefficient is represented by a(f) and can be calculated using Thorp s formula [3]: 10 log(a(f)) = 0.11 f2 1 + f f f f (1.3) Fig.1.2. (from [1]) illustrates that higher frequencies experience higher attenuation. The power needed to transmit a signal increases exponentially with the distance. For 35 khz, the absorption coefficient is about 11.4 db/km. If relays are available, the shortest path between two nodes does not always have to be the one that needs the lowest power. A path with shorter hops may significantly reduce the required power level. For example, a 3 km link requires much more power than three links of 1 km each. 5

12 absorption coefficient [db/km] frequency [khz] Figure 1.2: Absorption coefficient, a(f), [db/km]. 1.4 Bandwidth and equalization Narrow-band SNR depends heavily on the distance because of the noise and attenuation patterns of the UAC. That means that the bandwidth will decrease when the distance of the link increases. This property is illustrated by representing 1 graphically SNR = in Fig.1.3. (from [1]). A(l,f)N(f) km 5km 1/AN [db] km 50km frequency [khz] Figure 1.3: Frequency-dependent part of narrow-band SNR, 1/A(l,f)N(f) from [1]. Practical spreading (k = 1.5) is used for the path loss A(l, f). Moderate shipping activity (s = 0.5) and no wind (w = 0) are used for the noise p.s.d. N(f). 6

13 Bandwidth is physically much more limited than in terrestrial systems. Our network is designed with a bandwidth of 1 khz, which roughly corresponds to a 1 kbps rate. 1.5 Multi-path and Doppler effect Inter-symbol interference (ISI) due to multi-path is very significant because the reflections of one symbol may affect the reception of the next symbols. Both the sea surface and the sea bottom reflect the acoustic waves and this can cause strong fading. Doppler effect is much more pronounced in the UAC than in the radio channel as the shifting in frequency and scaling in time are proportional to the following expression: v r /v p, where v r is the relative speed between transmitter and receiver and v p is the propagation speed of the waves. Although v r tends to be smaller in the water, the speed of the sound is much lower than the speed of light. As a result, underwater synchronization needs to be much more accurate. 1.6 Interference The fact that the attenuation increases exponentially with the distance provides a protection against interference. The minimum distance between two nodes at which they cause non-negligible interference to one another. to interfere one into each other is much smaller than in terrestrial networks. The signal to noise ratio (SNR) used in the simulations is 20dB Transmission area vs Interference area Let the transmission area be the area in which the packet would be correctly received. Let the interference area be the area in which the signal would cause interference to third nodes (when the packet would be received with a power level higher than the ambient noise). In Fig.1.4. both concepts are graphically represented for a given transmission. The relation between the radius of the transmission area, r T, and the radius of the interference area, r I, comes from the expression SNR = 10 (r I r T ) k a(f) r I r T (1.4) The transmission area depends on the power used to transmit the packet and the interference area has to be taken into account to calculate the potential collisions that our transmission may cause. 7

14 Figure 1.4: Diagram of transmission area and interference area of a given transmission. The TX node sends a packet P to the RX node using exactly the minimum power level that ensures that P is received Connectivity Fig.1.5. shows four different levels of connectivity that correspond to four different transmission power levels. A network is said to be connected when any node can send a packet to another node. The path that the packet will follow can include as many hops as required but none of them can be greater than the transmission range. When this it is not possible, we will say that the network is not connected (see Fig.1.5 (d)). Maximal connectivity is achieved when the sink can send a packet to another node using a single hop as it is illustrated in Fig.1.5. (a). Some communications need multi-hoping in a medium connected scenario (b). In a minimally connected scenario (c) if transmission power is further reduced, connectivity can not be guaranteed for any transmission. Therefore, minimal connectivity is defined by the minimum transmission range that ensures connectivity in the network. 8

