International Conference on Electrical, Control and Computer Engineering Pahang, Malaysia, June 21-22, 2011
Underwater Wireless Network Energy Efficiency and Optimal Data Packet Size Low Tang Jung
Azween B. Abdullah
Computer & Information Sciences Department Universiti Teknologi PETRONAS Tronoh, Malaysia
[email protected]
Computer & Information Sciences Department Universiti Teknologi PETRONAS Tronoh, Malaysia
[email protected] which infers that the network performance sensitivity is related to the choice of the packet size, this project shall also study the effects of packet size in relation to energy consumption.
Abstract—This paper addresses the issue of energy efficiency and the optimal data packet size/length in underwater wireless network communications in the context of effective and efficient data transmission. In light of the limited battery power of underwater sensor nodes, energy efficiency is investigated in relation to the optimization of data packet length to meet the low power constraints of the shallow water acoustic channel. The main contribution of this project is a data packet size optimization look-up table or graph of which viability is proven through virtual simulations using ns2 simulator and its MIRACLE packages on the Ubuntu platform.
Limited on-board energy poses a serious problem. The sensor nodes have only their own battery capacity to rely on since it is very difficult to recharge or replace them in the underwater scenario far from solar reach. Moreover, high transmitting power is needed in UWA and noisy underwater channel causes packet losses and retransmissions, wasting valuable energy. Since battery power of the sensors is limited, improving the effective data rates should be achieved with maximum energy efficiency in mind.
Keywords—underwater wireless communications, optimal packet size, energy efficiency, acoustic channel, link quality
I.
The trade-offs between using acoustic, radios or optical waves are examined in [4]. Electromagnetic signals are known to be easily absorbed in sea water. Optical waves have the added disadvantage of scattering. Thus the outlook of acoustic signaling is favorable relative to electromagnetic or optical waves elevating it to be the primary form of wireless underwater communication [6]. However, using acoustic waves as the transmitting media in UWA communication systems do have to face the challenges caused by the characteristics of acoustic propagation that demand higher energy or power for transmitting data packets.
INTRODUCTION
Underwater wireless communications, more specifically the underwater acoustic (UWA) communications, entails the employment of acoustic waves to send and receive information below water [1]. Credit to novel studies of underwater acoustic networks, communications and routing protocols, what was once the primarily researched for military purposes has the potential to be applied in off-shore oil industry, naval missions and the environmental domain [2][3]. Acoustic signals are used in the network because radio waves do not propagate well underwater and optical waves are affected by severe scattering.
UWA communication is affected by sound absorption in the water, energy spreading and the waveguide nature of the channel [2]. These factors induce absorption losses, extensive time-varying multipath, propagation delays, reverberation and Doppler spread [4][6]. In the medium range channel of shallow warm water such as the coastal region of South East Asian countries, acoustic propagation does occur as both the seasurface and the sea-floor act as reflectors [7]. Acoustic propagation in water poses a limitation to transmission data rates, causing low effective data rates [8]. Investigations are thus needed to find the correlations of data packet size and energy efficiency.
Lucrative benefits of UWA communication are not without costs. The challenges present in UWA communication channel include fading, multipath propagation delay, limited bandwidth and severe energy constraints of battery-powered sensor nodes [4]. Sound waves propagate much slower than electromagnetic waves, at a speed of 1500 m/s. Due to refraction, reflection and ambient noise of the underwater channel, packet loss rate is greater. Thus, identification of channel parameters which have profound implication on UWA network performance is needed. Although studies on underwater acoustic communication have been increasingly prevalent in recent decades, a comprehensive resource on channel parameter optimization, more specifically, techniques or algorithms for choosing the best packet size for efficient data transmission is not yet readily available. It is well known that energy efficiency is of extreme importance to UWA networks in enhancing the life span of the networks. This research focuses on finding the optimal data packet size for UWA data transmission with energy efficiency as the optimization metric. With reference to the works found in [5]
978-1-61284-230-1/11/$26.00 ©2011 IEEE
This research covers only UWA communication at very warm shallow tropical waters (50 m to 200 m) with a medium transmission range of 100m to 2km. Critical review of related works on energy efficiency and simulations of the underwater channel were conducted to develop an optimization mechanism that could be used to enhance channel performance. In view of the possible high cost in setting up a real physical underwater wireless sensor network, our research mainly used modeling,
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analysis and virtual simulations. Ubuntu platform, ns-2 network simulator, and the ns-miracle package were installed for underwater acoustic networks setup. Simulation programs and scripts were used to emulate the properties of the channel and to investigate alternative design solutions.
where,
The rest of the paper is presented as follows. Section II touches on the issue of energy and data packet size with respect to the UWA communications. The review of some related works is presented in Section III. Section IV is our results and their related discussions. Section V concludes the paper.