15 1.7 Limitations of acoustic transducers The existing acoustic modems are half-duplex. That means that they cannot receive and transmit at the same time. However, the high latency of the UAC helps to reduce the importance of this limitation. Because the propagation delay can be greater than the duration of the packet, two packets can be sent simultaneously by two nodes in the same channel and be correctly received. The transmission power is much higher than the reception power. On short links, this can cause power waste and receiver saturation if we do not adapt the transmission power to the link length. 1.8 Limitations of AUVs AUVs can t capitalize on commercial systems that use radio communications such as GPS. Thus, they have to rely on their own positioning system. AUVs move at a speed up to about 10 m/s. Therefore, the slow movement of underwater nodes facilitates the use of dynamic routing. 9

16 a) Maximal connectivity tx range = 7.5km Sink b) Medium connectivity tx range = 6.5 km Sink 10

17 c) Minimal connectivity tx range = 4.5 km 4.5 km Sink d) No connectivity tx range = 3.5 km Sink Figure 1.5: Connectivity changes when transmission range is reduced. Solid lines indicate shortest path routes from the sink (square on the right) to each one of the relays (circles). Dashed lines indicate overhearing between relays. The scenario is 10 km wide. 11

18 Chapter 2 The underwater network 2.1 Introduction The network considered in this project contains fixed and mobile nodes. The nodes are randomly located in order to encompass any possible network architecture. This means that any node can communicate directly with any other node without needing a centralized access point. Therefore, they have to follow a Medium access control (MAC) protocol. We have chosen DACAP [2] because it is a contention based protocol that works with asynchronous nodes, minimizes the energy consumption and maximizes throughput. However, [2] demonstrated the superior performance of DACAP only for network with maximal connectivity and fixed nodes, all the packets sent with the same power, and static routing by shortest path. In this thesis, we apply DACAP to different situations. For example we will send the packets with different power levels (adaptive power control). We will choose a transmission range that does not provide maximal connectivity (optimal transmission range) and that will make DACAP scalable to a situation when large coverage is needed. Finally, we will change the routing matrix over time (dynamic routing). We will look at a scenario with a certain density of randomly distributed nodes, both mobile and fixed. 2.2 Scenario The main scenario that we are addressing consists of a network of fixed and mobile nodes (AUVs). We are interested in making the AUVs communicate with a fixed node that will act as the sink of the network. In a lot of underwater applications it is required that the AUVs send images, measurements or any other type of data to the sink. Meanwhile, the sink will send control messages to the AUVs. A network of fixed nodes, that act as relays, will be used to connect the sink with the AUVs and vice versa. If two AUVs want to communicate, they will also have to use the network of relays. Therefore, we have three types of nodes in our scenario: N auv = 3 AUVs, N gate = 1 sink and N rel = 16 relays. 12

19 2.2.1 Surface size and initial node location All nodes are located on a two-dimensional surface of area A, length L1 and width L2 (A = L1 L2). The surface is divided into N rel squares, each of area equal to A/N rel. In each square we randomly locate one relay following a uniform distribution. The sink is located in the middle of the surface. In the most common scenario, there is only one sink with coordinates (L1/2, L2/2). In the rest of the thesis we will refer to this case. The AUVs will be randomly deployed. They can can freely move to any point in the scenario. The initial position of a given AUV i will be (x i, y i ). x i and y i follow uniform distribution in the ranges [0, L1] and [0, L2] respectively Figure 2.1: Example of deployment positions of the nodes of the network. Relays, sink and AUVs represented by circles, squares and triangles respectively Mobility The gateway and the relays are fixed, i.e. they can not change their position. The AUVs can move but they cannot leave the surface A. They follow a motion pattern that is a special case of the random way point model [4]. Each AUV starts 13

20 with a random speed, that is uniformly distributed between 0 and v max = 3m/s. They also start with a random course, uniformly distributed in [0, 2π). Let the course, φ, be the angle between the vectorial velocity of the AUV and the x axis. Every t c seconds the AUV can accelerate, increasing or decreasing its speed by a random number uniformly distributed between 0 and a max (maximum acceleration) and change its course turning left or right by a maximum of ϕ max degrees. Fig.2.2. shows the magnitudes involved in the movement of an AUV. In Fig.2.3. we can see how the AUV changes its course if it detects that it is leaving the area of operation. Finally, Fig.2.4. illustrates an example of the simulated AUVs movement. Figure 2.2: Example of the change in speed and course of an AUV Positioning Any node knows the initial deployment position of any other nodes. Each AUV also knows its position with a certain delay t p. If a dynamic routing technique is used, then the AUVs communicate their updated position to the rest of the nodes when necessary to change the routing matrices. Otherwise, we will simulate our scenario assuming that the nodes calculate the routing matrix with the deployment positions Frequency, attenuation and noise level Attenuation, A, depends on distance and frequency, as shown in Eq.1.3. The minimal power needed to send a signal between two nodes separated a distance d, can be approximated by P min = SNR A(d, f c ) N(f c ) B (2.1) 14

21 Figure 2.3: Example AUV avoiding to leave the scenario. where B is the bandwidth of the system around the center frequency f c and N is the power spectral density of ambient noise, as evaluated in [1] Transmission range and scalability The transmission range, R max, is a key parameter for the system design. First, we study the case of maximal connectivity, R max d max, where d max is the maximum distance between any two nodes. In this case, we can ensure that any node can communicate with any other node directly. The case without maximal connectivity, where R max < d max also has to be analyzed to make sure that the algorithm is scalable, i.e. that it can be used not only in a 25km 2 scenario with 20 nodes, but also, for example, in a 2500km 2 scenario with 2000 nodes. Let s assume that the area of operation is square shaped; then d max is about 70km. Transmission distance R max = 70km may be too large for the following reasons. First, high attenuation would require high power. Second, less bandwidth is available for a long distance. Transmission over 5km can be accomplished with a 10 times higher bandwidth than a transmission over 100km. As a result, networks would not be always maximally connected. Although it means that multi-hoping is necessary, it will also reduce the interference level and the power consumption. 15

22 Figure 2.4: Example of movements. Relays, sink and AUVs are represented by circles, squares and triangles respectively. AUVs follow a trajectory with random course and speed changes, restrained to a maximum speed, maximum turn per second and maximum acceleration. 16

23 Chapter 3 MAC protocols in underwater networks 3.1 Related work Nodes of mobile underwater ad hoc networks rely on batteries for their power supply. In underwater acoustic communications, power consumption is dominated by the transmission. Attenuation in the underwater acoustic channel is given by (1.3). As a result, techniques based on multi-hoping can lead to power savings. Many authors have focused on developing power-aware protocols for ad hoc radio networks, [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. Their studies are based on channels with attenuation proportional to d w, where d is the distance and w usually ranges between 2 and 4. However, as shown in [1], attenuation of acoustic waves in the water is proportional to a d, where a is the absorption coefficient and d is the distance. Some authors propose dynamic power control mechanisms [13]. In Chapter 5 we study the applicability of a similar technique to underwater networks. Finally, [10] and [16] propose dynamic routing techniques for undersea applications. An analytical solution to find the optimum transmission range that optimize the distanceenergy efficiency was given in [17]. They conclude that it strongly depends on the density of nodes in the network. However they have only addressed attenuation proportional to d w. Moreover, they have not considered that there is a minimum transmission range necessary to achieve connectivity. 3.2 MAC protocols There are two basic types of channel access techniques: deterministic and random channel allocation protocols. The former divide the channel into a fixed number of sub-channels and give the exclusivity of each sub-channel to a different node. Depending on the sub-channel criterion they can be classified as TDMA (Time division multi-access), FDMA (Frequency division multi-access) and CDMA (Code division 17