Fig. 3 shows the plot of energy efficiency against packet sizes, with and without power management, from the work of [10] which depicts the similar findings in [9].
II.
l h PER l+h
1
RELATED WORKS
0.9
Of the existing data packet size optimization mechanisms, energy efficiency-based optimization has only been examined relatively scarcely. Nevertheless mathematical models and experimental studies on the subject show a general accord on the importance of energy efficiency as the optimization metric since minimizing energy consumption is one of the main goals in UWA communication.
Energy efficiency
0.7
Useful energy
0.6 0.5 0.4 0.3 0.2 0.1 0 0
500
1000 1500 2000 2500 3000 3500 4000 Packet size in bits
4500 5000
Figure 2. Packet size against energy efficiency with and without forward error correction capability
0.7 With power management Without power management
0.6
Energy efficiency
0.5
(1 – PER) Energy Channel
Without FEC With BCH coding, t = 2
0.8
A. Energy Efficiency Energy efficiency represents the “useful fraction of the total energy expenditure in a communication link between the neighboring sensor nodes” [9]. In this context, useful energy is energy spent when all bits are transmitted correctly and energy lost occurs when packets are corrupted. Energy efficiency as a performance metric arose from the concept of energy channel. Fig. 1 [9] shows the notion of an energy channel to depict a useful energy consumed in correctly delivering a data payload. This model was used in their work to determine optimal packet size in radio wave wireless sensor network. In Fig.1 k1 and k2 are communication device constants, l is packet payload, α is the packet header, τ is the packet trailer, Edec is energy consumed in decoding the packet, and PER is the packet error rate.
k1(l+α+τ)+k2+Edec
is the actual data bits transmitted is packet header length in bits is packet error rate is the entire packet length in bits
0.4
0.3
0.2
k1l
0.1
(PER) 0
Energy lost
100
200
300
400 500 600 700 Payload length in bits
800
900 1000
Figure 3. Packet size against energy efficiency with and without power management
Figure 1. Notion of energy channel
Fig. 2 shows the plot of energy efficiency against packet sizes, with and without forward error correction, from the work of [9]. The plot clearly shows that in wireless sensor network communications there exist a packet size which gives peak energy efficiency.
B. Energy Per Useful Bit Vuran and Akyildiz [11] defined energy per useful bit (EPUB) as “the energy consumption for useful bit between a particular node and the sink” and the basic equation to represent their definition is given by,
In the work of [10] the author defined the energy efficiency without power management as the ratio between the amount of energy consumed for actual data transmitted and the energy consumed for the entire data packet transmitted in the form of, · 1
0
(2) where Eflow is the end-to-end energy consumption to transport a packet from a source to a destination, lD is the payload length, and PERe2e is the end-to-end packet error rate. It is claimed that
(1)
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In the simulation two nodes are created (one transmitter and one receiver/sink) at any one time for a one hop data packet rely with one constant bit rate (CBR) module per layer. A unidirectional Module/Link connects the two nodes. A single CBR packet flow is then started from one node to the other. The packet flow is in accordance to the ns2 MIRACLE layered framework. The transmitter CBR module, acting as an agent, generates data packet of the required size. If any packet collides, that packet is discarded. The MIRACLE physical layer (MPHY) uses binary phase shift keying (BPSK) modulation to send the data packet over the underwater channel to the receiver. The underwater channel is configured with Shannon channel characteristics. Some essential parameters used in the simulation are listed in Table I.
by minimizing the function can result in optimal packet size values that achieve high energy efficiency. Their work further introduced an optimization framework for energy minimization with delay and reliability constraints. They used the log-normal channel model which can model accurately the low power communications in WSN. Part of the results from their analysis is shown in Fig. 4 which shows different optimal packet length with various forward error control capabilities. -3
PER = 10
ARQ FEC(t=2) FEC(t=3)
TABLE I.