24 multi-access). These techniques are useful when there are a finite (small) number of users who continuously generate traffic. In this thesis the focus is on random channel access which is useful in scenarios with a large network of nodes, which transmit in a bursty manner at a relatively low duty cycle. Aloha is the predecessor and the basis of many contention oriented protocols [18]. However, its high collision rate requires too much power in underwater acoustic systems [2]. At the same time, because of the high latency, carrier sensing is not reliable. For this reason, MACA (Multiple Access with Collision Avoidance) [19] type of protocols have been considered for underwater networks. MACA is based on an exchange of RTS/CTS (Request to send/clear to send) that secures the communication. This protocol has been used in an experimental underwater network [20]. Current research is focusing on MACA type protocols such as S- FAMA (Slotted-Floor Acquisition Multiple Access) [21], PCAP (Propagation-delaytolerant Collision-Avoidance Protocol) [22] and DACAP (Distance-Aware Collision Avoidance Protocol) [2]. Another approach is looking at sleeping schedules such as those proposed in [23] and in [24]. DACAP offers good throughput performance, and, unlike S-FAMA, it is suitable for asynchronous nodes. DACAP was designed for applications with and without acknowledgments. However, it was only analyzed for maximal connected networks with fixed nodes. 3.3 DACAP protocol The Distance aware collision avoidance protocol (DACAP) is a contention based MAC protocol specifically designed for the UAC [2]. DACAP uses asynchronous nodes and it is designed to reduce the power consumed in collisions. Collisions are a problem in underwater networks. The high propagation delay slows the process of collision detection and retransmission. Moreover, retransmissions require a lot of power due to the high attenuation of the UAC. Consequently, avoiding collisions is a priority. At the same time the low speed of sound requires a long handshake duration. DACAP has a shorter handshake than other protocols. It capitalizes on the receiver s tolerance to interference when the two nodes are closer than the maximal transmission range. The protocol is characterized in the following way. When a node A receives a request-to-send (RTS) from another node B, its partner, it immediately answers with a clear-to-send (CTS). When its partner B receives the CTS it waits for Tw(U/c) seconds before starting sending the first data packet. Tw depends on U/c, half of the round-trip time of the RTS/CTS. If at any time before respectively receiving or sending the data packet, A or B hear any other RTS: they will send a short warning packet to their partner. When a node receives a warning packet, it postpones the transmission of the data packet and enters a back-off state. All the packets are sent with the same power. The waiting time is chosen to avoid the collision of any packet sent from a shorter distance and to tolerate the interference of a packet sent from a 18

25 longer distance if the distance between A and B is short Waiting time DACAP can be used with or without acknowledgments (ACKs). Let t min be a configurable parameter that defines the minimum hand-shake length. If the distance between most of the links is equal to the transmission range then t min needs to be as long as the maximal round trip time. When some links are shorter than the transmission range, t min can be reduced. In the case without ACKs, the sender will defer the transmission of the data packet if it overhears a CTS within the next t min after sending the RTS or if it receives a warning. The receiver will send a warning if it overhears a RTS within 2T t min seconds after sending a CTS. T is the maximum propagation delay for the maximum transmission range of our scenario. If U + D is the minimal distance to an interfering node for which we can still correctly receive our packet, and t data is the duration of the data packet, then we can determine: { tmin 2U/c, U/c < t T w (U/c) = 1 (3.1) 2(U + D)/c t min, U/c > t 1 where t 1 = t min min( D/c, t data, 2T t min ) 2 Also Tw > 2 D/c to avoid collisions from In the case with ACKs the sender postpones the transmission if it overhears either a CTS within t min or an RTS within T seconds after sending the RTS. Moreover it postpones transmission if it receives a warning form its partner. Meanwhile, the receiver sends a warning if it overhears an RTS within 2T t min seconds or a CTS within 2T Tw min seconds after sending a CTS. Where Tw min is a parameter, a predefined minimum waiting time. For the calculation of Tw we also need to define t data as the maximum difference between the duration of two data packets. where 2(U + D)/c t min, U/c (t 1, t 2 ) T w (U/c) = 2(U + D)/c T W min U/c > max(t 2, min(t 3, t 1 )) t min 2U/c other t 1 = t min min( D/c, t data, 2T t min ) 2 t 2 = t min t data 2 t 3 = t min + T W min 2 D/c 4 and Tw(U/c) > max(2 D, Tw c min). 19 (3.2) (3.3)