FEC(t=5) FEC(t=7)
Parameter Payload length Distance Frequency Bandwidth Protocol Constant bit rate (CBR)
Payload Header Trailer
FEC(t=9)
0
50
100 Packet Length (bytes)
150
200
Figure 4. Optimal packet length with different error control techniques
Setting 10 – 1000 bits 100m – 1000m 8.2 KHz 6 KHz Aloha 0.01s, 0.03s, 0.05s
In the simulation, the packet header has 10 bits and the payload is varied from 10 bits to 1000 bits. The time period between each packet is determined by the constant bit rate (CBR) interval. The MAC protocol is deployed in the MIRACLE MAC (MMAC) layer for media access. ALOHA protocol floods the network with packets and has no carrier sensing functionality. If there is a packet collision, resend the packet later. Once a packet is received, an acknowledgement (ACK) will be sent by the receiver. It should be mentioned here that CSMA protocol was also used in our simulation for comparative studies purpose but this studies shall be presented in other papers. The idea is that CSMA senses whether a network is busy before transmitting a packet thus lowering collision probability for providing a better link performance.
Their other results did show that although the inclusion of FEC schemes can permit longer packet length without affecting the energy efficiency, it was proven that this scenario will only hold up to a certain packet length. That is an upper bound limit of packet length exists since when high packet lengths cannot be accommodated, energy efficiency decreases. Simulations were also performed to find the expected BER, latency, and energy consumption. In their proposed cross-layer framework, medium access collisions, routing choices and wireless channel are taken into consideration to find the optimal packet size. III.
ESSENTIAL SIMULATION PARAMETERS
METHODOLOGY
Fig. 5 shows the general scenario setup of the underwater environment for our simulations. A cluster of 100 nodes is placed in the middle of a body of water with a dimension of 2km x 2km x 200m. This is to avoid reflection effects near the water surface and the water bottom. The depth of 200m is chosen to simulate the shallow water environment. One sink is place roughly at the centre of the cluster to collect data packets from other nodes. Distance range between the sink and a source node is 100m to 1km. The maximum transmission range of the nodes is to be 1km.
IV.
RESULTS AND DISCUSSION
Our simulation has adopted the energy efficiency definition from the work of [10]. A database was constructed from the outcomes of the simulation, from which the graph of packet size against energy efficiency under different link bit error rate (BER) is plotted as in Fig. 6 below. 1.0 0.9
BER = 0.0001
0.8 Energy efficiency
0.7 BER = 0.001
0.6 0.5 0.4 BER = 0.01
0.3 0.2 0.1 0
100
200
300
400 500 600 Packet size (bits))
700
800
900
Figure 6. Energy efficiency vs packet size with different BERs
Figure 5. The general scenario
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It is obvious from this plot that an optimal packet size N can be obtained from each BER. That is, N can be easily determined by choosing the point of maximum energy efficiency from the graph. For example, the optimal packet size for a link quality of 0.001 is given by 100 bits with the maximum energy efficiency of 84%.
2000
Packet size (bits)
1500
It can be seen from the plot that the energy efficiency decreases with increasing BER, denoting that the more unreliable the channel is the more energy is wasted. These phenomena can be explained in the sense that when the link quality deteriorates, more data packets would be corrupted. In return the demand for packet retransmissions thus increases resulting in more energy being consumed for these retrainsmissions. No doubt the energy efficiency would suffer when the retransmission of packets are frequent.
1000
500
0 1E-5
1E-4
1E-3 BER
1E-2
1E-1
Figure 7. Optimal packet size against BERs
It is interesting to observe that the energy efficiency for link with low BER drops more gently after the peak than link with high BER. It brings out a point here that energy efficiency may not suffer much deterioration under good link quality even with a large packet size. For instance, with a BER of 0.0001 the optimal packet length can be varied practically from 150 bits to 900 bits with the energy efficiency maintained at/or above 90%. This, in turn, may helps to produce a higher throughput efficiency with the opportunity to load the transmitted packets with larger payload. A snapshot of the database structure constructed from the outcomes of the simulation and which was used to plot the graph of Figure 6 is given in Table II. TABLE II.
V.