26 3.3.2 Back offs and fairness The reception of an RTS or a CTS will make a node A go into a back off state. It will go back to an idle state if it overhears the end of the other transmission or if one of the nodes already using the channel sends an RTS to the node A. This makes the protocol unfair but achieves better overall information flow Comparative performance Fig.3.1. and Fig.3.2(a). show that DACAP achieves a better throughput than S- FAMA. Aloha achieves better throughput at lower loads but at higher loads its high collision rate reduce its performance. Fig.3.2.(b) and Fig. 3.3.(a) show that DACAP wastes similar power on collision compared to S-FAMA but clearly outperforms Aloha. Finally, Fig.3.3.(b) shows that the average end to end delay of a packet in a DACAP network is higher than the one in an Aloha network but lower than the one in a S-FAMA network DACAP S FAMA CS ALOHA Throughput Offered load Figure 3.1: Throughput of Carrier Sensing ALOHA, Slotted FAMA and DACAP(t min = T) for a transmission range of 7 km (maximally connected network) without ACKs. The tolerance to interference is given by D=3500 m. From [25] Limitations of DACAP DACAP has only been simulated in maximally connected networks consisting of fixed nodes sending all the packets with the same power. However, maximal connectivity is not an option if we want to scale up the network. There is a physical limit on the maximum power that can be used. Moreover, bandwidth decreases for longer transmission ranges [1]. Mobile nodes are essential for many underwater applications. When a fixed node wants to communicate with a mobile node, it will not know for sure where the mobile node is located. This can be a problem in networks that are not maximally connected. Sending all the packets with the same power is very inefficient because packets suffer much lower attenuation over short links than over long links. However, DACAP uses this fact to protect the packets 20

27 DACAP S FAMA CS ALOHA 10 2 DACAP S FAMA CS ALOHA Throughput Power waste (%) Offered load (a) Offered load (b) Figure 3.2: Throughput (a) and ratio of power wasted (b) of Carrier sensing ALOHA, Slotted FAMA and DACAP(t min = 2T) for a transmission range of 2.5 km without ACKs. The solid lines represent the results for zero tolerance to interference ( D=2.5 km) and the dashed ones, those with D=625 m (suppressed for S-FAMA and DACAP in (b) for the sake of clarity). From [25] 10 2 DACAP S FAMA CS ALOHA DACAP S FAMA CS ALOHA 1200 power waste (%) 10 1 end to end delay (secs) transmission range (km) Transmission range (km) a) (b) Figure 3.3: (a) percentage of power wasted in collisions and transmitting control packets; (b) average end to end delay. To compare fairly, packets that were delivered only in some cases were assumed to have 1800 sec delay in the others, while the delay of those that were never delivered is being ignored. From [25] over short links from interferences coming from distant nodes. Our techniques try to cope with all these limitations. Simulation was conducted to assess the network performance. 21

28 3.3.5 Conclusion DACAP is a protocol that addresses the issue of minimizing power consumption while maximizing throughput in underwater networks. We have designed several techniques to reduce the power consumption and they will be implemented applying it. 22

29 Chapter 4 Simulations 4.1 Simulation strategy Simulations are based on MATLAB and SIMULINK implementations Packet generation Only the sink and the AUVs generate data packets. The performance of the algorithm is measured for different generation rates, λ g. The time interval between the generation of two packets by the same node, t g, follows an exponential distribution. Its average is is given by t g = 1 λ g (4.1) The offered load, OL, can be calculated as: OL = N gen λ g t send t data t send (4.2) where t data is the duration of the data packet and N gen = N auv + N gate, being N auv the number of AUVs and N gate the number of sinks Results The performance can be assessed through three basic indicators: Total energy: is the total energy used during the simulation time. Throughput: is the end to end throughput, defined Throughput = N f t data t end (4.3) where N f is the number of packets that reach their final destination in t end seconds. 23

30 Some other indicators are going to be checked to ensure that the performance measures are valid: Average end-to-end delay: the average time it takes for one packet to reach its final destination. Throughput and end-to-end delay follow the same trends. Therefore, only throughput results will be shown. N f : the number of packets that reach their final destination. Its value helps to check that the simulation is valid because all the generated packets were correctly transmitted. N c : the total number of collisions. According to [2] N c should be very low in the DACAP algorithm. 4.2 Simulation parameters The constants used in the simulation can be divided into two categories: parameters and variables. Physical parameters: they refer to certain properties of the transmission, usually related to the physical layer, such as the frequency of the carrier or the spreading factor used for propagation modeling. Protocol parameters: they are DACAP variables such as the duration of the data packet. Their values have been taken from [25]. Scenario parameters: they determine the characteristics of the scenario such as the number of AUVs, relays and sinks, or the duration of the simulation. Scenario variables: for instance, the position of the fixed nodes, the distances or the trasnmission range that provides minimal connectivity are examples of this type of variables. Algorithm variables: they are the values that one can modify in order to increase the energy efficiency of the algorithm. For instance, the transmission range, R max, is one of these variables. In the following sections, those parameters that remain constant through all the simulations, are described Physical parameters Frequency [f c ]: 35 khz (see [25]) offsets the trade-off between the higher available bandwidth for higher frequencies and the lower attenuation of lower frequencies. 24

31 Bandwidth [B]: 1 khz is the bandwidth used in the scenario. Signal to noise ratio [SNR]: 20dB is the signal to noise ratio used in the simulations. Noise power level [NF] -14dB (see [1]). Absorption coefficient [af] 11.4 db/km, using Thorp s formula and f = 35kHz. Spreading factor [k]: a value of 1.5 (practical spreading) is used (see [1]). Speed of the sound in the water [c] 1.5 (km/s) Protocol parameters Duration of the data packet [t data ] = 2 seconds. Duration of the RTS packet [t RTS ] = 0.01 seconds. Duration of the CTS packet [t CTS ] = 0.01 seconds. Duration of the ACK packet [t ACK ] = seconds. Duration of the WARN packet [t WARN ] = seconds. Duration of the RCP packet [t RCP ] = 0.02 seconds Scenario parameters Number of relays [N rel ] = 16. Number of sinks [N gate ] = 1. Number of AUVs [N auv ] = 3. Area of the scenario [A] = 25 km 2. Side of the square assigned to a relay [l] = 1.25 km. Maximum speed of an AUV [v max ] = 3m/s. AUVs are powered by electrical engines and will not achieve high speeds in order to be able to fulfill their task and to save battery life. Maximum turn [φ max ] = 30 grades per t c seconds. Maximum acceleration [a max ] = 1 meter per t c seconds. Time interval between accelerations [t c ] = 10 seconds. We believe that most applications will not need a quicker response from the AUV. 25

32 Simulation duration [t sim ] = 1500 seconds are enough to receive all the packets for most of the offered loads tot he network below 2. New packet generation duration [t send ] = 100 seconds are enough to send a meaningful number of packets for each of the offered loads that we are testing. 26