SNAPSHOT OF DATABASE STRUCTURE
Pckt Size (bits)
EPUB (mJ/bit)
16
1.9668
96
1.0728
176
1.0302
256
1.0151
336
1.0074
416
Figure 8. Application for UWA experiment
1.0027
BER 0.01 0.001 0.0001 0.01 0.001 0.0001 0.01 0.001 0.0001 0.01 0.001 0.0001 0.01 0.001 0.0001 0.01 0.001 0.0001
PER 0.1485 0.0159 0.0016 0.6190 0.0916 0.0096 0.8295 0.1615 0.0174 0.9237 0.2260 0.0253 0.9658 0.2855 0.0330 0.9847 0.3405 0.0407
CONCLUSIONS AND FURTHER WORKS
The authors have conducted simulation works, via ns2 simulator, to investigate the relatioship between optimal data packet size and the energy efficiency in underwater wireless communications particularly to the underwater acoustic link. A database was constructed to represent a look-up graph or as a look-up table that can be used to determine the optimal packet size that gives the optimum energy efficiency. Energy efficiency as the optimization metric was chosen to emphasize on energy conservation in the power-limited environment of underwater acoustic network and communications. The importance of packet size optimization was shown by the sharp drop in energy efficiency for certain packet sizes as can be seen in Figure 6.
Energy Efficiency 0.4257 0.4921 0.4992 0.3493 0.8327 0.9079 0.1628 0.8004 0.9379 0.0739 0.7499 0.9443 0.0333 0.6975 0.9439 0.0150 0.6469 0.9408
Our further works include the following. First, to enhance the intial application prototype that will be used to test and clarify the simulated outcomes in a laboratory testbed. Second, to develop an algorithm, based on a predefined or a preconstructed look-up table/graph, to determine the optimal packet size for a given set of channel characteristics by maximizing the energy efficiency metric. This is with the aim to ensure a UW wireless link can reliably and efficiently send and receive data packets with an optimal length. Third, to investigate the current findings under other MAC protocols.
It should be noted here that the database can also be used to plot the graph of Figure 7 to show how the optimal packet sizes relate to BER. A sample plot here shows a non-liner or near exponential drop of packet size as the link quality reduces by decades. As an extension to the simulation works on ns2 the authors have developed an application that will be used in the next stage of our research to verify the simulated outcomes through laboratory setup. The initial prototype is shown in Figure 8.
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REFERENCES [1]
Wikipedia. Retrieved: February 18, 2010 from Wikipedia website on “underwater wireless communications definition and principles” . [2] M. Stojanovic, M., “Underwater acoustic communication,” Northeastern University, Department of Electrical and Computer Engineering, Boston, MA, USA. [3] P.C. Etter,2003, “Underwater acoustic modeling and simulation (3rd edition), London and New York, Spoon Press, 2003. [4] I.F. Akyildiz, D. Pompili, T. Melodia, “Challenges for efficient communication in underwater acoustic sensor networks,” ACM SIGBED Review, Vol. 1, Issue 2, pp 3-8, 2004. [5] S. Basagni, C. Petrioli, R. Petroccia, M. Stojanovic M, “Choosing the packet size in multi-hop underwater networks,” Northern University, Boston, MA, USA, 2009. [6] J. Preisig, “Acoustic propagation considerations for underwater acoustic communications network development,” Int. Conference on Mobile Computing and Networking, 1st ACM International Workshop on Underwater Networks, pp 1-5, 2006. [7] M. Chitre, “A high-frequency warm shallow water acoustic communications channel model and measurements,” Journal of Acoustic Society of America 122 (5): 2580-2586, 2007. [8] Liverpool John Moores University. Retrieved: February 20, 2010 from Liverpool John Moores University on “Liverpool Logistics Offshore and Marine Research Institute (Loom)” . [9] Y. Sankarasubramaniam, I.F. Akyildiz, S.W. McLaughlin, “Energy efficiency based packet size optimization in wireless sensor networks,” IEEE Internal Workshop on Sensor Network Protocols and Applications, pp. 8 – 46, 2003. [10] J. Inwhee, “Optimal packet length with energy efficiency for wireless sensor networks,” IEEE International Symposium on Circuits and Systems, Vol. 3, pp. 2955 – 2957,2005. [11] M.C. Vuran, I.F. Akyildiz, “Cross-layer packet size optimization for wireless terrestrial, undewater, and underground sensor networks,” IEEE INFOCOM ’08, Phoenix, Arizona, 2008.
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