33 Chapter 5 Adaptive power control 5.1 Introduction Power control ensures that each packet is sent with just the minimal power that is needed to ensure a certain SNR. The use of packets with lower power not only saves energy but it also causes less interference. The DACAP was originally tested with all the packets being sent with the same power (see [2]). The adaptive power control technique is based on sending each packet with a higher power level if the link is long (and lower if the link is short). This reduces the interference but at the same time, the receiver of a short link will loose part of its tolerance to the interference caused by other transmissions. 5.2 Design In the case without power control, the power to send any packet is determined by the following expression: P = SNR A(R max, f c ) N(f c ) B (5.1) where B is the bandwidth around the center frequency f c, R max is the transmission range that we have allowed for our scenario, N(f c ) is the noise power spectral density and A(R max, f c ) is the attenuation given by (1.3). P is going to be the same for any node and any link. The main advantage is the simplicity of this solution. The adaptive power control is based on transmitting the packet with the minimum power to be received considering a certain SNR and the distance to the next hop. In the power control mode the power used to send a packet from node i to node j follows this expression (see (2.2.4)): P = SNR A(d ij, f c ) N(f c ) B (5.2) where d ij is the distance between node i and node j. Nodes need an adaptive distance estimation method if they want to use the power control mode. 27

34 5.2.1 Distance estimation in power control mode Because data packets are much longer than any other type of packet, they are the main contributors to the energy consumption. When sending a data packet, a node can use the round-trip time from the previous RTS/CTS in order to estimate the distance to its partner. Other types of packets cannot benefit from this distance estimation technique. If the destination node is a fixed node, the sender uses the deployment node positions to calculate the distance. If it is a mobile node, then the transmission range is selected as in the mode without power control (see (5.2)). Figure 5.1: Uncertainty in the position of an AUV. Each inter-node distance is different but can be measured with the RTS/CTS round trip time Cross effect: interference area The power control mode not only saves power but also reduces potential collisions. In Fig.5.2. and Fig.5.3. we compare the transmission and interferences of a algorithm with power control and another one without power control. The interference area of the latter is much larger than the one using power control. 5.3 Simulation results Both modes have been tested. The routing criterion chosen is minimum distance routing. We have run the simulation for different transmission ranges, R max. The most relevant cases are: a) maximally connected network (R max 7km in our scenario) and b) minimally connected network, R con. A maximally connected network 28

35 Figure 5.2: Transmission and interference areas with power control. is the case where we will be able to notice the difference between both modes. In a minimally connected network both results should be closer because there is a much smaller difference between the actual distance to the next node and the maximal distance that the packet can travel (R max ). Fig.5.4. (a) illustrates that in a maximally connected network the power control mode can help to save about 20 db (99% power saving) for any offered load in the range between (0, 2]. Fig.5.4. (b) shows that in a minimally connected network the saving is about 3dBs, 50% less energy consumption. Fig.5.5. shows that the throughput of the protocol is not affected by the power control mode. We have run simulations for the DACAP protocol with and without ACKs, achieving similar results and conclusions in both cases (see Fig.5.6). 5.4 Conclusion The power control was shown to save between 3 db and 20 db (between 50% and 99% of the total energy consumption), depending on the transmission power 29

36 Figure 5.3: Transmission and interference areas with and without power control. Power control saves power and reduces interferences. used. To implement adaptative power control, distance estimation is based on the RTS/CTS round trip time that DACAP already calculates. This technique does not reduce the overall performance of the protocol. The loss of tolerance to interference is balanced by the lower interference introduced by packets with less power. 30

37 DACAP wo ACKs Maximally connected (Rmax=7km) without power control with power control DACAP wo ACKs Rmax=Rcon=1.5km without power control with power control Total energy [db] Total energy [db] Offered load (a) Offered load (b) Figure 5.4: Total energy with and without power control - without ACKs. The total energy [dbs re µpa] results show that (a) if the scenario is maximally connected (R max = 7km), then the power control saves about 20dB (99%) and (b) if R max = R con = 1.5km the it uses about 3dB (50%) less than the without power control mode. The routing criterion is the shortest path DACAP wo ACKs Maximally connected (Rmax=7km) without power control with power control DACAPwoACKs Rmax=Rcon=1.5km without power control with power control Throughput Offered load (a) Throughput Offered load (b) Figure 5.5: Throughput for with and without power control (a) in a maximally connected network and (b) in network with minimal connectivity. Without ACKs. 31

38 DACAP with ACKs Maximally connected (Rmax=7km) 200 without power control with power control 180 Total energy [db] Offered load Figure 5.6: Total energy consumption with and without power control for a maximally connected network with ACKs. Maximally connected scenario (R max = 7km). The routing criterion is the shortest path. 32

39 Chapter 6 Optimal transmission range 6.1 Introduction DACAP should work in situations without maximal connectivity in order to be scalable to a situation where large coverage is needed. Optimization of the transmission range was considered in [17]. However, this analysis was conducted for radio propagation conditions and minimal connectivity was not taken into account. A reduction in the transmission range will have several implications. First, power will be saved, since the power required to transmit a signal over multiple hops is less than the power needed to transmit the signal over a single long hop. If large power is used then we will obtain maximal connectivity but also a lot of interferences that will cause a drop in throughput. Otherwise, if power is too low, there will be no connectivity between any pair of nodes. If a medium power level is used, we will reduce interference but now multi-hoping will be needed. We look at shortest path routing and we ask the following question: Is there an optimal transmission power to be used by the nodes? We address this question from the viewpoint of minimizing the total power consumption and maximizing the total throughput. In order to simplify the implementation, all the packets are sent with the same power. 6.2 Design Maximally connected networks need R max d max where R max is the transmission range and d max is the maximum distance that can separate two nodes. The transmission range is the maximal distance at which a packet can be correctly received. Actually, the physical limit of the transmission range is explained by the maximum power level that our transducer is allowed to use. This power level P can be regulated by the algorithm. The direct relation between P and the transmission range, R max, is given by Eq.5.2. In Fig.6.1. it is shown that a transmission with reduced power may cause less interferences than a maximally connected one. 33

40 Figure 6.1: Optimal transmission range. Interference area is smaller than in a maximally connected scenario. 6.3 Simulation results Reducing the transmission range is a way to substantially increase the performance of the protocol. This technique improves the energy consumption, the throughput and the delay. Its implementation is much easier than other techniques because it only requires reducing the maximum transmission range that is allowed for any packet. The technique works so well because by reducing the transmission range the protocol is forced to follow multi-hop paths but at the same time is requested to search for the shortest path. This multi-hop strategy redistributes the utilization of the relays and it leads to lower waiting times. Relays that before were not used, because they were too close to the sink or to the AUVs, now play a key role in the transmissions. The use of a lower transmission range reduces power consumption and collisions. Our results show that there is an optimal transmission power to use for a given density of nodes. Fig.6.2. and Fig.6.3. illustrate the results of the simulation. More importantly, results show that there is an optimal transmission power to use for 34

41 190 DACAP with ACKs 180 Maximal connectivity 170 Total energy [db] Connectivity not guaranteed 130 Minimal connectivity 120 Optimal range Transmission range [km] Figure 6.2: Total energy consumption using the DACAP protocol with acknowledgments for different transmission ranges. Simulation time is 1000 seconds. a given density of nodes. It is the power needed to establish the minimal level of connectivity. Fig.6.2 shows the total energy consumption, i.e. total energy consumed by all the nodes in the network during the simulation time. It demonstrates that substantial savings (about 65dBs) in total power are available in the power controlled scenario as compared to the maximal connected one. Fig.6.3. shows the total throughput achieved with different transmission ranges. This results reveals an interesting observation. Optimal range minimizes energy and maximizes throughput Optimal transmission range calculation The transmission range that provides minimum connectivity (called R con ) depends on the deployment positions of the fixed nodes. It has to satisfy two conditions: 1. Neighborhood condition: Each point Q in the scenario is at least connected with one fixed node. Being connected means that the distance between Q and its closest node is lower than R con. 2. Connectivity condition: From any point Q a packet can be sent to any fixed node (no hop can be greater than the transmission range). The method that we are going to apply to obtain R con for a given scenario (determined by number of fixed nodes and their deployment positions) is based on first 35